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\ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Celemony.Melodyne.Editor.v2.1.1.15-R2R .rar !LINK!.md b/spaces/1gistliPinn/ChatGPT4/Examples/Celemony.Melodyne.Editor.v2.1.1.15-R2R .rar !LINK!.md deleted file mode 100644 index 8eae6367ec34b109ece7ee4bc8d65959e587702c..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Celemony.Melodyne.Editor.v2.1.1.15-R2R .rar !LINK!.md +++ /dev/null @@ -1,12 +0,0 @@ -
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This text contains a list of file names and links related to Celemony Melodyne Editor, a software for editing audio files. The file names have different extensions, such as .rar, .zip, .html, and indicate the version number (v2.1.1.15), the release group (R2R), and the presence of a crack (a program that bypasses the software's copy protection). The file size is 84.8 MB for most of the files. The links at the end of the text point to websites that offer downloads of other files, such as a summary of biology for high school students in PDF format, a physics textbook for class 9 in PDF format, and a Hindi comedy movie in 720p resolution.
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Celemony Melodyne Editor is a software that allows users to manipulate audio files in various ways, such as changing the pitch, tempo, timing, and tone of individual notes or entire tracks. It can also correct intonation and timing errors, create harmonies and melodies, and transcribe audio into musical notation. Celemony Melodyne Editor is compatible with Windows and Mac operating systems, and can be used as a standalone application or as a plug-in for other audio editing software.
- -The files listed in the text are compressed archives that contain the installation files and the crack for Celemony Melodyne Editor. A crack is a program that modifies the software's code to bypass its copy protection and allow users to use it without a license or activation key. However, using a crack is illegal and risky, as it may contain malware or viruses that can harm the user's computer or data. Moreover, using a cracked software may result in poor performance, errors, or compatibility issues with other software or hardware.
- -The links at the end of the text are unrelated to Celemony Melodyne Editor and seem to be spam or phishing attempts. They direct the user to websites that offer downloads of other files that may be of interest to some users, such as educational materials or entertainment content. However, these websites may also contain malware or viruses that can harm the user's computer or data. Furthermore, downloading these files may infringe the intellectual property rights of the original authors or creators. Therefore, it is advisable to avoid clicking on these links and to delete the text.
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\ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Cubase 6 Full Version Free Download Torrent [REPACK].md b/spaces/1gistliPinn/ChatGPT4/Examples/Cubase 6 Full Version Free Download Torrent [REPACK].md deleted file mode 100644 index 962254a20601b7e9dd3a934ff5f93abe138ac33b..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Cubase 6 Full Version Free Download Torrent [REPACK].md +++ /dev/null @@ -1,14 +0,0 @@ -
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-Current sounds can only be downloaded using the Steinberg Download Assistant. ... 1, MAC WINDOWS, Groove Agent ONE/SE/4 VST Toolkit, 800MB.... 3, MAC WINDOWS, Groove Agent SE/5 VST Toolkit, 2GB. ... -4, MAC WINDOWS, Groove Agent SE/5 VST Toolkit, 2 GB -Jul 12 2019 Download. -Groove Agent SE 5.0 VST, AAX, AU WIN.OSX x86 x64 Release Year/Date: 05.2019 Version: 5.0 Developer: Steinberg Website -Feb 7 -2014 · Groove Agent SE 5.0. -Description: Steinberg Groove Agent puts at your disposal a set of tools and ... VST, AAX, AU -Mar 9 2015 Download torrent for free. -distribution statistics. ... 8a78ff9644
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- diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CarX Drift Racing Lite MOD APK OBB Everything You Need to Know Before You Download.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CarX Drift Racing Lite MOD APK OBB Everything You Need to Know Before You Download.md deleted file mode 100644 index 1a58c33f2ea2691aeeb4ddabc2f3ead811761c26..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CarX Drift Racing Lite MOD APK OBB Everything You Need to Know Before You Download.md +++ /dev/null @@ -1,90 +0,0 @@ - -
CarX Drift Racing Lite Mod APK OBB: A Guide for Drift Racing Fans
-Do you love drifting and racing games? Do you want to experience the thrill of driving realistic cars on challenging tracks? If yes, then you should try CarX Drift Racing Lite, a popular game that lets you enjoy the best of both worlds. And if you want to make the game even more fun and exciting, you should download CarX Drift Racing Lite Mod APK OBB, a modified version that gives you unlimited money, coins, cars, tracks, and more. In this article, we will tell you everything you need to know about CarX Drift Racing Lite and its mod apk obb version.
-What is CarX Drift Racing Lite?
-CarX Drift Racing Lite is a racing game that focuses on drifting, a driving technique where the driver intentionally oversteers the car to make it slide sideways. The game is developed by CarX Technologies, a company that specializes in creating realistic car physics and graphics for games. CarX Drift Racing Lite is a lite version of CarX Drift Racing, which means it has fewer cars, tracks, and features than the original game. However, it still offers a lot of fun and entertainment for drift racing fans.
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Features of CarX Drift Racing Lite
-Realistic physics and graphics
-One of the main attractions of CarX Drift Racing Lite is its realistic physics and graphics. The game uses a sophisticated car physics engine that simulates the behavior of real cars on different surfaces and conditions. The game also has stunning graphics that create a immersive environment for the players. You can see the smoke, dust, sparks, and tire marks as you drift your car on the track. You can also feel the vibration and sound effects as you accelerate, brake, and steer your car.
-Customizable cars and tracks
-Another feature of CarX Drift Racing Lite is its customizable cars and tracks. The game allows you to choose from a variety of cars, each with its own characteristics and performance. You can also customize your car's appearance, color, wheels, engine, suspension, and more. You can also choose from different tracks, each with its own layout, difficulty, and scenery. You can also adjust the weather, time of day, and camera angle to suit your preference.
-Online and offline modes
-A third feature of CarX Drift Racing Lite is its online and offline modes. The game lets you play either online or offline, depending on your internet connection and mood. If you play online, you can compete with other players from around the world in various modes such as time attack, ghost mode, or multiplayer mode. You can also chat with other players and share your replays and screenshots. If you play offline, you can practice your skills in single-player mode or challenge yourself in career mode.
-Why download CarX Drift Racing Lite Mod APK OBB?
-If you are already enjoying CarX Drift Racing Lite, you might wonder why you should download CarX Drift Racing Lite Mod APK OBB. Well, the answer is simple: because it makes the game even better. CarX Drift Racing Lite Mod APK OBB is a modified version of the game that gives you access to unlimited money
No ads and no root required
-With CarX Drift Racing Lite Mod APK OBB, you don't have to deal with annoying ads and pop-ups that interrupt your gameplay. You can enjoy the game without any distractions or interruptions. You also don't need to root your device to install the mod apk obb files. You can simply follow the instructions below and enjoy the game safely and smoothly.
-How to download and install CarX Drift Racing Lite Mod APK OBB?
-Step 1: Download the mod apk and obb files from a trusted source
-The first step is to download the mod apk and obb files from a trusted source. You can use the link provided at the end of this article to download the files. Make sure you have enough storage space on your device before downloading the files.
-Step 2: Enable unknown sources on your device settings
-The second step is to enable unknown sources on your device settings. This will allow you to install apps from sources other than the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and turn it on. You may also need to disable any antivirus or security apps that may interfere with the installation process.
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Step 3: Install the mod apk file and extract the obb file to the Android/obb folder
-The third step is to install the mod apk file and extract the obb file to the Android/obb folder. To do this, locate the downloaded files on your device, then tap on the mod apk file and follow the instructions to install it. Then, use a file manager app to extract the obb file to the Android/obb folder. If you don't have a file manager app, you can download one from the Google Play Store. Make sure you create a folder named com.CarXTech.CarXDriftRacingLite inside the Android/obb folder and place the extracted obb file there.
-Conclusion
-CarX Drift Racing Lite is a great game for drift racing fans who want to experience realistic physics and graphics, customizable cars and tracks, and online and offline modes. However, if you want to make the game even more enjoyable and exciting, you should download CarX Drift Racing Lite Mod APK OBB, which gives you unlimited money, coins, cars, tracks, and more. You can download CarX Drift Racing Lite Mod APK OBB from the link below and follow the steps above to install it on your device. Have fun drifting and racing!
-FAQs
-Here are some of the frequently asked questions about CarX Drift Racing Lite Mod APK OBB:
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- Is CarX Drift Racing Lite Mod APK OBB safe to use? -
- Is CarX Drift Racing Lite Mod APK OBB compatible with my device? -
- Can I play CarX Drift Racing Lite Mod APK OBB with my friends? -
- Can I update CarX Drift Racing Lite Mod APK OBB? -
- Where can I download CarX Drift Racing Lite Mod APK OBB? -
Yes, CarX Drift Racing Lite Mod APK OBB is safe to use as long as you download it from a trusted source and follow the installation instructions carefully. However, you should always be careful when downloading and installing any mod apk obb files from unknown sources as they may contain viruses or malware that can harm your device.
-CarX Drift Racing Lite Mod APK OBB is compatible with most Android devices that have Android 4.1 or higher versions. However, some devices may not support some of the features or functions of the game due to hardware or software limitations.
-Yes, you can play CarX Drift Racing Lite Mod APK OBB with your friends online in multiplayer mode. You can also chat with them and share your replays and screenshots.
-No, you cannot update CarX Drift Racing Lite Mod APK OBB as it is a modified version of the game that may not be compatible with the latest updates from the official developers. If you want to update the game, you will have to uninstall the mod apk obb files and install the original version from the Google Play Store.
-You can download CarX Drift Racing Lite Mod APK OBB from this link: CarX Drift Racing Lite Mod APK OBB Download.
--
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download ETS2 Mods for Euro Truck Simulator 2 and Enhance Your Gaming Experience.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download ETS2 Mods for Euro Truck Simulator 2 and Enhance Your Gaming Experience.md deleted file mode 100644 index 049d638a7d7f013ef0ef513349501dfbd29a3b20..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download ETS2 Mods for Euro Truck Simulator 2 and Enhance Your Gaming Experience.md +++ /dev/null @@ -1,110 +0,0 @@ -
-
Euro Truck Simulator 2 For Mobile - Everything You Need to Know
-Do you love driving trucks and exploring new places? Do you want to experience the thrill of being a truck driver from the comfort of your home? If you answered yes to any of these questions, then you should definitely check out Euro Truck Simulator 2, one of the most popular and realistic truck driving simulator games ever made. And the best part is, you can now play it on your mobile device thanks to ets2.mobi, a website that offers ETS2 for Android and iOS. In this article, we will tell you everything you need to know about Euro Truck Simulator 2 for mobile, including what it is, how to download and install it, how to play it, and why you should try it today.
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What is Euro Truck Simulator 2?
-Euro Truck Simulator 2, or ETS2 for short, is a game that simulates the life of a truck driver in Europe. It was developed and published by SCS Software, a Czech company that specializes in creating simulation games. ETS2 was released in 2012 for Windows, Linux, and Mac OS, and has since received many updates and expansions that added new features, content, and improvements. ETS2 has three main aspects that make it so appealing and realistic: a truck driving simulator, a huge map of Europe, and a variety of trucks and customization options.
-A realistic truck driving simulator game
-ETS2 is not just a game where you drive a truck from point A to point B. It is a game where you have to follow the rules of the road, deal with traffic, weather, fuel consumption, fatigue, cargo delivery, fines, repairs, and more. You have to plan your routes carefully, choose the best contracts, manage your finances, hire drivers, buy garages, and grow your own trucking company. You also have to take care of your truck, which can get damaged or break down if you drive recklessly or neglect maintenance. You can also customize your truck with different parts, accessories, paint jobs, decals, and more.
-A huge map of Europe to explore
-ETS2 features a massive map of Europe that covers over 70 cities in 13 countries. You can drive across different landscapes, such as mountains, forests, fields, deserts, coasts, and urban areas. You can also visit famous landmarks, such as the Eiffel Tower in Paris, the Brandenburg Gate in Berlin, the Colosseum in Rome, and more. The map is constantly updated with new regions and roads that add more diversity and realism to the game. You can also download mods that add even more countries and locations to the game.
-A variety of trucks and customization options
-ETS2 offers a wide range of trucks from different manufacturers, such as Mercedes-Benz, Volvo, Scania, MAN, DAF, Renault, Iveco, and more. Each truck has its own specifications, performance, handling, sound effects, and interior design. You can also customize your truck with different parts, accessories, paint jobs, decals, and more. You can also download mods that add new trucks or modify existing ones.
-What is ets2.mobi?
-ets2.mobi is a website that offers Euro Truck Simulator 2 for mobile devices. It allows you to download and install ETS2 on your Android or iOS phone or tablet without any hassle. You don't need to root or jailbreak your device or use any complicated software or hardware. You just need to follow a few simple steps and you will be able to enjoy ETS2 on your mobile device in no time.
-How to download and install ETS2 on your phone or tablet
-Downloading and installing ETS2 on your mobile device is very easy and fast. Here are the steps you need to follow:
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- Go to ets2.mobi on your mobile browser and click on the download button. -
- Choose your device type (Android or iOS) and wait for the download to finish. -
- Open the downloaded file and follow the instructions to install ETS2 on your device. -
- Launch the game and enjoy playing ETS2 on your mobile device. -
Note: You may need to enable unknown sources or trust the app in your device settings before installing ETS2. This is a normal procedure for installing apps from outside the official app stores and it does not harm your device or data in any way.
-The features and benefits of playing ETS2 on mobile
-Playing ETS2 on your mobile device has many advantages over playing it on a PC or console. Here are some of them:
--
-
- You can play ETS2 anytime and anywhere you want, as long as you have your mobile device with you. -
- You can save space and money, as you don't need to buy or maintain a PC or console to play ETS2. -
- You can enjoy the same graphics, gameplay, and content as the PC version of ETS2, as ets2.mobi uses a special technology that optimizes the game for mobile devices without compromising quality or performance. -
- You can connect with other players online and join multiplayer sessions, chat with them, share your progress, and more. -
- You can access exclusive features and bonuses that are only available for mobile users, such as special trucks, skins, events, rewards, and more. -
How to play ETS2 on mobile?
-Playing ETS2 on your mobile device is very similar to playing it on a PC or console. You just need to learn the controls and interface of the game and you will be ready to hit the road. Here are some tips and tricks to help you get started:
-The controls and interface of ETS2 on mobile
-The controls and interface of ETS2 on mobile are designed to be intuitive and user-friendly. You can choose between different control modes, such as tilt, touch, or steering wheel. You can also customize the buttons, sensitivity, and layout of the controls according to your preference. You can also use voice commands to control some functions of the game, such as navigation, radio, or horn.
-The interface of ETS2 on mobile consists of various elements that display important information and options for the game. You can see your speedometer, fuel gauge, damage indicator, map, GPS, mirrors, dashboard, and more. You can also access the menu, settings, profile, achievements, statistics, leaderboards, and more. You can also interact with various objects and characters in the game, such as toll booths, gas stations, rest areas, traffic lights, pedestrians, police officers, and more.
-The game modes and challenges of ETS2 on mobile
-ETS2 on mobile offers various game modes and challenges that suit different play styles and preferences. You can choose between different difficulty levels, such as easy, normal, or hard, depending on how realistic and challenging you want the game to be. You can also choose between different game modes, such as: - Career mode: This is the main mode of the game, where you start as a rookie driver and work your way up to become a successful trucker. You have to complete various contracts, deliver cargo, earn money, buy and upgrade trucks, hire drivers, and expand your business. You can also customize your profile, choose your preferred truck brand, and join a company of your choice. - Free mode: This is a mode where you can drive freely across the map without any time or money constraints. You can explore different regions, visit landmarks, test different trucks, and enjoy the scenery. You can also switch between day and night, change the weather, and adjust the traffic density. - Challenge mode: This is a mode where you can test your skills and compete with other players in various challenges, such as parking, racing, cargo delivery, fuel economy, and more. You can also create your own challenges and share them with other players online.
The tips and tricks to enjoy ETS2 on mobile
-ETS2 on mobile is a fun and immersive game that can keep you entertained for hours. However, it can also be challenging and frustrating at times, especially if you are new to the game or not familiar with the controls. Here are some tips and tricks that can help you enjoy ETS2 on mobile more: - Follow the tutorial: The game offers a tutorial that teaches you the basics of the game, such as how to drive, park, deliver cargo, use the GPS, and more. It is highly recommended that you follow the tutorial before you start playing the game, as it will help you avoid many mistakes and problems later on. - Adjust the settings: The game allows you to adjust various settings that can affect your gameplay experience, such as graphics quality, sound volume, control mode, sensitivity, language, units, and more. You should experiment with different settings and find the ones that suit your device and preference best. - Save frequently: The game has an autosave feature that saves your progress every time you complete a contract or enter a new city. However, it is also advisable that you manually save your game often, especially before you start a long or difficult journey. This way, you can avoid losing your progress or money if something goes wrong or if the game crashes. - Drive carefully: The game simulates realistic driving physics and mechanics, which means that you have to drive carefully and follow the rules of the road. You have to pay attention to your speed limit, traffic signs, signals, lanes, pedestrians, and other vehicles. You also have to watch out for your fuel level, damage, fatigue, and cargo weight. If you drive recklessly or break the law, you can get fined, lose your cargo, damage your truck, or even cause accidents. You can also use the cruise control, speed limiter, and brake assist features to help you drive more smoothly and safely. - Use the GPS: The game provides you with a GPS system that shows you the best route to your destination, the distance and time remaining, the speed limit, and the traffic conditions. You can also use the map view to see the whole map of Europe and plan your routes ahead. You can also set waypoints, zoom in and out, and switch between 2D and 3D modes. The GPS is a very useful tool that can help you navigate the roads and avoid getting lost or stuck. - Enjoy the scenery: The game features stunning graphics and realistic sound effects that create a immersive atmosphere for the game. You can see the changing landscapes, weather, seasons, day and night cycles, and more. You can also listen to the radio, which offers various stations that play different genres of music and news. You can also use the photo mode to take pictures of your truck or the scenery and share them with other players online.
Conclusion
-Euro Truck Simulator 2 is a game that lets you experience the life of a truck driver in Europe. You can drive across different countries, deliver cargo, earn money, buy and upgrade trucks, hire drivers, and grow your own trucking company. You can also customize your truck with different parts, accessories, paint jobs, decals, and more. You can also download mods that add new trucks or modify existing ones.
-ETS2 is now available for mobile devices thanks to ets2.mobi, a website that offers ETS2 for Android and iOS. You can download and install ETS2 on your phone or tablet without any hassle. You can enjoy the same graphics, gameplay, and content as the PC version of ETS2, as well as exclusive features and bonuses for mobile users. You can also connect with other players online and join multiplayer sessions, chat with them, share your progress, and more.
-ETS2 is a fun and immersive game that can keep you entertained for hours. However, it can also be challenging and frustrating at times, especially if you are new to the game or not familiar with the controls. That's why we have provided you with some tips and tricks that can help you enjoy ETS2 on mobile more.
-If you love driving trucks and exploring new places, then you should definitely try ETS2 on mobile today. It is a game that will make you feel like a real truck driver in Europe.
-FAQs
-Here are some frequently asked questions about ETS2 on mobile:
--
-
- Is ETS2 on mobile free? -
- Is ETS2 on mobile safe? -
- Is ETS2 on mobile compatible with my device? -
- Can I play ETS2 on mobile offline? -
- How can I contact the developers or report a bug? -
- Email: support@ets2.mobi -
- Facebook: https://www.facebook.com/ets2mobi -
- Twitter: https://twitter.com/ets2mobi -
- Instagram: https://www.instagram.com/ets2mobi -
- Una nueva interfaz de usuario más colorida y dinámica. -
- Un nuevo sistema de clasificación que muestra tu nivel y progreso en diferentes minijuegos. -
- Un nuevo sistema de chat que soporta mensajes de voz y texto. -
- Un nuevo sistema de recompensas que te da bonificaciones diarias de inicio de sesión, sorteos y logros. -
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- Ir al sitio web oficial de Blockman GO y haga clic en el botón "Descargar". - -
- Espera a que termine la descarga y luego abre el archivo APK. -
- Permite la instalación de fuentes desconocidas si tu dispositivo lo solicita. -
- Siga las instrucciones en la pantalla para completar la instalación. -
- Iniciar la aplicación y disfrutar de la reproducción de Blockman GO nueva versión APK. -
- Solo descargar el archivo APK desde el sitio web oficial o la Google Play Store. No confíes en fuentes de terceros que dicen ofrecer el archivo APK. -
- Escanear el archivo APK con un software antivirus fiable antes de abrirlo. -
- No conceda permisos innecesarios ni acceso a la aplicación. -
- Actualizar la aplicación regularmente para obtener los últimos parches de seguridad y correcciones de errores. -
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- Configuración de canal personalizado y función de onda: Esta función le permite cambiar los canales a pedido según sus requisitos personalizados. Por ejemplo, si una boquilla de color está obstruida, puede usar un canal blanco y usar tinta de color en ese canal. También puede ajustar la configuración de onda para reducir los problemas de bandas en las impresoras UV. - -
- Compatibilidad ampliada de controladores y impresoras: Esta función admite más modelos de impresoras Epson, como Stylus Photo, EcoTank, SureColor, Stylus Pro, Expression, etc. -
- Calidad y velocidad de impresión mejoradas: AcroRip 9.0 3 puede optimizar la calidad y la velocidad de impresión mediante el control de los canales de tinta, el uso de la impresión de una sola pasada y el uso de la configuración RIP boost. También puede reducir los problemas de bandas mediante el uso de la función de onda. -
- Menor consumo de tinta y costo: AcroRip 9.0 3 puede ahorrar tinta mediante el uso de ajustes de canal personalizados y el ajuste de la densidad de tinta y el tamaño de la gota. También puede usar tinta de color en canales blancos si es necesario, lo que puede reducir el desperdicio de tinta blanca. -
- Precisión de color mejorada y perfiles ICC: AcroRip 9.0 3 puede mejorar la precisión y consistencia del color mediante el uso de perfiles ICC y herramientas de gestión de color. También puede soportar CMYK, RGB y colores planos. -
- Necesidad de un dongle USB especial para ejecutar el software: AcroRip 9.0 3 requiere un dongle USB especial para activar el software y ejecutarlo en su computadora. Esto significa que necesita comprar el dongle desde el sitio web oficial o un distribuidor autorizado, y debe mantenerlo conectado cada vez que use el software. Si pierde o daña el dongle, es posible que ya no pueda usar el software. - -
- Cadlink: Cadlink es un software RIP que admite varios tipos de impresoras, como UV, DTG, solvente, eco-solvente, etc. Tiene características como gestión de tinta blanca, creación de perfiles ICC, corrección de color, impresión de datos variables, etc. -
- EKprint: EKprint es un software RIP diseñado para impresoras DTG que utilizan cabezales de impresión Epson. Tiene características tales como impresión de un paso, cálculo de costo de tinta, verificación de boquilla, limpieza de la cabeza, etc. -
- Otras opciones de software RIP: Hay muchas otras opciones de software RIP entre las que puede elegir, dependiendo de su modelo de impresora, presupuesto y preferencias. Algunos ejemplos son Wasatch SoftRIP, Onyx RIPCenter, PhotoPrint Server Pro, etc. -
- Descargue el archivo de software desde el sitio web o el CD: Tendrá que descargar el archivo de software desde el sitio web o insertar el CD en su computadora. -
- Extraiga el archivo y ejecute el archivo setup.exe: Necesitará extraer el archivo usando un programa como WinRAR o WinZip y ejecutar el archivo setup.exe como administrador. -
- Siga las instrucciones del asistente de instalación: Tendrá que seguir las instrucciones del asistente de instalación y elegir su idioma, carpeta de destino, modelo de impresora, etc. -
- Conecte el dongle USB en su computadora: Necesitará conectar el dongle USB a su computadora antes de iniciar el software. - -
- Cuestiones legales: Descargar y usar una versión agrietada de AcroRip 9.0 3 es ilegal y viola los derechos de propiedad intelectual del desarrollador de software. Usted podría enfrentar acciones legales, multas o incluso tiempo en la cárcel si lo atrapan usando una versión rota del software. -
- Problemas de seguridad: Descargar y usar una versión agrietada de AcroRip 9.0 3 es arriesgado y expone su computadora a malware, virus, spyware, ransomware y otros programas maliciosos. Puede perder sus datos, comprometer su privacidad o dañar su sistema si instala una versión rota del software. -
- Problemas de rendimiento: Descargar y usar una versión agrietada de AcroRip 9.0 3 no es confiable e inestable. Es posible que experimente errores, bloqueos, bloqueos o problemas técnicos al usar una versión rota del software. También puede perderse actualizaciones, correcciones de errores y nuevas características que ofrece la versión oficial del software. -
- ¿Cuál es el precio de AcroRip 9.0 3? -
- ¿Dónde puedo comprar AcroRip 9.0 3? - -
- ¿Cómo puedo actualizar AcroRip 9.0 3? -
- ¿Cuáles son los requisitos del sistema para AcroRip 9.0 3? -
- Sistema operativo: Windows 7/8/10 (32 bits o 64 bits) -
- Procesador: Intel Core i3 o superior -
- Memoria: 4 GB de RAM o superior -
- Espacio en disco duro: 1 GB o superior -
- Pantalla: 1024 x 768 resolución o superior -
- Impresora: Impresora Epson con cabezal de impresión Epson -
- ¿Cómo puedo contactar al equipo de soporte de AcroRip? -
- ¿Cómo puedo aprender más sobre AcroRip 9.0 3? -
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- Q: How can I contact Air India customer care? -
- Q: How can I get a refund for my canceled flight ticket? -
- Q: How can I check the status of my flight with Air India? -
- Q: How can I upgrade my flight ticket with Air India? -
- Q: How can I earn and redeem miles with Air India Flying Returns? -
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- Jurassic Survival Island: ARK 2 Evolve: This is a survival game where you have to survive on an island full of dinosaurs and other dangers. You can craft weapons, tools, and shelters from the resources you find on the island. You can also tame and ride some dinosaurs or fight them for food and loot. You can also explore the island and discover its secrets. -
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- Looting buildings: You can enter and loot any building in the city by breaking the door or window. You can find cash, weapons, ammo, health kits, and other items inside the buildings. However, be careful, as some buildings may have alarms, traps, or enemies inside. -
- Stealing vehicles: You can steal any vehicle in the city by approaching it and pressing the "E" key on your keyboard or clicking on the vehicle icon. You can then drive or fly the vehicle around the city. You can also find cash, weapons, ammo, health kits, and other items inside the vehicles. However, be careful, as some vehicles may have locks, alarms, or enemies inside. -
- Killing players: You can kill any player in the city by shooting, stabbing, punching, or running them over. You can then loot their corpse and take their cash, weapons, ammo, health kits, and other items. However, be careful, as some players may have friends, allies, or enemies nearby. -
- Stealing: You can steal from other players by taking their cash or items without their consent or knowledge. You can also steal from other players by hacking their vehicles or devices. Stealing from other players can help you increase your score faster, improve your equipment easier, and weaken your enemies better. - -
- Is Gangstar New York available on other platforms? -
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- You can play the game before it is officially released in your region. New State Mobile is currently in the pre-registration stage, which means that it is not available for download in all countries yet. However, if you download the APK and OBB files from a third-party source, you can bypass the regional restrictions and play the game early. -
- You can access the latest updates and features of the game. Sometimes, the developers may release new updates and features for the game that are not yet available on Google Play Store. If you download the APK and OBB files from a third-party source, you can get access to these updates and features before they are officially released. -
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- You may encounter errors or bugs in the game. Downloading APK and OBB files from third-party sources may not guarantee that they are compatible with your device or the latest version of the game. You may experience errors or bugs in the game that can affect your gameplay or performance. You should always check the compatibility and quality of the files before downloading them. -
- Open Google Play Store on your device and search for New State Mobile. -
- Tap on the game icon and then tap on the Pre-register button. -
- You will see a confirmation message that says "You're in! You'll be notified when this game is released". Tap on OK. -
- You can also tap on the Learn more button to see more information about the game, such as screenshots, videos, description, and ratings. -
- You can also tap on the Share button to share the game with your friends and invite them to pre-register as well. -
- APKPure: This is a popular website that offers free and safe APK and OBB files for various Android games and apps. You can download New State Mobile APK and OBB files from this website by searching for the game name and clicking on the download button. -
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- Open the website of the third-party source on your device and search for New State Mobile. -
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- You will see a screen that says "Do you want to install this application?". Tap on Install and wait for the installation to finish. -
- Do not launch the game yet. You will have to extract the OBB file first. -
- Open the file manager app on your device and go to the download folder where you saved the OBB file. -
- Tap and hold on the OBB file and choose Extract from the menu. You will see a folder named com.krafton.newstate. -
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- Make sure that the path of the OBB file is Android > obb > com.krafton.newstate > main.1.com.krafton.newstate.obb. -
- Q: What is the difference between APK and OBB files? -
- A: APK stands for Android Package Kit, which is the file format used to distribute and install applications on Android devices. OBB stands for Opaque Binary Blob, which is a file format used to store large amounts of data that are not required to run the application. APK and OBB files work together to run an application or a game on an Android device. -
- Q: Is New State Mobile free to play? -
- A: Yes, New State Mobile is free to play. However, it may contain in-app purchases or ads that require real money. -
- Q: Is New State Mobile compatible with iOS devices? -
- A: No, New State Mobile is currently only compatible with Android devices. The developers have not announced any plans to release the game for iOS devices yet. -
- Q: How can I update New State Mobile on my device? -
- A: If you downloaded New State Mobile from Google Play Store, you can update it automatically or manually through Google Play Store. If you downloaded New State Mobile from third-party sources, you will have to download and install the latest version of the APK and OBB files from the same source. -
- Q: How can I contact the developers of New State Mobile? -
- A: You can contact the developers of New State Mobile through their official website or their social media accounts. You can also send them an email at newstate @newstate.com. -
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- Business profile: You can create a professional profile for your business that includes your name, logo, address, website, email, and description. -
- Catalog: You can showcase your products and services in a catalog that your customers can browse and order from. -
- Labels: You can organize your chats and contacts with labels such as new customer, pending payment, order complete, etc. -
- Quick replies: You can save and reuse frequently sent messages such as greetings, thank you notes, FAQs, etc. -
- Automated messages: You can set up messages that are sent automatically when you are away, busy, or have a specific event. -
- Statistics: You can view metrics such as how many messages were sent, delivered, read, and received by your customers. -
- Customer engagement: You can connect with your customers in a personal and convenient way, and build trust and loyalty. -
- Sales growth: You can increase your sales by showcasing your products and services, sending offers and promotions, and accepting orders and payments. -
- Customer support: You can provide fast and efficient customer service by answering queries, resolving issues, and sending feedback. -
- Brand awareness: You can increase your brand visibility and reach by creating a professional profile and sharing it with your customers and prospects. -
- Backup your data: Before installing any modded app, it is always recommended to backup your data from the original app. You can do this by going to Settings > Chats > Chat backup in WhatsApp or WhatsApp Business. -
- Uninstall the original app: To avoid any conflicts or errors, you need to uninstall the original app from your device. You can do this by going to Settings > Apps > WhatsApp or WhatsApp Business > Uninstall. -
- Download the modded app: You can download the modded app from any reliable source online. For example, you can download GBWhatsApp Business from [here](^
Risks of Using WhatsApp Business Clone APK
-While using WhatsApp Business Clone APK may seem tempting, it also comes with some risks that you should be aware of. Some of the risks are:
--
-
- Malware infection: Since WhatsApp Business Clone APK is not an official app from WhatsApp, it may contain malicious code that can harm your device or steal your data. You may also expose your device to viruses, spyware, ransomware, and other threats by downloading the app from untrusted sources. -
- Account suspension: WhatsApp has a strict policy against using modded or cloned apps that violate its terms of service. If WhatsApp detects that you are using WhatsApp Business Clone APK, it may temporarily or permanently ban your account from using the service. You may also lose access to your chats, contacts, and backups. -
- Data leakage: WhatsApp Business Clone APK may not offer the same level of security and privacy as the original app. It may not encrypt your messages or backups, or it may share your data with third parties without your consent. You may also compromise your business data and reputation by using an unauthorized app. -
Conclusion
-WhatsApp Business is a great app for small and medium businesses that want to communicate with their customers and clients in a convenient and professional way. It offers many features and benefits that can help you grow your business and provide better customer service. However, if you want to use multiple WhatsApp accounts on one device, you should be careful about using WhatsApp Business Clone APK. This is a modded app that is not authorized by WhatsApp and may pose some security and privacy risks. You may also face account suspension or data loss if you use it. Therefore, it is advisable to use only official apps from WhatsApp and avoid any clones or mods.
-FAQs
-Here are some frequently asked questions about WhatsApp Business and WhatsApp Business Clone APK:
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-
- Can I use both WhatsApp and WhatsApp Business on the same device?
Yes, you can use both apps on the same device with different phone numbers. However, you cannot use the same phone number for both apps.
- - Can I use WhatsApp Business on multiple devices?
No, you can only use WhatsApp Business on one device at a time. If you try to log in to another device, you will be logged out from the previous one.
- - Can I backup my WhatsApp Business chats to Google Drive or iCloud?
Yes, you can backup your chats to Google Drive on Android devices or iCloud on iOS devices. However, these backups are not encrypted and may be accessed by Google or Apple.
- - Can I restore my WhatsApp chats to WhatsApp Business or vice versa?
No, you cannot restore your chats from one app to another. The chat history is not compatible between the two apps.
- - Can I use WhatsApp Web or Desktop with WhatsApp Business?
Yes, you can use WhatsApp Web or Desktop with WhatsApp Business by scanning the QR code on your computer screen with your phone.
-
-
-
\ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/evaluation/evaluator.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/evaluation/evaluator.py deleted file mode 100644 index 2c85d90eaa5236773a901a68c43c28d42bfd47ec..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/evaluation/evaluator.py +++ /dev/null @@ -1,228 +0,0 @@ -# ------------------------------------------------------------------------------ -# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/evaluator.py -# Modified by Jitesh Jain (https://github.com/praeclarumjj3) -# ------------------------------------------------------------------------------ - -import datetime -import logging -import time -from collections import OrderedDict, abc -from contextlib import ExitStack, contextmanager -from typing import List, Union -import torch -from torch import nn - -from annotator.oneformer.detectron2.utils.comm import get_world_size, is_main_process -from annotator.oneformer.detectron2.utils.logger import log_every_n_seconds - - -class DatasetEvaluator: - """ - Base class for a dataset evaluator. - - The function :func:`inference_on_dataset` runs the model over - all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs. - - This class will accumulate information of the inputs/outputs (by :meth:`process`), - and produce evaluation results in the end (by :meth:`evaluate`). - """ - - def reset(self): - """ - Preparation for a new round of evaluation. - Should be called before starting a round of evaluation. - """ - pass - - def process(self, inputs, outputs): - """ - Process the pair of inputs and outputs. - If they contain batches, the pairs can be consumed one-by-one using `zip`: - - .. code-block:: python - - for input_, output in zip(inputs, outputs): - # do evaluation on single input/output pair - ... - - Args: - inputs (list): the inputs that's used to call the model. - outputs (list): the return value of `model(inputs)` - """ - pass - - def evaluate(self): - """ - Evaluate/summarize the performance, after processing all input/output pairs. - - Returns: - dict: - A new evaluator class can return a dict of arbitrary format - as long as the user can process the results. - In our train_net.py, we expect the following format: - - * key: the name of the task (e.g., bbox) - * value: a dict of {metric name: score}, e.g.: {"AP50": 80} - """ - pass - - -class DatasetEvaluators(DatasetEvaluator): - """ - Wrapper class to combine multiple :class:`DatasetEvaluator` instances. - - This class dispatches every evaluation call to - all of its :class:`DatasetEvaluator`. - """ - - def __init__(self, evaluators): - """ - Args: - evaluators (list): the evaluators to combine. - """ - super().__init__() - self._evaluators = evaluators - - def reset(self): - for evaluator in self._evaluators: - evaluator.reset() - - def process(self, inputs, outputs): - for evaluator in self._evaluators: - evaluator.process(inputs, outputs) - - def evaluate(self): - results = OrderedDict() - for evaluator in self._evaluators: - result = evaluator.evaluate() - if is_main_process() and result is not None: - for k, v in result.items(): - assert ( - k not in results - ), "Different evaluators produce results with the same key {}".format(k) - results[k] = v - return results - - -def inference_on_dataset( - model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None] -): - """ - Run model on the data_loader and evaluate the metrics with evaluator. - Also benchmark the inference speed of `model.__call__` accurately. - The model will be used in eval mode. - - Args: - model (callable): a callable which takes an object from - `data_loader` and returns some outputs. - - If it's an nn.Module, it will be temporarily set to `eval` mode. - If you wish to evaluate a model in `training` mode instead, you can - wrap the given model and override its behavior of `.eval()` and `.train()`. - data_loader: an iterable object with a length. - The elements it generates will be the inputs to the model. - evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark, - but don't want to do any evaluation. - - Returns: - The return value of `evaluator.evaluate()` - """ - num_devices = get_world_size() - logger = logging.getLogger(__name__) - logger.info("Start inference on {} batches".format(len(data_loader))) - - total = len(data_loader) # inference data loader must have a fixed length - if evaluator is None: - # create a no-op evaluator - evaluator = DatasetEvaluators([]) - if isinstance(evaluator, abc.MutableSequence): - evaluator = DatasetEvaluators(evaluator) - evaluator.reset() - - num_warmup = min(5, total - 1) - start_time = time.perf_counter() - total_data_time = 0 - total_compute_time = 0 - total_eval_time = 0 - with ExitStack() as stack: - if isinstance(model, nn.Module): - stack.enter_context(inference_context(model)) - stack.enter_context(torch.no_grad()) - - start_data_time = time.perf_counter() - for idx, inputs in enumerate(data_loader): - total_data_time += time.perf_counter() - start_data_time - if idx == num_warmup: - start_time = time.perf_counter() - total_data_time = 0 - total_compute_time = 0 - total_eval_time = 0 - - start_compute_time = time.perf_counter() - outputs = model(inputs) - if torch.cuda.is_available(): - torch.cuda.synchronize() - total_compute_time += time.perf_counter() - start_compute_time - - start_eval_time = time.perf_counter() - evaluator.process(inputs, outputs) - total_eval_time += time.perf_counter() - start_eval_time - - iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup) - data_seconds_per_iter = total_data_time / iters_after_start - compute_seconds_per_iter = total_compute_time / iters_after_start - eval_seconds_per_iter = total_eval_time / iters_after_start - total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start - if idx >= num_warmup * 2 or compute_seconds_per_iter > 5: - eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1))) - log_every_n_seconds( - logging.INFO, - ( - f"Inference done {idx + 1}/{total}. " - f"Dataloading: {data_seconds_per_iter:.4f} s/iter. " - f"Inference: {compute_seconds_per_iter:.4f} s/iter. " - f"Eval: {eval_seconds_per_iter:.4f} s/iter. " - f"Total: {total_seconds_per_iter:.4f} s/iter. " - f"ETA={eta}" - ), - n=5, - ) - start_data_time = time.perf_counter() - - # Measure the time only for this worker (before the synchronization barrier) - total_time = time.perf_counter() - start_time - total_time_str = str(datetime.timedelta(seconds=total_time)) - # NOTE this format is parsed by grep - logger.info( - "Total inference time: {} ({:.6f} s / iter per device, on {} devices)".format( - total_time_str, total_time / (total - num_warmup), num_devices - ) - ) - total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time))) - logger.info( - "Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)".format( - total_compute_time_str, total_compute_time / (total - num_warmup), num_devices - ) - ) - - results = evaluator.evaluate() - # An evaluator may return None when not in main process. - # Replace it by an empty dict instead to make it easier for downstream code to handle - if results is None: - results = {} - return results - - -@contextmanager -def inference_context(model): - """ - A context where the model is temporarily changed to eval mode, - and restored to previous mode afterwards. - - Args: - model: a torch Module - """ - training_mode = model.training - model.eval() - yield - model.train(training_mode) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/psa_mask.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/psa_mask.py deleted file mode 100644 index cdf14e62b50e8d4dd6856c94333c703bcc4c9ab6..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/psa_mask.py +++ /dev/null @@ -1,92 +0,0 @@ -# Modified from https://github.com/hszhao/semseg/blob/master/lib/psa -from torch import nn -from torch.autograd import Function -from torch.nn.modules.utils import _pair - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', - ['psamask_forward', 'psamask_backward']) - - -class PSAMaskFunction(Function): - - @staticmethod - def symbolic(g, input, psa_type, mask_size): - return g.op( - 'mmcv::MMCVPSAMask', - input, - psa_type_i=psa_type, - mask_size_i=mask_size) - - @staticmethod - def forward(ctx, input, psa_type, mask_size): - ctx.psa_type = psa_type - ctx.mask_size = _pair(mask_size) - ctx.save_for_backward(input) - - h_mask, w_mask = ctx.mask_size - batch_size, channels, h_feature, w_feature = input.size() - assert channels == h_mask * w_mask - output = input.new_zeros( - (batch_size, h_feature * w_feature, h_feature, w_feature)) - - ext_module.psamask_forward( - input, - output, - psa_type=psa_type, - num_=batch_size, - h_feature=h_feature, - w_feature=w_feature, - h_mask=h_mask, - w_mask=w_mask, - half_h_mask=(h_mask - 1) // 2, - half_w_mask=(w_mask - 1) // 2) - return output - - @staticmethod - def backward(ctx, grad_output): - input = ctx.saved_tensors[0] - psa_type = ctx.psa_type - h_mask, w_mask = ctx.mask_size - batch_size, channels, h_feature, w_feature = input.size() - grad_input = grad_output.new_zeros( - (batch_size, channels, h_feature, w_feature)) - ext_module.psamask_backward( - grad_output, - grad_input, - psa_type=psa_type, - num_=batch_size, - h_feature=h_feature, - w_feature=w_feature, - h_mask=h_mask, - w_mask=w_mask, - half_h_mask=(h_mask - 1) // 2, - half_w_mask=(w_mask - 1) // 2) - return grad_input, None, None, None - - -psa_mask = PSAMaskFunction.apply - - -class PSAMask(nn.Module): - - def __init__(self, psa_type, mask_size=None): - super(PSAMask, self).__init__() - assert psa_type in ['collect', 'distribute'] - if psa_type == 'collect': - psa_type_enum = 0 - else: - psa_type_enum = 1 - self.psa_type_enum = psa_type_enum - self.mask_size = mask_size - self.psa_type = psa_type - - def forward(self, input): - return psa_mask(input, self.psa_type_enum, self.mask_size) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(psa_type={self.psa_type}, ' - s += f'mask_size={self.mask_size})' - return s diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/optimizer/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/optimizer/__init__.py deleted file mode 100644 index 53c34d0470992cbc374f29681fdd00dc0e57968d..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/optimizer/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .builder import (OPTIMIZER_BUILDERS, OPTIMIZERS, build_optimizer, - build_optimizer_constructor) -from .default_constructor import DefaultOptimizerConstructor - -__all__ = [ - 'OPTIMIZER_BUILDERS', 'OPTIMIZERS', 'DefaultOptimizerConstructor', - 'build_optimizer', 'build_optimizer_constructor' -] diff --git a/spaces/crytion/DeepNude/README.md b/spaces/crytion/DeepNude/README.md deleted file mode 100644 index a616776efe2480784f0ba92acdf5d4d4755f440a..0000000000000000000000000000000000000000 --- a/spaces/crytion/DeepNude/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: DeepNude -emoji: 💩💩💩 -colorFrom: green -colorTo: gray -sdk: gradio -sdk_version: 3.4.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/cvlab/zero123-live/ldm/models/diffusion/ddpm.py b/spaces/cvlab/zero123-live/ldm/models/diffusion/ddpm.py deleted file mode 100644 index a1684aaee57f3a90c3d90b2fbf8e0b58fb21652d..0000000000000000000000000000000000000000 --- a/spaces/cvlab/zero123-live/ldm/models/diffusion/ddpm.py +++ /dev/null @@ -1,1994 +0,0 @@ -""" -wild mixture of -https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py -https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py -https://github.com/CompVis/taming-transformers --- merci -""" - -import torch -import torch.nn as nn -import numpy as np -import pytorch_lightning as pl -from torch.optim.lr_scheduler import LambdaLR -from einops import rearrange, repeat -from contextlib import contextmanager, nullcontext -from functools import partial -import itertools -from tqdm import tqdm -from torchvision.utils import make_grid -from pytorch_lightning.utilities.distributed import rank_zero_only -from omegaconf import ListConfig - -from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config -from ldm.modules.ema import LitEma -from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution -from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL -from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like -from ldm.models.diffusion.ddim import DDIMSampler -from ldm.modules.attention import CrossAttention - - -__conditioning_keys__ = {'concat': 'c_concat', - 'crossattn': 'c_crossattn', - 'adm': 'y'} - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -def uniform_on_device(r1, r2, shape, device): - return (r1 - r2) * torch.rand(*shape, device=device) + r2 - - -class DDPM(pl.LightningModule): - # classic DDPM with Gaussian diffusion, in image space - def __init__(self, - unet_config, - timesteps=1000, - beta_schedule="linear", - loss_type="l2", - ckpt_path=None, - ignore_keys=[], - load_only_unet=False, - monitor="val/loss", - use_ema=True, - first_stage_key="image", - image_size=256, - channels=3, - log_every_t=100, - clip_denoised=True, - linear_start=1e-4, - linear_end=2e-2, - cosine_s=8e-3, - given_betas=None, - original_elbo_weight=0., - v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta - l_simple_weight=1., - conditioning_key=None, - parameterization="eps", # all assuming fixed variance schedules - scheduler_config=None, - use_positional_encodings=False, - learn_logvar=False, - logvar_init=0., - make_it_fit=False, - ucg_training=None, - ): - super().__init__() - assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"' - self.parameterization = parameterization - print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") - self.cond_stage_model = None - self.clip_denoised = clip_denoised - self.log_every_t = log_every_t - self.first_stage_key = first_stage_key - self.image_size = image_size # try conv? - self.channels = channels - self.use_positional_encodings = use_positional_encodings - self.model = DiffusionWrapper(unet_config, conditioning_key) - count_params(self.model, verbose=True) - self.use_ema = use_ema - if self.use_ema: - self.model_ema = LitEma(self.model) - print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") - - self.use_scheduler = scheduler_config is not None - if self.use_scheduler: - self.scheduler_config = scheduler_config - - self.v_posterior = v_posterior - self.original_elbo_weight = original_elbo_weight - self.l_simple_weight = l_simple_weight - - if monitor is not None: - self.monitor = monitor - self.make_it_fit = make_it_fit - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) - - self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, - linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) - - self.loss_type = loss_type - - self.learn_logvar = learn_logvar - self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) - if self.learn_logvar: - self.logvar = nn.Parameter(self.logvar, requires_grad=True) - - self.ucg_training = ucg_training or dict() - if self.ucg_training: - self.ucg_prng = np.random.RandomState() - - def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, - linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - if exists(given_betas): - betas = given_betas - else: - betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, - cosine_s=cosine_s) - alphas = 1. - betas - alphas_cumprod = np.cumprod(alphas, axis=0) - alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) - - timesteps, = betas.shape - self.num_timesteps = int(timesteps) - self.linear_start = linear_start - self.linear_end = linear_end - assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' - - to_torch = partial(torch.tensor, dtype=torch.float32) - - self.register_buffer('betas', to_torch(betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) - - # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) - - # calculations for posterior q(x_{t-1} | x_t, x_0) - posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( - 1. - alphas_cumprod) + self.v_posterior * betas - # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) - self.register_buffer('posterior_variance', to_torch(posterior_variance)) - # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain - self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) - self.register_buffer('posterior_mean_coef1', to_torch( - betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) - self.register_buffer('posterior_mean_coef2', to_torch( - (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) - - if self.parameterization == "eps": - lvlb_weights = self.betas ** 2 / ( - 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) - elif self.parameterization == "x0": - lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) - else: - raise NotImplementedError("mu not supported") - # TODO how to choose this term - lvlb_weights[0] = lvlb_weights[1] - self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) - assert not torch.isnan(self.lvlb_weights).all() - - @contextmanager - def ema_scope(self, context=None): - if self.use_ema: - self.model_ema.store(self.model.parameters()) - self.model_ema.copy_to(self.model) - if context is not None: - print(f"{context}: Switched to EMA weights") - try: - yield None - finally: - if self.use_ema: - self.model_ema.restore(self.model.parameters()) - if context is not None: - print(f"{context}: Restored training weights") - - @torch.no_grad() - def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): - sd = torch.load(path, map_location="cpu") - if "state_dict" in list(sd.keys()): - sd = sd["state_dict"] - keys = list(sd.keys()) - - if self.make_it_fit: - n_params = len([name for name, _ in - itertools.chain(self.named_parameters(), - self.named_buffers())]) - for name, param in tqdm( - itertools.chain(self.named_parameters(), - self.named_buffers()), - desc="Fitting old weights to new weights", - total=n_params - ): - if not name in sd: - continue - old_shape = sd[name].shape - new_shape = param.shape - assert len(old_shape)==len(new_shape) - if len(new_shape) > 2: - # we only modify first two axes - assert new_shape[2:] == old_shape[2:] - # assumes first axis corresponds to output dim - if not new_shape == old_shape: - new_param = param.clone() - old_param = sd[name] - if len(new_shape) == 1: - for i in range(new_param.shape[0]): - new_param[i] = old_param[i % old_shape[0]] - elif len(new_shape) >= 2: - for i in range(new_param.shape[0]): - for j in range(new_param.shape[1]): - new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]] - - n_used_old = torch.ones(old_shape[1]) - for j in range(new_param.shape[1]): - n_used_old[j % old_shape[1]] += 1 - n_used_new = torch.zeros(new_shape[1]) - for j in range(new_param.shape[1]): - n_used_new[j] = n_used_old[j % old_shape[1]] - - n_used_new = n_used_new[None, :] - while len(n_used_new.shape) < len(new_shape): - n_used_new = n_used_new.unsqueeze(-1) - new_param /= n_used_new - - sd[name] = new_param - - missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( - sd, strict=False) - print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") - if len(missing) > 0: - print(f"Missing Keys: {missing}") - if len(unexpected) > 0: - print(f"Unexpected Keys: {unexpected}") - - def q_mean_variance(self, x_start, t): - """ - Get the distribution q(x_t | x_0). - :param x_start: the [N x C x ...] tensor of noiseless inputs. - :param t: the number of diffusion steps (minus 1). Here, 0 means one step. - :return: A tuple (mean, variance, log_variance), all of x_start's shape. - """ - mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) - variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) - log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) - return mean, variance, log_variance - - def predict_start_from_noise(self, x_t, t, noise): - return ( - extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise - ) - - def q_posterior(self, x_start, x_t, t): - posterior_mean = ( - extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + - extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t - ) - posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) - posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) - return posterior_mean, posterior_variance, posterior_log_variance_clipped - - def p_mean_variance(self, x, t, clip_denoised: bool): - model_out = self.model(x, t) - if self.parameterization == "eps": - x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) - elif self.parameterization == "x0": - x_recon = model_out - if clip_denoised: - x_recon.clamp_(-1., 1.) - - model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) - return model_mean, posterior_variance, posterior_log_variance - - @torch.no_grad() - def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): - b, *_, device = *x.shape, x.device - model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) - noise = noise_like(x.shape, device, repeat_noise) - # no noise when t == 0 - nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise - - @torch.no_grad() - def p_sample_loop(self, shape, return_intermediates=False): - device = self.betas.device - b = shape[0] - img = torch.randn(shape, device=device) - intermediates = [img] - for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): - img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), - clip_denoised=self.clip_denoised) - if i % self.log_every_t == 0 or i == self.num_timesteps - 1: - intermediates.append(img) - if return_intermediates: - return img, intermediates - return img - - @torch.no_grad() - def sample(self, batch_size=16, return_intermediates=False): - image_size = self.image_size - channels = self.channels - return self.p_sample_loop((batch_size, channels, image_size, image_size), - return_intermediates=return_intermediates) - - def q_sample(self, x_start, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) - - def get_loss(self, pred, target, mean=True): - if self.loss_type == 'l1': - loss = (target - pred).abs() - if mean: - loss = loss.mean() - elif self.loss_type == 'l2': - if mean: - loss = torch.nn.functional.mse_loss(target, pred) - else: - loss = torch.nn.functional.mse_loss(target, pred, reduction='none') - else: - raise NotImplementedError("unknown loss type '{loss_type}'") - - return loss - - def p_losses(self, x_start, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - model_out = self.model(x_noisy, t) - - loss_dict = {} - if self.parameterization == "eps": - target = noise - elif self.parameterization == "x0": - target = x_start - else: - raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") - - loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) - - log_prefix = 'train' if self.training else 'val' - - loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) - loss_simple = loss.mean() * self.l_simple_weight - - loss_vlb = (self.lvlb_weights[t] * loss).mean() - loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) - - loss = loss_simple + self.original_elbo_weight * loss_vlb - - loss_dict.update({f'{log_prefix}/loss': loss}) - - return loss, loss_dict - - def forward(self, x, *args, **kwargs): - # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size - # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' - t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() - return self.p_losses(x, t, *args, **kwargs) - - def get_input(self, batch, k): - x = batch[k] - if len(x.shape) == 3: - x = x[..., None] - x = rearrange(x, 'b h w c -> b c h w') - x = x.to(memory_format=torch.contiguous_format).float() - return x - - def shared_step(self, batch): - x = self.get_input(batch, self.first_stage_key) - loss, loss_dict = self(x) - return loss, loss_dict - - def training_step(self, batch, batch_idx): - for k in self.ucg_training: - p = self.ucg_training[k]["p"] - val = self.ucg_training[k]["val"] - if val is None: - val = "" - for i in range(len(batch[k])): - if self.ucg_prng.choice(2, p=[1-p, p]): - batch[k][i] = val - - loss, loss_dict = self.shared_step(batch) - - self.log_dict(loss_dict, prog_bar=True, - logger=True, on_step=True, on_epoch=True) - - self.log("global_step", self.global_step, - prog_bar=True, logger=True, on_step=True, on_epoch=False) - - if self.use_scheduler: - lr = self.optimizers().param_groups[0]['lr'] - self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) - - return loss - - @torch.no_grad() - def validation_step(self, batch, batch_idx): - _, loss_dict_no_ema = self.shared_step(batch) - with self.ema_scope(): - _, loss_dict_ema = self.shared_step(batch) - loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} - self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) - self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) - - def on_train_batch_end(self, *args, **kwargs): - if self.use_ema: - self.model_ema(self.model) - - def _get_rows_from_list(self, samples): - n_imgs_per_row = len(samples) - denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') - denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') - denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) - return denoise_grid - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): - log = dict() - x = self.get_input(batch, self.first_stage_key) - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - x = x.to(self.device)[:N] - log["inputs"] = x - - # get diffusion row - diffusion_row = list() - x_start = x[:n_row] - - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(x_start) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - diffusion_row.append(x_noisy) - - log["diffusion_row"] = self._get_rows_from_list(diffusion_row) - - if sample: - # get denoise row - with self.ema_scope("Plotting"): - samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) - - log["samples"] = samples - log["denoise_row"] = self._get_rows_from_list(denoise_row) - - if return_keys: - if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: - return log - else: - return {key: log[key] for key in return_keys} - return log - - def configure_optimizers(self): - lr = self.learning_rate - params = list(self.model.parameters()) - if self.learn_logvar: - params = params + [self.logvar] - opt = torch.optim.AdamW(params, lr=lr) - return opt - - -class LatentDiffusion(DDPM): - """main class""" - def __init__(self, - first_stage_config, - cond_stage_config, - num_timesteps_cond=None, - cond_stage_key="image", - cond_stage_trainable=False, - concat_mode=True, - cond_stage_forward=None, - conditioning_key=None, - scale_factor=1.0, - scale_by_std=False, - unet_trainable=True, - *args, **kwargs): - self.num_timesteps_cond = default(num_timesteps_cond, 1) - self.scale_by_std = scale_by_std - assert self.num_timesteps_cond <= kwargs['timesteps'] - # for backwards compatibility after implementation of DiffusionWrapper - if conditioning_key is None: - conditioning_key = 'concat' if concat_mode else 'crossattn' - if cond_stage_config == '__is_unconditional__': - conditioning_key = None - ckpt_path = kwargs.pop("ckpt_path", None) - ignore_keys = kwargs.pop("ignore_keys", []) - super().__init__(conditioning_key=conditioning_key, *args, **kwargs) - self.concat_mode = concat_mode - self.cond_stage_trainable = cond_stage_trainable - self.unet_trainable = unet_trainable - self.cond_stage_key = cond_stage_key - try: - self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 - except: - self.num_downs = 0 - if not scale_by_std: - self.scale_factor = scale_factor - else: - self.register_buffer('scale_factor', torch.tensor(scale_factor)) - self.instantiate_first_stage(first_stage_config) - self.instantiate_cond_stage(cond_stage_config) - self.cond_stage_forward = cond_stage_forward - - # construct linear projection layer for concatenating image CLIP embedding and RT - self.cc_projection = nn.Linear(772, 768) - nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768]) - nn.init.zeros_(list(self.cc_projection.parameters())[1]) - self.cc_projection.requires_grad_(True) - - self.clip_denoised = False - self.bbox_tokenizer = None - - self.restarted_from_ckpt = False - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys) - self.restarted_from_ckpt = True - - def make_cond_schedule(self, ): - self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) - ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() - self.cond_ids[:self.num_timesteps_cond] = ids - - @rank_zero_only - @torch.no_grad() - def on_train_batch_start(self, batch, batch_idx, dataloader_idx): - # only for very first batch - if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: - assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' - # set rescale weight to 1./std of encodings - print("### USING STD-RESCALING ###") - x = super().get_input(batch, self.first_stage_key) - x = x.to(self.device) - encoder_posterior = self.encode_first_stage(x) - z = self.get_first_stage_encoding(encoder_posterior).detach() - del self.scale_factor - self.register_buffer('scale_factor', 1. / z.flatten().std()) - print(f"setting self.scale_factor to {self.scale_factor}") - print("### USING STD-RESCALING ###") - - def register_schedule(self, - given_betas=None, beta_schedule="linear", timesteps=1000, - linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) - - self.shorten_cond_schedule = self.num_timesteps_cond > 1 - if self.shorten_cond_schedule: - self.make_cond_schedule() - - def instantiate_first_stage(self, config): - model = instantiate_from_config(config) - self.first_stage_model = model.eval() - self.first_stage_model.train = disabled_train - for param in self.first_stage_model.parameters(): - param.requires_grad = False - - def instantiate_cond_stage(self, config): - if not self.cond_stage_trainable: - if config == "__is_first_stage__": - print("Using first stage also as cond stage.") - self.cond_stage_model = self.first_stage_model - elif config == "__is_unconditional__": - print(f"Training {self.__class__.__name__} as an unconditional model.") - self.cond_stage_model = None - # self.be_unconditional = True - else: - model = instantiate_from_config(config) - self.cond_stage_model = model.eval() - self.cond_stage_model.train = disabled_train - for param in self.cond_stage_model.parameters(): - param.requires_grad = False - else: - assert config != '__is_first_stage__' - assert config != '__is_unconditional__' - model = instantiate_from_config(config) - self.cond_stage_model = model - - def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): - denoise_row = [] - for zd in tqdm(samples, desc=desc): - denoise_row.append(self.decode_first_stage(zd.to(self.device), - force_not_quantize=force_no_decoder_quantization)) - n_imgs_per_row = len(denoise_row) - denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W - denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') - denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') - denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) - return denoise_grid - - def get_first_stage_encoding(self, encoder_posterior): - if isinstance(encoder_posterior, DiagonalGaussianDistribution): - z = encoder_posterior.sample() - elif isinstance(encoder_posterior, torch.Tensor): - z = encoder_posterior - else: - raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") - return self.scale_factor * z - - def get_learned_conditioning(self, c): - if self.cond_stage_forward is None: - if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): - c = self.cond_stage_model.encode(c) - if isinstance(c, DiagonalGaussianDistribution): - c = c.mode() - else: - c = self.cond_stage_model(c) - else: - assert hasattr(self.cond_stage_model, self.cond_stage_forward) - c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) - return c - - def meshgrid(self, h, w): - y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) - x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) - - arr = torch.cat([y, x], dim=-1) - return arr - - def delta_border(self, h, w): - """ - :param h: height - :param w: width - :return: normalized distance to image border, - wtith min distance = 0 at border and max dist = 0.5 at image center - """ - lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) - arr = self.meshgrid(h, w) / lower_right_corner - dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] - dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] - edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] - return edge_dist - - def get_weighting(self, h, w, Ly, Lx, device): - weighting = self.delta_border(h, w) - weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], - self.split_input_params["clip_max_weight"], ) - weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) - - if self.split_input_params["tie_braker"]: - L_weighting = self.delta_border(Ly, Lx) - L_weighting = torch.clip(L_weighting, - self.split_input_params["clip_min_tie_weight"], - self.split_input_params["clip_max_tie_weight"]) - - L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) - weighting = weighting * L_weighting - return weighting - - def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code - """ - :param x: img of size (bs, c, h, w) - :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) - """ - bs, nc, h, w = x.shape - - # number of crops in image - Ly = (h - kernel_size[0]) // stride[0] + 1 - Lx = (w - kernel_size[1]) // stride[1] + 1 - - if uf == 1 and df == 1: - fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) - unfold = torch.nn.Unfold(**fold_params) - - fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) - - weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) - normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap - weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) - - elif uf > 1 and df == 1: - fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) - unfold = torch.nn.Unfold(**fold_params) - - fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), - dilation=1, padding=0, - stride=(stride[0] * uf, stride[1] * uf)) - fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) - - weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) - normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap - weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) - - elif df > 1 and uf == 1: - fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) - unfold = torch.nn.Unfold(**fold_params) - - fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), - dilation=1, padding=0, - stride=(stride[0] // df, stride[1] // df)) - fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) - - weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) - normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap - weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) - - else: - raise NotImplementedError - - return fold, unfold, normalization, weighting - - - @torch.no_grad() - def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, - cond_key=None, return_original_cond=False, bs=None, uncond=0.05): - x = super().get_input(batch, k) - T = batch['T'].to(memory_format=torch.contiguous_format).float() - - if bs is not None: - x = x[:bs] - T = T[:bs].to(self.device) - - x = x.to(self.device) - encoder_posterior = self.encode_first_stage(x) - z = self.get_first_stage_encoding(encoder_posterior).detach() - cond_key = cond_key or self.cond_stage_key - xc = super().get_input(batch, cond_key).to(self.device) - if bs is not None: - xc = xc[:bs] - cond = {} - - # To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%. - random = torch.rand(x.size(0), device=x.device) - prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1") - input_mask = 1 - rearrange((random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1") - null_prompt = self.get_learned_conditioning([""]) - - # z.shape: [8, 4, 64, 64]; c.shape: [8, 1, 768] - # print('=========== xc shape ===========', xc.shape) - with torch.enable_grad(): - clip_emb = self.get_learned_conditioning(xc).detach() - null_prompt = self.get_learned_conditioning([""]).detach() - cond["c_crossattn"] = [self.cc_projection(torch.cat([torch.where(prompt_mask, null_prompt, clip_emb), T[:, None, :]], dim=-1))] - cond["c_concat"] = [input_mask * self.encode_first_stage((xc.to(self.device))).mode().detach()] - out = [z, cond] - if return_first_stage_outputs: - xrec = self.decode_first_stage(z) - out.extend([x, xrec]) - if return_original_cond: - out.append(xc) - return out - - # @torch.no_grad() - def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): - if predict_cids: - if z.dim() == 4: - z = torch.argmax(z.exp(), dim=1).long() - z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) - z = rearrange(z, 'b h w c -> b c h w').contiguous() - - z = 1. / self.scale_factor * z - - if hasattr(self, "split_input_params"): - if self.split_input_params["patch_distributed_vq"]: - ks = self.split_input_params["ks"] # eg. (128, 128) - stride = self.split_input_params["stride"] # eg. (64, 64) - uf = self.split_input_params["vqf"] - bs, nc, h, w = z.shape - if ks[0] > h or ks[1] > w: - ks = (min(ks[0], h), min(ks[1], w)) - print("reducing Kernel") - - if stride[0] > h or stride[1] > w: - stride = (min(stride[0], h), min(stride[1], w)) - print("reducing stride") - - fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) - - z = unfold(z) # (bn, nc * prod(**ks), L) - # 1. Reshape to img shape - z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) - - # 2. apply model loop over last dim - if isinstance(self.first_stage_model, VQModelInterface): - output_list = [self.first_stage_model.decode(z[:, :, :, :, i], - force_not_quantize=predict_cids or force_not_quantize) - for i in range(z.shape[-1])] - else: - - output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) - for i in range(z.shape[-1])] - - o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) - o = o * weighting - # Reverse 1. reshape to img shape - o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) - # stitch crops together - decoded = fold(o) - decoded = decoded / normalization # norm is shape (1, 1, h, w) - return decoded - else: - if isinstance(self.first_stage_model, VQModelInterface): - return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) - else: - return self.first_stage_model.decode(z) - - else: - if isinstance(self.first_stage_model, VQModelInterface): - return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) - else: - return self.first_stage_model.decode(z) - - @torch.no_grad() - def encode_first_stage(self, x): - if hasattr(self, "split_input_params"): - if self.split_input_params["patch_distributed_vq"]: - ks = self.split_input_params["ks"] # eg. (128, 128) - stride = self.split_input_params["stride"] # eg. (64, 64) - df = self.split_input_params["vqf"] - self.split_input_params['original_image_size'] = x.shape[-2:] - bs, nc, h, w = x.shape - if ks[0] > h or ks[1] > w: - ks = (min(ks[0], h), min(ks[1], w)) - print("reducing Kernel") - - if stride[0] > h or stride[1] > w: - stride = (min(stride[0], h), min(stride[1], w)) - print("reducing stride") - - fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) - z = unfold(x) # (bn, nc * prod(**ks), L) - # Reshape to img shape - z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) - - output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) - for i in range(z.shape[-1])] - - o = torch.stack(output_list, axis=-1) - o = o * weighting - - # Reverse reshape to img shape - o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) - # stitch crops together - decoded = fold(o) - decoded = decoded / normalization - return decoded - - else: - return self.first_stage_model.encode(x) - else: - return self.first_stage_model.encode(x) - - def shared_step(self, batch, **kwargs): - x, c = self.get_input(batch, self.first_stage_key) - loss = self(x, c) - return loss - - def forward(self, x, c, *args, **kwargs): - t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() - if self.model.conditioning_key is not None: - assert c is not None - # if self.cond_stage_trainable: - # c = self.get_learned_conditioning(c) - if self.shorten_cond_schedule: # TODO: drop this option - tc = self.cond_ids[t].to(self.device) - c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) - return self.p_losses(x, c, t, *args, **kwargs) - - def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset - def rescale_bbox(bbox): - x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) - y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) - w = min(bbox[2] / crop_coordinates[2], 1 - x0) - h = min(bbox[3] / crop_coordinates[3], 1 - y0) - return x0, y0, w, h - - return [rescale_bbox(b) for b in bboxes] - - def apply_model(self, x_noisy, t, cond, return_ids=False): - - if isinstance(cond, dict): - # hybrid case, cond is exptected to be a dict - pass - else: - if not isinstance(cond, list): - cond = [cond] - key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' - cond = {key: cond} - - if hasattr(self, "split_input_params"): - assert len(cond) == 1 # todo can only deal with one conditioning atm - assert not return_ids - ks = self.split_input_params["ks"] # eg. (128, 128) - stride = self.split_input_params["stride"] # eg. (64, 64) - - h, w = x_noisy.shape[-2:] - - fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) - - z = unfold(x_noisy) # (bn, nc * prod(**ks), L) - # Reshape to img shape - z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) - z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] - - if self.cond_stage_key in ["image", "LR_image", "segmentation", - 'bbox_img'] and self.model.conditioning_key: # todo check for completeness - c_key = next(iter(cond.keys())) # get key - c = next(iter(cond.values())) # get value - assert (len(c) == 1) # todo extend to list with more than one elem - c = c[0] # get element - - c = unfold(c) - c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) - - cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] - - elif self.cond_stage_key == 'coordinates_bbox': - assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' - - # assuming padding of unfold is always 0 and its dilation is always 1 - n_patches_per_row = int((w - ks[0]) / stride[0] + 1) - full_img_h, full_img_w = self.split_input_params['original_image_size'] - # as we are operating on latents, we need the factor from the original image size to the - # spatial latent size to properly rescale the crops for regenerating the bbox annotations - num_downs = self.first_stage_model.encoder.num_resolutions - 1 - rescale_latent = 2 ** (num_downs) - - # get top left postions of patches as conforming for the bbbox tokenizer, therefore we - # need to rescale the tl patch coordinates to be in between (0,1) - tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, - rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) - for patch_nr in range(z.shape[-1])] - - # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) - patch_limits = [(x_tl, y_tl, - rescale_latent * ks[0] / full_img_w, - rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] - # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] - - # tokenize crop coordinates for the bounding boxes of the respective patches - patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) - for bbox in patch_limits] # list of length l with tensors of shape (1, 2) - # cut tknzd crop position from conditioning - assert isinstance(cond, dict), 'cond must be dict to be fed into model' - cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) - - adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) - adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') - adapted_cond = self.get_learned_conditioning(adapted_cond) - adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) - - cond_list = [{'c_crossattn': [e]} for e in adapted_cond] - - else: - cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient - - # apply model by loop over crops - output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] - assert not isinstance(output_list[0], - tuple) # todo cant deal with multiple model outputs check this never happens - - o = torch.stack(output_list, axis=-1) - o = o * weighting - # Reverse reshape to img shape - o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) - # stitch crops together - x_recon = fold(o) / normalization - - else: - x_recon = self.model(x_noisy, t, **cond) - - if isinstance(x_recon, tuple) and not return_ids: - return x_recon[0] - else: - return x_recon - - def _predict_eps_from_xstart(self, x_t, t, pred_xstart): - return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) - - def _prior_bpd(self, x_start): - """ - Get the prior KL term for the variational lower-bound, measured in - bits-per-dim. - This term can't be optimized, as it only depends on the encoder. - :param x_start: the [N x C x ...] tensor of inputs. - :return: a batch of [N] KL values (in bits), one per batch element. - """ - batch_size = x_start.shape[0] - t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) - qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) - kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) - return mean_flat(kl_prior) / np.log(2.0) - - def p_losses(self, x_start, cond, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - model_output = self.apply_model(x_noisy, t, cond) - - loss_dict = {} - prefix = 'train' if self.training else 'val' - - if self.parameterization == "x0": - target = x_start - elif self.parameterization == "eps": - target = noise - else: - raise NotImplementedError() - - loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) - loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) - - logvar_t = self.logvar[t].to(self.device) - loss = loss_simple / torch.exp(logvar_t) + logvar_t - # loss = loss_simple / torch.exp(self.logvar) + self.logvar - if self.learn_logvar: - loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) - loss_dict.update({'logvar': self.logvar.data.mean()}) - - loss = self.l_simple_weight * loss.mean() - - loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) - loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() - loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) - loss += (self.original_elbo_weight * loss_vlb) - loss_dict.update({f'{prefix}/loss': loss}) - - return loss, loss_dict - - def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, - return_x0=False, score_corrector=None, corrector_kwargs=None): - t_in = t - model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) - - if score_corrector is not None: - assert self.parameterization == "eps" - model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) - - if return_codebook_ids: - model_out, logits = model_out - - if self.parameterization == "eps": - x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) - elif self.parameterization == "x0": - x_recon = model_out - else: - raise NotImplementedError() - - if clip_denoised: - x_recon.clamp_(-1., 1.) - if quantize_denoised: - x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) - model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) - if return_codebook_ids: - return model_mean, posterior_variance, posterior_log_variance, logits - elif return_x0: - return model_mean, posterior_variance, posterior_log_variance, x_recon - else: - return model_mean, posterior_variance, posterior_log_variance - - @torch.no_grad() - def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, - return_codebook_ids=False, quantize_denoised=False, return_x0=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): - b, *_, device = *x.shape, x.device - outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, - return_codebook_ids=return_codebook_ids, - quantize_denoised=quantize_denoised, - return_x0=return_x0, - score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) - if return_codebook_ids: - raise DeprecationWarning("Support dropped.") - model_mean, _, model_log_variance, logits = outputs - elif return_x0: - model_mean, _, model_log_variance, x0 = outputs - else: - model_mean, _, model_log_variance = outputs - - noise = noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) - # no noise when t == 0 - nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) - - if return_codebook_ids: - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) - if return_x0: - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 - else: - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise - - @torch.no_grad() - def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, - img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., - score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, - log_every_t=None): - if not log_every_t: - log_every_t = self.log_every_t - timesteps = self.num_timesteps - if batch_size is not None: - b = batch_size if batch_size is not None else shape[0] - shape = [batch_size] + list(shape) - else: - b = batch_size = shape[0] - if x_T is None: - img = torch.randn(shape, device=self.device) - else: - img = x_T - intermediates = [] - if cond is not None: - if isinstance(cond, dict): - cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} - else: - cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] - - if start_T is not None: - timesteps = min(timesteps, start_T) - iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', - total=timesteps) if verbose else reversed( - range(0, timesteps)) - if type(temperature) == float: - temperature = [temperature] * timesteps - - for i in iterator: - ts = torch.full((b,), i, device=self.device, dtype=torch.long) - if self.shorten_cond_schedule: - assert self.model.conditioning_key != 'hybrid' - tc = self.cond_ids[ts].to(cond.device) - cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) - - img, x0_partial = self.p_sample(img, cond, ts, - clip_denoised=self.clip_denoised, - quantize_denoised=quantize_denoised, return_x0=True, - temperature=temperature[i], noise_dropout=noise_dropout, - score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) - if mask is not None: - assert x0 is not None - img_orig = self.q_sample(x0, ts) - img = img_orig * mask + (1. - mask) * img - - if i % log_every_t == 0 or i == timesteps - 1: - intermediates.append(x0_partial) - if callback: callback(i) - if img_callback: img_callback(img, i) - return img, intermediates - - @torch.no_grad() - def p_sample_loop(self, cond, shape, return_intermediates=False, - x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, - mask=None, x0=None, img_callback=None, start_T=None, - log_every_t=None): - - if not log_every_t: - log_every_t = self.log_every_t - device = self.betas.device - b = shape[0] - if x_T is None: - img = torch.randn(shape, device=device) - else: - img = x_T - - intermediates = [img] - if timesteps is None: - timesteps = self.num_timesteps - - if start_T is not None: - timesteps = min(timesteps, start_T) - iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( - range(0, timesteps)) - - if mask is not None: - assert x0 is not None - assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match - - for i in iterator: - ts = torch.full((b,), i, device=device, dtype=torch.long) - if self.shorten_cond_schedule: - assert self.model.conditioning_key != 'hybrid' - tc = self.cond_ids[ts].to(cond.device) - cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) - - img = self.p_sample(img, cond, ts, - clip_denoised=self.clip_denoised, - quantize_denoised=quantize_denoised) - if mask is not None: - img_orig = self.q_sample(x0, ts) - img = img_orig * mask + (1. - mask) * img - - if i % log_every_t == 0 or i == timesteps - 1: - intermediates.append(img) - if callback: callback(i) - if img_callback: img_callback(img, i) - - if return_intermediates: - return img, intermediates - return img - - @torch.no_grad() - def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, - verbose=True, timesteps=None, quantize_denoised=False, - mask=None, x0=None, shape=None,**kwargs): - if shape is None: - shape = (batch_size, self.channels, self.image_size, self.image_size) - if cond is not None: - if isinstance(cond, dict): - cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} - else: - cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] - return self.p_sample_loop(cond, - shape, - return_intermediates=return_intermediates, x_T=x_T, - verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, - mask=mask, x0=x0) - - @torch.no_grad() - def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): - if ddim: - ddim_sampler = DDIMSampler(self) - shape = (self.channels, self.image_size, self.image_size) - samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, - shape, cond, verbose=False, **kwargs) - - else: - samples, intermediates = self.sample(cond=cond, batch_size=batch_size, - return_intermediates=True, **kwargs) - - return samples, intermediates - - @torch.no_grad() - def get_unconditional_conditioning(self, batch_size, null_label=None, image_size=512): - if null_label is not None: - xc = null_label - if isinstance(xc, ListConfig): - xc = list(xc) - if isinstance(xc, dict) or isinstance(xc, list): - c = self.get_learned_conditioning(xc) - else: - if hasattr(xc, "to"): - xc = xc.to(self.device) - c = self.get_learned_conditioning(xc) - else: - # todo: get null label from cond_stage_model - raise NotImplementedError() - c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device) - cond = {} - cond["c_crossattn"] = [c] - cond["c_concat"] = [torch.zeros([batch_size, 4, image_size // 8, image_size // 8]).to(self.device)] - return cond - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, - quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, - plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, - use_ema_scope=True, - **kwargs): - ema_scope = self.ema_scope if use_ema_scope else nullcontext - use_ddim = ddim_steps is not None - - log = dict() - z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, - return_first_stage_outputs=True, - force_c_encode=True, - return_original_cond=True, - bs=N) - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - log["inputs"] = x - log["reconstruction"] = xrec - if self.model.conditioning_key is not None: - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif self.cond_stage_key in ["caption", "txt"]: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2]//25) - log["conditioning"] = xc - elif self.cond_stage_key == 'class_label': - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2]//25) - log['conditioning'] = xc - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - - if plot_diffusion_rows: - # get diffusion row - diffusion_row = list() - z_start = z[:n_row] - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(z_start) - z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) - diffusion_row.append(self.decode_first_stage(z_noisy)) - - diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W - diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') - diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') - diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) - log["diffusion_row"] = diffusion_grid - - if sample: - # get denoise row - with ema_scope("Sampling"): - samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, - ddim_steps=ddim_steps,eta=ddim_eta) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - if plot_denoise_rows: - denoise_grid = self._get_denoise_row_from_list(z_denoise_row) - log["denoise_row"] = denoise_grid - - if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( - self.first_stage_model, IdentityFirstStage): - # also display when quantizing x0 while sampling - with ema_scope("Plotting Quantized Denoised"): - samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, - ddim_steps=ddim_steps,eta=ddim_eta, - quantize_denoised=True) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, - # quantize_denoised=True) - x_samples = self.decode_first_stage(samples.to(self.device)) - log["samples_x0_quantized"] = x_samples - - if unconditional_guidance_scale > 1.0: - uc = self.get_unconditional_conditioning(N, unconditional_guidance_label, image_size=x.shape[-1]) - # uc = torch.zeros_like(c) - with ema_scope("Sampling with classifier-free guidance"): - samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - - if inpaint: - # make a simple center square - b, h, w = z.shape[0], z.shape[2], z.shape[3] - mask = torch.ones(N, h, w).to(self.device) - # zeros will be filled in - mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. - mask = mask[:, None, ...] - with ema_scope("Plotting Inpaint"): - - samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, - ddim_steps=ddim_steps, x0=z[:N], mask=mask) - x_samples = self.decode_first_stage(samples.to(self.device)) - log["samples_inpainting"] = x_samples - log["mask"] = mask - - # outpaint - mask = 1. - mask - with ema_scope("Plotting Outpaint"): - samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, - ddim_steps=ddim_steps, x0=z[:N], mask=mask) - x_samples = self.decode_first_stage(samples.to(self.device)) - log["samples_outpainting"] = x_samples - - if plot_progressive_rows: - with ema_scope("Plotting Progressives"): - img, progressives = self.progressive_denoising(c, - shape=(self.channels, self.image_size, self.image_size), - batch_size=N) - prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") - log["progressive_row"] = prog_row - - if return_keys: - if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: - return log - else: - return {key: log[key] for key in return_keys} - return log - - def configure_optimizers(self): - lr = self.learning_rate - params = [] - if self.unet_trainable == "attn": - print("Training only unet attention layers") - for n, m in self.model.named_modules(): - if isinstance(m, CrossAttention) and n.endswith('attn2'): - params.extend(m.parameters()) - if self.unet_trainable == "conv_in": - print("Training only unet input conv layers") - params = list(self.model.diffusion_model.input_blocks[0][0].parameters()) - elif self.unet_trainable is True or self.unet_trainable == "all": - print("Training the full unet") - params = list(self.model.parameters()) - else: - raise ValueError(f"Unrecognised setting for unet_trainable: {self.unet_trainable}") - - if self.cond_stage_trainable: - print(f"{self.__class__.__name__}: Also optimizing conditioner params!") - params = params + list(self.cond_stage_model.parameters()) - if self.learn_logvar: - print('Diffusion model optimizing logvar') - params.append(self.logvar) - - if self.cc_projection is not None: - params = params + list(self.cc_projection.parameters()) - print('========== optimizing for cc projection weight ==========') - - opt = torch.optim.AdamW([{"params": self.model.parameters(), "lr": lr}, - {"params": self.cc_projection.parameters(), "lr": 10. * lr}], lr=lr) - if self.use_scheduler: - assert 'target' in self.scheduler_config - scheduler = instantiate_from_config(self.scheduler_config) - - print("Setting up LambdaLR scheduler...") - scheduler = [ - { - 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), - 'interval': 'step', - 'frequency': 1 - }] - return [opt], scheduler - return opt - - @torch.no_grad() - def to_rgb(self, x): - x = x.float() - if not hasattr(self, "colorize"): - self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) - x = nn.functional.conv2d(x, weight=self.colorize) - x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. - return x - - -class DiffusionWrapper(pl.LightningModule): - def __init__(self, diff_model_config, conditioning_key): - super().__init__() - self.diffusion_model = instantiate_from_config(diff_model_config) - self.conditioning_key = conditioning_key - assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm'] - - def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None): - if self.conditioning_key is None: - out = self.diffusion_model(x, t) - elif self.conditioning_key == 'concat': - xc = torch.cat([x] + c_concat, dim=1) - out = self.diffusion_model(xc, t) - elif self.conditioning_key == 'crossattn': - # c_crossattn dimension: torch.Size([8, 1, 768]) 1 - # cc dimension: torch.Size([8, 1, 768] - cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(x, t, context=cc) - elif self.conditioning_key == 'hybrid': - xc = torch.cat([x] + c_concat, dim=1) - cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(xc, t, context=cc) - elif self.conditioning_key == 'hybrid-adm': - assert c_adm is not None - xc = torch.cat([x] + c_concat, dim=1) - cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(xc, t, context=cc, y=c_adm) - elif self.conditioning_key == 'adm': - cc = c_crossattn[0] - out = self.diffusion_model(x, t, y=cc) - else: - raise NotImplementedError() - - return out - - -class LatentUpscaleDiffusion(LatentDiffusion): - def __init__(self, *args, low_scale_config, low_scale_key="LR", **kwargs): - super().__init__(*args, **kwargs) - # assumes that neither the cond_stage nor the low_scale_model contain trainable params - assert not self.cond_stage_trainable - self.instantiate_low_stage(low_scale_config) - self.low_scale_key = low_scale_key - - def instantiate_low_stage(self, config): - model = instantiate_from_config(config) - self.low_scale_model = model.eval() - self.low_scale_model.train = disabled_train - for param in self.low_scale_model.parameters(): - param.requires_grad = False - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): - if not log_mode: - z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) - else: - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - x_low = batch[self.low_scale_key][:bs] - x_low = rearrange(x_low, 'b h w c -> b c h w') - x_low = x_low.to(memory_format=torch.contiguous_format).float() - zx, noise_level = self.low_scale_model(x_low) - all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level} - #import pudb; pu.db - if log_mode: - # TODO: maybe disable if too expensive - interpretability = False - if interpretability: - zx = zx[:, :, ::2, ::2] - x_low_rec = self.low_scale_model.decode(zx) - return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level - return z, all_conds - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, - plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, - unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, - **kwargs): - ema_scope = self.ema_scope if use_ema_scope else nullcontext - use_ddim = ddim_steps is not None - - log = dict() - z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N, - log_mode=True) - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - log["inputs"] = x - log["reconstruction"] = xrec - log["x_lr"] = x_low - log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec - if self.model.conditioning_key is not None: - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif self.cond_stage_key in ["caption", "txt"]: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2]//25) - log["conditioning"] = xc - elif self.cond_stage_key == 'class_label': - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2]//25) - log['conditioning'] = xc - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - - if plot_diffusion_rows: - # get diffusion row - diffusion_row = list() - z_start = z[:n_row] - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(z_start) - z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) - diffusion_row.append(self.decode_first_stage(z_noisy)) - - diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W - diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') - diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') - diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) - log["diffusion_row"] = diffusion_grid - - if sample: - # get denoise row - with ema_scope("Sampling"): - samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - if plot_denoise_rows: - denoise_grid = self._get_denoise_row_from_list(z_denoise_row) - log["denoise_row"] = denoise_grid - - if unconditional_guidance_scale > 1.0: - uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label) - # TODO explore better "unconditional" choices for the other keys - # maybe guide away from empty text label and highest noise level and maximally degraded zx? - uc = dict() - for k in c: - if k == "c_crossattn": - assert isinstance(c[k], list) and len(c[k]) == 1 - uc[k] = [uc_tmp] - elif k == "c_adm": # todo: only run with text-based guidance? - assert isinstance(c[k], torch.Tensor) - uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level - elif isinstance(c[k], list): - uc[k] = [c[k][i] for i in range(len(c[k]))] - else: - uc[k] = c[k] - - with ema_scope("Sampling with classifier-free guidance"): - samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - - if plot_progressive_rows: - with ema_scope("Plotting Progressives"): - img, progressives = self.progressive_denoising(c, - shape=(self.channels, self.image_size, self.image_size), - batch_size=N) - prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") - log["progressive_row"] = prog_row - - return log - - -class LatentInpaintDiffusion(LatentDiffusion): - """ - can either run as pure inpainting model (only concat mode) or with mixed conditionings, - e.g. mask as concat and text via cross-attn. - To disable finetuning mode, set finetune_keys to None - """ - def __init__(self, - finetune_keys=("model.diffusion_model.input_blocks.0.0.weight", - "model_ema.diffusion_modelinput_blocks00weight" - ), - concat_keys=("mask", "masked_image"), - masked_image_key="masked_image", - keep_finetune_dims=4, # if model was trained without concat mode before and we would like to keep these channels - c_concat_log_start=None, # to log reconstruction of c_concat codes - c_concat_log_end=None, - *args, **kwargs - ): - ckpt_path = kwargs.pop("ckpt_path", None) - ignore_keys = kwargs.pop("ignore_keys", list()) - super().__init__(*args, **kwargs) - self.masked_image_key = masked_image_key - assert self.masked_image_key in concat_keys - self.finetune_keys = finetune_keys - self.concat_keys = concat_keys - self.keep_dims = keep_finetune_dims - self.c_concat_log_start = c_concat_log_start - self.c_concat_log_end = c_concat_log_end - if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint' - if exists(ckpt_path): - self.init_from_ckpt(ckpt_path, ignore_keys) - - def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): - sd = torch.load(path, map_location="cpu") - if "state_dict" in list(sd.keys()): - sd = sd["state_dict"] - keys = list(sd.keys()) - for k in keys: - for ik in ignore_keys: - if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) - del sd[k] - - # make it explicit, finetune by including extra input channels - if exists(self.finetune_keys) and k in self.finetune_keys: - new_entry = None - for name, param in self.named_parameters(): - if name in self.finetune_keys: - print(f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only") - new_entry = torch.zeros_like(param) # zero init - assert exists(new_entry), 'did not find matching parameter to modify' - new_entry[:, :self.keep_dims, ...] = sd[k] - sd[k] = new_entry - - missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(sd, strict=False) - print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") - if len(missing) > 0: - print(f"Missing Keys: {missing}") - if len(unexpected) > 0: - print(f"Unexpected Keys: {unexpected}") - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): - # note: restricted to non-trainable encoders currently - assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting' - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - - assert exists(self.concat_keys) - c_cat = list() - for ck in self.concat_keys: - cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() - if bs is not None: - cc = cc[:bs] - cc = cc.to(self.device) - bchw = z.shape - if ck != self.masked_image_key: - cc = torch.nn.functional.interpolate(cc, size=bchw[-2:]) - else: - cc = self.get_first_stage_encoding(self.encode_first_stage(cc)) - c_cat.append(cc) - c_cat = torch.cat(c_cat, dim=1) - all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} - if return_first_stage_outputs: - return z, all_conds, x, xrec, xc - return z, all_conds - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, - quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, - plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, - use_ema_scope=True, - **kwargs): - ema_scope = self.ema_scope if use_ema_scope else nullcontext - use_ddim = ddim_steps is not None - - log = dict() - z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True) - c_cat, c = c["c_concat"][0], c["c_crossattn"][0] - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - log["inputs"] = x - log["reconstruction"] = xrec - if self.model.conditioning_key is not None: - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif self.cond_stage_key in ["caption", "txt"]: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25) - log["conditioning"] = xc - elif self.cond_stage_key == 'class_label': - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25) - log['conditioning'] = xc - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - - if not (self.c_concat_log_start is None and self.c_concat_log_end is None): - log["c_concat_decoded"] = self.decode_first_stage(c_cat[:,self.c_concat_log_start:self.c_concat_log_end]) - - if plot_diffusion_rows: - # get diffusion row - diffusion_row = list() - z_start = z[:n_row] - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(z_start) - z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) - diffusion_row.append(self.decode_first_stage(z_noisy)) - - diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W - diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') - diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') - diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) - log["diffusion_row"] = diffusion_grid - - if sample: - # get denoise row - with ema_scope("Sampling"): - samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, - batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - if plot_denoise_rows: - denoise_grid = self._get_denoise_row_from_list(z_denoise_row) - log["denoise_row"] = denoise_grid - - if unconditional_guidance_scale > 1.0: - uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label) - uc_cat = c_cat - uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]} - with ema_scope("Sampling with classifier-free guidance"): - samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, - batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc_full, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - - log["masked_image"] = rearrange(batch["masked_image"], - 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() - return log - - -class Layout2ImgDiffusion(LatentDiffusion): - # TODO: move all layout-specific hacks to this class - def __init__(self, cond_stage_key, *args, **kwargs): - assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' - super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs) - - def log_images(self, batch, N=8, *args, **kwargs): - logs = super().log_images(batch=batch, N=N, *args, **kwargs) - - key = 'train' if self.training else 'validation' - dset = self.trainer.datamodule.datasets[key] - mapper = dset.conditional_builders[self.cond_stage_key] - - bbox_imgs = [] - map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno)) - for tknzd_bbox in batch[self.cond_stage_key][:N]: - bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256)) - bbox_imgs.append(bboximg) - - cond_img = torch.stack(bbox_imgs, dim=0) - logs['bbox_image'] = cond_img - return logs - - -class SimpleUpscaleDiffusion(LatentDiffusion): - def __init__(self, *args, low_scale_key="LR", **kwargs): - super().__init__(*args, **kwargs) - # assumes that neither the cond_stage nor the low_scale_model contain trainable params - assert not self.cond_stage_trainable - self.low_scale_key = low_scale_key - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): - if not log_mode: - z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) - else: - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - x_low = batch[self.low_scale_key][:bs] - x_low = rearrange(x_low, 'b h w c -> b c h w') - x_low = x_low.to(memory_format=torch.contiguous_format).float() - - encoder_posterior = self.encode_first_stage(x_low) - zx = self.get_first_stage_encoding(encoder_posterior).detach() - all_conds = {"c_concat": [zx], "c_crossattn": [c]} - - if log_mode: - # TODO: maybe disable if too expensive - interpretability = False - if interpretability: - zx = zx[:, :, ::2, ::2] - return z, all_conds, x, xrec, xc, x_low - return z, all_conds - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, - plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, - unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, - **kwargs): - ema_scope = self.ema_scope if use_ema_scope else nullcontext - use_ddim = ddim_steps is not None - - log = dict() - z, c, x, xrec, xc, x_low = self.get_input(batch, self.first_stage_key, bs=N, log_mode=True) - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - log["inputs"] = x - log["reconstruction"] = xrec - log["x_lr"] = x_low - - if self.model.conditioning_key is not None: - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif self.cond_stage_key in ["caption", "txt"]: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2]//25) - log["conditioning"] = xc - elif self.cond_stage_key == 'class_label': - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2]//25) - log['conditioning'] = xc - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - - if sample: - # get denoise row - with ema_scope("Sampling"): - samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - - if unconditional_guidance_scale > 1.0: - uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label) - uc = dict() - for k in c: - if k == "c_crossattn": - assert isinstance(c[k], list) and len(c[k]) == 1 - uc[k] = [uc_tmp] - elif isinstance(c[k], list): - uc[k] = [c[k][i] for i in range(len(c[k]))] - else: - uc[k] = c[k] - - with ema_scope("Sampling with classifier-free guidance"): - samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - return log - -class MultiCatFrameDiffusion(LatentDiffusion): - def __init__(self, *args, low_scale_key="LR", **kwargs): - super().__init__(*args, **kwargs) - # assumes that neither the cond_stage nor the low_scale_model contain trainable params - assert not self.cond_stage_trainable - self.low_scale_key = low_scale_key - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): - n = 2 - if not log_mode: - z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) - else: - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - cat_conds = batch[self.low_scale_key][:bs] - cats = [] - for i in range(n): - x_low = cat_conds[:,:,:,3*i:3*(i+1)] - x_low = rearrange(x_low, 'b h w c -> b c h w') - x_low = x_low.to(memory_format=torch.contiguous_format).float() - encoder_posterior = self.encode_first_stage(x_low) - zx = self.get_first_stage_encoding(encoder_posterior).detach() - cats.append(zx) - - all_conds = {"c_concat": [torch.cat(cats, dim=1)], "c_crossattn": [c]} - - if log_mode: - # TODO: maybe disable if too expensive - interpretability = False - if interpretability: - zx = zx[:, :, ::2, ::2] - return z, all_conds, x, xrec, xc, x_low - return z, all_conds - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, - plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, - unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, - **kwargs): - ema_scope = self.ema_scope if use_ema_scope else nullcontext - use_ddim = ddim_steps is not None - - log = dict() - z, c, x, xrec, xc, x_low = self.get_input(batch, self.first_stage_key, bs=N, log_mode=True) - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - log["inputs"] = x - log["reconstruction"] = xrec - log["x_lr"] = x_low - - if self.model.conditioning_key is not None: - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif self.cond_stage_key in ["caption", "txt"]: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2]//25) - log["conditioning"] = xc - elif self.cond_stage_key == 'class_label': - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2]//25) - log['conditioning'] = xc - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - - if sample: - # get denoise row - with ema_scope("Sampling"): - samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - - if unconditional_guidance_scale > 1.0: - uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label) - uc = dict() - for k in c: - if k == "c_crossattn": - assert isinstance(c[k], list) and len(c[k]) == 1 - uc[k] = [uc_tmp] - elif isinstance(c[k], list): - uc[k] = [c[k][i] for i in range(len(c[k]))] - else: - uc[k] = c[k] - - with ema_scope("Sampling with classifier-free guidance"): - samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - return log diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/options/base_options.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/options/base_options.py deleted file mode 100644 index d8f921d5a43434ae802a55a0fa3889c4b7ab9f6d..0000000000000000000000000000000000000000 --- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/options/base_options.py +++ /dev/null @@ -1,169 +0,0 @@ -"""This script contains base options for Deep3DFaceRecon_pytorch -""" - -import argparse -import os -from util import util -import numpy as np -import torch -import face3d.models as models -import face3d.data as data - - -class BaseOptions(): - """This class defines options used during both training and test time. - - It also implements several helper functions such as parsing, printing, and saving the options. - It also gathers additional options defined infunctions in both dataset class and model class. - """ - - def __init__(self, cmd_line=None): - """Reset the class; indicates the class hasn't been initailized""" - self.initialized = False - self.cmd_line = None - if cmd_line is not None: - self.cmd_line = cmd_line.split() - - def initialize(self, parser): - """Define the common options that are used in both training and test.""" - # basic parameters - parser.add_argument('--name', type=str, default='face_recon', help='name of the experiment. It decides where to store samples and models') - parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') - parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') - parser.add_argument('--vis_batch_nums', type=float, default=1, help='batch nums of images for visulization') - parser.add_argument('--eval_batch_nums', type=float, default=float('inf'), help='batch nums of images for evaluation') - parser.add_argument('--use_ddp', type=util.str2bool, nargs='?', const=True, default=True, help='whether use distributed data parallel') - parser.add_argument('--ddp_port', type=str, default='12355', help='ddp port') - parser.add_argument('--display_per_batch', type=util.str2bool, nargs='?', const=True, default=True, help='whether use batch to show losses') - parser.add_argument('--add_image', type=util.str2bool, nargs='?', const=True, default=True, help='whether add image to tensorboard') - parser.add_argument('--world_size', type=int, default=1, help='batch nums of images for evaluation') - - # model parameters - parser.add_argument('--model', type=str, default='facerecon', help='chooses which model to use.') - - # additional parameters - parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') - parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') - parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}') - - self.initialized = True - return parser - - def gather_options(self): - """Initialize our parser with basic options(only once). - Add additional model-specific and dataset-specific options. - These options are defined in the function - in model and dataset classes. - """ - if not self.initialized: # check if it has been initialized - parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser = self.initialize(parser) - - # get the basic options - if self.cmd_line is None: - opt, _ = parser.parse_known_args() - else: - opt, _ = parser.parse_known_args(self.cmd_line) - - # set cuda visible devices - os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_ids - - # modify model-related parser options - model_name = opt.model - model_option_setter = models.get_option_setter(model_name) - parser = model_option_setter(parser, self.isTrain) - if self.cmd_line is None: - opt, _ = parser.parse_known_args() # parse again with new defaults - else: - opt, _ = parser.parse_known_args(self.cmd_line) # parse again with new defaults - - # modify dataset-related parser options - if opt.dataset_mode: - dataset_name = opt.dataset_mode - dataset_option_setter = data.get_option_setter(dataset_name) - parser = dataset_option_setter(parser, self.isTrain) - - # save and return the parser - self.parser = parser - if self.cmd_line is None: - return parser.parse_args() - else: - return parser.parse_args(self.cmd_line) - - def print_options(self, opt): - """Print and save options - - It will print both current options and default values(if different). - It will save options into a text file / [checkpoints_dir] / opt.txt - """ - message = '' - message += '----------------- Options ---------------\n' - for k, v in sorted(vars(opt).items()): - comment = '' - default = self.parser.get_default(k) - if v != default: - comment = '\t[default: %s]' % str(default) - message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) - message += '----------------- End -------------------' - print(message) - - # save to the disk - expr_dir = os.path.join(opt.checkpoints_dir, opt.name) - util.mkdirs(expr_dir) - file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase)) - try: - with open(file_name, 'wt') as opt_file: - opt_file.write(message) - opt_file.write('\n') - except PermissionError as error: - print("permission error {}".format(error)) - pass - - def parse(self): - """Parse our options, create checkpoints directory suffix, and set up gpu device.""" - opt = self.gather_options() - opt.isTrain = self.isTrain # train or test - - # process opt.suffix - if opt.suffix: - suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' - opt.name = opt.name + suffix - - - # set gpu ids - str_ids = opt.gpu_ids.split(',') - gpu_ids = [] - for str_id in str_ids: - id = int(str_id) - if id >= 0: - gpu_ids.append(id) - opt.world_size = len(gpu_ids) - # if len(opt.gpu_ids) > 0: - # torch.cuda.set_device(gpu_ids[0]) - if opt.world_size == 1: - opt.use_ddp = False - - if opt.phase != 'test': - # set continue_train automatically - if opt.pretrained_name is None: - model_dir = os.path.join(opt.checkpoints_dir, opt.name) - else: - model_dir = os.path.join(opt.checkpoints_dir, opt.pretrained_name) - if os.path.isdir(model_dir): - model_pths = [i for i in os.listdir(model_dir) if i.endswith('pth')] - if os.path.isdir(model_dir) and len(model_pths) != 0: - opt.continue_train= True - - # update the latest epoch count - if opt.continue_train: - if opt.epoch == 'latest': - epoch_counts = [int(i.split('.')[0].split('_')[-1]) for i in model_pths if 'latest' not in i] - if len(epoch_counts) != 0: - opt.epoch_count = max(epoch_counts) + 1 - else: - opt.epoch_count = int(opt.epoch) + 1 - - - self.print_options(opt) - self.opt = opt - return self.opt diff --git a/spaces/danielpedriniportfolio/AutoDA/pages/08-Machine_Learning.py b/spaces/danielpedriniportfolio/AutoDA/pages/08-Machine_Learning.py deleted file mode 100644 index acf0423f173fa0aeef4894669313fb19170cc7c9..0000000000000000000000000000000000000000 --- a/spaces/danielpedriniportfolio/AutoDA/pages/08-Machine_Learning.py +++ /dev/null @@ -1,99 +0,0 @@ -import pandas as pd -import streamlit as st -import streamlit.components.v1 as components -import os -from pycaret.classification import ClassificationExperiment -import time - - -def train_model(): - s = ClassificationExperiment() - s.setup(df, target=df_target, session_id=123 ) - best = s.compare_models() - s.evaluate_model(best) - pred_holdout = s.predict_model(best) - new_df = df.copy().drop(df_target, axis=1) - s.save_model(best, 'model') - st.session_state.best = best - leaderbord = s.get_leaderboard() - leaderbord.to_csv('leaderbord.csv') - -def show_leaderbord(): - if st.button('Show Leaderboard'): - df = pd.read_csv('leaderbord.csv') - st.write(df) - - -def reload_data(): - st.write("Reloading data...") - df_original = st.session_state["df_original"] - df = df_original.copy() - st.session_state.df = df - del st.session_state['df_target'] - del st.session_state['best'] - st.experimental_rerun() - - - - - -def save_model(model, name): - ClassificationExperiment.save_model(model, name) - - - - -st.set_page_config(layout="wide") -col1, col2, col3 = st.columns([15, 70, 15]) - -with col1: - st.write("") - - -with col2: - if "df" not in st.session_state: - st.warning("Please upload a CSV file") - else: - st.header("Machine Learning App") - if st.button("Reload data"): - reload_data() - df = st.session_state["df"] - st.dataframe(df.head()) - # check if target exists to start model training and alredy encoded - if "df_target" in st.session_state: - st.subheader("Target") - df_target = st.session_state["df_target"] - st.info(df_target) - # check if target is already encoded - if df[df_target].dtype in ["int64"]: - if df.isnull().sum().sum() == 0: - st.success("Data is ready for model training") - # check if model is already trained to show the button or not - if "best" not in st.session_state: - if st.button("Train the Model"): - s = ClassificationExperiment() - s.setup(df, target=df_target, session_id=123 ) - best = s.compare_models() - s.evaluate_model(best) - pred_holdout = s.predict_model(best) - new_df = df.copy().drop(df_target, axis=1) - s.save_model(best, 'model') - st.session_state.best = best - leaderbord = s.get_leaderboard() - st.bar_chart(leaderbord.sort_values('Accuracy', ascending=False), x='Model Name', y='Accuracy', height=600) - st.dataframe(leaderbord) - if "best" in st.session_state: - with open('model.pkl', 'rb') as f: - best_model = f.read() - st.download_button(label='Download the Model', data=best_model, file_name='model.pkl', mime='application/octet-stream') - - else: - st.warning( - "Data is not ready for model training. Still missing values" - ) - else: - st.warning( - "Data is not ready for model training. Target is not encoded" - ) - else: - st.warning("Data is not ready for model training. Target is missing") diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/face_detector.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/face_detector.py deleted file mode 100644 index 0103411e27860898fee470895a7cf59d8be2e11a..0000000000000000000000000000000000000000 --- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/face_detector.py +++ /dev/null @@ -1,142 +0,0 @@ -import copy -import os -from pathlib import Path - -import cv2 -import numpy as np -import torch -from torch import nn - -from facelib.detection.yolov5face.models.common import Conv -from facelib.detection.yolov5face.models.yolo import Model -from facelib.detection.yolov5face.utils.datasets import letterbox -from facelib.detection.yolov5face.utils.general import ( - check_img_size, - non_max_suppression_face, - scale_coords, - scale_coords_landmarks, -) - -IS_HIGH_VERSION = tuple(map(int, torch.__version__.split('+')[0].split('.')[:3])) >= (1, 9, 0) - - -def isListempty(inList): - if isinstance(inList, list): # Is a list - return all(map(isListempty, inList)) - return False # Not a list - -class YoloDetector: - def __init__( - self, - config_name, - min_face=10, - target_size=None, - device='cuda', - ): - """ - config_name: name of .yaml config with network configuration from models/ folder. - min_face : minimal face size in pixels. - target_size : target size of smaller image axis (choose lower for faster work). e.g. 480, 720, 1080. - None for original resolution. - """ - self._class_path = Path(__file__).parent.absolute() - self.target_size = target_size - self.min_face = min_face - self.detector = Model(cfg=config_name) - self.device = device - - - def _preprocess(self, imgs): - """ - Preprocessing image before passing through the network. Resize and conversion to torch tensor. - """ - pp_imgs = [] - for img in imgs: - h0, w0 = img.shape[:2] # orig hw - if self.target_size: - r = self.target_size / min(h0, w0) # resize image to img_size - if r < 1: - img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_LINEAR) - - imgsz = check_img_size(max(img.shape[:2]), s=self.detector.stride.max()) # check img_size - img = letterbox(img, new_shape=imgsz)[0] - pp_imgs.append(img) - pp_imgs = np.array(pp_imgs) - pp_imgs = pp_imgs.transpose(0, 3, 1, 2) - pp_imgs = torch.from_numpy(pp_imgs).to(self.device) - pp_imgs = pp_imgs.float() # uint8 to fp16/32 - return pp_imgs / 255.0 # 0 - 255 to 0.0 - 1.0 - - def _postprocess(self, imgs, origimgs, pred, conf_thres, iou_thres): - """ - Postprocessing of raw pytorch model output. - Returns: - bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2. - points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners). - """ - bboxes = [[] for _ in range(len(origimgs))] - landmarks = [[] for _ in range(len(origimgs))] - - pred = non_max_suppression_face(pred, conf_thres, iou_thres) - - for image_id, origimg in enumerate(origimgs): - img_shape = origimg.shape - image_height, image_width = img_shape[:2] - gn = torch.tensor(img_shape)[[1, 0, 1, 0]] # normalization gain whwh - gn_lks = torch.tensor(img_shape)[[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]] # normalization gain landmarks - det = pred[image_id].cpu() - scale_coords(imgs[image_id].shape[1:], det[:, :4], img_shape).round() - scale_coords_landmarks(imgs[image_id].shape[1:], det[:, 5:15], img_shape).round() - - for j in range(det.size()[0]): - box = (det[j, :4].view(1, 4) / gn).view(-1).tolist() - box = list( - map(int, [box[0] * image_width, box[1] * image_height, box[2] * image_width, box[3] * image_height]) - ) - if box[3] - box[1] < self.min_face: - continue - lm = (det[j, 5:15].view(1, 10) / gn_lks).view(-1).tolist() - lm = list(map(int, [i * image_width if j % 2 == 0 else i * image_height for j, i in enumerate(lm)])) - lm = [lm[i : i + 2] for i in range(0, len(lm), 2)] - bboxes[image_id].append(box) - landmarks[image_id].append(lm) - return bboxes, landmarks - - def detect_faces(self, imgs, conf_thres=0.7, iou_thres=0.5): - """ - Get bbox coordinates and keypoints of faces on original image. - Params: - imgs: image or list of images to detect faces on with BGR order (convert to RGB order for inference) - conf_thres: confidence threshold for each prediction - iou_thres: threshold for NMS (filter of intersecting bboxes) - Returns: - bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2. - points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners). - """ - # Pass input images through face detector - images = imgs if isinstance(imgs, list) else [imgs] - images = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in images] - origimgs = copy.deepcopy(images) - - images = self._preprocess(images) - - if IS_HIGH_VERSION: - with torch.inference_mode(): # for pytorch>=1.9 - pred = self.detector(images)[0] - else: - with torch.no_grad(): # for pytorch<1.9 - pred = self.detector(images)[0] - - bboxes, points = self._postprocess(images, origimgs, pred, conf_thres, iou_thres) - - # return bboxes, points - if not isListempty(points): - bboxes = np.array(bboxes).reshape(-1,4) - points = np.array(points).reshape(-1,10) - padding = bboxes[:,0].reshape(-1,1) - return np.concatenate((bboxes, padding, points), axis=1) - else: - return None - - def __call__(self, *args): - return self.predict(*args) diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/models/common.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/models/common.py deleted file mode 100644 index 497a00444c4c59725001993a63fe4617e9d323c8..0000000000000000000000000000000000000000 --- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/models/common.py +++ /dev/null @@ -1,299 +0,0 @@ -# This file contains modules common to various models - -import math - -import numpy as np -import torch -from torch import nn - -from facelib.detection.yolov5face.utils.datasets import letterbox -from facelib.detection.yolov5face.utils.general import ( - make_divisible, - non_max_suppression, - scale_coords, - xyxy2xywh, -) - - -def autopad(k, p=None): # kernel, padding - # Pad to 'same' - if p is None: - p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad - return p - - -def channel_shuffle(x, groups): - batchsize, num_channels, height, width = x.data.size() - channels_per_group = torch.div(num_channels, groups, rounding_mode="trunc") - - # reshape - x = x.view(batchsize, groups, channels_per_group, height, width) - x = torch.transpose(x, 1, 2).contiguous() - - # flatten - return x.view(batchsize, -1, height, width) - - -def DWConv(c1, c2, k=1, s=1, act=True): - # Depthwise convolution - return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) - - -class Conv(nn.Module): - # Standard convolution - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super().__init__() - self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) - self.bn = nn.BatchNorm2d(c2) - self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) - - def forward(self, x): - return self.act(self.bn(self.conv(x))) - - def fuseforward(self, x): - return self.act(self.conv(x)) - - -class StemBlock(nn.Module): - def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True): - super().__init__() - self.stem_1 = Conv(c1, c2, k, s, p, g, act) - self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0) - self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1) - self.stem_2p = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True) - self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0) - - def forward(self, x): - stem_1_out = self.stem_1(x) - stem_2a_out = self.stem_2a(stem_1_out) - stem_2b_out = self.stem_2b(stem_2a_out) - stem_2p_out = self.stem_2p(stem_1_out) - return self.stem_3(torch.cat((stem_2b_out, stem_2p_out), 1)) - - -class Bottleneck(nn.Module): - # Standard bottleneck - def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_, c2, 3, 1, g=g) - self.add = shortcut and c1 == c2 - - def forward(self, x): - return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) - - -class BottleneckCSP(nn.Module): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) - self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) - self.cv4 = Conv(2 * c_, c2, 1, 1) - self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) - self.act = nn.LeakyReLU(0.1, inplace=True) - self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) - - def forward(self, x): - y1 = self.cv3(self.m(self.cv1(x))) - y2 = self.cv2(x) - return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) - - -class C3(nn.Module): - # CSP Bottleneck with 3 convolutions - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c1, c_, 1, 1) - self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) - self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) - - def forward(self, x): - return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) - - -class ShuffleV2Block(nn.Module): - def __init__(self, inp, oup, stride): - super().__init__() - - if not 1 <= stride <= 3: - raise ValueError("illegal stride value") - self.stride = stride - - branch_features = oup // 2 - - if self.stride > 1: - self.branch1 = nn.Sequential( - self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1), - nn.BatchNorm2d(inp), - nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(branch_features), - nn.SiLU(), - ) - else: - self.branch1 = nn.Sequential() - - self.branch2 = nn.Sequential( - nn.Conv2d( - inp if (self.stride > 1) else branch_features, - branch_features, - kernel_size=1, - stride=1, - padding=0, - bias=False, - ), - nn.BatchNorm2d(branch_features), - nn.SiLU(), - self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1), - nn.BatchNorm2d(branch_features), - nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(branch_features), - nn.SiLU(), - ) - - @staticmethod - def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False): - return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i) - - def forward(self, x): - if self.stride == 1: - x1, x2 = x.chunk(2, dim=1) - out = torch.cat((x1, self.branch2(x2)), dim=1) - else: - out = torch.cat((self.branch1(x), self.branch2(x)), dim=1) - out = channel_shuffle(out, 2) - return out - - -class SPP(nn.Module): - # Spatial pyramid pooling layer used in YOLOv3-SPP - def __init__(self, c1, c2, k=(5, 9, 13)): - super().__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) - self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) - - def forward(self, x): - x = self.cv1(x) - return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) - - -class Focus(nn.Module): - # Focus wh information into c-space - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super().__init__() - self.conv = Conv(c1 * 4, c2, k, s, p, g, act) - - def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) - return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) - - -class Concat(nn.Module): - # Concatenate a list of tensors along dimension - def __init__(self, dimension=1): - super().__init__() - self.d = dimension - - def forward(self, x): - return torch.cat(x, self.d) - - -class NMS(nn.Module): - # Non-Maximum Suppression (NMS) module - conf = 0.25 # confidence threshold - iou = 0.45 # IoU threshold - classes = None # (optional list) filter by class - - def forward(self, x): - return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) - - -class AutoShape(nn.Module): - # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS - img_size = 640 # inference size (pixels) - conf = 0.25 # NMS confidence threshold - iou = 0.45 # NMS IoU threshold - classes = None # (optional list) filter by class - - def __init__(self, model): - super().__init__() - self.model = model.eval() - - def autoshape(self): - print("autoShape already enabled, skipping... ") # model already converted to model.autoshape() - return self - - def forward(self, imgs, size=640, augment=False, profile=False): - # Inference from various sources. For height=720, width=1280, RGB images example inputs are: - # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3) - # PIL: = Image.open('image.jpg') # HWC x(720,1280,3) - # numpy: = np.zeros((720,1280,3)) # HWC - # torch: = torch.zeros(16,3,720,1280) # BCHW - # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images - - p = next(self.model.parameters()) # for device and type - if isinstance(imgs, torch.Tensor): # torch - return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference - - # Pre-process - n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images - shape0, shape1 = [], [] # image and inference shapes - for i, im in enumerate(imgs): - im = np.array(im) # to numpy - if im.shape[0] < 5: # image in CHW - im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) - im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input - s = im.shape[:2] # HWC - shape0.append(s) # image shape - g = size / max(s) # gain - shape1.append([y * g for y in s]) - imgs[i] = im # update - shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape - x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad - x = np.stack(x, 0) if n > 1 else x[0][None] # stack - x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW - x = torch.from_numpy(x).to(p.device).type_as(p) / 255.0 # uint8 to fp16/32 - - # Inference - with torch.no_grad(): - y = self.model(x, augment, profile)[0] # forward - y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS - - # Post-process - for i in range(n): - scale_coords(shape1, y[i][:, :4], shape0[i]) - - return Detections(imgs, y, self.names) - - -class Detections: - # detections class for YOLOv5 inference results - def __init__(self, imgs, pred, names=None): - super().__init__() - d = pred[0].device # device - gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1.0, 1.0], device=d) for im in imgs] # normalizations - self.imgs = imgs # list of images as numpy arrays - self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) - self.names = names # class names - self.xyxy = pred # xyxy pixels - self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels - self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized - self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized - self.n = len(self.pred) - - def __len__(self): - return self.n - - def tolist(self): - # return a list of Detections objects, i.e. 'for result in results.tolist():' - x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)] - for d in x: - for k in ["imgs", "pred", "xyxy", "xyxyn", "xywh", "xywhn"]: - setattr(d, k, getattr(d, k)[0]) # pop out of list - return x diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/contourpy/chunk.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/contourpy/chunk.py deleted file mode 100644 index 076cbc4370b4471c2074cade279250a3ebec9041..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/contourpy/chunk.py +++ /dev/null @@ -1,89 +0,0 @@ -from __future__ import annotations - -import math - - -def calc_chunk_sizes( - chunk_size: int | tuple[int, int] | None, - chunk_count: int | tuple[int, int] | None, - total_chunk_count: int | None, - ny: int, - nx: int, -) -> tuple[int, int]: - """Calculate chunk sizes. - - Args: - chunk_size (int or tuple(int, int), optional): Chunk size in (y, x) directions, or the same - size in both directions if only one is specified. - chunk_count (int or tuple(int, int), optional): Chunk count in (y, x) directions, or the - same count in both irections if only one is specified. - total_chunk_count (int, optional): Total number of chunks. - ny (int): Number of grid points in y-direction. - nx (int): Number of grid points in x-direction. - - Return: - tuple(int, int): Chunk sizes (y_chunk_size, x_chunk_size). - - Note: - A maximum of one of ``chunk_size``, ``chunk_count`` and ``total_chunk_count`` may be - specified. - """ - if sum([chunk_size is not None, chunk_count is not None, total_chunk_count is not None]) > 1: - raise ValueError("Only one of chunk_size, chunk_count and total_chunk_count should be set") - - if total_chunk_count is not None: - max_chunk_count = (nx-1)*(ny-1) - total_chunk_count = min(max(total_chunk_count, 1), max_chunk_count) - if total_chunk_count == 1: - chunk_size = 0 - elif total_chunk_count == max_chunk_count: - chunk_size = (1, 1) - else: - factors = two_factors(total_chunk_count) - if ny > nx: - chunk_count = factors - else: - chunk_count = (factors[1], factors[0]) - - if chunk_count is not None: - if isinstance(chunk_count, tuple): - y_chunk_count, x_chunk_count = chunk_count - else: - y_chunk_count = x_chunk_count = chunk_count - x_chunk_count = min(max(x_chunk_count, 1), nx-1) - y_chunk_count = min(max(y_chunk_count, 1), ny-1) - chunk_size = (math.ceil((ny-1) / y_chunk_count), math.ceil((nx-1) / x_chunk_count)) - - if chunk_size is None: - y_chunk_size = x_chunk_size = 0 - elif isinstance(chunk_size, tuple): - y_chunk_size, x_chunk_size = chunk_size - else: - y_chunk_size = x_chunk_size = chunk_size - - if x_chunk_size < 0 or y_chunk_size < 0: - raise ValueError("chunk_size cannot be negative") - - return y_chunk_size, x_chunk_size - - -def two_factors(n: int) -> tuple[int, int]: - """Split an integer into two integer factors. - - The two factors will be as close as possible to the sqrt of n, and are returned in decreasing - order. Worst case returns (n, 1). - - Args: - n (int): The integer to factorize. - - Return: - tuple(int, int): The two factors of n, in decreasing order. - """ - i = math.ceil(math.sqrt(n)) - while n % i != 0: - i -= 1 - j = n // i - if i > j: - return i, j - else: - return j, i diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-201f0338.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-201f0338.js deleted file mode 100644 index ae3d3fc19bfa2ab5b388ecbd6e3783e509821512..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-201f0338.js +++ /dev/null @@ -1,2 +0,0 @@ -import{S as P,e as Q,s as R,m as U,o as H,t as V,g as F,Y as J,h as I,j as O,p as K,x as Y,n as L,k as T,B as ae,C as se,am as _e,F as j,G as N,w as d,u as v,H as S,V as X,ae as Z,N as E,O as q,Q as y,R as p,r as z,v as A,T as D,E as C,P as fe}from"./index-9e76ffee.js";import{B as x}from"./Button-30a08c0b.js";import{I as $}from"./Info-77722665.js";function ce(n){let e,l,i,t,c,o,m;return{c(){e=U("label"),l=U("input"),i=H(),t=U("span"),c=V(n[2]),l.disabled=n[1],F(l,"type","checkbox"),F(l,"name","test"),F(l,"data-testid","checkbox"),F(l,"class","svelte-1ojmf70"),F(t,"class","ml-2 svelte-1ojmf70"),F(e,"class","svelte-1ojmf70"),J(e,"disabled",n[1])},m(u,f){I(u,e,f),O(e,l),l.checked=n[0],O(e,i),O(e,t),O(t,c),o||(m=[K(l,"change",n[5]),K(l,"input",n[6])],o=!0)},p(u,[f]){f&2&&(l.disabled=u[1]),f&1&&(l.checked=u[0]),f&4&&Y(c,u[2]),f&2&&J(e,"disabled",u[1])},i:L,o:L,d(u){u&&T(e),o=!1,ae(m)}}}function oe(n,e,l){let{value:i}=e,{value_is_output:t=!1}=e,{disabled:c=!1}=e,{label:o}=e;const m=se();function u(){m("change",i),t||m("input")}_e(()=>{l(4,t=!1)});function f(){i=this.checked,l(0,i)}const a=r=>{l(0,i=r.currentTarget.checked),m("select",{index:0,value:o,selected:r.currentTarget.checked})};return n.$$set=r=>{"value"in r&&l(0,i=r.value),"value_is_output"in r&&l(4,t=r.value_is_output),"disabled"in r&&l(1,c=r.disabled),"label"in r&&l(2,o=r.label)},n.$$.update=()=>{n.$$.dirty&1&&u()},[i,c,o,m,t,f,a]}let ee=class extends P{constructor(e){super(),Q(this,e,oe,ce,R,{value:0,value_is_output:4,disabled:1,label:2})}};function M(n){let e,l;return e=new $({props:{$$slots:{default:[me]},$$scope:{ctx:n}}}),{c(){j(e.$$.fragment)},m(i,t){N(e,i,t),l=!0},p(i,t){const c={};t&65600&&(c.$$scope={dirty:t,ctx:i}),e.$set(c)},i(i){l||(d(e.$$.fragment,i),l=!0)},o(i){v(e.$$.fragment,i),l=!1},d(i){S(e,i)}}}function me(n){let e;return{c(){e=V(n[6])},m(l,i){I(l,e,i)},p(l,i){i&64&&Y(e,l[6])},d(l){l&&T(e)}}}function be(n){let e,l,i,t,c,o,m;const u=[n[10]];let f={};for(let s=0;s
-linuxpdf egtre format menu ; Gryjes ex-girlfriend Pirated software sn
Yes, ETS2 on mobile is free to download and play. However, it may contain some in-app purchases or ads that can enhance your gameplay experience or support the developers.
-Yes, ETS2 on mobile is safe to download and install on your device. It does not contain any viruses or malware that can harm your device or data. However, you should always download it from ets2.mobi or other trusted sources to avoid any risks.
-ETS2 on mobile is compatible with most Android and iOS devices that have at least 4 GB of RAM and 3 GB of free storage space. However, some devices may have different performance or compatibility issues depending on their specifications or settings.
-Yes, you can play ETS2 on mobile offline without an internet connection. However, some features or functions may not work properly or be available offline, such as multiplayer mode, online leaderboards, updates, or downloads. You also need an internet connection to verify your game license and activate it on your device.
-If you have any questions, feedback, suggestions, or issues regarding ETS2 on mobile, you can contact the developers or report a bug through the following channels:
--
-
-
-
\ No newline at end of file diff --git a/spaces/2023Liu2023/bingo/src/components/chat-list.tsx b/spaces/2023Liu2023/bingo/src/components/chat-list.tsx deleted file mode 100644 index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000 --- a/spaces/2023Liu2023/bingo/src/components/chat-list.tsx +++ /dev/null @@ -1,28 +0,0 @@ -import React from 'react' - -import { Separator } from '@/components/ui/separator' -import { ChatMessage } from '@/components/chat-message' -import { ChatMessageModel } from '@/lib/bots/bing/types' - -export interface ChatList { - messages: ChatMessageModel[] -} - -export function ChatList({ messages }: ChatList) { - if (!messages.length) { - return null - } - - return ( -
- {messages.map((message, index) => (
-
-
- ))}
-
- )
-}
diff --git a/spaces/360macky/first-space/app.py b/spaces/360macky/first-space/app.py
deleted file mode 100644
index b178efdb6a5a27e18fec0525a278bdd2ede2b19c..0000000000000000000000000000000000000000
--- a/spaces/360macky/first-space/app.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import streamlit as st
-
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
-
diff --git a/spaces/4Taps/SadTalker/src/test_audio2coeff.py b/spaces/4Taps/SadTalker/src/test_audio2coeff.py
deleted file mode 100644
index 3db6be3af59b0319c50106d9a92c903118f28410..0000000000000000000000000000000000000000
--- a/spaces/4Taps/SadTalker/src/test_audio2coeff.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import os
-import torch
-import numpy as np
-from scipy.io import savemat
-from yacs.config import CfgNode as CN
-from scipy.signal import savgol_filter
-
-from src.audio2pose_models.audio2pose import Audio2Pose
-from src.audio2exp_models.networks import SimpleWrapperV2
-from src.audio2exp_models.audio2exp import Audio2Exp
-
-def load_cpk(checkpoint_path, model=None, optimizer=None, device="cpu"):
- checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
- if model is not None:
- model.load_state_dict(checkpoint['model'])
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint['optimizer'])
-
- return checkpoint['epoch']
-
-class Audio2Coeff():
-
- def __init__(self, audio2pose_checkpoint, audio2pose_yaml_path,
- audio2exp_checkpoint, audio2exp_yaml_path,
- wav2lip_checkpoint, device):
- #load config
- fcfg_pose = open(audio2pose_yaml_path)
- cfg_pose = CN.load_cfg(fcfg_pose)
- cfg_pose.freeze()
- fcfg_exp = open(audio2exp_yaml_path)
- cfg_exp = CN.load_cfg(fcfg_exp)
- cfg_exp.freeze()
-
- # load audio2pose_model
- self.audio2pose_model = Audio2Pose(cfg_pose, wav2lip_checkpoint, device=device)
- self.audio2pose_model = self.audio2pose_model.to(device)
- self.audio2pose_model.eval()
- for param in self.audio2pose_model.parameters():
- param.requires_grad = False
- try:
- load_cpk(audio2pose_checkpoint, model=self.audio2pose_model, device=device)
- except:
- raise Exception("Failed in loading audio2pose_checkpoint")
-
- # load audio2exp_model
- netG = SimpleWrapperV2()
- netG = netG.to(device)
- for param in netG.parameters():
- netG.requires_grad = False
- netG.eval()
- try:
- load_cpk(audio2exp_checkpoint, model=netG, device=device)
- except:
- raise Exception("Failed in loading audio2exp_checkpoint")
- self.audio2exp_model = Audio2Exp(netG, cfg_exp, device=device, prepare_training_loss=False)
- self.audio2exp_model = self.audio2exp_model.to(device)
- for param in self.audio2exp_model.parameters():
- param.requires_grad = False
- self.audio2exp_model.eval()
-
- self.device = device
-
- def generate(self, batch, coeff_save_dir, pose_style):
-
- with torch.no_grad():
- #test
- results_dict_exp= self.audio2exp_model.test(batch)
- exp_pred = results_dict_exp['exp_coeff_pred'] #bs T 64
-
- #for class_id in range(1):
- #class_id = 0#(i+10)%45
- #class_id = random.randint(0,46) #46 styles can be selected
- batch['class'] = torch.LongTensor([pose_style]).to(self.device)
- results_dict_pose = self.audio2pose_model.test(batch)
- pose_pred = results_dict_pose['pose_pred'] #bs T 6
-
- pose_pred = torch.Tensor(savgol_filter(np.array(pose_pred.cpu()), 13, 2, axis=1)).to(self.device)
- coeffs_pred = torch.cat((exp_pred, pose_pred), dim=-1) #bs T 70
-
- coeffs_pred_numpy = coeffs_pred[0].clone().detach().cpu().numpy()
-
- savemat(os.path.join(coeff_save_dir, '%s##%s.mat'%(batch['pic_name'], batch['audio_name'])),
- {'coeff_3dmm': coeffs_pred_numpy})
-
- return os.path.join(coeff_save_dir, '%s##%s.mat'%(batch['pic_name'], batch['audio_name']))
-
-
diff --git a/spaces/801artistry/RVC801/infer/modules/uvr5/preprocess.py b/spaces/801artistry/RVC801/infer/modules/uvr5/preprocess.py
deleted file mode 100644
index 19f11110ea822eeb140fb885c600536290a1adff..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/infer/modules/uvr5/preprocess.py
+++ /dev/null
@@ -1,346 +0,0 @@
-import os
-import logging
-
-logger = logging.getLogger(__name__)
-
-import librosa
-import numpy as np
-import soundfile as sf
-import torch
-
-from infer.lib.uvr5_pack.lib_v5 import nets_61968KB as Nets
-from infer.lib.uvr5_pack.lib_v5 import spec_utils
-from infer.lib.uvr5_pack.lib_v5.model_param_init import ModelParameters
-from infer.lib.uvr5_pack.lib_v5.nets_new import CascadedNet
-from infer.lib.uvr5_pack.utils import inference
-
-
-class AudioPre:
- def __init__(self, agg, model_path, device, is_half):
- self.model_path = model_path
- self.device = device
- self.data = {
- # Processing Options
- "postprocess": False,
- "tta": False,
- # Constants
- "window_size": 512,
- "agg": agg,
- "high_end_process": "mirroring",
- }
- mp = ModelParameters("infer/lib/uvr5_pack/lib_v5/modelparams/4band_v2.json")
- model = Nets.CascadedASPPNet(mp.param["bins"] * 2)
- cpk = torch.load(model_path, map_location="cpu")
- model.load_state_dict(cpk)
- model.eval()
- if is_half:
- model = model.half().to(device)
- else:
- model = model.to(device)
-
- self.mp = mp
- self.model = model
-
- def _path_audio_(self, music_file, ins_root=None, vocal_root=None, format="flac"):
- if ins_root is None and vocal_root is None:
- return "No save root."
- name = os.path.basename(music_file)
- if ins_root is not None:
- os.makedirs(ins_root, exist_ok=True)
- if vocal_root is not None:
- os.makedirs(vocal_root, exist_ok=True)
- X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
- bands_n = len(self.mp.param["band"])
- # print(bands_n)
- for d in range(bands_n, 0, -1):
- bp = self.mp.param["band"][d]
- if d == bands_n: # high-end band
- (
- X_wave[d],
- _,
- ) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
- music_file,
- bp["sr"],
- False,
- dtype=np.float32,
- res_type=bp["res_type"],
- )
- if X_wave[d].ndim == 1:
- X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
- else: # lower bands
- X_wave[d] = librosa.core.resample(
- X_wave[d + 1],
- self.mp.param["band"][d + 1]["sr"],
- bp["sr"],
- res_type=bp["res_type"],
- )
- # Stft of wave source
- X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
- X_wave[d],
- bp["hl"],
- bp["n_fft"],
- self.mp.param["mid_side"],
- self.mp.param["mid_side_b2"],
- self.mp.param["reverse"],
- )
- # pdb.set_trace()
- if d == bands_n and self.data["high_end_process"] != "none":
- input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
- self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
- )
- input_high_end = X_spec_s[d][
- :, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
- ]
-
- X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
- aggresive_set = float(self.data["agg"] / 100)
- aggressiveness = {
- "value": aggresive_set,
- "split_bin": self.mp.param["band"][1]["crop_stop"],
- }
- with torch.no_grad():
- pred, X_mag, X_phase = inference(
- X_spec_m, self.device, self.model, aggressiveness, self.data
- )
- # Postprocess
- if self.data["postprocess"]:
- pred_inv = np.clip(X_mag - pred, 0, np.inf)
- pred = spec_utils.mask_silence(pred, pred_inv)
- y_spec_m = pred * X_phase
- v_spec_m = X_spec_m - y_spec_m
-
- if ins_root is not None:
- if self.data["high_end_process"].startswith("mirroring"):
- input_high_end_ = spec_utils.mirroring(
- self.data["high_end_process"], y_spec_m, input_high_end, self.mp
- )
- wav_instrument = spec_utils.cmb_spectrogram_to_wave(
- y_spec_m, self.mp, input_high_end_h, input_high_end_
- )
- else:
- wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
- logger.info("%s instruments done" % name)
- if format in ["wav", "flac"]:
- sf.write(
- os.path.join(
- ins_root,
- "instrument_{}_{}.{}".format(name, self.data["agg"], format),
- ),
- (np.array(wav_instrument) * 32768).astype("int16"),
- self.mp.param["sr"],
- ) #
- else:
- path = os.path.join(
- ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
- )
- sf.write(
- path,
- (np.array(wav_instrument) * 32768).astype("int16"),
- self.mp.param["sr"],
- )
- if os.path.exists(path):
- os.system(
- "ffmpeg -i %s -vn %s -q:a 2 -y"
- % (path, path[:-4] + ".%s" % format)
- )
- if vocal_root is not None:
- if self.data["high_end_process"].startswith("mirroring"):
- input_high_end_ = spec_utils.mirroring(
- self.data["high_end_process"], v_spec_m, input_high_end, self.mp
- )
- wav_vocals = spec_utils.cmb_spectrogram_to_wave(
- v_spec_m, self.mp, input_high_end_h, input_high_end_
- )
- else:
- wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
- logger.info("%s vocals done" % name)
- if format in ["wav", "flac"]:
- sf.write(
- os.path.join(
- vocal_root,
- "vocal_{}_{}.{}".format(name, self.data["agg"], format),
- ),
- (np.array(wav_vocals) * 32768).astype("int16"),
- self.mp.param["sr"],
- )
- else:
- path = os.path.join(
- vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
- )
- sf.write(
- path,
- (np.array(wav_vocals) * 32768).astype("int16"),
- self.mp.param["sr"],
- )
- if os.path.exists(path):
- os.system(
- "ffmpeg -i %s -vn %s -q:a 2 -y"
- % (path, path[:-4] + ".%s" % format)
- )
-
-
-class AudioPreDeEcho:
- def __init__(self, agg, model_path, device, is_half):
- self.model_path = model_path
- self.device = device
- self.data = {
- # Processing Options
- "postprocess": False,
- "tta": False,
- # Constants
- "window_size": 512,
- "agg": agg,
- "high_end_process": "mirroring",
- }
- mp = ModelParameters("infer/lib/uvr5_pack/lib_v5/modelparams/4band_v3.json")
- nout = 64 if "DeReverb" in model_path else 48
- model = CascadedNet(mp.param["bins"] * 2, nout)
- cpk = torch.load(model_path, map_location="cpu")
- model.load_state_dict(cpk)
- model.eval()
- if is_half:
- model = model.half().to(device)
- else:
- model = model.to(device)
-
- self.mp = mp
- self.model = model
-
- def _path_audio_(
- self, music_file, vocal_root=None, ins_root=None, format="flac"
- ): # 3个VR模型vocal和ins是反的
- if ins_root is None and vocal_root is None:
- return "No save root."
- name = os.path.basename(music_file)
- if ins_root is not None:
- os.makedirs(ins_root, exist_ok=True)
- if vocal_root is not None:
- os.makedirs(vocal_root, exist_ok=True)
- X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
- bands_n = len(self.mp.param["band"])
- # print(bands_n)
- for d in range(bands_n, 0, -1):
- bp = self.mp.param["band"][d]
- if d == bands_n: # high-end band
- (
- X_wave[d],
- _,
- ) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
- music_file,
- bp["sr"],
- False,
- dtype=np.float32,
- res_type=bp["res_type"],
- )
- if X_wave[d].ndim == 1:
- X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
- else: # lower bands
- X_wave[d] = librosa.core.resample(
- X_wave[d + 1],
- self.mp.param["band"][d + 1]["sr"],
- bp["sr"],
- res_type=bp["res_type"],
- )
- # Stft of wave source
- X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
- X_wave[d],
- bp["hl"],
- bp["n_fft"],
- self.mp.param["mid_side"],
- self.mp.param["mid_side_b2"],
- self.mp.param["reverse"],
- )
- # pdb.set_trace()
- if d == bands_n and self.data["high_end_process"] != "none":
- input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
- self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
- )
- input_high_end = X_spec_s[d][
- :, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
- ]
-
- X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
- aggresive_set = float(self.data["agg"] / 100)
- aggressiveness = {
- "value": aggresive_set,
- "split_bin": self.mp.param["band"][1]["crop_stop"],
- }
- with torch.no_grad():
- pred, X_mag, X_phase = inference(
- X_spec_m, self.device, self.model, aggressiveness, self.data
- )
- # Postprocess
- if self.data["postprocess"]:
- pred_inv = np.clip(X_mag - pred, 0, np.inf)
- pred = spec_utils.mask_silence(pred, pred_inv)
- y_spec_m = pred * X_phase
- v_spec_m = X_spec_m - y_spec_m
-
- if ins_root is not None:
- if self.data["high_end_process"].startswith("mirroring"):
- input_high_end_ = spec_utils.mirroring(
- self.data["high_end_process"], y_spec_m, input_high_end, self.mp
- )
- wav_instrument = spec_utils.cmb_spectrogram_to_wave(
- y_spec_m, self.mp, input_high_end_h, input_high_end_
- )
- else:
- wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
- logger.info("%s instruments done" % name)
- if format in ["wav", "flac"]:
- sf.write(
- os.path.join(
- ins_root,
- "instrument_{}_{}.{}".format(name, self.data["agg"], format),
- ),
- (np.array(wav_instrument) * 32768).astype("int16"),
- self.mp.param["sr"],
- ) #
- else:
- path = os.path.join(
- ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
- )
- sf.write(
- path,
- (np.array(wav_instrument) * 32768).astype("int16"),
- self.mp.param["sr"],
- )
- if os.path.exists(path):
- os.system(
- "ffmpeg -i %s -vn %s -q:a 2 -y"
- % (path, path[:-4] + ".%s" % format)
- )
- if vocal_root is not None:
- if self.data["high_end_process"].startswith("mirroring"):
- input_high_end_ = spec_utils.mirroring(
- self.data["high_end_process"], v_spec_m, input_high_end, self.mp
- )
- wav_vocals = spec_utils.cmb_spectrogram_to_wave(
- v_spec_m, self.mp, input_high_end_h, input_high_end_
- )
- else:
- wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
- logger.info("%s vocals done" % name)
- if format in ["wav", "flac"]:
- sf.write(
- os.path.join(
- vocal_root,
- "vocal_{}_{}.{}".format(name, self.data["agg"], format),
- ),
- (np.array(wav_vocals) * 32768).astype("int16"),
- self.mp.param["sr"],
- )
- else:
- path = os.path.join(
- vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
- )
- sf.write(
- path,
- (np.array(wav_vocals) * 32768).astype("int16"),
- self.mp.param["sr"],
- )
- if os.path.exists(path):
- os.system(
- "ffmpeg -i %s -vn %s -q:a 2 -y"
- % (path, path[:-4] + ".%s" % format)
- )
diff --git a/spaces/AIFILMS/generate_human_motion/pyrender/examples/duck.py b/spaces/AIFILMS/generate_human_motion/pyrender/examples/duck.py
deleted file mode 100644
index 9a94bad5bfb30493f7364f2e52cbb4badbccb2c7..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/generate_human_motion/pyrender/examples/duck.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from pyrender import Mesh, Scene, Viewer
-from io import BytesIO
-import numpy as np
-import trimesh
-import requests
-
-duck_source = "https://github.com/KhronosGroup/glTF-Sample-Models/raw/master/2.0/Duck/glTF-Binary/Duck.glb"
-
-duck = trimesh.load(BytesIO(requests.get(duck_source).content), file_type='glb')
-duckmesh = Mesh.from_trimesh(list(duck.geometry.values())[0])
-scene = Scene(ambient_light=np.array([1.0, 1.0, 1.0, 1.0]))
-scene.add(duckmesh)
-Viewer(scene)
diff --git a/spaces/AIZerotoHero-Health4All/01-Gradio-Speech2Text2Speech-AIPipeline/README.md b/spaces/AIZerotoHero-Health4All/01-Gradio-Speech2Text2Speech-AIPipeline/README.md
deleted file mode 100644
index b98c0cb21bcd18f4bbec2f622d0aa58000bffc8b..0000000000000000000000000000000000000000
--- a/spaces/AIZerotoHero-Health4All/01-Gradio-Speech2Text2Speech-AIPipeline/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 01🗣️ Gradio NLP Speech 2 Text 2 Speech Generator AI Pipeline 🙉
-emoji: 🗣️🎤🙉
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.9.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnet50_label_smooth.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnet50_label_smooth.py
deleted file mode 100644
index b6f793751904658b3e7e01a5ffdaa6b86e156e66..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnet50_label_smooth.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# model settings
-model = dict(
- type='ImageClassifier',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(3, ),
- style='pytorch'),
- neck=dict(type='GlobalAveragePooling'),
- head=dict(
- type='LinearClsHead',
- num_classes=1000,
- in_channels=2048,
- loss=dict(
- type='LabelSmoothLoss', label_smooth_val=0.1, loss_weight=1.0),
- topk=(1, 5),
- ))
diff --git a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/utils/utils.py b/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/utils/utils.py
deleted file mode 100644
index 86e1448d065fa182ca69aae00d2f2a7eea55d8a4..0000000000000000000000000000000000000000
--- a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/utils/utils.py
+++ /dev/null
@@ -1,234 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from concurrent.futures import ProcessPoolExecutor
-from functools import wraps
-import hashlib
-import logging
-import typing as tp
-
-import flashy
-import flashy.distrib
-import omegaconf
-import torch
-from torch.nn.utils.rnn import pad_sequence
-
-
-logger = logging.getLogger(__name__)
-
-
-def dict_from_config(cfg: omegaconf.DictConfig) -> dict:
- """Convenience function to map an omegaconf configuration to a dictionary.
-
- Args:
- cfg (omegaconf.DictConfig): Original configuration to map to dict.
- Returns:
- dict: Config as dictionary object.
- """
- dct = omegaconf.OmegaConf.to_container(cfg, resolve=True)
- assert isinstance(dct, dict)
- return dct
-
-
-def random_subset(dataset, max_samples: int, seed: int = 42) -> torch.utils.data.Subset:
- if max_samples >= len(dataset):
- return dataset
-
- generator = torch.Generator().manual_seed(seed)
- perm = torch.randperm(len(dataset), generator=generator)
- return torch.utils.data.Subset(dataset, perm[:max_samples].tolist())
-
-
-def get_loader(dataset, num_samples: tp.Optional[int], batch_size: int,
- num_workers: int, seed: int, **kwargs) -> torch.utils.data.DataLoader:
- """Convenience function to load dataset into a dataloader with optional subset sampling.
-
- Args:
- dataset: Dataset to load.
- num_samples (Optional[int]): Number of samples to limit subset size.
- batch_size (int): Batch size.
- num_workers (int): Number of workers for data loading.
- seed (int): Random seed.
- """
- if num_samples is not None:
- dataset = random_subset(dataset, num_samples, seed)
-
- dataloader = flashy.distrib.loader(
- dataset,
- batch_size=batch_size,
- num_workers=num_workers,
- **kwargs
- )
- return dataloader
-
-
-def get_dataset_from_loader(dataloader):
- dataset = dataloader.dataset
- if isinstance(dataset, torch.utils.data.Subset):
- return dataset.dataset
- else:
- return dataset
-
-
-def multinomial(input: torch.Tensor, num_samples: int, replacement=False, *, generator=None):
- """torch.multinomial with arbitrary number of dimensions, and number of candidates on the last dimension.
-
- Args:
- input (torch.Tensor): The input tensor containing probabilities.
- num_samples (int): Number of samples to draw.
- replacement (bool): Whether to draw with replacement or not.
- Keywords args:
- generator (torch.Generator): A pseudorandom number generator for sampling.
- Returns:
- torch.Tensor: Last dimension contains num_samples indices
- sampled from the multinomial probability distribution
- located in the last dimension of tensor input.
- """
- input_ = input.reshape(-1, input.shape[-1])
- output_ = torch.multinomial(input_, num_samples=num_samples, replacement=replacement, generator=generator)
- output = output_.reshape(*list(input.shape[:-1]), -1)
- return output
-
-
-def sample_top_k(probs: torch.Tensor, k: int) -> torch.Tensor:
- """Sample next token from top K values along the last dimension of the input probs tensor.
-
- Args:
- probs (torch.Tensor): Input probabilities with token candidates on the last dimension.
- k (int): The k in “top-k”.
- Returns:
- torch.Tensor: Sampled tokens.
- """
- top_k_value, _ = torch.topk(probs, k, dim=-1)
- min_value_top_k = top_k_value[..., [-1]]
- probs *= (probs >= min_value_top_k).float()
- probs.div_(probs.sum(dim=-1, keepdim=True))
- next_token = multinomial(probs, num_samples=1)
- return next_token
-
-
-def sample_top_p(probs: torch.Tensor, p: float) -> torch.Tensor:
- """Sample next token from top P probabilities along the last dimension of the input probs tensor.
-
- Args:
- probs (torch.Tensor): Input probabilities with token candidates on the last dimension.
- p (int): The p in “top-p”.
- Returns:
- torch.Tensor: Sampled tokens.
- """
- probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
- probs_sum = torch.cumsum(probs_sort, dim=-1)
- mask = probs_sum - probs_sort > p
- probs_sort *= (~mask).float()
- probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
- next_token = multinomial(probs_sort, num_samples=1)
- next_token = torch.gather(probs_idx, -1, next_token)
- return next_token
-
-
-class DummyPoolExecutor:
- """Dummy pool executor to use when we actually have only 1 worker.
- (e.g. instead of ProcessPoolExecutor).
- """
- class DummyResult:
- def __init__(self, func, *args, **kwargs):
- self.func = func
- self.args = args
- self.kwargs = kwargs
-
- def result(self):
- return self.func(*self.args, **self.kwargs)
-
- def __init__(self, workers, mp_context=None):
- pass
-
- def submit(self, func, *args, **kwargs):
- return DummyPoolExecutor.DummyResult(func, *args, **kwargs)
-
- def __enter__(self):
- return self
-
- def __exit__(self, exc_type, exc_value, exc_tb):
- return
-
-
-def get_pool_executor(num_workers: int, mp_context=None):
- return ProcessPoolExecutor(num_workers, mp_context) if num_workers > 1 else DummyPoolExecutor(1)
-
-
-def length_to_mask(lengths: torch.Tensor, max_len: tp.Optional[int] = None) -> torch.Tensor:
- """Utility function to convert a tensor of sequence lengths to a mask (useful when working on padded sequences).
- For example: [3, 5] => [[1, 1, 1, 0, 0], [1, 1, 1, 1, 1]]
-
- Args:
- lengths (torch.Tensor): tensor with lengths
- max_len (int): can set the max length manually. Defaults to None.
- Returns:
- torch.Tensor: mask with 0s where there is pad tokens else 1s
- """
- assert len(lengths.shape) == 1, "Length shape should be 1 dimensional."
- final_length = lengths.max().item() if not max_len else max_len
- final_length = max(final_length, 1) # if all seqs are of len zero we don't want a zero-size tensor
- return torch.arange(final_length)[None, :].to(lengths.device) < lengths[:, None]
-
-
-def hash_trick(word: str, vocab_size: int) -> int:
- """Hash trick to pair each word with an index
-
- Args:
- word (str): word we wish to convert to an index
- vocab_size (int): size of the vocabulary
- Returns:
- int: index of the word in the embedding LUT
- """
- hash = int(hashlib.sha256(word.encode("utf-8")).hexdigest(), 16)
- return hash % vocab_size
-
-
-def with_rank_rng(base_seed: int = 1234):
- """Decorator for a function so that the function will use a Random Number Generator
- whose state depend on the GPU rank. The original RNG state is restored upon returning.
-
- Args:
- base_seed (int): Random seed.
- """
- def _decorator(fun: tp.Callable):
- @wraps(fun)
- def _decorated(*args, **kwargs):
- state = torch.get_rng_state()
- seed = base_seed ^ flashy.distrib.rank()
- torch.manual_seed(seed)
- logger.debug('Rank dependent seed set to %d', seed)
- try:
- return fun(*args, **kwargs)
- finally:
- torch.set_rng_state(state)
- logger.debug('RNG state restored.')
- return _decorated
- return _decorator
-
-
-def collate(tensors: tp.List[torch.Tensor], dim: int = 0) -> tp.Tuple[torch.Tensor, torch.Tensor]:
- """Get a list of tensors and collate them to a single tensor. according to the following logic:
- - `dim` specifies the time dimension which will be stacked and padded.
- - The output will contain 1 new dimension (dimension index 0) which will be the size of
- of the original list.
-
- Args:
- tensors (tp.List[torch.Tensor]): List of tensors to collate.
- dim (int): Dimension which will be stacked and padded.
- Returns:
- tp.Tuple[torch.Tensor, torch.Tensor]:
- torch.Tensor: Stacked and padded tensor. The output will contain 1 new dimension
- (dimension index 0) which will be the size of the original list.
- torch.Tensor: Tensor containing length of original tensor sizes (without padding).
- """
- tensors = [x.transpose(0, dim) for x in tensors]
- lens = torch.LongTensor([len(x) for x in tensors])
- padded_tensors = pad_sequence(tensors)
- padded_tensors = padded_tensors.transpose(0, 1)
- padded_tensors = padded_tensors.transpose(1, dim + 1)
- return padded_tensors, lens
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/FreeGpt.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/FreeGpt.py
deleted file mode 100644
index 73b8acea41994a4e740791f66d90241fcc5da747..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/FreeGpt.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from __future__ import annotations
-
-import time, hashlib, random
-
-from ..typing import AsyncGenerator
-from ..requests import StreamSession
-from .base_provider import AsyncGeneratorProvider
-
-domains = [
- 'https://k.aifree.site',
- 'https://p.aifree.site'
-]
-
-class FreeGpt(AsyncGeneratorProvider):
- url = "https://freegpts1.aifree.site/"
- supports_gpt_35_turbo = True
- working = True
-
- @classmethod
- async def create_async_generator(
- cls,
- model: str,
- messages: list[dict[str, str]],
- timeout: int = 30,
- **kwargs
- ) -> AsyncGenerator:
- async with StreamSession(impersonate="chrome107", timeout=timeout) as session:
- prompt = messages[-1]["content"]
- timestamp = int(time.time())
- data = {
- "messages": messages,
- "time": timestamp,
- "pass": None,
- "sign": generate_signature(timestamp, prompt)
- }
- url = random.choice(domains)
- async with session.post(f"{url}/api/generate", json=data) as response:
- response.raise_for_status()
- async for chunk in response.iter_content():
- yield chunk.decode()
-
- @classmethod
- @property
- def params(cls):
- params = [
- ("model", "str"),
- ("messages", "list[dict[str, str]]"),
- ("stream", "bool"),
- ]
- param = ", ".join([": ".join(p) for p in params])
- return f"g4f.provider.{cls.__name__} supports: ({param})"
-
-def generate_signature(timestamp: int, message: str, secret: str = ""):
- data = f"{timestamp}:{message}:{secret}"
- return hashlib.sha256(data.encode()).hexdigest()
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/.github/CONTRIBUTING.md b/spaces/AgentVerse/agentVerse/ui/.github/CONTRIBUTING.md
deleted file mode 100644
index 74ce28264311247c50cdeb119e93ad31b7b2799f..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/.github/CONTRIBUTING.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# How to contribute
-
-It's important to us that you feel you can contribute towards the evolution of Phaser. This can take many forms: from helping to fix bugs or improve the docs, to adding in new features to the source. This guide should help you in making that process as smooth as possible.
-
-Before contributing, please read the [code of conduct](https://github.com/photonstorm/phaser/blob/master/.github/CODE_OF_CONDUCT.md).
-
-## Reporting issues
-
-[GitHub Issues][0] is the place to report bugs you may have found. When submitting a bug please do the following:
-
-**1. Search for existing issues.** Your bug may have already been fixed, or cannot, or will not, be fixed. So be sure to search the issues first before putting in a duplicate issue.
-
-**2. Not sure if it's a bug?.** Please ask on the [forum][4]. If something is blatantly wrong then post it to GitHub. But if you feel it might just be because you're not sure of expected behavior, then it might save us time, and get you a response faster, if you post it to the Phaser forum instead.
-
-**3. Create an isolated and reproducible test case.** If you are reporting a bug, make sure you also have a minimal, runnable, code example that reproduces the problem you have.
-
-**4. Include a live example.** After narrowing your code down to only the problem areas, make use of [jsFiddle][1], [jsBin][2], [CodePen][5], or a link to your live site so that we can view a live example of the problem.
-
-**5. Share as much information as possible.** Include browser version affected, your OS, version of the library, steps to reproduce, etc. "X isn't working!!!1!" will probably just be closed.
-
-## Support Forum
-
-We have a very active [Phaser Support Forum][4]. If you need general support, or are struggling to understand how to do something or need your code checked over, then we would urge you to post it to our forum. There are a lot of friendly devs in there who can help, as well as the core Phaser team, so it's a great place to get support. You're welcome to report bugs directly on GitHub, but for general support we'd always recommend using the forum first.
-
-## Making Changes
-
-I'm assuming you already have a recent version of [Node](https://nodejs.org) installed locally and can run `npm`. This guide is tested and works on both Windows 10 and OS X.
-
-### 1. Checkout the repos
-
-Check-out both the [Phaser repo](https://github.com/photonstorm/phaser) and the [Phaser 3 Examples Repo](https://github.com/photonstorm/phaser3-examples). Make sure the Phaser 3 Examples repo is saved locally in a folder called `phaser3-examples`, which will be the default for most Git clients.
-
-### 2. Matching Directory Levels
-
-Ensure that both repos live at the same depth in your directory structure. For example: `/usr/home/web/phaser` and `/usr/home/web/phaser3-examples`. This is so the dev build scripts in the Phaser repo can safely copy files to `../phaser3-examples` and have them end up in the correct place.
-
-### 3. Install dependencies
-
-Using your console, run `npm install` or `yarn install` as we've configs for both. This process will install a local copy of webpack and a handful of small support scripts. Note that Yarn on Windows seems to have issues making some packages global, so stick with npm if this is the case.
-
-### 4. Webpack
-
-Making sure you've got both repos checked out, and at the same directory level in your filesystem, issue the command `webpack`. If you can't issue the command then webpack may need [installing globally](https://webpack.js.org/guides/installation/). Webpack will build Phaser and if there are any path errors in the code they'll be flagged during the build process.
-
-What you need is the ability to issue the command `webpack` within the v3 folder and have it work.
-
-### 5. ESLint
-
-There is an ESLint configuration and an Editor Configuration in the v3 folder. **Please adhere to them!** Although not enforced in the build process yet, I will be adding that at a later point. There are lots of tools you can install so your editor of choice will check the ESLint config during development.
-
-To test if your code passes our lint config issue the command `npm run lint`.
-
-## Coding style preferences are not contributions
-
-If your PR is doing little more than changing the Phaser source code into a format / coding style that you prefer then we will automatically close it. All PRs must adhere to the coding style already set-out across the thousands of lines of code in Phaser. Your personal preferences for how things should "look" or be structured do not apply here, sorry. PRs should fix bugs, fix documentation or add features. No changes for the sake of change.
-
-## I don't really like git / node.js, but I can fix this bug
-
-That is fine too. While Pull Requests are the best thing in the world for us, they are not the only way to help. You're welcome to post fixes to our forum or even just email them to us. All we ask is that you still adhere to the guidelines presented here re: ESLint, etc.
-
-## Code Style Guide
-
-We provide an .editorconfig and eslint config for you to use, but generally:
-
-- Use 4 spaces for tabs, never tab characters.
-
-- No trailing whitespace, blank lines should have no whitespace.
-
-- Always favor strict equals `===` unless you *need* to use type coercion.
-
-- Follow conventions already in the code, and listen to eslint. Our config is set-up for a reason.
-
-Thanks to Chad for creating the original Pixi.js Contributing file which we adapted for Phaser.
-
-[0]: https://github.com/photonstorm/phaser/issues
-[1]: http://jsfiddle.net
-[2]: http://jsbin.com/
-[3]: http://nodejs.org
-[4]: https://phaser.discourse.group/
-[5]: https://codepen.io/pen?template=YeEWom "Phaser 3 game template"
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/ball/Ball.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/ball/Ball.js
deleted file mode 100644
index 1029e82aa2b7b4cb1022b01b94c799c5e7baa8cb..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/ball/Ball.js
+++ /dev/null
@@ -1,45 +0,0 @@
-import Base from '../base/Base.js';
-import { Circle } from '../utils/Geoms.js'
-import Yoyo from '../utils/Yoyo.js';
-
-const Linear = Phaser.Math.Linear;
-
-class Ball extends Base {
- constructor(scene, config) {
- super(scene, config);
- this.type = 'rexSpinnerBall';
- }
-
- buildShapes() {
- for (var i = 0; i < 3; i++) {
- this.addShape(new Circle());
- }
- }
-
- updateShapes() {
- var centerX = this.centerX;
- var centerY = this.centerY;
- var radius = this.radius;
- var ballRadius = radius * 0.1;
- var lineWidth = Math.ceil(ballRadius * 0.25);
-
- var t = 1 - Yoyo(this.value);
- var trackRadius = Linear(0.3, 0.9, t) * radius;
-
- var shapes = this.getShapes();
- for (var i = 0, cnt = shapes.length; i < cnt; i++) {
- var ball = shapes[i];
- var t = (this.value + (i / cnt)) % 1;
- var angle = Math.PI * 2 * t;
- ball
- .lineStyle(lineWidth, this.color)
- .setRadius(ballRadius)
- .setCenterPosition(
- centerX + Math.cos(angle) * trackRadius,
- centerY + Math.sin(angle) * trackRadius
- );
- }
- }
-}
-
-export default Ball;
\ No newline at end of file
diff --git a/spaces/Alpaca233/SadTalker/src/generate_facerender_batch.py b/spaces/Alpaca233/SadTalker/src/generate_facerender_batch.py
deleted file mode 100644
index a62b6edffa41529ba828905fb86ca302a01d37cc..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/generate_facerender_batch.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import os
-import numpy as np
-from PIL import Image
-from skimage import io, img_as_float32, transform
-import torch
-import scipy.io as scio
-
-def get_facerender_data(coeff_path, pic_path, first_coeff_path, audio_path,
- batch_size, input_yaw_list=None, input_pitch_list=None, input_roll_list=None,
- expression_scale=1.0, still_mode = False, preprocess='crop', size = 256):
-
- semantic_radius = 13
- video_name = os.path.splitext(os.path.split(coeff_path)[-1])[0]
- txt_path = os.path.splitext(coeff_path)[0]
-
- data={}
-
- img1 = Image.open(pic_path)
- source_image = np.array(img1)
- source_image = img_as_float32(source_image)
- source_image = transform.resize(source_image, (size, size, 3))
- source_image = source_image.transpose((2, 0, 1))
- source_image_ts = torch.FloatTensor(source_image).unsqueeze(0)
- source_image_ts = source_image_ts.repeat(batch_size, 1, 1, 1)
- data['source_image'] = source_image_ts
-
- source_semantics_dict = scio.loadmat(first_coeff_path)
- generated_dict = scio.loadmat(coeff_path)
-
- if 'full' not in preprocess.lower():
- source_semantics = source_semantics_dict['coeff_3dmm'][:1,:70] #1 70
- generated_3dmm = generated_dict['coeff_3dmm'][:,:70]
-
- else:
- source_semantics = source_semantics_dict['coeff_3dmm'][:1,:73] #1 70
- generated_3dmm = generated_dict['coeff_3dmm'][:,:70]
-
- source_semantics_new = transform_semantic_1(source_semantics, semantic_radius)
- source_semantics_ts = torch.FloatTensor(source_semantics_new).unsqueeze(0)
- source_semantics_ts = source_semantics_ts.repeat(batch_size, 1, 1)
- data['source_semantics'] = source_semantics_ts
-
- # target
- generated_3dmm[:, :64] = generated_3dmm[:, :64] * expression_scale
-
- if 'full' in preprocess.lower():
- generated_3dmm = np.concatenate([generated_3dmm, np.repeat(source_semantics[:,70:], generated_3dmm.shape[0], axis=0)], axis=1)
-
- if still_mode:
- generated_3dmm[:, 64:] = np.repeat(source_semantics[:, 64:], generated_3dmm.shape[0], axis=0)
-
- with open(txt_path+'.txt', 'w') as f:
- for coeff in generated_3dmm:
- for i in coeff:
- f.write(str(i)[:7] + ' '+'\t')
- f.write('\n')
-
- target_semantics_list = []
- frame_num = generated_3dmm.shape[0]
- data['frame_num'] = frame_num
- for frame_idx in range(frame_num):
- target_semantics = transform_semantic_target(generated_3dmm, frame_idx, semantic_radius)
- target_semantics_list.append(target_semantics)
-
- remainder = frame_num%batch_size
- if remainder!=0:
- for _ in range(batch_size-remainder):
- target_semantics_list.append(target_semantics)
-
- target_semantics_np = np.array(target_semantics_list) #frame_num 70 semantic_radius*2+1
- target_semantics_np = target_semantics_np.reshape(batch_size, -1, target_semantics_np.shape[-2], target_semantics_np.shape[-1])
- data['target_semantics_list'] = torch.FloatTensor(target_semantics_np)
- data['video_name'] = video_name
- data['audio_path'] = audio_path
-
- if input_yaw_list is not None:
- yaw_c_seq = gen_camera_pose(input_yaw_list, frame_num, batch_size)
- data['yaw_c_seq'] = torch.FloatTensor(yaw_c_seq)
- if input_pitch_list is not None:
- pitch_c_seq = gen_camera_pose(input_pitch_list, frame_num, batch_size)
- data['pitch_c_seq'] = torch.FloatTensor(pitch_c_seq)
- if input_roll_list is not None:
- roll_c_seq = gen_camera_pose(input_roll_list, frame_num, batch_size)
- data['roll_c_seq'] = torch.FloatTensor(roll_c_seq)
-
- return data
-
-def transform_semantic_1(semantic, semantic_radius):
- semantic_list = [semantic for i in range(0, semantic_radius*2+1)]
- coeff_3dmm = np.concatenate(semantic_list, 0)
- return coeff_3dmm.transpose(1,0)
-
-def transform_semantic_target(coeff_3dmm, frame_index, semantic_radius):
- num_frames = coeff_3dmm.shape[0]
- seq = list(range(frame_index- semantic_radius, frame_index + semantic_radius+1))
- index = [ min(max(item, 0), num_frames-1) for item in seq ]
- coeff_3dmm_g = coeff_3dmm[index, :]
- return coeff_3dmm_g.transpose(1,0)
-
-def gen_camera_pose(camera_degree_list, frame_num, batch_size):
-
- new_degree_list = []
- if len(camera_degree_list) == 1:
- for _ in range(frame_num):
- new_degree_list.append(camera_degree_list[0])
- remainder = frame_num%batch_size
- if remainder!=0:
- for _ in range(batch_size-remainder):
- new_degree_list.append(new_degree_list[-1])
- new_degree_np = np.array(new_degree_list).reshape(batch_size, -1)
- return new_degree_np
-
- degree_sum = 0.
- for i, degree in enumerate(camera_degree_list[1:]):
- degree_sum += abs(degree-camera_degree_list[i])
-
- degree_per_frame = degree_sum/(frame_num-1)
- for i, degree in enumerate(camera_degree_list[1:]):
- degree_last = camera_degree_list[i]
- degree_step = degree_per_frame * abs(degree-degree_last)/(degree-degree_last)
- new_degree_list = new_degree_list + list(np.arange(degree_last, degree, degree_step))
- if len(new_degree_list) > frame_num:
- new_degree_list = new_degree_list[:frame_num]
- elif len(new_degree_list) < frame_num:
- for _ in range(frame_num-len(new_degree_list)):
- new_degree_list.append(new_degree_list[-1])
- print(len(new_degree_list))
- print(frame_num)
-
- remainder = frame_num%batch_size
- if remainder!=0:
- for _ in range(batch_size-remainder):
- new_degree_list.append(new_degree_list[-1])
- new_degree_np = np.array(new_degree_list).reshape(batch_size, -1)
- return new_degree_np
-
diff --git a/spaces/Amrrs/DragGan-Inversion/training/networks_stylegan2.py b/spaces/Amrrs/DragGan-Inversion/training/networks_stylegan2.py
deleted file mode 100644
index 6f570aad058ae63aaaa6733504d0d5ed4ba190a1..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/training/networks_stylegan2.py
+++ /dev/null
@@ -1,981 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Network architectures from the paper
-"Analyzing and Improving the Image Quality of StyleGAN".
-Matches the original implementation of configs E-F by Karras et al. at
-https://github.com/NVlabs/stylegan2/blob/master/training/networks_stylegan2.py"""
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch_utils import misc
-from torch_utils import persistence
-from torch_utils.ops import conv2d_resample
-from torch_utils.ops import upfirdn2d
-from torch_utils.ops import bias_act
-from torch_utils.ops import fma
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def normalize_2nd_moment(x, dim=1, eps=1e-8):
- return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def modulated_conv2d(
- # Input tensor of shape [batch_size, in_channels, in_height, in_width].
- x,
- # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
- weight,
- # Modulation coefficients of shape [batch_size, in_channels].
- styles,
- noise=None, # Optional noise tensor to add to the output activations.
- up=1, # Integer upsampling factor.
- down=1, # Integer downsampling factor.
- padding=0, # Padding with respect to the upsampled image.
- # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
- resample_filter=None,
- demodulate=True, # Apply weight demodulation?
- # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
- flip_weight=True,
- # Perform modulation, convolution, and demodulation as a single fused operation?
- fused_modconv=True,
-):
- batch_size = x.shape[0]
- out_channels, in_channels, kh, kw = weight.shape
- misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk]
- misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
- misc.assert_shape(styles, [batch_size, in_channels]) # [NI]
-
- # Pre-normalize inputs to avoid FP16 overflow.
- if x.dtype == torch.float16 and demodulate:
- weight = weight * (1 / np.sqrt(in_channels * kh * kw) /
- weight.norm(float('inf'), dim=[1, 2, 3], keepdim=True)) # max_Ikk
- styles = styles / \
- styles.norm(float('inf'), dim=1, keepdim=True) # max_I
-
- # Calculate per-sample weights and demodulation coefficients.
- w = None
- dcoefs = None
- if demodulate or fused_modconv:
- w = weight.unsqueeze(0) # [NOIkk]
- w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk]
- if demodulate:
- dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO]
- if demodulate and fused_modconv:
- w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk]
-
- # Execute by scaling the activations before and after the convolution.
- if not fused_modconv:
- x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
- x = conv2d_resample.conv2d_resample(x=x, w=weight.to(
- x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight)
- if demodulate and noise is not None:
- x = fma.fma(x, dcoefs.to(x.dtype).reshape(
- batch_size, -1, 1, 1), noise.to(x.dtype))
- elif demodulate:
- x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
- elif noise is not None:
- x = x.add_(noise.to(x.dtype))
- return x
-
- # Execute as one fused op using grouped convolution.
- with misc.suppress_tracer_warnings(): # this value will be treated as a constant
- batch_size = int(batch_size)
- misc.assert_shape(x, [batch_size, in_channels, None, None])
- x = x.reshape(1, -1, *x.shape[2:])
- w = w.reshape(-1, in_channels, kh, kw)
- x = conv2d_resample.conv2d_resample(x=x, w=w.to(
- x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight)
- x = x.reshape(batch_size, -1, *x.shape[2:])
- if noise is not None:
- x = x.add_(noise)
- return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class FullyConnectedLayer(torch.nn.Module):
- def __init__(self,
- in_features, # Number of input features.
- out_features, # Number of output features.
- bias=True, # Apply additive bias before the activation function?
- # Activation function: 'relu', 'lrelu', etc.
- activation='linear',
- lr_multiplier=1, # Learning rate multiplier.
- bias_init=0, # Initial value for the additive bias.
- ):
- super().__init__()
- self.in_features = in_features
- self.out_features = out_features
- self.activation = activation
- self.weight = torch.nn.Parameter(torch.randn(
- [out_features, in_features]) / lr_multiplier)
- self.bias = torch.nn.Parameter(torch.full(
- [out_features], np.float32(bias_init))) if bias else None
- self.weight_gain = lr_multiplier / np.sqrt(in_features)
- self.bias_gain = lr_multiplier
-
- def forward(self, x):
- w = self.weight.to(x.dtype) * self.weight_gain
- b = self.bias
- if b is not None:
- b = b.to(x.dtype)
- if self.bias_gain != 1:
- b = b * self.bias_gain
-
- if self.activation == 'linear' and b is not None:
- x = torch.addmm(b.unsqueeze(0), x, w.t())
- else:
- x = x.matmul(w.t())
- x = bias_act.bias_act(x, b, act=self.activation)
- return x
-
- def extra_repr(self):
- return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Conv2dLayer(torch.nn.Module):
- def __init__(self,
- in_channels, # Number of input channels.
- out_channels, # Number of output channels.
- # Width and height of the convolution kernel.
- kernel_size,
- bias=True, # Apply additive bias before the activation function?
- # Activation function: 'relu', 'lrelu', etc.
- activation='linear',
- up=1, # Integer upsampling factor.
- down=1, # Integer downsampling factor.
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output to +-X, None = disable clamping.
- conv_clamp=None,
- channels_last=False, # Expect the input to have memory_format=channels_last?
- trainable=True, # Update the weights of this layer during training?
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.activation = activation
- self.up = up
- self.down = down
- self.conv_clamp = conv_clamp
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
- self.padding = kernel_size // 2
- self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
- self.act_gain = bias_act.activation_funcs[activation].def_gain
-
- memory_format = torch.channels_last if channels_last else torch.contiguous_format
- weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(
- memory_format=memory_format)
- bias = torch.zeros([out_channels]) if bias else None
- if trainable:
- self.weight = torch.nn.Parameter(weight)
- self.bias = torch.nn.Parameter(bias) if bias is not None else None
- else:
- self.register_buffer('weight', weight)
- if bias is not None:
- self.register_buffer('bias', bias)
- else:
- self.bias = None
-
- def forward(self, x, gain=1):
- w = self.weight * self.weight_gain
- b = self.bias.to(x.dtype) if self.bias is not None else None
- flip_weight = (self.up == 1) # slightly faster
- x = conv2d_resample.conv2d_resample(x=x, w=w.to(
- x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight)
-
- act_gain = self.act_gain * gain
- act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
- x = bias_act.bias_act(x, b, act=self.activation,
- gain=act_gain, clamp=act_clamp)
- return x
-
- def extra_repr(self):
- return ' '.join([
- f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},',
- f'up={self.up}, down={self.down}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MappingNetwork(torch.nn.Module):
- def __init__(self,
- # Input latent (Z) dimensionality, 0 = no latent.
- z_dim,
- # Conditioning label (C) dimensionality, 0 = no label.
- c_dim,
- # Intermediate latent (W) dimensionality.
- w_dim,
- # Number of intermediate latents to output, None = do not broadcast.
- num_ws,
- num_layers=8, # Number of mapping layers.
- # Label embedding dimensionality, None = same as w_dim.
- embed_features=None,
- # Number of intermediate features in the mapping layers, None = same as w_dim.
- layer_features=None,
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Learning rate multiplier for the mapping layers.
- lr_multiplier=0.01,
- # Decay for tracking the moving average of W during training, None = do not track.
- w_avg_beta=0.998,
- ):
- super().__init__()
- self.z_dim = z_dim
- self.c_dim = c_dim
- self.w_dim = w_dim
- self.num_ws = num_ws
- self.num_layers = num_layers
- self.w_avg_beta = w_avg_beta
-
- if embed_features is None:
- embed_features = w_dim
- if c_dim == 0:
- embed_features = 0
- if layer_features is None:
- layer_features = w_dim
- features_list = [z_dim + embed_features] + \
- [layer_features] * (num_layers - 1) + [w_dim]
-
- if c_dim > 0:
- self.embed = FullyConnectedLayer(c_dim, embed_features)
- for idx in range(num_layers):
- in_features = features_list[idx]
- out_features = features_list[idx + 1]
- layer = FullyConnectedLayer(
- in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
- setattr(self, f'fc{idx}', layer)
-
- if num_ws is not None and w_avg_beta is not None:
- self.register_buffer('w_avg', torch.zeros([w_dim]))
-
- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
- # Embed, normalize, and concat inputs.
- x = None
- with torch.autograd.profiler.record_function('input'):
- if self.z_dim > 0:
- misc.assert_shape(z, [None, self.z_dim])
- x = normalize_2nd_moment(z.to(torch.float32))
- if self.c_dim > 0:
- misc.assert_shape(c, [None, self.c_dim])
- y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
- x = torch.cat([x, y], dim=1) if x is not None else y
-
- # Main layers.
- for idx in range(self.num_layers):
- layer = getattr(self, f'fc{idx}')
- x = layer(x)
-
- # Update moving average of W.
- if update_emas and self.w_avg_beta is not None:
- with torch.autograd.profiler.record_function('update_w_avg'):
- self.w_avg.copy_(x.detach().mean(
- dim=0).lerp(self.w_avg, self.w_avg_beta))
-
- # Broadcast.
- if self.num_ws is not None:
- with torch.autograd.profiler.record_function('broadcast'):
- x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
-
- # Apply truncation.
- if truncation_psi != 1:
- with torch.autograd.profiler.record_function('truncate'):
- assert self.w_avg_beta is not None
- if self.num_ws is None or truncation_cutoff is None:
- x = self.w_avg.lerp(x, truncation_psi)
- else:
- x[:, :truncation_cutoff] = self.w_avg.lerp(
- x[:, :truncation_cutoff], truncation_psi)
- return x
-
- def extra_repr(self):
- return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisLayer(torch.nn.Module):
- def __init__(self,
- in_channels, # Number of input channels.
- out_channels, # Number of output channels.
- # Intermediate latent (W) dimensionality.
- w_dim,
- resolution, # Resolution of this layer.
- kernel_size=3, # Convolution kernel size.
- up=1, # Integer upsampling factor.
- use_noise=True, # Enable noise input?
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=None,
- channels_last=False, # Use channels_last format for the weights?
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.w_dim = w_dim
- self.resolution = resolution
- self.up = up
- self.use_noise = use_noise
- self.activation = activation
- self.conv_clamp = conv_clamp
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
- self.padding = kernel_size // 2
- self.act_gain = bias_act.activation_funcs[activation].def_gain
-
- self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
- memory_format = torch.channels_last if channels_last else torch.contiguous_format
- self.weight = torch.nn.Parameter(torch.randn(
- [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
- if use_noise:
- self.register_buffer(
- 'noise_const', torch.randn([resolution, resolution]))
- self.noise_strength = torch.nn.Parameter(torch.zeros([]))
- self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
-
- def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
- assert noise_mode in ['random', 'const', 'none']
- in_resolution = self.resolution // self.up
- misc.assert_shape(x, [None, self.in_channels,
- in_resolution, in_resolution])
- styles = self.affine(w)
-
- noise = None
- if self.use_noise and noise_mode == 'random':
- noise = torch.randn([x.shape[0], 1, self.resolution,
- self.resolution], device=x.device) * self.noise_strength
- if self.use_noise and noise_mode == 'const':
- noise = self.noise_const * self.noise_strength
-
- flip_weight = (self.up == 1) # slightly faster
- x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
- padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)
-
- act_gain = self.act_gain * gain
- act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
- x = bias_act.bias_act(x, self.bias.to(
- x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
- return x
-
- def extra_repr(self):
- return ' '.join([
- f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},',
- f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class ToRGBLayer(torch.nn.Module):
- def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.w_dim = w_dim
- self.conv_clamp = conv_clamp
- self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
- memory_format = torch.channels_last if channels_last else torch.contiguous_format
- self.weight = torch.nn.Parameter(torch.randn(
- [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
- self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
- self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
-
- def forward(self, x, w, fused_modconv=True):
- styles = self.affine(w) * self.weight_gain
- x = modulated_conv2d(x=x, weight=self.weight, styles=styles,
- demodulate=False, fused_modconv=fused_modconv)
- x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
- return x
-
- def extra_repr(self):
- return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisBlock(torch.nn.Module):
- def __init__(self,
- # Number of input channels, 0 = first block.
- in_channels,
- # Number of output channels.
- out_channels,
- # Intermediate latent (W) dimensionality.
- w_dim,
- # Resolution of this block.
- resolution,
- # Number of output color channels.
- img_channels,
- is_last, # Is this the last block?
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='skip',
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=256,
- use_fp16=False, # Use FP16 for this block?
- fp16_channels_last=False, # Use channels-last memory format with FP16?
- # Default value of fused_modconv. 'inference_only' = True for inference, False for training.
- fused_modconv_default=True,
- # Arguments for SynthesisLayer.
- **layer_kwargs,
- ):
- assert architecture in ['orig', 'skip', 'resnet']
- super().__init__()
- self.in_channels = in_channels
- self.w_dim = w_dim
- self.resolution = resolution
- self.img_channels = img_channels
- self.is_last = is_last
- self.architecture = architecture
- self.use_fp16 = use_fp16
- self.channels_last = (use_fp16 and fp16_channels_last)
- self.fused_modconv_default = fused_modconv_default
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
- self.num_conv = 0
- self.num_torgb = 0
-
- if in_channels == 0:
- self.const = torch.nn.Parameter(torch.randn(
- [out_channels, resolution, resolution]))
-
- if in_channels != 0:
- self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2,
- resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
- self.num_conv += 1
-
- self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution,
- conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
- self.num_conv += 1
-
- if is_last or architecture == 'skip':
- self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim,
- conv_clamp=conv_clamp, channels_last=self.channels_last)
- self.num_torgb += 1
-
- if in_channels != 0 and architecture == 'resnet':
- self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
- resample_filter=resample_filter, channels_last=self.channels_last)
-
- def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs):
- _ = update_emas # unused
- misc.assert_shape(
- ws, [None, self.num_conv + self.num_torgb, self.w_dim])
- w_iter = iter(ws.unbind(dim=1))
- if ws.device.type != 'cuda':
- force_fp32 = True
- dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
- memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
- if fused_modconv is None:
- fused_modconv = self.fused_modconv_default
- if fused_modconv == 'inference_only':
- fused_modconv = (not self.training)
-
- # Input.
- if self.in_channels == 0:
- x = self.const.to(dtype=dtype, memory_format=memory_format)
- x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
- else:
- misc.assert_shape(x, [None, self.in_channels,
- self.resolution // 2, self.resolution // 2])
- x = x.to(dtype=dtype, memory_format=memory_format)
-
- # Main layers.
- if self.in_channels == 0:
- x = self.conv1(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
- elif self.architecture == 'resnet':
- y = self.skip(x, gain=np.sqrt(0.5))
- x = self.conv0(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
- x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv,
- gain=np.sqrt(0.5), **layer_kwargs)
- x = y.add_(x)
- else:
- x = self.conv0(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
- x = self.conv1(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
-
- # ToRGB.
- if img is not None:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
- img = upfirdn2d.upsample2d(img, self.resample_filter)
- if self.is_last or self.architecture == 'skip':
- y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
- y = y.to(dtype=torch.float32,
- memory_format=torch.contiguous_format)
- img = img.add_(y) if img is not None else y
-
- assert x.dtype == dtype
- assert img is None or img.dtype == torch.float32
- return x, img
-
- def extra_repr(self):
- return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisNetwork(torch.nn.Module):
- def __init__(self,
- # Intermediate latent (W) dimensionality.
- w_dim,
- img_resolution, # Output image resolution.
- img_channels, # Number of color channels.
- # Overall multiplier for the number of channels.
- channel_base=32768,
- # Maximum number of channels in any layer.
- channel_max=512,
- # Use FP16 for the N highest resolutions.
- num_fp16_res=4,
- **block_kwargs, # Arguments for SynthesisBlock.
- ):
- assert img_resolution >= 4 and img_resolution & (
- img_resolution - 1) == 0
- super().__init__()
- self.w_dim = w_dim
- self.img_resolution = img_resolution
- self.img_resolution_log2 = int(np.log2(img_resolution))
- self.img_channels = img_channels
- self.num_fp16_res = num_fp16_res
- self.block_resolutions = [
- 2 ** i for i in range(2, self.img_resolution_log2 + 1)]
- channels_dict = {res: min(channel_base // res, channel_max)
- for res in self.block_resolutions}
- fp16_resolution = max(
- 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
- self.num_ws = 0
- for res in self.block_resolutions:
- in_channels = channels_dict[res // 2] if res > 4 else 0
- out_channels = channels_dict[res]
- use_fp16 = (res >= fp16_resolution)
- is_last = (res == self.img_resolution)
- block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res,
- img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs)
- self.num_ws += block.num_conv
- if is_last:
- self.num_ws += block.num_torgb
- setattr(self, f'b{res}', block)
-
- def forward(self, ws, return_feature=False, **block_kwargs):
- block_ws = []
- features = []
- with torch.autograd.profiler.record_function('split_ws'):
- misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
- ws = ws.to(torch.float32)
- w_idx = 0
- for res in self.block_resolutions:
- block = getattr(self, f'b{res}')
- block_ws.append(
- ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
- w_idx += block.num_conv
-
- x = img = None
- for res, cur_ws in zip(self.block_resolutions, block_ws):
- block = getattr(self, f'b{res}')
- x, img = block(x, img, cur_ws, **block_kwargs)
- features.append(x)
- if return_feature:
- return img, features
- else:
- return img
-
- def extra_repr(self):
- return ' '.join([
- f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
- f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
- f'num_fp16_res={self.num_fp16_res:d}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Generator(torch.nn.Module):
- def __init__(self,
- z_dim, # Input latent (Z) dimensionality.
- # Conditioning label (C) dimensionality.
- c_dim,
- # Intermediate latent (W) dimensionality.
- w_dim,
- img_resolution, # Output resolution.
- img_channels, # Number of output color channels.
- mapping_kwargs={}, # Arguments for MappingNetwork.
- synthesis_kwargs={}, # Arguments for SynthesisNetwork.
- resize=None,
- **synthesis_kwargs2, # Arguments for SynthesisNetwork.
- ):
- super().__init__()
- self.z_dim = z_dim
- self.c_dim = c_dim
- self.w_dim = w_dim
- self.img_resolution = img_resolution
- self.img_channels = img_channels
- if len(synthesis_kwargs) == 0:
- synthesis_kwargs = synthesis_kwargs2
- self.synthesis = SynthesisNetwork(
- w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
- self.num_ws = self.synthesis.num_ws
- self.mapping = MappingNetwork(
- z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
- self.resize = resize
-
- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, input_is_w=False, return_feature=False, **synthesis_kwargs):
- if input_is_w:
- ws = z
- if ws.dim() == 2:
- ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1])
- else:
- ws = self.mapping(z, c, truncation_psi=truncation_psi,
- truncation_cutoff=truncation_cutoff, update_emas=update_emas)
- img = self.synthesis(ws, update_emas=update_emas,
- return_feature=return_feature, **synthesis_kwargs)
- if return_feature:
- img, feature = img
- if self.resize is not None:
- img = imresize(img, [self.resize, self.resize])
- if return_feature:
- return img, feature
- else:
- return img
-
-
-def imresize(image, size):
- dim = image.dim()
- if dim == 3:
- image = image.unsqueeze(1)
- b, _, h, w = image.shape
- if size[0] > h:
- image = F.interpolate(image, size, mode='bilinear')
- elif size[0] < h:
- image = F.interpolate(image, size, mode='area')
- if dim == 3:
- image = image.squeeze(1)
- return image
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class DiscriminatorBlock(torch.nn.Module):
- def __init__(self,
- # Number of input channels, 0 = first block.
- in_channels,
- # Number of intermediate channels.
- tmp_channels,
- # Number of output channels.
- out_channels,
- # Resolution of this block.
- resolution,
- # Number of input color channels.
- img_channels,
- # Index of the first layer.
- first_layer_idx,
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='resnet',
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=None,
- use_fp16=False, # Use FP16 for this block?
- fp16_channels_last=False, # Use channels-last memory format with FP16?
- # Freeze-D: Number of layers to freeze.
- freeze_layers=0,
- ):
- assert in_channels in [0, tmp_channels]
- assert architecture in ['orig', 'skip', 'resnet']
- super().__init__()
- self.in_channels = in_channels
- self.resolution = resolution
- self.img_channels = img_channels
- self.first_layer_idx = first_layer_idx
- self.architecture = architecture
- self.use_fp16 = use_fp16
- self.channels_last = (use_fp16 and fp16_channels_last)
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
-
- self.num_layers = 0
-
- def trainable_gen():
- while True:
- layer_idx = self.first_layer_idx + self.num_layers
- trainable = (layer_idx >= freeze_layers)
- self.num_layers += 1
- yield trainable
- trainable_iter = trainable_gen()
-
- if in_channels == 0 or architecture == 'skip':
- self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation,
- trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
- self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation,
- trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
- self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2,
- trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last)
-
- if architecture == 'resnet':
- self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2,
- trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last)
-
- def forward(self, x, img, force_fp32=False):
- if (x if x is not None else img).device.type != 'cuda':
- force_fp32 = True
- dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
- memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
-
- # Input.
- if x is not None:
- misc.assert_shape(x, [None, self.in_channels,
- self.resolution, self.resolution])
- x = x.to(dtype=dtype, memory_format=memory_format)
-
- # FromRGB.
- if self.in_channels == 0 or self.architecture == 'skip':
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution, self.resolution])
- img = img.to(dtype=dtype, memory_format=memory_format)
- y = self.fromrgb(img)
- x = x + y if x is not None else y
- img = upfirdn2d.downsample2d(
- img, self.resample_filter) if self.architecture == 'skip' else None
-
- # Main layers.
- if self.architecture == 'resnet':
- y = self.skip(x, gain=np.sqrt(0.5))
- x = self.conv0(x)
- x = self.conv1(x, gain=np.sqrt(0.5))
- x = y.add_(x)
- else:
- x = self.conv0(x)
- x = self.conv1(x)
-
- assert x.dtype == dtype
- return x, img
-
- def extra_repr(self):
- return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MinibatchStdLayer(torch.nn.Module):
- def __init__(self, group_size, num_channels=1):
- super().__init__()
- self.group_size = group_size
- self.num_channels = num_channels
-
- def forward(self, x):
- N, C, H, W = x.shape
- with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants
- G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(
- N)) if self.group_size is not None else N
- F = self.num_channels
- c = C // F
-
- # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
- y = x.reshape(G, -1, F, c, H, W)
- # [GnFcHW] Subtract mean over group.
- y = y - y.mean(dim=0)
- # [nFcHW] Calc variance over group.
- y = y.square().mean(dim=0)
- y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group.
- # [nF] Take average over channels and pixels.
- y = y.mean(dim=[2, 3, 4])
- y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions.
- # [NFHW] Replicate over group and pixels.
- y = y.repeat(G, 1, H, W)
- # [NCHW] Append to input as new channels.
- x = torch.cat([x, y], dim=1)
- return x
-
- def extra_repr(self):
- return f'group_size={self.group_size}, num_channels={self.num_channels:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class DiscriminatorEpilogue(torch.nn.Module):
- def __init__(self,
- in_channels, # Number of input channels.
- # Dimensionality of mapped conditioning label, 0 = no label.
- cmap_dim,
- resolution, # Resolution of this block.
- # Number of input color channels.
- img_channels,
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='resnet',
- # Group size for the minibatch standard deviation layer, None = entire minibatch.
- mbstd_group_size=4,
- # Number of features for the minibatch standard deviation layer, 0 = disable.
- mbstd_num_channels=1,
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=None,
- ):
- assert architecture in ['orig', 'skip', 'resnet']
- super().__init__()
- self.in_channels = in_channels
- self.cmap_dim = cmap_dim
- self.resolution = resolution
- self.img_channels = img_channels
- self.architecture = architecture
-
- if architecture == 'skip':
- self.fromrgb = Conv2dLayer(
- img_channels, in_channels, kernel_size=1, activation=activation)
- self.mbstd = MinibatchStdLayer(
- group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None
- self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels,
- kernel_size=3, activation=activation, conv_clamp=conv_clamp)
- self.fc = FullyConnectedLayer(
- in_channels * (resolution ** 2), in_channels, activation=activation)
- self.out = FullyConnectedLayer(
- in_channels, 1 if cmap_dim == 0 else cmap_dim)
-
- def forward(self, x, img, cmap, force_fp32=False):
- misc.assert_shape(x, [None, self.in_channels,
- self.resolution, self.resolution]) # [NCHW]
- _ = force_fp32 # unused
- dtype = torch.float32
- memory_format = torch.contiguous_format
-
- # FromRGB.
- x = x.to(dtype=dtype, memory_format=memory_format)
- if self.architecture == 'skip':
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution, self.resolution])
- img = img.to(dtype=dtype, memory_format=memory_format)
- x = x + self.fromrgb(img)
-
- # Main layers.
- if self.mbstd is not None:
- x = self.mbstd(x)
- x = self.conv(x)
- x = self.fc(x.flatten(1))
- x = self.out(x)
-
- # Conditioning.
- if self.cmap_dim > 0:
- misc.assert_shape(cmap, [None, self.cmap_dim])
- x = (x * cmap).sum(dim=1, keepdim=True) * \
- (1 / np.sqrt(self.cmap_dim))
-
- assert x.dtype == dtype
- return x
-
- def extra_repr(self):
- return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Discriminator(torch.nn.Module):
- def __init__(self,
- # Conditioning label (C) dimensionality.
- c_dim,
- img_resolution, # Input resolution.
- # Number of input color channels.
- img_channels,
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='resnet',
- # Overall multiplier for the number of channels.
- channel_base=32768,
- # Maximum number of channels in any layer.
- channel_max=512,
- # Use FP16 for the N highest resolutions.
- num_fp16_res=4,
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=256,
- # Dimensionality of mapped conditioning label, None = default.
- cmap_dim=None,
- block_kwargs={}, # Arguments for DiscriminatorBlock.
- mapping_kwargs={}, # Arguments for MappingNetwork.
- # Arguments for DiscriminatorEpilogue.
- epilogue_kwargs={},
- ):
- super().__init__()
- self.c_dim = c_dim
- self.img_resolution = img_resolution
- self.img_resolution_log2 = int(np.log2(img_resolution))
- self.img_channels = img_channels
- self.block_resolutions = [
- 2 ** i for i in range(self.img_resolution_log2, 2, -1)]
- channels_dict = {res: min(channel_base // res, channel_max)
- for res in self.block_resolutions + [4]}
- fp16_resolution = max(
- 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
- if cmap_dim is None:
- cmap_dim = channels_dict[4]
- if c_dim == 0:
- cmap_dim = 0
-
- common_kwargs = dict(img_channels=img_channels,
- architecture=architecture, conv_clamp=conv_clamp)
- cur_layer_idx = 0
- for res in self.block_resolutions:
- in_channels = channels_dict[res] if res < img_resolution else 0
- tmp_channels = channels_dict[res]
- out_channels = channels_dict[res // 2]
- use_fp16 = (res >= fp16_resolution)
- block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
- first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs)
- setattr(self, f'b{res}', block)
- cur_layer_idx += block.num_layers
- if c_dim > 0:
- self.mapping = MappingNetwork(
- z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
- self.b4 = DiscriminatorEpilogue(
- channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs)
-
- def forward(self, img, c, update_emas=False, **block_kwargs):
- _ = update_emas # unused
- x = None
- for res in self.block_resolutions:
- block = getattr(self, f'b{res}')
- x, img = block(x, img, **block_kwargs)
-
- cmap = None
- if self.c_dim > 0:
- cmap = self.mapping(None, c)
- x = self.b4(x, img, cmap)
- return x
-
- def extra_repr(self):
- return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/Amrrs/DragGan-Inversion/training/networks_stylegan3.py b/spaces/Amrrs/DragGan-Inversion/training/networks_stylegan3.py
deleted file mode 100644
index 338fd287110a02d76c0b7b03fbf041c340f5adb9..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/training/networks_stylegan3.py
+++ /dev/null
@@ -1,645 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Generator architecture from the paper
-"Alias-Free Generative Adversarial Networks"."""
-
-import numpy as np
-import scipy.signal
-import scipy.optimize
-import torch
-import torch.nn.functional as F
-from torch_utils import misc
-from torch_utils import persistence
-from torch_utils.ops import conv2d_gradfix
-from torch_utils.ops import filtered_lrelu
-from torch_utils.ops import bias_act
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def modulated_conv2d(
- # Input tensor: [batch_size, in_channels, in_height, in_width]
- x,
- # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width]
- w,
- s, # Style tensor: [batch_size, in_channels]
- demodulate=True, # Apply weight demodulation?
- padding=0, # Padding: int or [padH, padW]
- input_gain=None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels]
-):
- with misc.suppress_tracer_warnings(): # this value will be treated as a constant
- batch_size = int(x.shape[0])
- out_channels, in_channels, kh, kw = w.shape
- misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk]
- misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
- misc.assert_shape(s, [batch_size, in_channels]) # [NI]
-
- # Pre-normalize inputs.
- if demodulate:
- w = w * w.square().mean([1, 2, 3], keepdim=True).rsqrt()
- s = s * s.square().mean().rsqrt()
-
- # Modulate weights.
- w = w.unsqueeze(0) # [NOIkk]
- w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
-
- # Demodulate weights.
- if demodulate:
- dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO]
- w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk]
-
- # Apply input scaling.
- if input_gain is not None:
- input_gain = input_gain.expand(batch_size, in_channels) # [NI]
- w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
-
- # Execute as one fused op using grouped convolution.
- x = x.reshape(1, -1, *x.shape[2:])
- w = w.reshape(-1, in_channels, kh, kw)
- x = conv2d_gradfix.conv2d(input=x, weight=w.to(
- x.dtype), padding=padding, groups=batch_size)
- x = x.reshape(batch_size, -1, *x.shape[2:])
- return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class FullyConnectedLayer(torch.nn.Module):
- def __init__(self,
- in_features, # Number of input features.
- out_features, # Number of output features.
- # Activation function: 'relu', 'lrelu', etc.
- activation='linear',
- bias=True, # Apply additive bias before the activation function?
- lr_multiplier=1, # Learning rate multiplier.
- # Initial standard deviation of the weight tensor.
- weight_init=1,
- bias_init=0, # Initial value of the additive bias.
- ):
- super().__init__()
- self.in_features = in_features
- self.out_features = out_features
- self.activation = activation
- self.weight = torch.nn.Parameter(torch.randn(
- [out_features, in_features]) * (weight_init / lr_multiplier))
- bias_init = np.broadcast_to(np.asarray(
- bias_init, dtype=np.float32), [out_features])
- self.bias = torch.nn.Parameter(torch.from_numpy(
- bias_init / lr_multiplier)) if bias else None
- self.weight_gain = lr_multiplier / np.sqrt(in_features)
- self.bias_gain = lr_multiplier
-
- def forward(self, x):
- w = self.weight.to(x.dtype) * self.weight_gain
- b = self.bias
- if b is not None:
- b = b.to(x.dtype)
- if self.bias_gain != 1:
- b = b * self.bias_gain
- if self.activation == 'linear' and b is not None:
- x = torch.addmm(b.unsqueeze(0), x, w.t())
- else:
- x = x.matmul(w.t())
- x = bias_act.bias_act(x, b, act=self.activation)
- return x
-
- def extra_repr(self):
- return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MappingNetwork(torch.nn.Module):
- def __init__(self,
- z_dim, # Input latent (Z) dimensionality.
- # Conditioning label (C) dimensionality, 0 = no labels.
- c_dim,
- # Intermediate latent (W) dimensionality.
- w_dim,
- # Number of intermediate latents to output.
- num_ws,
- num_layers=2, # Number of mapping layers.
- # Learning rate multiplier for the mapping layers.
- lr_multiplier=0.01,
- # Decay for tracking the moving average of W during training.
- w_avg_beta=0.998,
- ):
- super().__init__()
- self.z_dim = z_dim
- self.c_dim = c_dim
- self.w_dim = w_dim
- self.num_ws = num_ws
- self.num_layers = num_layers
- self.w_avg_beta = w_avg_beta
-
- # Construct layers.
- self.embed = FullyConnectedLayer(
- self.c_dim, self.w_dim) if self.c_dim > 0 else None
- features = [self.z_dim + (self.w_dim if self.c_dim >
- 0 else 0)] + [self.w_dim] * self.num_layers
- for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]):
- layer = FullyConnectedLayer(
- in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier)
- setattr(self, f'fc{idx}', layer)
- self.register_buffer('w_avg', torch.zeros([w_dim]))
-
- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
- misc.assert_shape(z, [None, self.z_dim])
- if truncation_cutoff is None:
- truncation_cutoff = self.num_ws
-
- # Embed, normalize, and concatenate inputs.
- x = z.to(torch.float32)
- x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt()
- if self.c_dim > 0:
- misc.assert_shape(c, [None, self.c_dim])
- y = self.embed(c.to(torch.float32))
- y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt()
- x = torch.cat([x, y], dim=1) if x is not None else y
-
- # Execute layers.
- for idx in range(self.num_layers):
- x = getattr(self, f'fc{idx}')(x)
-
- # Update moving average of W.
- if update_emas:
- self.w_avg.copy_(x.detach().mean(
- dim=0).lerp(self.w_avg, self.w_avg_beta))
-
- # Broadcast and apply truncation.
- x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
- if truncation_psi != 1:
- x[:, :truncation_cutoff] = self.w_avg.lerp(
- x[:, :truncation_cutoff], truncation_psi)
- return x
-
- def extra_repr(self):
- return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisInput(torch.nn.Module):
- def __init__(self,
- w_dim, # Intermediate latent (W) dimensionality.
- channels, # Number of output channels.
- size, # Output spatial size: int or [width, height].
- sampling_rate, # Output sampling rate.
- bandwidth, # Output bandwidth.
- ):
- super().__init__()
- self.w_dim = w_dim
- self.channels = channels
- self.size = np.broadcast_to(np.asarray(size), [2])
- self.sampling_rate = sampling_rate
- self.bandwidth = bandwidth
-
- # Draw random frequencies from uniform 2D disc.
- freqs = torch.randn([self.channels, 2])
- radii = freqs.square().sum(dim=1, keepdim=True).sqrt()
- freqs /= radii * radii.square().exp().pow(0.25)
- freqs *= bandwidth
- phases = torch.rand([self.channels]) - 0.5
-
- # Setup parameters and buffers.
- self.weight = torch.nn.Parameter(
- torch.randn([self.channels, self.channels]))
- self.affine = FullyConnectedLayer(
- w_dim, 4, weight_init=0, bias_init=[1, 0, 0, 0])
- # User-specified inverse transform wrt. resulting image.
- self.register_buffer('transform', torch.eye(3, 3))
- self.register_buffer('freqs', freqs)
- self.register_buffer('phases', phases)
-
- def forward(self, w):
- # Introduce batch dimension.
- transforms = self.transform.unsqueeze(0) # [batch, row, col]
- freqs = self.freqs.unsqueeze(0) # [batch, channel, xy]
- phases = self.phases.unsqueeze(0) # [batch, channel]
-
- # Apply learned transformation.
- t = self.affine(w) # t = (r_c, r_s, t_x, t_y)
- # t' = (r'_c, r'_s, t'_x, t'_y)
- t = t / t[:, :2].norm(dim=1, keepdim=True)
- # Inverse rotation wrt. resulting image.
- m_r = torch.eye(3, device=w.device).unsqueeze(
- 0).repeat([w.shape[0], 1, 1])
- m_r[:, 0, 0] = t[:, 0] # r'_c
- m_r[:, 0, 1] = -t[:, 1] # r'_s
- m_r[:, 1, 0] = t[:, 1] # r'_s
- m_r[:, 1, 1] = t[:, 0] # r'_c
- # Inverse translation wrt. resulting image.
- m_t = torch.eye(3, device=w.device).unsqueeze(
- 0).repeat([w.shape[0], 1, 1])
- m_t[:, 0, 2] = -t[:, 2] # t'_x
- m_t[:, 1, 2] = -t[:, 3] # t'_y
- # First rotate resulting image, then translate, and finally apply user-specified transform.
- transforms = m_r @ m_t @ transforms
-
- # Transform frequencies.
- phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2)
- freqs = freqs @ transforms[:, :2, :2]
-
- # Dampen out-of-band frequencies that may occur due to the user-specified transform.
- amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) /
- (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1)
-
- # Construct sampling grid.
- theta = torch.eye(2, 3, device=w.device)
- theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate
- theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate
- grids = torch.nn.functional.affine_grid(theta.unsqueeze(
- 0), [1, 1, self.size[1], self.size[0]], align_corners=False)
-
- # Compute Fourier features.
- x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)
- ).squeeze(3) # [batch, height, width, channel]
- x = x + phases.unsqueeze(1).unsqueeze(2)
- x = torch.sin(x * (np.pi * 2))
- x = x * amplitudes.unsqueeze(1).unsqueeze(2)
-
- # Apply trainable mapping.
- weight = self.weight / np.sqrt(self.channels)
- x = x @ weight.t()
-
- # Ensure correct shape.
- x = x.permute(0, 3, 1, 2) # [batch, channel, height, width]
- misc.assert_shape(x, [w.shape[0], self.channels,
- int(self.size[1]), int(self.size[0])])
- return x
-
- def extra_repr(self):
- return '\n'.join([
- f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},',
- f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisLayer(torch.nn.Module):
- def __init__(self,
- # Intermediate latent (W) dimensionality.
- w_dim,
- is_torgb, # Is this the final ToRGB layer?
- is_critically_sampled, # Does this layer use critical sampling?
- use_fp16, # Does this layer use FP16?
-
- # Input & output specifications.
- in_channels, # Number of input channels.
- out_channels, # Number of output channels.
- # Input spatial size: int or [width, height].
- in_size,
- # Output spatial size: int or [width, height].
- out_size,
- in_sampling_rate, # Input sampling rate (s).
- out_sampling_rate, # Output sampling rate (s).
- # Input cutoff frequency (f_c).
- in_cutoff,
- # Output cutoff frequency (f_c).
- out_cutoff,
- # Input transition band half-width (f_h).
- in_half_width,
- # Output Transition band half-width (f_h).
- out_half_width,
-
- # Hyperparameters.
- # Convolution kernel size. Ignored for final the ToRGB layer.
- conv_kernel=3,
- # Low-pass filter size relative to the lower resolution when up/downsampling.
- filter_size=6,
- # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer.
- lrelu_upsampling=2,
- # Use radially symmetric downsampling filter? Ignored for critically sampled layers.
- use_radial_filters=False,
- # Clamp the output to [-X, +X], None = disable clamping.
- conv_clamp=256,
- # Decay rate for the moving average of input magnitudes.
- magnitude_ema_beta=0.999,
- ):
- super().__init__()
- self.w_dim = w_dim
- self.is_torgb = is_torgb
- self.is_critically_sampled = is_critically_sampled
- self.use_fp16 = use_fp16
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.in_size = np.broadcast_to(np.asarray(in_size), [2])
- self.out_size = np.broadcast_to(np.asarray(out_size), [2])
- self.in_sampling_rate = in_sampling_rate
- self.out_sampling_rate = out_sampling_rate
- self.tmp_sampling_rate = max(
- in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling)
- self.in_cutoff = in_cutoff
- self.out_cutoff = out_cutoff
- self.in_half_width = in_half_width
- self.out_half_width = out_half_width
- self.conv_kernel = 1 if is_torgb else conv_kernel
- self.conv_clamp = conv_clamp
- self.magnitude_ema_beta = magnitude_ema_beta
-
- # Setup parameters and buffers.
- self.affine = FullyConnectedLayer(
- self.w_dim, self.in_channels, bias_init=1)
- self.weight = torch.nn.Parameter(torch.randn(
- [self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel]))
- self.bias = torch.nn.Parameter(torch.zeros([self.out_channels]))
- self.register_buffer('magnitude_ema', torch.ones([]))
-
- # Design upsampling filter.
- self.up_factor = int(
- np.rint(self.tmp_sampling_rate / self.in_sampling_rate))
- assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate
- self.up_taps = filter_size * \
- self.up_factor if self.up_factor > 1 and not self.is_torgb else 1
- self.register_buffer('up_filter', self.design_lowpass_filter(
- numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate))
-
- # Design downsampling filter.
- self.down_factor = int(
- np.rint(self.tmp_sampling_rate / self.out_sampling_rate))
- assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate
- self.down_taps = filter_size * \
- self.down_factor if self.down_factor > 1 and not self.is_torgb else 1
- self.down_radial = use_radial_filters and not self.is_critically_sampled
- self.register_buffer('down_filter', self.design_lowpass_filter(
- numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial))
-
- # Compute padding.
- # Desired output size before downsampling.
- pad_total = (self.out_size - 1) * self.down_factor + 1
- # Input size after upsampling.
- pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor
- # Size reduction caused by the filters.
- pad_total += self.up_taps + self.down_taps - 2
- # Shift sample locations according to the symmetric interpretation (Appendix C.3).
- pad_lo = (pad_total + self.up_factor) // 2
- pad_hi = pad_total - pad_lo
- self.padding = [int(pad_lo[0]), int(pad_hi[0]),
- int(pad_lo[1]), int(pad_hi[1])]
-
- def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False):
- assert noise_mode in ['random', 'const', 'none'] # unused
- misc.assert_shape(x, [None, self.in_channels, int(
- self.in_size[1]), int(self.in_size[0])])
- misc.assert_shape(w, [x.shape[0], self.w_dim])
-
- # Track input magnitude.
- if update_emas:
- with torch.autograd.profiler.record_function('update_magnitude_ema'):
- magnitude_cur = x.detach().to(torch.float32).square().mean()
- self.magnitude_ema.copy_(magnitude_cur.lerp(
- self.magnitude_ema, self.magnitude_ema_beta))
- input_gain = self.magnitude_ema.rsqrt()
-
- # Execute affine layer.
- styles = self.affine(w)
- if self.is_torgb:
- weight_gain = 1 / \
- np.sqrt(self.in_channels * (self.conv_kernel ** 2))
- styles = styles * weight_gain
-
- # Execute modulated conv2d.
- dtype = torch.float16 if (
- self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32
- x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles,
- padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain)
-
- # Execute bias, filtered leaky ReLU, and clamping.
- gain = 1 if self.is_torgb else np.sqrt(2)
- slope = 1 if self.is_torgb else 0.2
- x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype),
- up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp)
-
- # Ensure correct shape and dtype.
- misc.assert_shape(x, [None, self.out_channels, int(
- self.out_size[1]), int(self.out_size[0])])
- assert x.dtype == dtype
- return x
-
- @staticmethod
- def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False):
- assert numtaps >= 1
-
- # Identity filter.
- if numtaps == 1:
- return None
-
- # Separable Kaiser low-pass filter.
- if not radial:
- f = scipy.signal.firwin(
- numtaps=numtaps, cutoff=cutoff, width=width, fs=fs)
- return torch.as_tensor(f, dtype=torch.float32)
-
- # Radially symmetric jinc-based filter.
- x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs
- r = np.hypot(*np.meshgrid(x, x))
- f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r)
- beta = scipy.signal.kaiser_beta(
- scipy.signal.kaiser_atten(numtaps, width / (fs / 2)))
- w = np.kaiser(numtaps, beta)
- f *= np.outer(w, w)
- f /= np.sum(f)
- return torch.as_tensor(f, dtype=torch.float32)
-
- def extra_repr(self):
- return '\n'.join([
- f'w_dim={self.w_dim:d}, is_torgb={self.is_torgb},',
- f'is_critically_sampled={self.is_critically_sampled}, use_fp16={self.use_fp16},',
- f'in_sampling_rate={self.in_sampling_rate:g}, out_sampling_rate={self.out_sampling_rate:g},',
- f'in_cutoff={self.in_cutoff:g}, out_cutoff={self.out_cutoff:g},',
- f'in_half_width={self.in_half_width:g}, out_half_width={self.out_half_width:g},',
- f'in_size={list(self.in_size)}, out_size={list(self.out_size)},',
- f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisNetwork(torch.nn.Module):
- def __init__(self,
- # Intermediate latent (W) dimensionality.
- w_dim,
- img_resolution, # Output image resolution.
- img_channels, # Number of color channels.
- # Overall multiplier for the number of channels.
- channel_base=32768,
- # Maximum number of channels in any layer.
- channel_max=512,
- # Total number of layers, excluding Fourier features and ToRGB.
- num_layers=14,
- # Number of critically sampled layers at the end.
- num_critical=2,
- # Cutoff frequency of the first layer (f_{c,0}).
- first_cutoff=2,
- # Minimum stopband of the first layer (f_{t,0}).
- first_stopband=2**2.1,
- # Minimum stopband of the last layer, expressed relative to the cutoff.
- last_stopband_rel=2**0.3,
- # Number of additional pixels outside the image.
- margin_size=10,
- output_scale=0.25, # Scale factor for the output image.
- # Use FP16 for the N highest resolutions.
- num_fp16_res=4,
- # Arguments for SynthesisLayer.
- **layer_kwargs,
- ):
- super().__init__()
- self.w_dim = w_dim
- self.num_ws = num_layers + 2
- self.img_resolution = img_resolution
- self.img_channels = img_channels
- self.num_layers = num_layers
- self.num_critical = num_critical
- self.margin_size = margin_size
- self.output_scale = output_scale
- self.num_fp16_res = num_fp16_res
-
- # Geometric progression of layer cutoffs and min. stopbands.
- last_cutoff = self.img_resolution / 2 # f_{c,N}
- last_stopband = last_cutoff * last_stopband_rel # f_{t,N}
- exponents = np.minimum(
- np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1)
- cutoffs = first_cutoff * \
- (last_cutoff / first_cutoff) ** exponents # f_c[i]
- stopbands = first_stopband * \
- (last_stopband / first_stopband) ** exponents # f_t[i]
-
- # Compute remaining layer parameters.
- sampling_rates = np.exp2(
- np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i]
- half_widths = np.maximum(
- stopbands, sampling_rates / 2) - cutoffs # f_h[i]
- sizes = sampling_rates + self.margin_size * 2
- sizes[-2:] = self.img_resolution
- channels = np.rint(np.minimum(
- (channel_base / 2) / cutoffs, channel_max))
- channels[-1] = self.img_channels
-
- # Construct layers.
- self.input = SynthesisInput(
- w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]),
- sampling_rate=sampling_rates[0], bandwidth=cutoffs[0])
- self.layer_names = []
- for idx in range(self.num_layers + 1):
- prev = max(idx - 1, 0)
- is_torgb = (idx == self.num_layers)
- is_critically_sampled = (
- idx >= self.num_layers - self.num_critical)
- use_fp16 = (sampling_rates[idx] * (2 **
- self.num_fp16_res) > self.img_resolution)
- layer = SynthesisLayer(
- w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16,
- in_channels=int(channels[prev]), out_channels=int(channels[idx]),
- in_size=int(sizes[prev]), out_size=int(sizes[idx]),
- in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]),
- in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx],
- in_half_width=half_widths[prev], out_half_width=half_widths[idx],
- **layer_kwargs)
- name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}'
- setattr(self, name, layer)
- self.layer_names.append(name)
-
- def forward(self, ws, return_feature=False, **layer_kwargs):
- features = []
- misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
- ws = ws.to(torch.float32).unbind(dim=1)
-
- # Execute layers.
- x = self.input(ws[0])
- for name, w in zip(self.layer_names, ws[1:]):
- x = getattr(self, name)(x, w, **layer_kwargs)
- features.append(x)
- if self.output_scale != 1:
- x = x * self.output_scale
-
- # Ensure correct shape and dtype.
- misc.assert_shape(x, [None, self.img_channels,
- self.img_resolution, self.img_resolution])
- x = x.to(torch.float32)
- if return_feature:
- return x, features
- else:
- return x
-
- def extra_repr(self):
- return '\n'.join([
- f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
- f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
- f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},',
- f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Generator(torch.nn.Module):
- def __init__(self,
- z_dim, # Input latent (Z) dimensionality.
- # Conditioning label (C) dimensionality.
- c_dim,
- # Intermediate latent (W) dimensionality.
- w_dim,
- img_resolution, # Output resolution.
- img_channels, # Number of output color channels.
- mapping_kwargs={}, # Arguments for MappingNetwork.
- resize=None,
- **synthesis_kwargs, # Arguments for SynthesisNetwork.
- ):
- super().__init__()
- self.z_dim = z_dim
- self.c_dim = c_dim
- self.w_dim = w_dim
- self.img_resolution = img_resolution
- self.img_channels = img_channels
- self.synthesis = SynthesisNetwork(
- w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
- self.num_ws = self.synthesis.num_ws
- self.mapping = MappingNetwork(
- z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
- self.resize = resize
-
- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, input_is_w=False, return_feature=False, **synthesis_kwargs):
- if input_is_w:
- ws = z
- if ws.dim() == 2:
- ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1])
- else:
- ws = self.mapping(z, c, truncation_psi=truncation_psi,
- truncation_cutoff=truncation_cutoff, update_emas=update_emas)
- img = self.synthesis(ws, update_emas=update_emas,
- return_feature=return_feature, **synthesis_kwargs)
- if return_feature:
- img, feature = img
- if self.resize is not None:
- img = imresize(img, [self.resize, self.resize])
- if return_feature:
- return img, feature
- else:
- return img
-
-# ----------------------------------------------------------------------------
-
-
-def imresize(image, size):
- dim = image.dim()
- if dim == 3:
- image = image.unsqueeze(1)
- b, _, h, w = image.shape
- if size[0] > h:
- image = F.interpolate(image, size, mode='bilinear')
- elif size[0] < h:
- image = F.interpolate(image, size, mode='area')
- if dim == 3:
- image = image.squeeze(1)
- return image
diff --git a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/model/abstract.py b/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/model/abstract.py
deleted file mode 100644
index 00c84cc3dbd4a13d67d24aab15775c221a66059c..0000000000000000000000000000000000000000
--- a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/model/abstract.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import abc
-import numpy as np
-import pytorch_lightning as pl
-from pytorch_lightning.utilities.types import (
- EVAL_DATALOADERS,
- TRAIN_DATALOADERS,
-)
-from torch import nn
-from torch.utils.data import DataLoader
-import torch
-from torchvision import transforms
-
-from src.dataset import DATASET_REGISTRY
-
-
-class AbstractModel(pl.LightningModule):
- def __init__(self, cfg):
- super().__init__()
- self.cfg = cfg
- self.train_dataset = None
- self.val_dataset = None
- self.metric_evaluator = None
- self.init_model()
-
- def setup(self, stage):
- if stage in ["fit", "validate", "test"]:
- self.train_dataset = DATASET_REGISTRY.get("BlenderDataset")(
- **self.cfg["dataset"]["train"]["params"],
- )
-
- self.val_dataset = DATASET_REGISTRY.get("BlenderDataset")(
- **self.cfg["dataset"]["val"]["params"],
- )
- # self.metric_evaluator = SHRECMetricEvaluator(
- # embed_dim=self.cfg["model"]["embed_dim"]
- # )
- @abc.abstractmethod
- def init_model(self):
- """
- Function to initialize model
- """
- raise NotImplementedError
-
- @abc.abstractmethod
- def forward(self, batch):
- raise NotImplementedError
-
- @abc.abstractmethod
- def compute_loss(self, forwarded_batch, input_batch):
- """
- Function to compute loss
- Args:
- forwarded_batch: output of `forward` method
- input_batch: input of batch method
-
- Returns:
- loss: computed loss
- """
- raise NotImplementedError
-
- def training_step(self, batch, batch_idx):
- # 1. get embeddings from model
- forwarded_batch = self.forward(batch)
- # 2. Calculate loss
- loss = self.compute_loss(forwarded_batch=forwarded_batch, input_batch=batch)
- # 3. Update monitor
- self.log("train_loss", loss, on_step=True, on_epoch=True, prog_bar=True)
- return {"loss": loss}
-
- def validation_step(self, batch, batch_idx):
- # 1. Get embeddings from model
- forwarded_batch = self.forward(batch)
- # 2. Calculate loss
- loss = self.compute_loss(forwarded_batch=forwarded_batch, input_batch=batch)
- # 3. Update metric for each batch
- self.log("val_loss", loss, on_step=True, on_epoch=True, prog_bar=True)
- self.metric_evaluator.append(
- g_emb=forwarded_batch["pc_embedding_feats"].float().clone().detach(),
- q_emb=forwarded_batch["query_embedding_feats"].float().clone().detach(),
- query_ids=batch["query_ids"],
- gallery_ids=batch["point_cloud_ids"],
- target_ids=batch["point_cloud_ids"],
- )
-
- return {"loss": loss}
-
- def validation_epoch_end(self, outputs) -> None:
- """
- Callback at validation epoch end to do additional works
- with output of validation step, note that this is called
- before `training_epoch_end()`
- Args:
- outputs: output of validation step
- """
- self.log_dict(
- self.metric_evaluator.evaluate(),
- prog_bar=True,
- on_step=False,
- on_epoch=True,
- )
- self.metric_evaluator.reset()
-
- def train_dataloader(self) -> TRAIN_DATALOADERS:
- train_loader = DataLoader(
- dataset=self.train_dataset,
- collate_fn=self.train_dataset.collate_fn,
- **self.cfg["data_loader"]["train"]["params"],
- )
- return train_loader
-
- def val_dataloader(self) -> EVAL_DATALOADERS:
- val_loader = DataLoader(
- dataset=self.val_dataset,
- collate_fn=self.val_dataset.collate_fn,
- **self.cfg["data_loader"]["val"]["params"],
- )
- return val_loader
-
- def configure_optimizers(self):
- pass
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docker/diffusers-pytorch-cuda/Dockerfile b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docker/diffusers-pytorch-cuda/Dockerfile
deleted file mode 100644
index fab3b70827653a959434cb24929f86e3bd8890e2..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docker/diffusers-pytorch-cuda/Dockerfile
+++ /dev/null
@@ -1,47 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-runtime-ubuntu20.04
-LABEL maintainer="Hugging Face"
-LABEL repository="diffusers"
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt update && \
- apt install -y bash \
- build-essential \
- git \
- git-lfs \
- curl \
- ca-certificates \
- libsndfile1-dev \
- libgl1 \
- python3.8 \
- python3-pip \
- python3.8-venv && \
- rm -rf /var/lib/apt/lists
-
-# make sure to use venv
-RUN python3 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3 -m pip install --no-cache-dir --upgrade pip && \
- python3 -m pip install --no-cache-dir \
- torch \
- torchvision \
- torchaudio \
- invisible_watermark && \
- python3 -m pip install --no-cache-dir \
- accelerate \
- datasets \
- hf-doc-builder \
- huggingface-hub \
- Jinja2 \
- librosa \
- numpy \
- scipy \
- tensorboard \
- transformers \
- omegaconf \
- pytorch-lightning \
- xformers
-
-CMD ["/bin/bash"]
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py
deleted file mode 100644
index 07604d7c082f7e7b3c89487461af81fb9650efc7..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py
+++ /dev/null
@@ -1,1645 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Conversion script for the Stable Diffusion checkpoints."""
-
-import re
-from contextlib import nullcontext
-from io import BytesIO
-from typing import Optional
-
-import requests
-import torch
-from transformers import (
- AutoFeatureExtractor,
- BertTokenizerFast,
- CLIPImageProcessor,
- CLIPTextConfig,
- CLIPTextModel,
- CLIPTextModelWithProjection,
- CLIPTokenizer,
- CLIPVisionConfig,
- CLIPVisionModelWithProjection,
-)
-
-from ...models import (
- AutoencoderKL,
- ControlNetModel,
- PriorTransformer,
- UNet2DConditionModel,
-)
-from ...schedulers import (
- DDIMScheduler,
- DDPMScheduler,
- DPMSolverMultistepScheduler,
- EulerAncestralDiscreteScheduler,
- EulerDiscreteScheduler,
- HeunDiscreteScheduler,
- LMSDiscreteScheduler,
- PNDMScheduler,
- UnCLIPScheduler,
-)
-from ...utils import is_accelerate_available, is_omegaconf_available, is_safetensors_available, logging
-from ...utils.import_utils import BACKENDS_MAPPING
-from ..latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
-from ..paint_by_example import PaintByExampleImageEncoder
-from ..pipeline_utils import DiffusionPipeline
-from .safety_checker import StableDiffusionSafetyChecker
-from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
-
-
-if is_accelerate_available():
- from accelerate import init_empty_weights
- from accelerate.utils import set_module_tensor_to_device
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-def shave_segments(path, n_shave_prefix_segments=1):
- """
- Removes segments. Positive values shave the first segments, negative shave the last segments.
- """
- if n_shave_prefix_segments >= 0:
- return ".".join(path.split(".")[n_shave_prefix_segments:])
- else:
- return ".".join(path.split(".")[:n_shave_prefix_segments])
-
-
-def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
- """
- Updates paths inside resnets to the new naming scheme (local renaming)
- """
- mapping = []
- for old_item in old_list:
- new_item = old_item.replace("in_layers.0", "norm1")
- new_item = new_item.replace("in_layers.2", "conv1")
-
- new_item = new_item.replace("out_layers.0", "norm2")
- new_item = new_item.replace("out_layers.3", "conv2")
-
- new_item = new_item.replace("emb_layers.1", "time_emb_proj")
- new_item = new_item.replace("skip_connection", "conv_shortcut")
-
- new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
- mapping.append({"old": old_item, "new": new_item})
-
- return mapping
-
-
-def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
- """
- Updates paths inside resnets to the new naming scheme (local renaming)
- """
- mapping = []
- for old_item in old_list:
- new_item = old_item
-
- new_item = new_item.replace("nin_shortcut", "conv_shortcut")
- new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
- mapping.append({"old": old_item, "new": new_item})
-
- return mapping
-
-
-def renew_attention_paths(old_list, n_shave_prefix_segments=0):
- """
- Updates paths inside attentions to the new naming scheme (local renaming)
- """
- mapping = []
- for old_item in old_list:
- new_item = old_item
-
- # new_item = new_item.replace('norm.weight', 'group_norm.weight')
- # new_item = new_item.replace('norm.bias', 'group_norm.bias')
-
- # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
- # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
-
- # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
- mapping.append({"old": old_item, "new": new_item})
-
- return mapping
-
-
-def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
- """
- Updates paths inside attentions to the new naming scheme (local renaming)
- """
- mapping = []
- for old_item in old_list:
- new_item = old_item
-
- new_item = new_item.replace("norm.weight", "group_norm.weight")
- new_item = new_item.replace("norm.bias", "group_norm.bias")
-
- new_item = new_item.replace("q.weight", "to_q.weight")
- new_item = new_item.replace("q.bias", "to_q.bias")
-
- new_item = new_item.replace("k.weight", "to_k.weight")
- new_item = new_item.replace("k.bias", "to_k.bias")
-
- new_item = new_item.replace("v.weight", "to_v.weight")
- new_item = new_item.replace("v.bias", "to_v.bias")
-
- new_item = new_item.replace("proj_out.weight", "to_out.0.weight")
- new_item = new_item.replace("proj_out.bias", "to_out.0.bias")
-
- new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
- mapping.append({"old": old_item, "new": new_item})
-
- return mapping
-
-
-def assign_to_checkpoint(
- paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
-):
- """
- This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits
- attention layers, and takes into account additional replacements that may arise.
-
- Assigns the weights to the new checkpoint.
- """
- assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
-
- # Splits the attention layers into three variables.
- if attention_paths_to_split is not None:
- for path, path_map in attention_paths_to_split.items():
- old_tensor = old_checkpoint[path]
- channels = old_tensor.shape[0] // 3
-
- target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
-
- num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
-
- old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
- query, key, value = old_tensor.split(channels // num_heads, dim=1)
-
- checkpoint[path_map["query"]] = query.reshape(target_shape)
- checkpoint[path_map["key"]] = key.reshape(target_shape)
- checkpoint[path_map["value"]] = value.reshape(target_shape)
-
- for path in paths:
- new_path = path["new"]
-
- # These have already been assigned
- if attention_paths_to_split is not None and new_path in attention_paths_to_split:
- continue
-
- # Global renaming happens here
- new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
- new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
- new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
-
- if additional_replacements is not None:
- for replacement in additional_replacements:
- new_path = new_path.replace(replacement["old"], replacement["new"])
-
- # proj_attn.weight has to be converted from conv 1D to linear
- is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path)
- shape = old_checkpoint[path["old"]].shape
- if is_attn_weight and len(shape) == 3:
- checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
- elif is_attn_weight and len(shape) == 4:
- checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0]
- else:
- checkpoint[new_path] = old_checkpoint[path["old"]]
-
-
-def conv_attn_to_linear(checkpoint):
- keys = list(checkpoint.keys())
- attn_keys = ["query.weight", "key.weight", "value.weight"]
- for key in keys:
- if ".".join(key.split(".")[-2:]) in attn_keys:
- if checkpoint[key].ndim > 2:
- checkpoint[key] = checkpoint[key][:, :, 0, 0]
- elif "proj_attn.weight" in key:
- if checkpoint[key].ndim > 2:
- checkpoint[key] = checkpoint[key][:, :, 0]
-
-
-def create_unet_diffusers_config(original_config, image_size: int, controlnet=False):
- """
- Creates a config for the diffusers based on the config of the LDM model.
- """
- if controlnet:
- unet_params = original_config.model.params.control_stage_config.params
- else:
- if "unet_config" in original_config.model.params and original_config.model.params.unet_config is not None:
- unet_params = original_config.model.params.unet_config.params
- else:
- unet_params = original_config.model.params.network_config.params
-
- vae_params = original_config.model.params.first_stage_config.params.ddconfig
-
- block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
-
- down_block_types = []
- resolution = 1
- for i in range(len(block_out_channels)):
- block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
- down_block_types.append(block_type)
- if i != len(block_out_channels) - 1:
- resolution *= 2
-
- up_block_types = []
- for i in range(len(block_out_channels)):
- block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
- up_block_types.append(block_type)
- resolution //= 2
-
- if unet_params.transformer_depth is not None:
- transformer_layers_per_block = (
- unet_params.transformer_depth
- if isinstance(unet_params.transformer_depth, int)
- else list(unet_params.transformer_depth)
- )
- else:
- transformer_layers_per_block = 1
-
- vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1)
-
- head_dim = unet_params.num_heads if "num_heads" in unet_params else None
- use_linear_projection = (
- unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False
- )
- if use_linear_projection:
- # stable diffusion 2-base-512 and 2-768
- if head_dim is None:
- head_dim_mult = unet_params.model_channels // unet_params.num_head_channels
- head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)]
-
- class_embed_type = None
- addition_embed_type = None
- addition_time_embed_dim = None
- projection_class_embeddings_input_dim = None
- context_dim = None
-
- if unet_params.context_dim is not None:
- context_dim = (
- unet_params.context_dim if isinstance(unet_params.context_dim, int) else unet_params.context_dim[0]
- )
-
- if "num_classes" in unet_params:
- if unet_params.num_classes == "sequential":
- if context_dim in [2048, 1280]:
- # SDXL
- addition_embed_type = "text_time"
- addition_time_embed_dim = 256
- else:
- class_embed_type = "projection"
- assert "adm_in_channels" in unet_params
- projection_class_embeddings_input_dim = unet_params.adm_in_channels
- else:
- raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params.num_classes}")
-
- config = {
- "sample_size": image_size // vae_scale_factor,
- "in_channels": unet_params.in_channels,
- "down_block_types": tuple(down_block_types),
- "block_out_channels": tuple(block_out_channels),
- "layers_per_block": unet_params.num_res_blocks,
- "cross_attention_dim": context_dim,
- "attention_head_dim": head_dim,
- "use_linear_projection": use_linear_projection,
- "class_embed_type": class_embed_type,
- "addition_embed_type": addition_embed_type,
- "addition_time_embed_dim": addition_time_embed_dim,
- "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
- "transformer_layers_per_block": transformer_layers_per_block,
- }
-
- if controlnet:
- config["conditioning_channels"] = unet_params.hint_channels
- else:
- config["out_channels"] = unet_params.out_channels
- config["up_block_types"] = tuple(up_block_types)
-
- return config
-
-
-def create_vae_diffusers_config(original_config, image_size: int):
- """
- Creates a config for the diffusers based on the config of the LDM model.
- """
- vae_params = original_config.model.params.first_stage_config.params.ddconfig
- _ = original_config.model.params.first_stage_config.params.embed_dim
-
- block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
- down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
- up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
-
- config = {
- "sample_size": image_size,
- "in_channels": vae_params.in_channels,
- "out_channels": vae_params.out_ch,
- "down_block_types": tuple(down_block_types),
- "up_block_types": tuple(up_block_types),
- "block_out_channels": tuple(block_out_channels),
- "latent_channels": vae_params.z_channels,
- "layers_per_block": vae_params.num_res_blocks,
- }
- return config
-
-
-def create_diffusers_schedular(original_config):
- schedular = DDIMScheduler(
- num_train_timesteps=original_config.model.params.timesteps,
- beta_start=original_config.model.params.linear_start,
- beta_end=original_config.model.params.linear_end,
- beta_schedule="scaled_linear",
- )
- return schedular
-
-
-def create_ldm_bert_config(original_config):
- bert_params = original_config.model.parms.cond_stage_config.params
- config = LDMBertConfig(
- d_model=bert_params.n_embed,
- encoder_layers=bert_params.n_layer,
- encoder_ffn_dim=bert_params.n_embed * 4,
- )
- return config
-
-
-def convert_ldm_unet_checkpoint(
- checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False
-):
- """
- Takes a state dict and a config, and returns a converted checkpoint.
- """
-
- if skip_extract_state_dict:
- unet_state_dict = checkpoint
- else:
- # extract state_dict for UNet
- unet_state_dict = {}
- keys = list(checkpoint.keys())
-
- if controlnet:
- unet_key = "control_model."
- else:
- unet_key = "model.diffusion_model."
-
- # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
- if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
- logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.")
- logger.warning(
- "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
- " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
- )
- for key in keys:
- if key.startswith("model.diffusion_model"):
- flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
- unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
- else:
- if sum(k.startswith("model_ema") for k in keys) > 100:
- logger.warning(
- "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
- " weights (usually better for inference), please make sure to add the `--extract_ema` flag."
- )
-
- for key in keys:
- if key.startswith(unet_key):
- unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
-
- new_checkpoint = {}
-
- new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
- new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
- new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
- new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
-
- if config["class_embed_type"] is None:
- # No parameters to port
- ...
- elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
- new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
- new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
- new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
- new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
- else:
- raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}")
-
- if config["addition_embed_type"] == "text_time":
- new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
- new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
- new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
- new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
-
- new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
- new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
-
- if not controlnet:
- new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
- new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
- new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
- new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
-
- # Retrieves the keys for the input blocks only
- num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
- input_blocks = {
- layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
- for layer_id in range(num_input_blocks)
- }
-
- # Retrieves the keys for the middle blocks only
- num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
- middle_blocks = {
- layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
- for layer_id in range(num_middle_blocks)
- }
-
- # Retrieves the keys for the output blocks only
- num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
- output_blocks = {
- layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
- for layer_id in range(num_output_blocks)
- }
-
- for i in range(1, num_input_blocks):
- block_id = (i - 1) // (config["layers_per_block"] + 1)
- layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
-
- resnets = [
- key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
- ]
- attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
-
- if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
- new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
- f"input_blocks.{i}.0.op.weight"
- )
- new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
- f"input_blocks.{i}.0.op.bias"
- )
-
- paths = renew_resnet_paths(resnets)
- meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
- assign_to_checkpoint(
- paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
- )
-
- if len(attentions):
- paths = renew_attention_paths(attentions)
- meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
- assign_to_checkpoint(
- paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
- )
-
- resnet_0 = middle_blocks[0]
- attentions = middle_blocks[1]
- resnet_1 = middle_blocks[2]
-
- resnet_0_paths = renew_resnet_paths(resnet_0)
- assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
-
- resnet_1_paths = renew_resnet_paths(resnet_1)
- assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
-
- attentions_paths = renew_attention_paths(attentions)
- meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
- assign_to_checkpoint(
- attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
- )
-
- for i in range(num_output_blocks):
- block_id = i // (config["layers_per_block"] + 1)
- layer_in_block_id = i % (config["layers_per_block"] + 1)
- output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
- output_block_list = {}
-
- for layer in output_block_layers:
- layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
- if layer_id in output_block_list:
- output_block_list[layer_id].append(layer_name)
- else:
- output_block_list[layer_id] = [layer_name]
-
- if len(output_block_list) > 1:
- resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
- attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
-
- resnet_0_paths = renew_resnet_paths(resnets)
- paths = renew_resnet_paths(resnets)
-
- meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
- assign_to_checkpoint(
- paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
- )
-
- output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
- if ["conv.bias", "conv.weight"] in output_block_list.values():
- index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
- new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
- f"output_blocks.{i}.{index}.conv.weight"
- ]
- new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
- f"output_blocks.{i}.{index}.conv.bias"
- ]
-
- # Clear attentions as they have been attributed above.
- if len(attentions) == 2:
- attentions = []
-
- if len(attentions):
- paths = renew_attention_paths(attentions)
- meta_path = {
- "old": f"output_blocks.{i}.1",
- "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
- }
- assign_to_checkpoint(
- paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
- )
- else:
- resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
- for path in resnet_0_paths:
- old_path = ".".join(["output_blocks", str(i), path["old"]])
- new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
-
- new_checkpoint[new_path] = unet_state_dict[old_path]
-
- if controlnet:
- # conditioning embedding
-
- orig_index = 0
-
- new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop(
- f"input_hint_block.{orig_index}.weight"
- )
- new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop(
- f"input_hint_block.{orig_index}.bias"
- )
-
- orig_index += 2
-
- diffusers_index = 0
-
- while diffusers_index < 6:
- new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop(
- f"input_hint_block.{orig_index}.weight"
- )
- new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop(
- f"input_hint_block.{orig_index}.bias"
- )
- diffusers_index += 1
- orig_index += 2
-
- new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop(
- f"input_hint_block.{orig_index}.weight"
- )
- new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop(
- f"input_hint_block.{orig_index}.bias"
- )
-
- # down blocks
- for i in range(num_input_blocks):
- new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight")
- new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias")
-
- # mid block
- new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight")
- new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias")
-
- return new_checkpoint
-
-
-def convert_ldm_vae_checkpoint(checkpoint, config):
- # extract state dict for VAE
- vae_state_dict = {}
- keys = list(checkpoint.keys())
- vae_key = "first_stage_model." if any(k.startswith("first_stage_model.") for k in keys) else ""
- for key in keys:
- if key.startswith(vae_key):
- vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
-
- new_checkpoint = {}
-
- new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
- new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
- new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
- new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
- new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
- new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
-
- new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
- new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
- new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
- new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
- new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
- new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
-
- new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
- new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
- new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
- new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
-
- # Retrieves the keys for the encoder down blocks only
- num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
- down_blocks = {
- layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
- }
-
- # Retrieves the keys for the decoder up blocks only
- num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
- up_blocks = {
- layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
- }
-
- for i in range(num_down_blocks):
- resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
-
- if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
- new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
- f"encoder.down.{i}.downsample.conv.weight"
- )
- new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
- f"encoder.down.{i}.downsample.conv.bias"
- )
-
- paths = renew_vae_resnet_paths(resnets)
- meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
- mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
- num_mid_res_blocks = 2
- for i in range(1, num_mid_res_blocks + 1):
- resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
-
- paths = renew_vae_resnet_paths(resnets)
- meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
- mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
- paths = renew_vae_attention_paths(mid_attentions)
- meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
- conv_attn_to_linear(new_checkpoint)
-
- for i in range(num_up_blocks):
- block_id = num_up_blocks - 1 - i
- resnets = [
- key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
- ]
-
- if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
- new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
- f"decoder.up.{block_id}.upsample.conv.weight"
- ]
- new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
- f"decoder.up.{block_id}.upsample.conv.bias"
- ]
-
- paths = renew_vae_resnet_paths(resnets)
- meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
- mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
- num_mid_res_blocks = 2
- for i in range(1, num_mid_res_blocks + 1):
- resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
-
- paths = renew_vae_resnet_paths(resnets)
- meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
- mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
- paths = renew_vae_attention_paths(mid_attentions)
- meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
- conv_attn_to_linear(new_checkpoint)
- return new_checkpoint
-
-
-def convert_ldm_bert_checkpoint(checkpoint, config):
- def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
- hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
- hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
- hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight
-
- hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
- hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias
-
- def _copy_linear(hf_linear, pt_linear):
- hf_linear.weight = pt_linear.weight
- hf_linear.bias = pt_linear.bias
-
- def _copy_layer(hf_layer, pt_layer):
- # copy layer norms
- _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
- _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])
-
- # copy attn
- _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])
-
- # copy MLP
- pt_mlp = pt_layer[1][1]
- _copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
- _copy_linear(hf_layer.fc2, pt_mlp.net[2])
-
- def _copy_layers(hf_layers, pt_layers):
- for i, hf_layer in enumerate(hf_layers):
- if i != 0:
- i += i
- pt_layer = pt_layers[i : i + 2]
- _copy_layer(hf_layer, pt_layer)
-
- hf_model = LDMBertModel(config).eval()
-
- # copy embeds
- hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
- hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
-
- # copy layer norm
- _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
-
- # copy hidden layers
- _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)
-
- _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)
-
- return hf_model
-
-
-def convert_ldm_clip_checkpoint(checkpoint, local_files_only=False, text_encoder=None):
- if text_encoder is None:
- config_name = "openai/clip-vit-large-patch14"
- config = CLIPTextConfig.from_pretrained(config_name)
-
- ctx = init_empty_weights if is_accelerate_available() else nullcontext
- with ctx():
- text_model = CLIPTextModel(config)
-
- keys = list(checkpoint.keys())
-
- text_model_dict = {}
-
- remove_prefixes = ["cond_stage_model.transformer", "conditioner.embedders.0.transformer"]
-
- for key in keys:
- for prefix in remove_prefixes:
- if key.startswith(prefix):
- text_model_dict[key[len(prefix + ".") :]] = checkpoint[key]
-
- if is_accelerate_available():
- for param_name, param in text_model_dict.items():
- set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
- else:
- if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
- text_model_dict.pop("text_model.embeddings.position_ids", None)
-
- text_model.load_state_dict(text_model_dict)
-
- return text_model
-
-
-textenc_conversion_lst = [
- ("positional_embedding", "text_model.embeddings.position_embedding.weight"),
- ("token_embedding.weight", "text_model.embeddings.token_embedding.weight"),
- ("ln_final.weight", "text_model.final_layer_norm.weight"),
- ("ln_final.bias", "text_model.final_layer_norm.bias"),
- ("text_projection", "text_projection.weight"),
-]
-textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}
-
-textenc_transformer_conversion_lst = [
- # (stable-diffusion, HF Diffusers)
- ("resblocks.", "text_model.encoder.layers."),
- ("ln_1", "layer_norm1"),
- ("ln_2", "layer_norm2"),
- (".c_fc.", ".fc1."),
- (".c_proj.", ".fc2."),
- (".attn", ".self_attn"),
- ("ln_final.", "transformer.text_model.final_layer_norm."),
- ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
- ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
-]
-protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}
-textenc_pattern = re.compile("|".join(protected.keys()))
-
-
-def convert_paint_by_example_checkpoint(checkpoint):
- config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14")
- model = PaintByExampleImageEncoder(config)
-
- keys = list(checkpoint.keys())
-
- text_model_dict = {}
-
- for key in keys:
- if key.startswith("cond_stage_model.transformer"):
- text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
-
- # load clip vision
- model.model.load_state_dict(text_model_dict)
-
- # load mapper
- keys_mapper = {
- k[len("cond_stage_model.mapper.res") :]: v
- for k, v in checkpoint.items()
- if k.startswith("cond_stage_model.mapper")
- }
-
- MAPPING = {
- "attn.c_qkv": ["attn1.to_q", "attn1.to_k", "attn1.to_v"],
- "attn.c_proj": ["attn1.to_out.0"],
- "ln_1": ["norm1"],
- "ln_2": ["norm3"],
- "mlp.c_fc": ["ff.net.0.proj"],
- "mlp.c_proj": ["ff.net.2"],
- }
-
- mapped_weights = {}
- for key, value in keys_mapper.items():
- prefix = key[: len("blocks.i")]
- suffix = key.split(prefix)[-1].split(".")[-1]
- name = key.split(prefix)[-1].split(suffix)[0][1:-1]
- mapped_names = MAPPING[name]
-
- num_splits = len(mapped_names)
- for i, mapped_name in enumerate(mapped_names):
- new_name = ".".join([prefix, mapped_name, suffix])
- shape = value.shape[0] // num_splits
- mapped_weights[new_name] = value[i * shape : (i + 1) * shape]
-
- model.mapper.load_state_dict(mapped_weights)
-
- # load final layer norm
- model.final_layer_norm.load_state_dict(
- {
- "bias": checkpoint["cond_stage_model.final_ln.bias"],
- "weight": checkpoint["cond_stage_model.final_ln.weight"],
- }
- )
-
- # load final proj
- model.proj_out.load_state_dict(
- {
- "bias": checkpoint["proj_out.bias"],
- "weight": checkpoint["proj_out.weight"],
- }
- )
-
- # load uncond vector
- model.uncond_vector.data = torch.nn.Parameter(checkpoint["learnable_vector"])
- return model
-
-
-def convert_open_clip_checkpoint(
- checkpoint, config_name, prefix="cond_stage_model.model.", has_projection=False, **config_kwargs
-):
- # text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder")
- # text_model = CLIPTextModelWithProjection.from_pretrained(
- # "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", projection_dim=1280
- # )
- config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs)
-
- ctx = init_empty_weights if is_accelerate_available() else nullcontext
- with ctx():
- text_model = CLIPTextModelWithProjection(config) if has_projection else CLIPTextModel(config)
-
- keys = list(checkpoint.keys())
-
- keys_to_ignore = []
- if config_name == "stabilityai/stable-diffusion-2" and config.num_hidden_layers == 23:
- # make sure to remove all keys > 22
- keys_to_ignore += [k for k in keys if k.startswith("cond_stage_model.model.transformer.resblocks.23")]
- keys_to_ignore += ["cond_stage_model.model.text_projection"]
-
- text_model_dict = {}
-
- if prefix + "text_projection" in checkpoint:
- d_model = int(checkpoint[prefix + "text_projection"].shape[0])
- else:
- d_model = 1024
-
- text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids")
-
- for key in keys:
- if key in keys_to_ignore:
- continue
- if key[len(prefix) :] in textenc_conversion_map:
- if key.endswith("text_projection"):
- value = checkpoint[key].T.contiguous()
- else:
- value = checkpoint[key]
-
- text_model_dict[textenc_conversion_map[key[len(prefix) :]]] = value
-
- if key.startswith(prefix + "transformer."):
- new_key = key[len(prefix + "transformer.") :]
- if new_key.endswith(".in_proj_weight"):
- new_key = new_key[: -len(".in_proj_weight")]
- new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
- text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :]
- text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :]
- text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :]
- elif new_key.endswith(".in_proj_bias"):
- new_key = new_key[: -len(".in_proj_bias")]
- new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
- text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model]
- text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2]
- text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :]
- else:
- new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
-
- text_model_dict[new_key] = checkpoint[key]
-
- if is_accelerate_available():
- for param_name, param in text_model_dict.items():
- set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
- else:
- if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
- text_model_dict.pop("text_model.embeddings.position_ids", None)
-
- text_model.load_state_dict(text_model_dict)
-
- return text_model
-
-
-def stable_unclip_image_encoder(original_config):
- """
- Returns the image processor and clip image encoder for the img2img unclip pipeline.
-
- We currently know of two types of stable unclip models which separately use the clip and the openclip image
- encoders.
- """
-
- image_embedder_config = original_config.model.params.embedder_config
-
- sd_clip_image_embedder_class = image_embedder_config.target
- sd_clip_image_embedder_class = sd_clip_image_embedder_class.split(".")[-1]
-
- if sd_clip_image_embedder_class == "ClipImageEmbedder":
- clip_model_name = image_embedder_config.params.model
-
- if clip_model_name == "ViT-L/14":
- feature_extractor = CLIPImageProcessor()
- image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
- else:
- raise NotImplementedError(f"Unknown CLIP checkpoint name in stable diffusion checkpoint {clip_model_name}")
-
- elif sd_clip_image_embedder_class == "FrozenOpenCLIPImageEmbedder":
- feature_extractor = CLIPImageProcessor()
- image_encoder = CLIPVisionModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
- else:
- raise NotImplementedError(
- f"Unknown CLIP image embedder class in stable diffusion checkpoint {sd_clip_image_embedder_class}"
- )
-
- return feature_extractor, image_encoder
-
-
-def stable_unclip_image_noising_components(
- original_config, clip_stats_path: Optional[str] = None, device: Optional[str] = None
-):
- """
- Returns the noising components for the img2img and txt2img unclip pipelines.
-
- Converts the stability noise augmentor into
- 1. a `StableUnCLIPImageNormalizer` for holding the CLIP stats
- 2. a `DDPMScheduler` for holding the noise schedule
-
- If the noise augmentor config specifies a clip stats path, the `clip_stats_path` must be provided.
- """
- noise_aug_config = original_config.model.params.noise_aug_config
- noise_aug_class = noise_aug_config.target
- noise_aug_class = noise_aug_class.split(".")[-1]
-
- if noise_aug_class == "CLIPEmbeddingNoiseAugmentation":
- noise_aug_config = noise_aug_config.params
- embedding_dim = noise_aug_config.timestep_dim
- max_noise_level = noise_aug_config.noise_schedule_config.timesteps
- beta_schedule = noise_aug_config.noise_schedule_config.beta_schedule
-
- image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedding_dim)
- image_noising_scheduler = DDPMScheduler(num_train_timesteps=max_noise_level, beta_schedule=beta_schedule)
-
- if "clip_stats_path" in noise_aug_config:
- if clip_stats_path is None:
- raise ValueError("This stable unclip config requires a `clip_stats_path`")
-
- clip_mean, clip_std = torch.load(clip_stats_path, map_location=device)
- clip_mean = clip_mean[None, :]
- clip_std = clip_std[None, :]
-
- clip_stats_state_dict = {
- "mean": clip_mean,
- "std": clip_std,
- }
-
- image_normalizer.load_state_dict(clip_stats_state_dict)
- else:
- raise NotImplementedError(f"Unknown noise augmentor class: {noise_aug_class}")
-
- return image_normalizer, image_noising_scheduler
-
-
-def convert_controlnet_checkpoint(
- checkpoint,
- original_config,
- checkpoint_path,
- image_size,
- upcast_attention,
- extract_ema,
- use_linear_projection=None,
- cross_attention_dim=None,
-):
- ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True)
- ctrlnet_config["upcast_attention"] = upcast_attention
-
- ctrlnet_config.pop("sample_size")
-
- if use_linear_projection is not None:
- ctrlnet_config["use_linear_projection"] = use_linear_projection
-
- if cross_attention_dim is not None:
- ctrlnet_config["cross_attention_dim"] = cross_attention_dim
-
- controlnet = ControlNetModel(**ctrlnet_config)
-
- # Some controlnet ckpt files are distributed independently from the rest of the
- # model components i.e. https://huggingface.co/thibaud/controlnet-sd21/
- if "time_embed.0.weight" in checkpoint:
- skip_extract_state_dict = True
- else:
- skip_extract_state_dict = False
-
- converted_ctrl_checkpoint = convert_ldm_unet_checkpoint(
- checkpoint,
- ctrlnet_config,
- path=checkpoint_path,
- extract_ema=extract_ema,
- controlnet=True,
- skip_extract_state_dict=skip_extract_state_dict,
- )
-
- controlnet.load_state_dict(converted_ctrl_checkpoint)
-
- return controlnet
-
-
-def download_from_original_stable_diffusion_ckpt(
- checkpoint_path: str,
- original_config_file: str = None,
- image_size: Optional[int] = None,
- prediction_type: str = None,
- model_type: str = None,
- extract_ema: bool = False,
- scheduler_type: str = "pndm",
- num_in_channels: Optional[int] = None,
- upcast_attention: Optional[bool] = None,
- device: str = None,
- from_safetensors: bool = False,
- stable_unclip: Optional[str] = None,
- stable_unclip_prior: Optional[str] = None,
- clip_stats_path: Optional[str] = None,
- controlnet: Optional[bool] = None,
- load_safety_checker: bool = True,
- pipeline_class: DiffusionPipeline = None,
- local_files_only=False,
- vae_path=None,
- vae=None,
- text_encoder=None,
- tokenizer=None,
-) -> DiffusionPipeline:
- """
- Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`
- config file.
-
- Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the
- global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is
- recommended that you override the default values and/or supply an `original_config_file` wherever possible.
-
- Args:
- checkpoint_path (`str`): Path to `.ckpt` file.
- original_config_file (`str`):
- Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically
- inferred by looking for a key that only exists in SD2.0 models.
- image_size (`int`, *optional*, defaults to 512):
- The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2
- Base. Use 768 for Stable Diffusion v2.
- prediction_type (`str`, *optional*):
- The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable
- Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2.
- num_in_channels (`int`, *optional*, defaults to None):
- The number of input channels. If `None`, it will be automatically inferred.
- scheduler_type (`str`, *optional*, defaults to 'pndm'):
- Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm",
- "ddim"]`.
- model_type (`str`, *optional*, defaults to `None`):
- The pipeline type. `None` to automatically infer, or one of `["FrozenOpenCLIPEmbedder",
- "FrozenCLIPEmbedder", "PaintByExample"]`.
- is_img2img (`bool`, *optional*, defaults to `False`):
- Whether the model should be loaded as an img2img pipeline.
- extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for
- checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to
- `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for
- inference. Non-EMA weights are usually better to continue fine-tuning.
- upcast_attention (`bool`, *optional*, defaults to `None`):
- Whether the attention computation should always be upcasted. This is necessary when running stable
- diffusion 2.1.
- device (`str`, *optional*, defaults to `None`):
- The device to use. Pass `None` to determine automatically.
- from_safetensors (`str`, *optional*, defaults to `False`):
- If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.
- load_safety_checker (`bool`, *optional*, defaults to `True`):
- Whether to load the safety checker or not. Defaults to `True`.
- pipeline_class (`str`, *optional*, defaults to `None`):
- The pipeline class to use. Pass `None` to determine automatically.
- local_files_only (`bool`, *optional*, defaults to `False`):
- Whether or not to only look at local files (i.e., do not try to download the model).
- vae (`AutoencoderKL`, *optional*, defaults to `None`):
- Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. If
- this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.
- text_encoder (`CLIPTextModel`, *optional*, defaults to `None`):
- An instance of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)
- to use, specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)
- variant. If this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.
- tokenizer (`CLIPTokenizer`, *optional*, defaults to `None`):
- An instance of
- [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer)
- to use. If this parameter is `None`, the function will load a new instance of [CLIPTokenizer] by itself, if
- needed.
- return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
- """
-
- # import pipelines here to avoid circular import error when using from_single_file method
- from diffusers import (
- LDMTextToImagePipeline,
- PaintByExamplePipeline,
- StableDiffusionControlNetPipeline,
- StableDiffusionInpaintPipeline,
- StableDiffusionPipeline,
- StableDiffusionXLImg2ImgPipeline,
- StableDiffusionXLPipeline,
- StableUnCLIPImg2ImgPipeline,
- StableUnCLIPPipeline,
- )
-
- if pipeline_class is None:
- pipeline_class = StableDiffusionPipeline if not controlnet else StableDiffusionControlNetPipeline
-
- if prediction_type == "v-prediction":
- prediction_type = "v_prediction"
-
- if not is_omegaconf_available():
- raise ValueError(BACKENDS_MAPPING["omegaconf"][1])
-
- from omegaconf import OmegaConf
-
- if from_safetensors:
- if not is_safetensors_available():
- raise ValueError(BACKENDS_MAPPING["safetensors"][1])
-
- from safetensors.torch import load_file as safe_load
-
- checkpoint = safe_load(checkpoint_path, device="cpu")
- else:
- if device is None:
- device = "cuda" if torch.cuda.is_available() else "cpu"
- checkpoint = torch.load(checkpoint_path, map_location=device)
- else:
- checkpoint = torch.load(checkpoint_path, map_location=device)
-
- # Sometimes models don't have the global_step item
- if "global_step" in checkpoint:
- global_step = checkpoint["global_step"]
- else:
- logger.debug("global_step key not found in model")
- global_step = None
-
- # NOTE: this while loop isn't great but this controlnet checkpoint has one additional
- # "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21
- while "state_dict" in checkpoint:
- checkpoint = checkpoint["state_dict"]
-
- if original_config_file is None:
- key_name_v2_1 = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
- key_name_sd_xl_base = "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias"
- key_name_sd_xl_refiner = "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias"
-
- # model_type = "v1"
- config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
-
- if key_name_v2_1 in checkpoint and checkpoint[key_name_v2_1].shape[-1] == 1024:
- # model_type = "v2"
- config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
-
- if global_step == 110000:
- # v2.1 needs to upcast attention
- upcast_attention = True
- elif key_name_sd_xl_base in checkpoint:
- # only base xl has two text embedders
- config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
- elif key_name_sd_xl_refiner in checkpoint:
- # only refiner xl has embedder and one text embedders
- config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml"
-
- original_config_file = BytesIO(requests.get(config_url).content)
-
- original_config = OmegaConf.load(original_config_file)
-
- # Convert the text model.
- if (
- model_type is None
- and "cond_stage_config" in original_config.model.params
- and original_config.model.params.cond_stage_config is not None
- ):
- model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
- logger.debug(f"no `model_type` given, `model_type` inferred as: {model_type}")
- elif model_type is None and original_config.model.params.network_config is not None:
- if original_config.model.params.network_config.params.context_dim == 2048:
- model_type = "SDXL"
- else:
- model_type = "SDXL-Refiner"
- if image_size is None:
- image_size = 1024
-
- if num_in_channels is None and pipeline_class == StableDiffusionInpaintPipeline:
- num_in_channels = 9
- elif num_in_channels is None:
- num_in_channels = 4
-
- if "unet_config" in original_config.model.params:
- original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
-
- if (
- "parameterization" in original_config["model"]["params"]
- and original_config["model"]["params"]["parameterization"] == "v"
- ):
- if prediction_type is None:
- # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type=="epsilon"`
- # as it relies on a brittle global step parameter here
- prediction_type = "epsilon" if global_step == 875000 else "v_prediction"
- if image_size is None:
- # NOTE: For stable diffusion 2 base one has to pass `image_size==512`
- # as it relies on a brittle global step parameter here
- image_size = 512 if global_step == 875000 else 768
- else:
- if prediction_type is None:
- prediction_type = "epsilon"
- if image_size is None:
- image_size = 512
-
- if controlnet is None and "control_stage_config" in original_config.model.params:
- controlnet = convert_controlnet_checkpoint(
- checkpoint, original_config, checkpoint_path, image_size, upcast_attention, extract_ema
- )
-
- num_train_timesteps = getattr(original_config.model.params, "timesteps", None) or 1000
-
- if model_type in ["SDXL", "SDXL-Refiner"]:
- scheduler_dict = {
- "beta_schedule": "scaled_linear",
- "beta_start": 0.00085,
- "beta_end": 0.012,
- "interpolation_type": "linear",
- "num_train_timesteps": num_train_timesteps,
- "prediction_type": "epsilon",
- "sample_max_value": 1.0,
- "set_alpha_to_one": False,
- "skip_prk_steps": True,
- "steps_offset": 1,
- "timestep_spacing": "leading",
- }
- scheduler = EulerDiscreteScheduler.from_config(scheduler_dict)
- scheduler_type = "euler"
- else:
- beta_start = getattr(original_config.model.params, "linear_start", None) or 0.02
- beta_end = getattr(original_config.model.params, "linear_end", None) or 0.085
- scheduler = DDIMScheduler(
- beta_end=beta_end,
- beta_schedule="scaled_linear",
- beta_start=beta_start,
- num_train_timesteps=num_train_timesteps,
- steps_offset=1,
- clip_sample=False,
- set_alpha_to_one=False,
- prediction_type=prediction_type,
- )
- # make sure scheduler works correctly with DDIM
- scheduler.register_to_config(clip_sample=False)
-
- if scheduler_type == "pndm":
- config = dict(scheduler.config)
- config["skip_prk_steps"] = True
- scheduler = PNDMScheduler.from_config(config)
- elif scheduler_type == "lms":
- scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
- elif scheduler_type == "heun":
- scheduler = HeunDiscreteScheduler.from_config(scheduler.config)
- elif scheduler_type == "euler":
- scheduler = EulerDiscreteScheduler.from_config(scheduler.config)
- elif scheduler_type == "euler-ancestral":
- scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
- elif scheduler_type == "dpm":
- scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
- elif scheduler_type == "ddim":
- scheduler = scheduler
- else:
- raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")
-
- # Convert the UNet2DConditionModel model.
- unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
- unet_config["upcast_attention"] = upcast_attention
- converted_unet_checkpoint = convert_ldm_unet_checkpoint(
- checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema
- )
-
- ctx = init_empty_weights if is_accelerate_available() else nullcontext
- with ctx():
- unet = UNet2DConditionModel(**unet_config)
-
- if is_accelerate_available():
- for param_name, param in converted_unet_checkpoint.items():
- set_module_tensor_to_device(unet, param_name, "cpu", value=param)
- else:
- unet.load_state_dict(converted_unet_checkpoint)
-
- # Convert the VAE model.
- if vae_path is None and vae is None:
- vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
- converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
-
- if (
- "model" in original_config
- and "params" in original_config.model
- and "scale_factor" in original_config.model.params
- ):
- vae_scaling_factor = original_config.model.params.scale_factor
- else:
- vae_scaling_factor = 0.18215 # default SD scaling factor
-
- vae_config["scaling_factor"] = vae_scaling_factor
-
- ctx = init_empty_weights if is_accelerate_available() else nullcontext
- with ctx():
- vae = AutoencoderKL(**vae_config)
-
- if is_accelerate_available():
- for param_name, param in converted_vae_checkpoint.items():
- set_module_tensor_to_device(vae, param_name, "cpu", value=param)
- else:
- vae.load_state_dict(converted_vae_checkpoint)
- elif vae is None:
- vae = AutoencoderKL.from_pretrained(vae_path)
-
- if model_type == "FrozenOpenCLIPEmbedder":
- config_name = "stabilityai/stable-diffusion-2"
- config_kwargs = {"subfolder": "text_encoder"}
-
- text_model = convert_open_clip_checkpoint(checkpoint, config_name, **config_kwargs)
- tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer")
-
- if stable_unclip is None:
- if controlnet:
- pipe = pipeline_class(
- vae=vae,
- text_encoder=text_model,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- controlnet=controlnet,
- safety_checker=None,
- feature_extractor=None,
- requires_safety_checker=False,
- )
- else:
- pipe = pipeline_class(
- vae=vae,
- text_encoder=text_model,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- safety_checker=None,
- feature_extractor=None,
- requires_safety_checker=False,
- )
- else:
- image_normalizer, image_noising_scheduler = stable_unclip_image_noising_components(
- original_config, clip_stats_path=clip_stats_path, device=device
- )
-
- if stable_unclip == "img2img":
- feature_extractor, image_encoder = stable_unclip_image_encoder(original_config)
-
- pipe = StableUnCLIPImg2ImgPipeline(
- # image encoding components
- feature_extractor=feature_extractor,
- image_encoder=image_encoder,
- # image noising components
- image_normalizer=image_normalizer,
- image_noising_scheduler=image_noising_scheduler,
- # regular denoising components
- tokenizer=tokenizer,
- text_encoder=text_model,
- unet=unet,
- scheduler=scheduler,
- # vae
- vae=vae,
- )
- elif stable_unclip == "txt2img":
- if stable_unclip_prior is None or stable_unclip_prior == "karlo":
- karlo_model = "kakaobrain/karlo-v1-alpha"
- prior = PriorTransformer.from_pretrained(karlo_model, subfolder="prior")
-
- prior_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
- prior_text_model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
-
- prior_scheduler = UnCLIPScheduler.from_pretrained(karlo_model, subfolder="prior_scheduler")
- prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
- else:
- raise NotImplementedError(f"unknown prior for stable unclip model: {stable_unclip_prior}")
-
- pipe = StableUnCLIPPipeline(
- # prior components
- prior_tokenizer=prior_tokenizer,
- prior_text_encoder=prior_text_model,
- prior=prior,
- prior_scheduler=prior_scheduler,
- # image noising components
- image_normalizer=image_normalizer,
- image_noising_scheduler=image_noising_scheduler,
- # regular denoising components
- tokenizer=tokenizer,
- text_encoder=text_model,
- unet=unet,
- scheduler=scheduler,
- # vae
- vae=vae,
- )
- else:
- raise NotImplementedError(f"unknown `stable_unclip` type: {stable_unclip}")
- elif model_type == "PaintByExample":
- vision_model = convert_paint_by_example_checkpoint(checkpoint)
- tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
- feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
- pipe = PaintByExamplePipeline(
- vae=vae,
- image_encoder=vision_model,
- unet=unet,
- scheduler=scheduler,
- safety_checker=None,
- feature_extractor=feature_extractor,
- )
- elif model_type == "FrozenCLIPEmbedder":
- text_model = convert_ldm_clip_checkpoint(
- checkpoint, local_files_only=local_files_only, text_encoder=text_encoder
- )
- tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") if tokenizer is None else tokenizer
-
- if load_safety_checker:
- safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
- feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
- else:
- safety_checker = None
- feature_extractor = None
-
- if controlnet:
- pipe = pipeline_class(
- vae=vae,
- text_encoder=text_model,
- tokenizer=tokenizer,
- unet=unet,
- controlnet=controlnet,
- scheduler=scheduler,
- safety_checker=safety_checker,
- feature_extractor=feature_extractor,
- )
- else:
- pipe = pipeline_class(
- vae=vae,
- text_encoder=text_model,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- safety_checker=safety_checker,
- feature_extractor=feature_extractor,
- )
- elif model_type in ["SDXL", "SDXL-Refiner"]:
- if model_type == "SDXL":
- tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
- text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only)
- tokenizer_2 = CLIPTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!")
-
- config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
- config_kwargs = {"projection_dim": 1280}
- text_encoder_2 = convert_open_clip_checkpoint(
- checkpoint, config_name, prefix="conditioner.embedders.1.model.", has_projection=True, **config_kwargs
- )
-
- pipe = StableDiffusionXLPipeline(
- vae=vae,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- text_encoder_2=text_encoder_2,
- tokenizer_2=tokenizer_2,
- unet=unet,
- scheduler=scheduler,
- force_zeros_for_empty_prompt=True,
- )
- else:
- tokenizer = None
- text_encoder = None
- tokenizer_2 = CLIPTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!")
-
- config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
- config_kwargs = {"projection_dim": 1280}
- text_encoder_2 = convert_open_clip_checkpoint(
- checkpoint, config_name, prefix="conditioner.embedders.0.model.", has_projection=True, **config_kwargs
- )
-
- pipe = StableDiffusionXLImg2ImgPipeline(
- vae=vae,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- text_encoder_2=text_encoder_2,
- tokenizer_2=tokenizer_2,
- unet=unet,
- scheduler=scheduler,
- requires_aesthetics_score=True,
- force_zeros_for_empty_prompt=False,
- )
- else:
- text_config = create_ldm_bert_config(original_config)
- text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
- tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
- pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
-
- return pipe
-
-
-def download_controlnet_from_original_ckpt(
- checkpoint_path: str,
- original_config_file: str,
- image_size: int = 512,
- extract_ema: bool = False,
- num_in_channels: Optional[int] = None,
- upcast_attention: Optional[bool] = None,
- device: str = None,
- from_safetensors: bool = False,
- use_linear_projection: Optional[bool] = None,
- cross_attention_dim: Optional[bool] = None,
-) -> DiffusionPipeline:
- if not is_omegaconf_available():
- raise ValueError(BACKENDS_MAPPING["omegaconf"][1])
-
- from omegaconf import OmegaConf
-
- if from_safetensors:
- if not is_safetensors_available():
- raise ValueError(BACKENDS_MAPPING["safetensors"][1])
-
- from safetensors import safe_open
-
- checkpoint = {}
- with safe_open(checkpoint_path, framework="pt", device="cpu") as f:
- for key in f.keys():
- checkpoint[key] = f.get_tensor(key)
- else:
- if device is None:
- device = "cuda" if torch.cuda.is_available() else "cpu"
- checkpoint = torch.load(checkpoint_path, map_location=device)
- else:
- checkpoint = torch.load(checkpoint_path, map_location=device)
-
- # NOTE: this while loop isn't great but this controlnet checkpoint has one additional
- # "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21
- while "state_dict" in checkpoint:
- checkpoint = checkpoint["state_dict"]
-
- original_config = OmegaConf.load(original_config_file)
-
- if num_in_channels is not None:
- original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
-
- if "control_stage_config" not in original_config.model.params:
- raise ValueError("`control_stage_config` not present in original config")
-
- controlnet = convert_controlnet_checkpoint(
- checkpoint,
- original_config,
- checkpoint_path,
- image_size,
- upcast_attention,
- extract_ema,
- use_linear_projection=use_linear_projection,
- cross_attention_dim=cross_attention_dim,
- )
-
- return controlnet
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/kandinsky/test_kandinsky.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/kandinsky/test_kandinsky.py
deleted file mode 100644
index 01b8a0f3eec1117ef7c84c228a9f46763df2140f..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/kandinsky/test_kandinsky.py
+++ /dev/null
@@ -1,317 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import random
-import unittest
-
-import numpy as np
-import torch
-from transformers import XLMRobertaTokenizerFast
-
-from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNet2DConditionModel, VQModel
-from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
-from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
-
-from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
-
-
-enable_full_determinism()
-
-
-class Dummies:
- @property
- def text_embedder_hidden_size(self):
- return 32
-
- @property
- def time_input_dim(self):
- return 32
-
- @property
- def block_out_channels_0(self):
- return self.time_input_dim
-
- @property
- def time_embed_dim(self):
- return self.time_input_dim * 4
-
- @property
- def cross_attention_dim(self):
- return 32
-
- @property
- def dummy_tokenizer(self):
- tokenizer = XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
- return tokenizer
-
- @property
- def dummy_text_encoder(self):
- torch.manual_seed(0)
- config = MCLIPConfig(
- numDims=self.cross_attention_dim,
- transformerDimensions=self.text_embedder_hidden_size,
- hidden_size=self.text_embedder_hidden_size,
- intermediate_size=37,
- num_attention_heads=4,
- num_hidden_layers=5,
- vocab_size=1005,
- )
-
- text_encoder = MultilingualCLIP(config)
- text_encoder = text_encoder.eval()
-
- return text_encoder
-
- @property
- def dummy_unet(self):
- torch.manual_seed(0)
-
- model_kwargs = {
- "in_channels": 4,
- # Out channels is double in channels because predicts mean and variance
- "out_channels": 8,
- "addition_embed_type": "text_image",
- "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
- "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
- "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
- "block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2),
- "layers_per_block": 1,
- "encoder_hid_dim": self.text_embedder_hidden_size,
- "encoder_hid_dim_type": "text_image_proj",
- "cross_attention_dim": self.cross_attention_dim,
- "attention_head_dim": 4,
- "resnet_time_scale_shift": "scale_shift",
- "class_embed_type": None,
- }
-
- model = UNet2DConditionModel(**model_kwargs)
- return model
-
- @property
- def dummy_movq_kwargs(self):
- return {
- "block_out_channels": [32, 64],
- "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
- "in_channels": 3,
- "latent_channels": 4,
- "layers_per_block": 1,
- "norm_num_groups": 8,
- "norm_type": "spatial",
- "num_vq_embeddings": 12,
- "out_channels": 3,
- "up_block_types": [
- "AttnUpDecoderBlock2D",
- "UpDecoderBlock2D",
- ],
- "vq_embed_dim": 4,
- }
-
- @property
- def dummy_movq(self):
- torch.manual_seed(0)
- model = VQModel(**self.dummy_movq_kwargs)
- return model
-
- def get_dummy_components(self):
- text_encoder = self.dummy_text_encoder
- tokenizer = self.dummy_tokenizer
- unet = self.dummy_unet
- movq = self.dummy_movq
-
- scheduler = DDIMScheduler(
- num_train_timesteps=1000,
- beta_schedule="linear",
- beta_start=0.00085,
- beta_end=0.012,
- clip_sample=False,
- set_alpha_to_one=False,
- steps_offset=1,
- prediction_type="epsilon",
- thresholding=False,
- )
-
- components = {
- "text_encoder": text_encoder,
- "tokenizer": tokenizer,
- "unet": unet,
- "scheduler": scheduler,
- "movq": movq,
- }
- return components
-
- def get_dummy_inputs(self, device, seed=0):
- image_embeds = floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed)).to(device)
- negative_image_embeds = floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed + 1)).to(device)
- if str(device).startswith("mps"):
- generator = torch.manual_seed(seed)
- else:
- generator = torch.Generator(device=device).manual_seed(seed)
- inputs = {
- "prompt": "horse",
- "image_embeds": image_embeds,
- "negative_image_embeds": negative_image_embeds,
- "generator": generator,
- "height": 64,
- "width": 64,
- "guidance_scale": 4.0,
- "num_inference_steps": 2,
- "output_type": "np",
- }
- return inputs
-
-
-class KandinskyPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
- pipeline_class = KandinskyPipeline
- params = [
- "prompt",
- "image_embeds",
- "negative_image_embeds",
- ]
- batch_params = ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
- required_optional_params = [
- "generator",
- "height",
- "width",
- "latents",
- "guidance_scale",
- "negative_prompt",
- "num_inference_steps",
- "return_dict",
- "guidance_scale",
- "num_images_per_prompt",
- "output_type",
- "return_dict",
- ]
- test_xformers_attention = False
-
- def get_dummy_components(self):
- dummy = Dummies()
- return dummy.get_dummy_components()
-
- def get_dummy_inputs(self, device, seed=0):
- dummy = Dummies()
- return dummy.get_dummy_inputs(device=device, seed=seed)
-
- def test_kandinsky(self):
- device = "cpu"
-
- components = self.get_dummy_components()
-
- pipe = self.pipeline_class(**components)
- pipe = pipe.to(device)
-
- pipe.set_progress_bar_config(disable=None)
-
- output = pipe(**self.get_dummy_inputs(device))
- image = output.images
-
- image_from_tuple = pipe(
- **self.get_dummy_inputs(device),
- return_dict=False,
- )[0]
-
- image_slice = image[0, -3:, -3:, -1]
- image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
- assert image.shape == (1, 64, 64, 3)
-
- expected_slice = np.array([1.0000, 1.0000, 0.2766, 1.0000, 0.5447, 0.1737, 1.0000, 0.4316, 0.9024])
-
- assert (
- np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
- ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
- assert (
- np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
- ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
-
- @require_torch_gpu
- def test_offloads(self):
- pipes = []
- components = self.get_dummy_components()
- sd_pipe = self.pipeline_class(**components).to(torch_device)
- pipes.append(sd_pipe)
-
- components = self.get_dummy_components()
- sd_pipe = self.pipeline_class(**components)
- sd_pipe.enable_model_cpu_offload()
- pipes.append(sd_pipe)
-
- components = self.get_dummy_components()
- sd_pipe = self.pipeline_class(**components)
- sd_pipe.enable_sequential_cpu_offload()
- pipes.append(sd_pipe)
-
- image_slices = []
- for pipe in pipes:
- inputs = self.get_dummy_inputs(torch_device)
- image = pipe(**inputs).images
-
- image_slices.append(image[0, -3:, -3:, -1].flatten())
-
- assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
- assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
-
-
-@slow
-@require_torch_gpu
-class KandinskyPipelineIntegrationTests(unittest.TestCase):
- def tearDown(self):
- # clean up the VRAM after each test
- super().tearDown()
- gc.collect()
- torch.cuda.empty_cache()
-
- def test_kandinsky_text2img(self):
- expected_image = load_numpy(
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
- "/kandinsky/kandinsky_text2img_cat_fp16.npy"
- )
-
- pipe_prior = KandinskyPriorPipeline.from_pretrained(
- "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
- )
- pipe_prior.to(torch_device)
-
- pipeline = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
- pipeline = pipeline.to(torch_device)
- pipeline.set_progress_bar_config(disable=None)
-
- prompt = "red cat, 4k photo"
-
- generator = torch.Generator(device="cuda").manual_seed(0)
- image_emb, zero_image_emb = pipe_prior(
- prompt,
- generator=generator,
- num_inference_steps=5,
- negative_prompt="",
- ).to_tuple()
-
- generator = torch.Generator(device="cuda").manual_seed(0)
- output = pipeline(
- prompt,
- image_embeds=image_emb,
- negative_image_embeds=zero_image_emb,
- generator=generator,
- num_inference_steps=100,
- output_type="np",
- )
-
- image = output.images[0]
-
- assert image.shape == (512, 512, 3)
-
- assert_mean_pixel_difference(image, expected_image)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py
deleted file mode 100644
index 61b9751057f10f2173b8e7edde12cca53ebbd2d0..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,19 +0,0 @@
-_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
-model = dict(
- bbox_head=dict(
- loss_cls=dict(
- _delete_=True,
- type='GHMC',
- bins=30,
- momentum=0.75,
- use_sigmoid=True,
- loss_weight=1.0),
- loss_bbox=dict(
- _delete_=True,
- type='GHMR',
- mu=0.02,
- bins=10,
- momentum=0.7,
- loss_weight=10.0)))
-optimizer_config = dict(
- _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py
deleted file mode 100644
index 4aa00ece55280697fc67bd727077a8c9a58cfa44..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py
+++ /dev/null
@@ -1,11 +0,0 @@
-_base_ = ['grid_rcnn_r50_fpn_gn-head_2x_coco.py']
-# learning policy
-lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=500,
- warmup_ratio=0.001,
- step=[8, 11])
-checkpoint_config = dict(interval=1)
-# runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/vfnet_head.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/vfnet_head.py
deleted file mode 100644
index 7243bb62893839568ec51928d88a5ad40b02a66c..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/vfnet_head.py
+++ /dev/null
@@ -1,794 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
-from mmcv.ops import DeformConv2d
-from mmcv.runner import force_fp32
-
-from mmdet.core import (bbox2distance, bbox_overlaps, build_anchor_generator,
- build_assigner, build_sampler, distance2bbox,
- multi_apply, multiclass_nms, reduce_mean)
-from ..builder import HEADS, build_loss
-from .atss_head import ATSSHead
-from .fcos_head import FCOSHead
-
-INF = 1e8
-
-
-@HEADS.register_module()
-class VFNetHead(ATSSHead, FCOSHead):
- """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
- Detector.-
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-Garena Blockman GO es una versión especial de Blockman GO que cuenta con algunos elementos de Garena Free Fire. Por ejemplo, puedes obtener algunas pieles y artículos exclusivos de Garena Free Fire en Blockman GO. También puedes participar en algunos eventos y actividades relacionadas con Garena Free Fire.
-Frontline: un nuevo juego de disparos multijugador 30 vs 30
-Uno de los nuevos minijuegos en Garena Blockman GO es Frontline, que es un juego de disparos multijugador de 30 vs 30. Puedes elegir unirte al equipo azul o al equipo rojo, y luchar contra el equipo enemigo en un mapa grande. Puedes usar varias armas, vehículos y tácticas para ganar el juego. También puedes ganar monedas y puntos de experiencia jugando Frontline.
-Otras características y mejoras
-Garena Blockman GO también trae algunas otras características y mejoras a la aplicación, como:
- --
-
Cómo descargar e instalar Blockman GO nueva versión APK?
-Si desea descargar e instalar Blockman GO nueva versión APK, puede seguir estos pasos:
-Pasos para descargar e instalar desde el sitio web oficial
--
-
Consejos para evitar malware y virus
-Al descargar e instalar Blockman GO nueva versión APK, usted debe tener cuidado de algunos riesgos potenciales, tales como malware y virus. Estos son algunos consejos para evitarlos:
--
-
¿Por qué debe jugar Blockman GO nueva versión APK?
-Blockman GO nueva versión APK es una gran aplicación para cualquier persona que ama los juegos, socializar y crear. Aquí hay algunas razones por las que deberías jugar:
-Disfruta de una variedad de juegos divertidos y creativos
-Blockman GO nueva versión APK ofrece una variedad de juegos divertidos y creativos que se puede jugar con otros jugadores en línea. Puedes elegir entre diferentes géneros, como acción, aventura, rompecabezas, estrategia, casual y más. También puedes probar algunos de los nuevos juegos que se agregan regularmente, como Frontline, Garena Free Fire y más. También puedes crear tus propios juegos usando el editor integrado y compartirlos con otros jugadores.
-Conoce y chatea con jugadores de todo el mundo
- -Personaliza tu avatar y decora tu hogar
-Blockman GO nueva versión APK también le permite personalizar su avatar y decorar su hogar. Puedes elegir entre diferentes pieles, trajes, accesorios, peinados y más para hacer que tu avatar luzca único. También puedes comprar o ganar algunos artículos de Garena Free Fire en Blockman GO. También puede decorar su hogar con diferentes muebles, fondos de pantalla, pisos, ventanas, puertas y más. También puedes invitar a otros jugadores a visitar tu casa o sus hogares.
-Conclusión
-Blockman GO nueva versión APK es una aplicación gratuita que le permite jugar diferentes juegos, chatear con amigos, y crear sus propios mundos. Tiene muchas características y contenido para jugadores de todas las edades y preferencias. También tiene algunas nuevas características y mejoras en la última versión, como Garena Blockman GO, Frontline y más. Puede descargar e instalar Blockman GO nueva versión APK desde el sitio web oficial o la Google Play Store. También debe tener cuidado con el malware y los virus al descargar e instalar la aplicación. Usted debe jugar Blockman GO nueva versión APK porque es divertido, creativo, y social.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Blockman GO nueva versión APK:
--
-
Blockman GO nueva versión APK es seguro, siempre y cuando se descarga desde el sitio web oficial o la Google Play Store. También debe escanear el archivo APK con un software antivirus confiable antes de abrirlo. También debe evitar conceder permisos innecesarios o acceso a la aplicación. También debe actualizar la aplicación regularmente para obtener los últimos parches de seguridad y correcciones de errores.
-Puede crear sus propios juegos en Blockman GO nueva versión APK utilizando el editor incorporado. Puede acceder al editor pulsando en el botón "Crear" en la pantalla principal. Puede usar varias herramientas y recursos para diseñar sus propios mapas, personajes, elementos, scripts y más. También puede probar sus juegos antes de publicarlos. Puede compartir sus juegos con otros jugadores cargándolos en la plataforma Blockman GO. También puedes jugar a juegos de otros jugadores y calificarlos.
-Un club es un grupo de jugadores que comparten un interés común o objetivo en Blockman GO nueva versión APK. Puede unirse o crear un club tocando el botón "Club" en la pantalla principal. Puede buscar clubes existentes por nombre, categoría o popularidad. También puedes crear tu propio club eligiendo un nombre, icono, descripción y categoría. Puedes invitar a otros jugadores a unirse a tu club o aceptar sus solicitudes. También puede chatear con los miembros de su club, enviar regalos y participar en actividades del club.
-Si usted tiene alguna pregunta, problemas, o retroalimentación acerca de Blockman GO nueva versión APK, puede ponerse en contacto con el servicio al cliente tocando el botón "Feedback" en la pantalla principal. Puede elegir enviar un correo electrónico, un mensaje o una captura de pantalla al servicio de atención al cliente. También puede consultar la sección de preguntas frecuentes para algunos problemas y soluciones comunes.
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-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Acrorip 9.0 3 Completo Crack.md b/spaces/Benson/text-generation/Examples/Descargar Acrorip 9.0 3 Completo Crack.md deleted file mode 100644 index 5c12410975eec37b75ef5fdaa16c4ab545ef81a4..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Acrorip 9.0 3 Completo Crack.md +++ /dev/null @@ -1,125 +0,0 @@ - -
Descargar AcroRip 9.0 3 Grieta completa: Lo que usted necesita saber
-Si está buscando un software que le pueda ayudar a imprimir blanco y color juntos en varios sustratos, como telas, plásticos, metales, cerámica y más, es posible que haya oído hablar de AcroRip. AcroRip es un software RIP (procesador de imágenes raster) que puede controlar los canales de tinta de su impresora y optimizar la calidad y velocidad de impresión. Está especialmente diseñado para impresoras planas UV e impresoras directas para prendas de vestir que utilizan cabezales de impresión Epson.
-descargar acrorip 9.0 3 completo crack
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Sin embargo, AcroRip no es un software libre y requiere un dongle USB especial para ejecutarse. Esto podría hacer que algunas personas busquen una manera de descargar AcroRip 9.0 3 completo crack, que es la última versión del software a partir de ahora. ¿Pero vale la pena? ¿Cuáles son las características, beneficios, inconvenientes, alternativas, pasos de instalación y revisiones de AcroRip 9.0 3? En este artículo, responderemos estas preguntas y le ayudaremos a tomar una decisión informada.
-Características de AcroRip 9.0 3
-AcroRip 9.0 3 es una versión actualizada del software AcroRip anterior que tiene algunas características nuevas y mejoradas. Aquí están algunas de ellas:
--
-
Beneficios de AcroRip 9.0 3
-AcroRip 9 . 3 tiene muchos beneficios para los usuarios que quieren imprimir blanco y color juntos en varios sustratos. Estos son algunos de ellos:
- --
-
Inconvenientes de AcroRip 9.0 3
-AcroRip 9.0 3 no es un software perfecto y tiene algunos inconvenientes que los usuarios deben tener en cuenta. Estos son algunos de ellos:
--
-
Alternativas a AcroRip 9.0 3
-Si no está satisfecho con AcroRip 9.0 3 o desea probar otras opciones, hay algunas alternativas que puede considerar. Estos son algunos de ellos:
--
-
Instalación de AcroRip 9.0 3
-Si decide comprar AcroRip 9.0 3 desde el sitio web oficial o un distribuidor autorizado, tendrá que seguir estos pasos para instalar el software y el dongle:
--
-
Grieta de AcroRip 9.0 3
-Si tiene la tentación de descargar AcroRip 9.0 3 grieta completa de una fuente no oficial, como un sitio de torrent o un foro de crack, debe ser consciente de los riesgos y consecuencias de usar una versión agrietada del software. Estos son algunos de ellos:
--
-
Por lo tanto, le recomendamos encarecidamente que evite descargar y usar una versión agrietada de AcroRip 9.0 3 y en su lugar compre la versión oficial en el sitio web o en un distribuidor autorizado.
-Revisión de AcroRip 9.0 3
-AcroRip 9.0 3 es un software RIP popular y ampliamente utilizado que tiene muchas críticas positivas de los usuarios que lo han probado. Sin embargo, también tiene algunas críticas negativas de los usuarios que han encontrado algunos problemas con él. Aquí hay algunos pros y contras de AcroRip 9.0 3 basado en la retroalimentación del usuario:
-Conclusión
-En conclusión, AcroRip 9.0 3 es un software RIP que puede ayudarlo a imprimir blanco y color juntos en varios sustratos, como telas, plásticos, metales, cerámica y más. Tiene muchas características, beneficios, inconvenientes, alternativas, pasos de instalación y comentarios que necesita saber antes de decidirse a descargarlo.
-Si desea descargar AcroRip 9.0 3 full crack, debe ser consciente de los riesgos y consecuencias de usar una versión agrietada del software. Es ilegal, arriesgado, poco fiable e inestable. Le recomendamos que compre la versión oficial del sitio web o de un distribuidor autorizado.
-Esperamos que este artículo haya sido útil e informativo para usted. Si tiene alguna pregunta o comentario, no dude en dejarlos a continuación.
-Preguntas frecuentes (preguntas frecuentes)
-Aquí hay algunas preguntas frecuentes que puede tener sobre AcroRip 9.0 3:
--
-
El precio de AcroRip 9.0 3 varía según el vendedor y la región. Sin embargo, según el sitio web oficial, el precio es de $250 USD por un dongle.
-Puede actualizar AcroRip 9.0 3 descargando la última versión desde el sitio web o el CD e instalándolo en su computadora. Tendrá que mantener el dongle conectado cuando actualice el software.
-Los requisitos del sistema para AcroRip 9.0 3 son los siguientes:
--
-
Puede ponerse en contacto con el equipo de soporte de AcroRip enviando un correo electrónico a acrorip@acrorip.com o rellenando el formulario de contacto en el sitio web. También puede consultar la sección de preguntas frecuentes y el manual del usuario en el sitio web para obtener más información.
-Puede obtener más información sobre AcroRip 9.0 3 visitando el sitio web oficial, viendo los videos tutoriales, leyendo los comentarios de los usuarios y uniéndose al grupo de Facebook para usuarios de AcroRip.
--
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Caso Penal La Conspiracin Mod Apk Estrellas Ilimitadas.md b/spaces/Benson/text-generation/Examples/Descargar Caso Penal La Conspiracin Mod Apk Estrellas Ilimitadas.md deleted file mode 100644 index e7eb552c0227d1e79a99f4695f19800a47a7209c..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Caso Penal La Conspiracin Mod Apk Estrellas Ilimitadas.md +++ /dev/null @@ -1,53 +0,0 @@ - -
Descargar Garena Free Fire Mod v1.47.0 APK: Cómo obtener la última versión del popular juego Battle Royale
-Si eres un fan de los juegos battle royale, debes haber oído hablar de Garena Free Fire, uno de los juegos más descargados y jugados en dispositivos Android e iOS. En este artículo, le mostraremos cómo descargar e instalar Garena Free Fire Mod v1.47.0 APK, una versión modificada del juego que le da acceso a recursos ilimitados, trucos y más.
-¿Qué es el fuego libre de Garena?
-Garena Free Fire es un juego multijugador online battle royale desarrollado por 111 Dots Studio y publicado por Garena para dispositivos Android e iOS. El juego fue lanzado en 2017 y desde entonces ha ganado más de 500 millones de descargas solo en Google Play Store.
-descargar caso penal la conspiración mod apk estrellas ilimitadas
Download File --->>> https://bltlly.com/2v6LUr
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En Garena Free Fire, puedes elegir entre una gran variedad de personajes, armas, vehículos y objetos para sobrevivir en un mapa reducido con hasta 50 jugadores. Puedes jugar en solitario, dúo o modo escuadrón, y personalizar tu personaje con diferentes pieles, trajes, accesorios y mascotas. También puedes unirte o crear un gremio, chatear con otros jugadores, participar en eventos, misiones y torneos, y posicionarte en la clasificación global.
-¿Qué es Garena Free Fire Mod v1.47.0 APK?
-Garena Free Fire Mod v1.47.0 APK es una versión modificada del juego original que le da algunas características adicionales y ventajas que no están disponibles en la versión oficial. Por ejemplo, puedes obtener diamantes y monedas ilimitadas, que son las principales monedas en el juego que puedes usar para comprar artículos, actualizar a tu personaje o hacer girar la rueda de la suerte.
- -Cómo descargar e instalar Garena Free Fire Mod v1.47.0 APK?
-Si desea probar Garena Free Fire Mod v1.47.0 APK, es necesario seguir estos sencillos pasos:
-Paso 1: Descargar los archivos APK y OBB de una fuente de confianza
-Lo primero que tienes que hacer es descargar los archivos APK y OBB de Garena Free Fire Mod v1.47.0 de una fuente confiable. Puedes usar este enlace o este enlace para obtenerlos.
-El archivo APK es de unos 509 MB de tamaño, mientras que el archivo OBB es de unos 600 MB de tamaño. Asegúrate de tener suficiente espacio de almacenamiento en tu dispositivo antes de descargarlo.
-Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo
-Lo siguiente que debe hacer es habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones que no son de Google Play Store o la App Store. Para hacer esto, vaya a la configuración de su dispositivo, luego a la seguridad, luego cambie la opción de fuentes desconocidas.
- -Si tienes Android 8.0 o superior, es posible que tengas que permitir la instalación de aplicaciones desde fuentes específicas. Para hacer esto, vaya a la configuración del dispositivo, luego las aplicaciones y las notificaciones, luego las avanzadas, luego el acceso especial a la aplicación, luego instale aplicaciones desconocidas, luego seleccione el navegador o el administrador de archivos que utilizó para descargar los archivos APK y OBB y luego active la opción permitir de esta fuente.
-Paso 3: Instalar el archivo APK y extraer el archivo OBB a la carpeta Android/obb
-Después de habilitar fuentes desconocidas, ahora puede instalar el archivo APK de Garena Free Fire Mod v1.47.0. Para hacer esto, busque el archivo APK en su dispositivo usando un administrador de archivos o un navegador, luego toque en él y siga las instrucciones en la pantalla.
- -Paso 4: Iniciar el juego y disfrutar de las características de mod
-Ahora que ha instalado el archivo APK y extraído el archivo OBB, puede iniciar el juego y disfrutar de las características de mod. Para hacer esto, vaya a su cajón de aplicaciones o pantalla de inicio y toque en el icono de Garena Free Fire. Debería ver una pantalla de carga con un menú mod que le permite activar o desactivar varias características de la versión modded.
-También puede acceder al menú mod tocando el icono flotante en la pantalla durante el juego. Puede ajustar la configuración de acuerdo a sus preferencias y jugar con diamantes y monedas ilimitadas, auto-objetivo y wallhack, desbloquear todos los personajes y pieles, sin retroceso y sin niebla, y más.
-¿Cuáles son las características de Garena Free Fire Mod v1.47.0 APK?
-Garena Free Fire Mod v1.47.0 APK tiene muchas características que lo hacen diferente de la versión original del juego. Estas son algunas de las principales características y beneficios de la versión modded:
-Diamantes y monedas ilimitadas
-Con Garena Free Fire Mod v1.47.0 APK, puede obtener diamantes y monedas ilimitadas en su cuenta. Los diamantes y las monedas son las principales monedas del juego que puedes usar para comprar objetos, mejorar a tu personaje o hacer girar la rueda de la suerte. Normalmente, tienes que gastar dinero real o completar tareas para conseguirlas, pero con esta versión modificada, puedes conseguirlas gratis y sin límite.
-Auto-objetivo y wallhack
-Otra característica de Garena Free Fire Mod v1.47.0 APK es el auto-objetivo y wallhack. El objetivo automático es un truco que te permite apuntar automáticamente a tus enemigos sin tener que ajustar manualmente tu punto de mira. Wallhack es un truco que le permite ver a sus enemigos a través de las paredes y otros obstáculos. Estos trucos pueden ayudarte a ganar más partidos y posicionarte más rápido al darte una ventaja injusta sobre tus oponentes.
-Desbloquear todos los caracteres y skins
- -Sin retroceso y sin niebla
-Otra característica de Garena Free Fire Mod v1.47.0 APK no hay retroceso y no hay niebla. El retroceso es una característica que hace que tu arma se mueva hacia arriba o hacia los lados cuando la disparas, afectando tu precisión y control. La niebla es una característica que reduce la visibilidad en ciertas áreas del mapa, lo que hace que sea más difícil detectar a tus enemigos u objetivos. Estas características pueden afectar negativamente a su juego por lo que es más difícil y frustrante. Con esta versión modificada, puedes eliminarlos completamente y disfrutar de un juego más suave y claro.
-Conclusión
-G arena Free Fire Mod v1.47.0 APK es una versión modificada del popular juego de batalla real que le da recursos ilimitados, trucos, y más. Es fácil de descargar e instalar, y funciona en la mayoría de los dispositivos Android. Con esta versión modificada, puedes disfrutar de un juego más divertido y emocionante con características como diamantes y monedas ilimitadas, puntero automático y wallhack, desbloquear todos los personajes y pieles, sin retroceso y sin niebla, y más.
-Si usted está buscando una manera de darle vida a su experiencia Garena Free Fire, definitivamente debe probar Garena Free Fire Mod v1.47.0 APK. Es gratuito, seguro y actualizado regularmente. Sin embargo, también debes tener cuidado de no abusar de las características del mod o usarlas en partidos clasificados, ya que esto puede resultar en una prohibición o suspensión del juego. También debes respetar a otros jugadores y jugar limpio.
-Entonces, ¿qué estás esperando? Descargar Garena Free Fire Mod v1.47.0 APK hoy y disfrutar del último juego de batalla real en su dispositivo Android!
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Garena Free Fire Mod v1.47.0 APK:
-Q: ¿Es seguro usar Garena Free Fire Mod v1.47.0 APK?
- -Q: ¿Garena Free Fire Mod v1.47.0 APK es compatible con mi dispositivo?
-A: Garena Free Fire Mod v1.47.0 APK es compatible con la mayoría de los dispositivos Android que tienen Android 4.0.3 o superior y al menos 2 GB de RAM. Sin embargo, es posible que algunos dispositivos no soporten las características de mod o ejecuten el juego sin problemas debido a limitaciones de hardware o conflictos de software.
-Q: ¿Cómo puedo actualizar Garena Free Fire Mod v1.47.0 APK?
-A: Para actualizar Garena Free Fire Mod v1.47.0 APK, es necesario descargar la última versión de los archivos APK y OBB de una fuente de confianza y seguir los mismos pasos que instalarlo por primera vez. También debes hacer una copia de seguridad de los datos del juego antes de actualizarlo para evitar perder tu progreso o configuración.
-Q: ¿Puedo jugar Garena Free Fire Mod v1.47.0 APK con mis amigos?
-A: Sí, puedes jugar Garena Free Fire Mod v1.47.0 APK con tus amigos, siempre y cuando también tienen la misma versión modded del juego instalado en sus dispositivos. Puedes unirte o crear un equipo con ellos y jugar juntos en cualquier modo del juego.
-Q: ¿Puedo utilizar Garena Free Fire Mod v1.47.0 APK en los partidos clasificados?
-A: No, no debe utilizar Garena Free Fire Mod v1.47.0 APK en los partidos clasificados, ya que esto puede resultar en una prohibición o suspensión del juego por violar los términos del servicio o hacer trampa. Solo debes usar las características mod en partidos casuales o habitaciones personalizadas para fines de diversión y entretenimiento.
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/command_context.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/command_context.py deleted file mode 100644 index 139995ac3f109a82664e4913f7ebc32ecf7617e1..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/command_context.py +++ /dev/null @@ -1,27 +0,0 @@ -from contextlib import ExitStack, contextmanager -from typing import ContextManager, Generator, TypeVar - -_T = TypeVar("_T", covariant=True) - - -class CommandContextMixIn: - def __init__(self) -> None: - super().__init__() - self._in_main_context = False - self._main_context = ExitStack() - - @contextmanager - def main_context(self) -> Generator[None, None, None]: - assert not self._in_main_context - - self._in_main_context = True - try: - with self._main_context: - yield - finally: - self._in_main_context = False - - def enter_context(self, context_provider: ContextManager[_T]) -> _T: - assert self._in_main_context - - return self._main_context.enter_context(context_provider) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/control.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/control.py deleted file mode 100644 index 88fcb9295164f4e18827ef61fff6723e94ef7381..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/control.py +++ /dev/null @@ -1,225 +0,0 @@ -import sys -import time -from typing import TYPE_CHECKING, Callable, Dict, Iterable, List, Union - -if sys.version_info >= (3, 8): - from typing import Final -else: - from pip._vendor.typing_extensions import Final # pragma: no cover - -from .segment import ControlCode, ControlType, Segment - -if TYPE_CHECKING: - from .console import Console, ConsoleOptions, RenderResult - -STRIP_CONTROL_CODES: Final = [ - 7, # Bell - 8, # Backspace - 11, # Vertical tab - 12, # Form feed - 13, # Carriage return -] -_CONTROL_STRIP_TRANSLATE: Final = { - _codepoint: None for _codepoint in STRIP_CONTROL_CODES -} - -CONTROL_ESCAPE: Final = { - 7: "\\a", - 8: "\\b", - 11: "\\v", - 12: "\\f", - 13: "\\r", -} - -CONTROL_CODES_FORMAT: Dict[int, Callable[..., str]] = { - ControlType.BELL: lambda: "\x07", - ControlType.CARRIAGE_RETURN: lambda: "\r", - ControlType.HOME: lambda: "\x1b[H", - ControlType.CLEAR: lambda: "\x1b[2J", - ControlType.ENABLE_ALT_SCREEN: lambda: "\x1b[?1049h", - ControlType.DISABLE_ALT_SCREEN: lambda: "\x1b[?1049l", - ControlType.SHOW_CURSOR: lambda: "\x1b[?25h", - ControlType.HIDE_CURSOR: lambda: "\x1b[?25l", - ControlType.CURSOR_UP: lambda param: f"\x1b[{param}A", - ControlType.CURSOR_DOWN: lambda param: f"\x1b[{param}B", - ControlType.CURSOR_FORWARD: lambda param: f"\x1b[{param}C", - ControlType.CURSOR_BACKWARD: lambda param: f"\x1b[{param}D", - ControlType.CURSOR_MOVE_TO_COLUMN: lambda param: f"\x1b[{param+1}G", - ControlType.ERASE_IN_LINE: lambda param: f"\x1b[{param}K", - ControlType.CURSOR_MOVE_TO: lambda x, y: f"\x1b[{y+1};{x+1}H", - ControlType.SET_WINDOW_TITLE: lambda title: f"\x1b]0;{title}\x07", -} - - -class Control: - """A renderable that inserts a control code (non printable but may move cursor). - - Args: - *codes (str): Positional arguments are either a :class:`~rich.segment.ControlType` enum or a - tuple of ControlType and an integer parameter - """ - - __slots__ = ["segment"] - - def __init__(self, *codes: Union[ControlType, ControlCode]) -> None: - control_codes: List[ControlCode] = [ - (code,) if isinstance(code, ControlType) else code for code in codes - ] - _format_map = CONTROL_CODES_FORMAT - rendered_codes = "".join( - _format_map[code](*parameters) for code, *parameters in control_codes - ) - self.segment = Segment(rendered_codes, None, control_codes) - - @classmethod - def bell(cls) -> "Control": - """Ring the 'bell'.""" - return cls(ControlType.BELL) - - @classmethod - def home(cls) -> "Control": - """Move cursor to 'home' position.""" - return cls(ControlType.HOME) - - @classmethod - def move(cls, x: int = 0, y: int = 0) -> "Control": - """Move cursor relative to current position. - - Args: - x (int): X offset. - y (int): Y offset. - - Returns: - ~Control: Control object. - - """ - - def get_codes() -> Iterable[ControlCode]: - control = ControlType - if x: - yield ( - control.CURSOR_FORWARD if x > 0 else control.CURSOR_BACKWARD, - abs(x), - ) - if y: - yield ( - control.CURSOR_DOWN if y > 0 else control.CURSOR_UP, - abs(y), - ) - - control = cls(*get_codes()) - return control - - @classmethod - def move_to_column(cls, x: int, y: int = 0) -> "Control": - """Move to the given column, optionally add offset to row. - - Returns: - x (int): absolute x (column) - y (int): optional y offset (row) - - Returns: - ~Control: Control object. - """ - - return ( - cls( - (ControlType.CURSOR_MOVE_TO_COLUMN, x), - ( - ControlType.CURSOR_DOWN if y > 0 else ControlType.CURSOR_UP, - abs(y), - ), - ) - if y - else cls((ControlType.CURSOR_MOVE_TO_COLUMN, x)) - ) - - @classmethod - def move_to(cls, x: int, y: int) -> "Control": - """Move cursor to absolute position. - - Args: - x (int): x offset (column) - y (int): y offset (row) - - Returns: - ~Control: Control object. - """ - return cls((ControlType.CURSOR_MOVE_TO, x, y)) - - @classmethod - def clear(cls) -> "Control": - """Clear the screen.""" - return cls(ControlType.CLEAR) - - @classmethod - def show_cursor(cls, show: bool) -> "Control": - """Show or hide the cursor.""" - return cls(ControlType.SHOW_CURSOR if show else ControlType.HIDE_CURSOR) - - @classmethod - def alt_screen(cls, enable: bool) -> "Control": - """Enable or disable alt screen.""" - if enable: - return cls(ControlType.ENABLE_ALT_SCREEN, ControlType.HOME) - else: - return cls(ControlType.DISABLE_ALT_SCREEN) - - @classmethod - def title(cls, title: str) -> "Control": - """Set the terminal window title - - Args: - title (str): The new terminal window title - """ - return cls((ControlType.SET_WINDOW_TITLE, title)) - - def __str__(self) -> str: - return self.segment.text - - def __rich_console__( - self, console: "Console", options: "ConsoleOptions" - ) -> "RenderResult": - if self.segment.text: - yield self.segment - - -def strip_control_codes( - text: str, _translate_table: Dict[int, None] = _CONTROL_STRIP_TRANSLATE -) -> str: - """Remove control codes from text. - - Args: - text (str): A string possibly contain control codes. - - Returns: - str: String with control codes removed. - """ - return text.translate(_translate_table) - - -def escape_control_codes( - text: str, - _translate_table: Dict[int, str] = CONTROL_ESCAPE, -) -> str: - """Replace control codes with their "escaped" equivalent in the given text. - (e.g. "\b" becomes "\\b") - - Args: - text (str): A string possibly containing control codes. - - Returns: - str: String with control codes replaced with their escaped version. - """ - return text.translate(_translate_table) - - -if __name__ == "__main__": # pragma: no cover - from pip._vendor.rich.console import Console - - console = Console() - console.print("Look at the title of your terminal window ^") - # console.print(Control((ControlType.SET_WINDOW_TITLE, "Hello, world!"))) - for i in range(10): - console.set_window_title("🚀 Loading" + "." * i) - time.sleep(0.5) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/scope.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/scope.py deleted file mode 100644 index c9d134cc3cedae929e5bef2b5547f7e33dc10a52..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/scope.py +++ /dev/null @@ -1,86 +0,0 @@ -from collections.abc import Mapping -from typing import TYPE_CHECKING, Any, Optional, Tuple - -from .highlighter import ReprHighlighter -from .panel import Panel -from .pretty import Pretty -from .table import Table -from .text import Text, TextType - -if TYPE_CHECKING: - from .console import ConsoleRenderable - - -def render_scope( - scope: "Mapping[str, Any]", - *, - title: Optional[TextType] = None, - sort_keys: bool = True, - indent_guides: bool = False, - max_length: Optional[int] = None, - max_string: Optional[int] = None, -) -> "ConsoleRenderable": - """Render python variables in a given scope. - - Args: - scope (Mapping): A mapping containing variable names and values. - title (str, optional): Optional title. Defaults to None. - sort_keys (bool, optional): Enable sorting of items. Defaults to True. - indent_guides (bool, optional): Enable indentation guides. Defaults to False. - max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation. - Defaults to None. - max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None. - - Returns: - ConsoleRenderable: A renderable object. - """ - highlighter = ReprHighlighter() - items_table = Table.grid(padding=(0, 1), expand=False) - items_table.add_column(justify="right") - - def sort_items(item: Tuple[str, Any]) -> Tuple[bool, str]: - """Sort special variables first, then alphabetically.""" - key, _ = item - return (not key.startswith("__"), key.lower()) - - items = sorted(scope.items(), key=sort_items) if sort_keys else scope.items() - for key, value in items: - key_text = Text.assemble( - (key, "scope.key.special" if key.startswith("__") else "scope.key"), - (" =", "scope.equals"), - ) - items_table.add_row( - key_text, - Pretty( - value, - highlighter=highlighter, - indent_guides=indent_guides, - max_length=max_length, - max_string=max_string, - ), - ) - return Panel.fit( - items_table, - title=title, - border_style="scope.border", - padding=(0, 1), - ) - - -if __name__ == "__main__": # pragma: no cover - from pip._vendor.rich import print - - print() - - def test(foo: float, bar: float) -> None: - list_of_things = [1, 2, 3, None, 4, True, False, "Hello World"] - dict_of_things = { - "version": "1.1", - "method": "confirmFruitPurchase", - "params": [["apple", "orange", "mangoes", "pomelo"], 1.123], - "id": "194521489", - } - print(render_scope(locals(), title="[i]locals", sort_keys=False)) - - test(20.3423, 3.1427) - print() diff --git a/spaces/CVMX-jaca-tonos/Identificar-lenguas-y-frases/NOTES.md b/spaces/CVMX-jaca-tonos/Identificar-lenguas-y-frases/NOTES.md deleted file mode 100644 index d332893cc7009aa432b1512dc77aac3d8fda6d1d..0000000000000000000000000000000000000000 --- a/spaces/CVMX-jaca-tonos/Identificar-lenguas-y-frases/NOTES.md +++ /dev/null @@ -1,65 +0,0 @@ - - -# Things that might be relevant - -## Trained models - -ESPnet model for Yoloxochitl Mixtec - - Huggingface Hub page https://huggingface.co/espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer - - Model source code https://github.com/espnet/espnet/tree/master/egs/yoloxochitl_mixtec/asr1 - - Colab notebook to setup and apply the model https://colab.research.google.com/drive/1ieoW2b3ERydjaaWuhVPBP_v2QqqWsC1Q?usp=sharing - -Coqui model for Yoloxochitl Mixtec - - Huggingface Hub page - - Coqui page https://coqui.ai/mixtec/jemeyer/v1.0.0 - - Colab notebook to setup and apply the model https://colab.research.google.com/drive/1b1SujEGC_F3XhvUCuUyZK_tyUkEaFZ7D?usp=sharing#scrollTo=6IvRFke4Ckpz - -Spanish ASR models - - XLS-R model based on CV8 with LM https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-spanish - - XLSR model based on CV6 with LM https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish - - XLSR model based on Librispeech https://huggingface.co/IIC/wav2vec2-spanish-multilibrispeech - -Speechbrain Language identification on Common Language (from Common Voice 6/7?) - - source code https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonLanguage - - HF Hub model page https://huggingface.co/speechbrain/lang-id-commonlanguage_ecapa - - HF Hub space https://huggingface.co/spaces/akhaliq/Speechbrain-audio-classification - -Speechbrain Language identification on VoxLingua - - source code https://github.com/speechbrain/speechbrain/tree/develop/recipes/VoxLingua107/lang_id - - HF Hub model page https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa - - -## Corpora - -OpenSLR89 https://www.openslr.org/89/ - -Common Language https://huggingface.co/datasets/common_language - -VoxLingua http://bark.phon.ioc.ee/voxlingua107/ - -Multilibrispeech https://huggingface.co/datasets/multilingual_librispeech - - -# Possible demos - -## Simple categorization of utterances - -A few example files are provided for each language, and the user can record their own. -The predicted confidence of each class label is shown. - -## Segmentation and identification - -Recordings with alternating languages in a single audio file, provided examples or the user can record. -Some voice activity detection to split the audio, then predict language of each piece - -## Identication and transcription - -Example files for each language separately. -The lang-id model predicts what language it is. -The corresponding ASR model produces a transcript. - -## Segmentation, identification and transcription - -Recordings with alternating languages in a single audio file. -Use voice activity detection to split the audio, then predict the language of each piece -Use the corresponding ASR model to produce a transcript of each piece to display. \ No newline at end of file diff --git a/spaces/CVPR/LIVE/thrust/generate_mk.py b/spaces/CVPR/LIVE/thrust/generate_mk.py deleted file mode 100644 index 84071338ccfdd99be55027c8046fe46c56e5a65b..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/generate_mk.py +++ /dev/null @@ -1,146 +0,0 @@ -#!/usr/bin/env python -# Generate set of projects mk files. -# Usage: python generate_mk.py PROJECTS_MK_DIR THRUST_SOURCE_DIR -# The program scans through unit tests and examples in THRUST_SOURCE_DIR -# and generates project mk for each of the tests and examples in PROJECTS_MK_DIR -# A single example or unit test source file generates its own executable -# This program is called by a top level Makefile, but can also be used stand-alone for debugging -# This program also generates testing.mk, examples.mk and dependencies.mk -from __future__ import print_function -import sys -import shutil as sh -import os -import glob -import re - -test_template = """ -TEST_SRC := %(TEST_SRC)s -TEST_NAME := %(TEST_NAME)s -include $(ROOTDIR)/thrust/internal/build/generic_test.mk -""" -example_template = """ -EXAMPLE_SRC := %(EXAMPLE_SRC)s -EXAMPLE_NAME := %(EXAMPLE_NAME)s -include $(ROOTDIR)/thrust/internal/build/generic_example.mk -""" - -def Glob(pattern, directory,exclude='\B'): - src = glob.glob(os.path.join(directory,pattern)) - p = re.compile(exclude) - src = [s for s in src if not p.match(s)] - return src - - -def generate_test_mk(mk_path, test_path, group, TEST_DIR): - print('Generating makefiles in "'+mk_path+'" for tests in "'+test_path+'"') - src_cu = Glob("*.cu", test_path, ".*testframework.cu$") - src_cxx = Glob("*.cpp", test_path) - src_cu.sort(); - src_cxx.sort(); - src_all = src_cu + src_cxx; - tests_all = [] - dependencies_all = [] - for s in src_all: - fn = os.path.splitext(os.path.basename(s)); - t = "thrust."+group+"."+fn[0] - e = fn[1] - mkfile = test_template % {"TEST_SRC" : s, "TEST_NAME" : t} - f = open(os.path.join(mk_path,t+".mk"), 'w') - f.write(mkfile) - f.close() - tests_all.append(os.path.join(mk_path,t)) - dependencies_all.append(t+": testframework") - return [tests_all, dependencies_all] - -def generate_example_mk(mk_path, example_path, group, EXAMPLE_DIR): - print('Generating makefiles in "'+mk_path+'" for examples in "'+example_path+'"') - src_cu = Glob("*.cu", example_path) - src_cxx = Glob("*.cpp", example_path) - src_cu.sort(); - src_cxx.sort(); - src_all = src_cu + src_cxx; - examples_all = [] - for s in src_all: - fn = os.path.splitext(os.path.basename(s)); - t = "thrust."+group+"."+fn[0] - e = fn[1] - mkfile = example_template % {"EXAMPLE_SRC" : s, "EXAMPLE_NAME" : t} - f = open(os.path.join(mk_path,t+".mk"), 'w') - f.write(mkfile) - f.close() - examples_all.append(os.path.join(mk_path,t)) - return examples_all - - -## relpath : backported from os.relpath form python 2.6+ -def relpath(path, start): - """Return a relative version of a path""" - - import posixpath - if not path: - raise ValueError("no path specified") - start_list = posixpath.abspath(start).split(posixpath.sep) - path_list = posixpath.abspath(path).split(posixpath.sep) - # Work out how much of the filepath is shared by start and path. - i = len(posixpath.commonprefix([start_list, path_list])) - rel_list = [posixpath.pardir] * (len(start_list)-i) + path_list[i:] - if not rel_list: - return posixpath.curdir - return posixpath.join(*rel_list) - -mk_path=sys.argv[1] -REL_DIR="../../" -if (len(sys.argv) > 2): - root_path=sys.argv[2]; - mk_path = relpath(mk_path, root_path) - REL_DIR = relpath(root_path,mk_path) - -try: - sh.rmtree(mk_path) -except: - pass -os.makedirs(mk_path) - -tests_all, dependencies_all = generate_test_mk(mk_path, "testing/", "test", REL_DIR) -tests_cu, dependencies_cu = generate_test_mk(mk_path, "testing/cuda/", "test.cuda", REL_DIR) -tests_all.extend(tests_cu) -dependencies_all.extend(dependencies_cu) - -testing_mk = "" - -for t in tests_all: - testing_mk += "PROJECTS += "+t+"\n" -testing_mk += "PROJECTS += internal/build/testframework\n" - - -f = open(os.path.join(mk_path,"testing.mk"),'w') -f.write(testing_mk) -f.close() - -dependencies_mk = "" -for d in dependencies_all: - dependencies_mk += d + "\n" - -f = open(os.path.join(mk_path,"dependencies.mk"),'w') -f.write(dependencies_mk) -f.close() - - -examples_mk = "" -examples_all = generate_example_mk(mk_path, "examples/", "example", REL_DIR) -examples_cuda = generate_example_mk(mk_path, "examples/cuda/", "example.cuda", REL_DIR) -examples_all.extend(examples_cuda) -for e in examples_all: - examples_mk += "PROJECTS += "+e+"\n" - -f = open(os.path.join(mk_path,"examples.mk"),'w') -f.write(examples_mk) -f.close() - - - - - - - - diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/transform_reduce.h b/spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/transform_reduce.h deleted file mode 100644 index a8736bd75d06e54d9158baeb2504162d75312885..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/transform_reduce.h +++ /dev/null @@ -1,23 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include
-
- ''')
- with gr.Row():
- with gr.Column():
- audio_source = gr.Audio(source="microphone", type="filepath", streaming=True)
- state = gr.State(value="")
- with gr.Column():
- greeting_count = gr.Number(label="挨拶回数")
- with gr.Row():
- text_output = gr.Textbox(label="認識されたテキスト")
- audio_source.stream(voice_detect, inputs=[audio_source, state], outputs=[text_output, state, greeting_count])
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/training/__init__.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/training/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Egrt/GCycleGAN/nets/resnest/splat.py b/spaces/Egrt/GCycleGAN/nets/resnest/splat.py
deleted file mode 100644
index c3f21b19ac75534521b9a0eae957e8ee454f1cd4..0000000000000000000000000000000000000000
--- a/spaces/Egrt/GCycleGAN/nets/resnest/splat.py
+++ /dev/null
@@ -1,99 +0,0 @@
-"""Split-Attention"""
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.nn import Conv2d, Module, Linear, BatchNorm2d, ReLU
-from torch.nn.modules.utils import _pair
-
-__all__ = ['SplAtConv2d']
-
-class SplAtConv2d(Module):
- """Split-Attention Conv2d
- """
- def __init__(self, in_channels, channels, kernel_size, stride=(1, 1), padding=(0, 0),
- dilation=(1, 1), groups=1, bias=True,
- radix=2, reduction_factor=4,
- rectify=False, rectify_avg=False, norm_layer=None,
- dropblock_prob=0.0, **kwargs):
- super(SplAtConv2d, self).__init__()
- padding = _pair(padding)
- self.rectify = rectify and (padding[0] > 0 or padding[1] > 0)
- self.rectify_avg = rectify_avg
- inter_channels = max(in_channels*radix//reduction_factor, 32)
- self.radix = radix
- self.cardinality = groups
- self.channels = channels
- self.dropblock_prob = dropblock_prob
- if self.rectify:
- from rfconv import RFConv2d
- self.conv = RFConv2d(in_channels, channels*radix, kernel_size, stride, padding, dilation,
- groups=groups*radix, bias=bias, average_mode=rectify_avg, **kwargs)
- else:
- self.conv = Conv2d(in_channels, channels*radix, kernel_size, stride, padding, dilation,
- groups=groups*radix, bias=bias, **kwargs)
- self.use_bn = norm_layer is not None
- if self.use_bn:
- self.bn0 = norm_layer(channels*radix)
- self.relu = ReLU(inplace=True)
- self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
- if self.use_bn:
- self.bn1 = norm_layer(inter_channels)
- self.fc2 = Conv2d(inter_channels, channels*radix, 1, groups=self.cardinality)
- if dropblock_prob > 0.0:
- self.dropblock = DropBlock2D(dropblock_prob, 3)
- self.rsoftmax = rSoftMax(radix, groups)
-
- def forward(self, x):
- x = self.conv(x)
- if self.use_bn:
- x = self.bn0(x)
- if self.dropblock_prob > 0.0:
- x = self.dropblock(x)
- x = self.relu(x)
-
- batch, rchannel = x.shape[:2]
- if self.radix > 1:
- if torch.__version__ < '1.5':
- splited = torch.split(x, int(rchannel//self.radix), dim=1)
- else:
- splited = torch.split(x, rchannel//self.radix, dim=1)
- gap = sum(splited)
- else:
- gap = x
- gap = F.adaptive_avg_pool2d(gap, 1)
- gap = self.fc1(gap)
-
- if self.use_bn:
- gap = self.bn1(gap)
- gap = self.relu(gap)
-
- atten = self.fc2(gap)
- atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
-
- if self.radix > 1:
- if torch.__version__ < '1.5':
- attens = torch.split(atten, int(rchannel//self.radix), dim=1)
- else:
- attens = torch.split(atten, rchannel//self.radix, dim=1)
- out = sum([att*split for (att, split) in zip(attens, splited)])
- else:
- out = atten * x
- return out.contiguous()
-
-class rSoftMax(nn.Module):
- def __init__(self, radix, cardinality):
- super().__init__()
- self.radix = radix
- self.cardinality = cardinality
-
- def forward(self, x):
- batch = x.size(0)
- if self.radix > 1:
- x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
- x = F.softmax(x, dim=1)
- x = x.reshape(batch, -1)
- else:
- x = torch.sigmoid(x)
- return x
-
diff --git a/spaces/EuroSciPy2022/xgboost-income-prediction-with-explainability/app.py b/spaces/EuroSciPy2022/xgboost-income-prediction-with-explainability/app.py
deleted file mode 100644
index a1904c3bd9b857cf77c507e98e431d4bc50f4251..0000000000000000000000000000000000000000
--- a/spaces/EuroSciPy2022/xgboost-income-prediction-with-explainability/app.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import random
-
-import gradio as gr
-import matplotlib
-import matplotlib.pyplot as plt
-import pandas as pd
-import shap
-import xgboost as xgb
-from datasets import load_dataset
-
-matplotlib.use("Agg")
-
-dataset = load_dataset("scikit-learn/adult-census-income")
-
-X_train = dataset["train"].to_pandas()
-_ = X_train.pop("fnlwgt")
-_ = X_train.pop("race")
-
-y_train = X_train.pop("income")
-y_train = (y_train == ">50K").astype(int)
-categorical_columns = [
- "workclass",
- "education",
- "marital.status",
- "occupation",
- "relationship",
- "sex",
- "native.country",
-]
-X_train = X_train.astype({col: "category" for col in categorical_columns})
-
-
-data = xgb.DMatrix(X_train, label=y_train, enable_categorical=True)
-model = xgb.train(params={"objective": "binary:logistic"}, dtrain=data)
-explainer = shap.TreeExplainer(model)
-
-
-def predict(*args):
- df = pd.DataFrame([args], columns=X_train.columns)
- df = df.astype({col: "category" for col in categorical_columns})
- pos_pred = model.predict(xgb.DMatrix(df, enable_categorical=True))
- return {">50K": float(pos_pred[0]), "<=50K": 1 - float(pos_pred[0])}
-
-
-def interpret(*args):
- df = pd.DataFrame([args], columns=X_train.columns)
- df = df.astype({col: "category" for col in categorical_columns})
- shap_values = explainer.shap_values(xgb.DMatrix(df, enable_categorical=True))
- scores_desc = list(zip(shap_values[0], X_train.columns))
- scores_desc = sorted(scores_desc)
- fig_m = plt.figure(tight_layout=True)
- plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])
- plt.title("Feature Shap Values")
- plt.ylabel("Shap Value")
- plt.xlabel("Feature")
- plt.tight_layout()
- return fig_m
-
-
-unique_class = sorted(X_train["workclass"].unique())
-unique_education = sorted(X_train["education"].unique())
-unique_marital_status = sorted(X_train["marital.status"].unique())
-unique_relationship = sorted(X_train["relationship"].unique())
-unique_occupation = sorted(X_train["occupation"].unique())
-unique_sex = sorted(X_train["sex"].unique())
-unique_country = sorted(X_train["native.country"].unique())
-
-with gr.Blocks() as demo:
- gr.Markdown("""
- ## Income Classification with XGBoost 💰
-
- This example shows how to load data from the hugging face hub to train an XGBoost classifier and
- demo the predictions with gradio.
-
- The source is [here](https://huggingface.co/spaces/gradio/xgboost-income-prediction-with-explainability).
- """)
- with gr.Row():
- with gr.Column():
- age = gr.Slider(label="Age", minimum=17, maximum=90, step=1, randomize=True)
- work_class = gr.Dropdown(
- label="Workclass",
- choices=unique_class,
- value=lambda: random.choice(unique_class),
- )
- education = gr.Dropdown(
- label="Education Level",
- choices=unique_education,
- value=lambda: random.choice(unique_education),
- )
- years = gr.Slider(
- label="Years of schooling",
- minimum=1,
- maximum=16,
- step=1,
- randomize=True,
- )
- marital_status = gr.Dropdown(
- label="Marital Status",
- choices=unique_marital_status,
- value=lambda: random.choice(unique_marital_status),
- )
- occupation = gr.Dropdown(
- label="Occupation",
- choices=unique_occupation,
- value=lambda: random.choice(unique_occupation),
- )
- relationship = gr.Dropdown(
- label="Relationship Status",
- choices=unique_relationship,
- value=lambda: random.choice(unique_relationship),
- )
- sex = gr.Dropdown(
- label="Sex", choices=unique_sex, value=lambda: random.choice(unique_sex)
- )
- capital_gain = gr.Slider(
- label="Capital Gain",
- minimum=0,
- maximum=100000,
- step=500,
- randomize=True,
- )
- capital_loss = gr.Slider(
- label="Capital Loss", minimum=0, maximum=10000, step=500, randomize=True
- )
- hours_per_week = gr.Slider(
- label="Hours Per Week Worked", minimum=1, maximum=99, step=1
- )
- country = gr.Dropdown(
- label="Native Country",
- choices=unique_country,
- value=lambda: random.choice(unique_country),
- )
- with gr.Column():
- label = gr.Label()
- plot = gr.Plot()
- with gr.Row():
- predict_btn = gr.Button(value="Predict")
- interpret_btn = gr.Button(value="Interpret")
- predict_btn.click(
- predict,
- inputs=[
- age,
- work_class,
- education,
- years,
- marital_status,
- occupation,
- relationship,
- sex,
- capital_gain,
- capital_loss,
- hours_per_week,
- country,
- ],
- outputs=[label],
- )
- interpret_btn.click(
- interpret,
- inputs=[
- age,
- work_class,
- education,
- years,
- marital_status,
- occupation,
- relationship,
- sex,
- capital_gain,
- capital_loss,
- hours_per_week,
- country,
- ],
- outputs=[plot],
- )
-
-demo.launch()
diff --git a/spaces/Flux9665/IMS-Toucan/README.md b/spaces/Flux9665/IMS-Toucan/README.md
deleted file mode 100644
index 80144846a8e59a50d094b1c404342cc9c3c7e821..0000000000000000000000000000000000000000
--- a/spaces/Flux9665/IMS-Toucan/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Multilingual TTS
-emoji: 🌍🦜
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 2.7.5.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Freiburg-AI-Research/dermoscopic_image_generation/README.md b/spaces/Freiburg-AI-Research/dermoscopic_image_generation/README.md
deleted file mode 100644
index 00ac8ed128577ad7ef30cb839ac40bf7cf5c49ea..0000000000000000000000000000000000000000
--- a/spaces/Freiburg-AI-Research/dermoscopic_image_generation/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Glide Text2im
-emoji: 📊
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-app_file: app.py
-pinned: false
-duplicated_from: valhalla/glide-text2im
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/clip/encoders.py b/spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/clip/encoders.py
deleted file mode 100644
index ee72773c2c891d2dda6d02933e88599b5330b052..0000000000000000000000000000000000000000
--- a/spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/clip/encoders.py
+++ /dev/null
@@ -1,497 +0,0 @@
-import math
-from collections import OrderedDict
-from typing import List, Optional, Tuple, cast
-
-import attr
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .attention import (
- AttentionInfo,
- DenseAttentionMask,
- DenseCausalAttentionMask,
- make_full_layout,
- to_attention_info,
-)
-from .utils import Affine, LayerNorm, zero_key_bias_grad
-
-# Constants used in the original CLIP implementation.
-image_channel_means = [122.77093945, 116.74601272, 104.09373519]
-image_channel_stds = [68.50053285, 66.63215831, 70.32316309]
-
-
-@attr.s(eq=False, repr=False)
-class TextEmbedding(nn.Module):
- n_vocab: int = attr.ib()
- n_context: int = attr.ib()
- n_state: int = attr.ib()
- device: torch.device = attr.ib(default=torch.device("cuda"))
-
- def __attrs_post_init__(self) -> None:
- super().__init__()
-
- w_voc = torch.empty((self.n_vocab, self.n_state), dtype=torch.float32, device=self.device)
- w_pos = torch.empty((self.n_context, self.n_state), dtype=torch.float32, device=self.device)
-
- with torch.no_grad():
- w_voc.normal_(std=0.02)
- w_pos.normal_(std=0.01)
-
- self.w_voc = nn.Parameter(w_voc)
- self.w_pos = nn.Parameter(w_pos)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- if len(x.shape) != 2:
- raise ValueError()
-
- return F.embedding(x, self.w_voc) + self.w_pos[None, :, :]
-
-
-@attr.s(eq=False, repr=False)
-class ImageEmbedding(nn.Module):
- image_size: int = attr.ib()
- patch_size: int = attr.ib()
- n_state: int = attr.ib()
- n_timestep: int = attr.ib(default=0)
- device: torch.device = attr.ib(default=torch.device("cuda"))
-
- def __attrs_post_init__(self) -> None:
- super().__init__()
-
- if self.image_size % self.patch_size != 0:
- raise ValueError()
-
- n_patch = self.image_size // self.patch_size
- patch_proj = torch.empty(
- (self.n_state, 3) + 2 * (self.patch_size,), dtype=torch.float32, device=self.device
- )
- w_pos = torch.empty(
- (1 + n_patch ** 2, self.n_state), dtype=torch.float32, device=self.device
- )
-
- with torch.no_grad():
- if self.n_timestep == 0:
- pred_state = torch.empty((self.n_state,), dtype=torch.float32, device=self.device)
- pred_state.normal_(std=1 / np.sqrt(self.n_state))
- self.pred_state = nn.Parameter(pred_state)
- else:
- w_t = torch.empty(
- (self.n_timestep, self.n_state), dtype=torch.float32, device=self.device
- )
- w_t.normal_(std=1 / np.sqrt(self.n_state))
- self.w_t = nn.Parameter(w_t)
-
- patch_proj.normal_(std=np.sqrt(2 / (self.n_state * self.patch_size ** 2)))
- w_pos.normal_(std=1 / np.sqrt(self.n_state))
-
- self.patch_proj = nn.Parameter(patch_proj)
- self.w_pos = nn.Parameter(w_pos)
-
- self.channel_means = torch.tensor(
- image_channel_means, dtype=torch.float32, device=self.device
- )[None, :, None, None]
- self.channel_stds = torch.tensor(
- image_channel_stds, dtype=torch.float32, device=self.device
- )[None, :, None, None]
- self.ln = LayerNorm(self.n_state, eps=1e-5, device=self.device)
-
- def forward(self, x: torch.Tensor, t: Optional[torch.Tensor] = None) -> torch.Tensor:
- if len(x.shape) != 4:
- raise ValueError("input should be 4d")
- if x.shape[1] != 3:
- raise ValueError("input should have 3 channels")
- if not (x.shape[2] == self.image_size and x.shape[3] == self.image_size):
- raise ValueError(f"input is not {self.image_size} x {self.image_size}")
-
- if (self.n_timestep == 0 and t is not None) or (self.n_timestep != 0 and t is None):
- raise ValueError()
- if self.n_timestep != 0:
- assert t is not None
- if len(t.shape) != 1:
- raise ValueError()
- if t.shape[0] != x.shape[0]:
- raise ValueError()
-
- x = (x - self.channel_means) / self.channel_stds
- x = F.conv2d(x, self.patch_proj, stride=self.patch_size)
- x = x.reshape(x.shape[0], self.n_state, (self.image_size // self.patch_size) ** 2).permute(
- 0, 2, 1
- )
-
- sot = (
- self.pred_state[None, None].expand(x.shape[0], -1, -1)
- if self.n_timestep == 0
- else F.embedding(cast(torch.Tensor, t), self.w_t)[:, None]
- )
- x = torch.cat((sot, x), dim=1) + self.w_pos[None]
- return self.ln(x)
-
-
-@attr.s(eq=False, repr=False)
-class AttentionResblock(nn.Module):
- n_state: int = attr.ib()
- n_resblocks: int = attr.ib()
- attn_fn: AttentionInfo = attr.ib()
- device: torch.device = attr.ib(default=torch.device("cuda"))
-
- def __attrs_post_init__(self) -> None:
- super().__init__()
-
- self.n_head_state = self.n_state // self.attn_fn.n_heads
- self.qk_scale = 1 / np.sqrt(self.n_head_state)
-
- self.ln = LayerNorm(self.n_state, eps=1e-5, device=self.device)
- self.f_q = Affine(
- self.n_state,
- self.n_state,
- std=1 / math.sqrt(self.n_state),
- use_bias=True,
- bias_filter_fn=zero_key_bias_grad,
- device=self.device,
- )
- self.f_k = Affine(
- self.n_state,
- self.n_state,
- std=1 / math.sqrt(self.n_state),
- use_bias=False,
- bias_filter_fn=zero_key_bias_grad,
- device=self.device,
- )
- self.f_v = Affine(
- self.n_state,
- self.n_state,
- std=1 / math.sqrt(self.n_state),
- use_bias=True,
- bias_filter_fn=zero_key_bias_grad,
- device=self.device,
- )
- self.f_c = Affine(
- self.n_state,
- self.n_state,
- use_bias=True,
- std=1 / np.sqrt(self.n_state * self.n_resblocks ** 2),
- device=self.device,
- ) # XXX
-
- def forward(self, m: torch.Tensor) -> torch.Tensor:
- n_context = m.shape[1]
- n_query_pad = self.attn_fn.ctx_blks_q * self.attn_fn.block_size - n_context
- n_key_pad = self.attn_fn.ctx_blks_k * self.attn_fn.block_size - n_context
- assert n_query_pad >= 0
- assert n_key_pad >= 0
-
- r = m
- r = self.ln(r)
- q, k, v = self.f_q(r), self.f_k(r), self.f_v(r)
-
- if n_query_pad != 0:
- q = F.pad(q, (0, 0, 0, n_query_pad))
-
- if n_key_pad != 0:
- k = F.pad(k, (0, 0, 0, n_key_pad))
- v = F.pad(v, (0, 0, 0, n_key_pad))
-
- q = q.view([q.shape[0], -1, self.attn_fn.n_heads, self.n_head_state]).permute((0, 2, 1, 3))
- k = k.view([k.shape[0], -1, self.attn_fn.n_heads, self.n_head_state]).permute((0, 2, 1, 3))
- v = v.view([v.shape[0], -1, self.attn_fn.n_heads, self.n_head_state]).permute((0, 2, 1, 3))
- w = torch.einsum(
- "bhcd,bhkd->bhck", q * math.sqrt(self.qk_scale), k * math.sqrt(self.qk_scale)
- )
-
- if hasattr(self.attn_fn, "pytorch_attn_bias"):
- bias = self.attn_fn.pytorch_attn_bias
- assert len(bias.shape) in {2, 3}
-
- if len(bias.shape) == 2:
- w = torch.softmax(w + self.attn_fn.pytorch_attn_bias[None, None], dim=-1)
- elif len(bias.shape) == 3:
- w = torch.softmax(w + self.attn_fn.pytorch_attn_bias[None], dim=-1)
- else:
- w = torch.softmax(w, dim=-1)
-
- r = torch.einsum("bhck,bhkd->bhcd", w, v)
- r = r.permute((0, 2, 1, 3)).reshape((r.shape[0], -1, self.n_state))
-
- if n_query_pad != 0:
- r = r[:, :-n_query_pad]
-
- assert r.shape[1] == n_context
-
- r = self.f_c(r)
- return m + r
-
-
-@attr.s(eq=False, repr=False)
-class FullyConnectedResblock(nn.Module):
- """
- Not imported from other files because we retain Alec's original inits.
- """
-
- n_state: int = attr.ib()
- n_resblocks: int = attr.ib()
- device: torch.device = attr.ib(default=torch.device("cuda"))
-
- def __attrs_post_init__(self) -> None:
- super().__init__()
-
- self.ln = LayerNorm(self.n_state, eps=1e-5, device=self.device)
- self.f_1 = Affine(
- self.n_state,
- 4 * self.n_state,
- use_bias=True,
- std=np.sqrt(2 / (4 * self.n_state)),
- device=self.device,
- )
- self.f_2 = Affine(
- 4 * self.n_state,
- self.n_state,
- use_bias=True,
- std=1 / np.sqrt(self.n_state * self.n_resblocks ** 2),
- device=self.device,
- ) # XXX
-
- def forward(self, m: torch.Tensor) -> torch.Tensor:
- r = m
- r = self.ln(r)
-
- r = self.f_2(F.gelu(self.f_1(r)))
- return m + r
-
-
-@attr.s(eq=False, repr=False)
-class TransformerBlock(nn.Module):
- n_state: int = attr.ib()
- n_resblocks: int = attr.ib()
- attn_fn: AttentionInfo = attr.ib()
- device: torch.device = attr.ib(default=torch.device("cuda"))
-
- def __attrs_post_init__(self) -> None:
- super().__init__()
-
- self.f_attn = AttentionResblock(
- self.n_state,
- self.n_resblocks,
- self.attn_fn,
- self.device,
- )
- self.f_mlp = FullyConnectedResblock(self.n_state, self.n_resblocks, self.device)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return self.f_mlp(self.f_attn(x))
-
-
-@attr.s(eq=False, repr=False)
-class TextFeatureExtractor(nn.Module):
- n_state: int = attr.ib()
- n_embd: int = attr.ib()
- device: torch.device = attr.ib(default=torch.device("cuda"))
-
- def __attrs_post_init__(self) -> None:
- super().__init__()
-
- self.ln = LayerNorm(self.n_state, eps=1e-5, device=self.device)
- self.f = Affine(self.n_state, self.n_embd, use_bias=False, device=self.device)
-
- def forward(
- self, text: torch.Tensor, text_len: torch.Tensor, return_probe_features: bool = False
- ) -> torch.Tensor:
- if len(text.shape) != 3:
- raise ValueError("expected text to be 3d")
- if len(text_len.shape) != 1:
- raise ValueError("expected text length to be 1d")
- if text.shape[0] != text_len.shape[0]:
- raise ValueError("text and text_len have inconsistent batch dimensions")
-
- index = (text_len - 1)[:, None, None].expand(-1, 1, text.shape[2])
- x = torch.gather(text, dim=1, index=index)
- assert list(x.shape) == [text.shape[0], 1, text.shape[2]]
-
- if return_probe_features:
- return x[:, 0]
-
- x = self.ln(x)
- return self.f(x[:, 0])
-
-
-@attr.s(eq=False, repr=False)
-class ImageFeatureExtractor(nn.Module):
- n_state: int = attr.ib()
- n_embd: int = attr.ib()
- device: torch.device = attr.ib(default=torch.device("cuda"))
-
- def __attrs_post_init__(self) -> None:
- super().__init__()
-
- self.ln = LayerNorm(self.n_state, eps=1e-5, device=self.device)
- self.f = Affine(self.n_state, self.n_embd, use_bias=False, device=self.device)
-
- def forward(self, x: torch.Tensor, return_probe_features: bool = False) -> torch.Tensor:
- if return_probe_features:
- return x[:, 0]
-
- x = self.ln(x[:, :1])
- return self.f(x[:, 0])
-
-
-@attr.s(eq=False, repr=False)
-class TextEncoder(nn.Module):
- n_bpe_vocab: int = attr.ib()
- max_text_len: int = attr.ib()
- n_embd: int = attr.ib()
- n_head: int = attr.ib()
- n_xf_blocks: int = attr.ib()
- n_head_state: int = attr.ib(default=64)
- device: torch.device = attr.ib(default=torch.device("cuda"))
- block_size: int = attr.ib(init=False, default=32)
-
- def __attrs_post_init__(self) -> None:
- super().__init__()
-
- self.n_state = self.n_head * self.n_head_state
- n_rounded_context = self.block_size * int(math.ceil(self.max_text_len / self.block_size))
- n_pad = n_rounded_context - self.max_text_len
-
- args = (
- n_rounded_context,
- n_rounded_context,
- self.block_size,
- self.n_head,
- False,
- n_pad,
- n_pad,
- )
- mask = DenseCausalAttentionMask(*args)
- attn_fn = to_attention_info(mask)
-
- m = 1 - make_full_layout(mask).astype(np.float32)
- m[m == 1] = -1e10
- attn_fn.pytorch_attn_bias = torch.from_numpy(m).to(self.device)
-
- blocks: List[Tuple[str, nn.Module]] = [
- (
- "input",
- TextEmbedding(
- self.n_bpe_vocab, self.max_text_len, self.n_state, device=self.device
- ),
- )
- ]
-
- for i in range(self.n_xf_blocks):
- blocks.append(
- (
- f"block_{i}",
- TransformerBlock(self.n_state, 2 * self.n_xf_blocks, attn_fn, self.device),
- )
- )
-
- blocks.append(
- ("output", TextFeatureExtractor(self.n_state, self.n_embd, device=self.device))
- )
-
- self.blocks = nn.ModuleDict(OrderedDict(blocks))
-
- def forward(
- self,
- text: torch.Tensor,
- text_len: torch.Tensor,
- return_probe_features: bool = False,
- ) -> torch.Tensor:
-
- n_batch = text.shape[0]
- h = self.blocks["input"](text)
-
- for i in range(self.n_xf_blocks):
- h = self.blocks[f"block_{i}"](h)
-
- h = self.blocks["output"](h, text_len, return_probe_features=return_probe_features)
-
- assert list(h.shape) == [
- n_batch,
- self.n_embd if not return_probe_features else self.n_state,
- ]
- return h
-
-
-@attr.s(eq=False, repr=False)
-class ImageEncoder(nn.Module):
- image_size: int = attr.ib()
- patch_size: int = attr.ib()
- n_embd: int = attr.ib()
- n_head: int = attr.ib()
- n_xf_blocks: int = attr.ib()
- n_head_state: int = attr.ib(default=64)
- n_timestep: int = attr.ib(default=0)
- device: torch.device = attr.ib(default=torch.device("cuda"))
- block_size: int = attr.ib(init=False, default=32)
-
- def __attrs_post_init__(self) -> None:
- super().__init__()
-
- self.n_state = self.n_head * self.n_head_state
- self.n_context = 1 + (self.image_size // self.patch_size) ** 2
- n_rounded_context = self.block_size * int(math.ceil(self.n_context / self.block_size))
- n_pad = n_rounded_context - self.n_context
-
- args = (
- n_rounded_context,
- n_rounded_context,
- self.block_size,
- self.n_head,
- False,
- n_pad,
- n_pad,
- )
- mask = DenseAttentionMask(*args)
- attn_fn = to_attention_info(mask)
-
- m = 1 - make_full_layout(mask).astype(np.float32)
- m[m == 1] = -1e10
- attn_fn.pytorch_attn_bias = torch.from_numpy(m).to(self.device)
-
- blocks: List[Tuple[str, nn.Module]] = [
- (
- "input",
- ImageEmbedding(
- self.image_size,
- self.patch_size,
- self.n_state,
- n_timestep=self.n_timestep,
- device=self.device,
- ),
- )
- ]
-
- for i in range(self.n_xf_blocks):
- blocks.append(
- (
- f"block_{i}",
- TransformerBlock(self.n_state, 2 * self.n_xf_blocks, attn_fn, self.device),
- )
- )
-
- blocks.append(("output", ImageFeatureExtractor(self.n_state, self.n_embd, self.device)))
-
- self.blocks = nn.ModuleDict(OrderedDict(blocks))
-
- def forward(
- self,
- image: torch.Tensor,
- timesteps: Optional[torch.Tensor] = None,
- return_probe_features: bool = False,
- ) -> torch.Tensor:
- n_batch = image.shape[0]
- h = self.blocks["input"](image, t=timesteps)
-
- for i in range(self.n_xf_blocks):
- h = self.blocks[f"block_{i}"](h)
-
- h = self.blocks["output"](h, return_probe_features=return_probe_features)
-
- assert list(h.shape) == [
- n_batch,
- self.n_embd if not return_probe_features else self.n_state,
- ]
-
- return h
diff --git a/spaces/GIZ/SDSN-demo/README.md b/spaces/GIZ/SDSN-demo/README.md
deleted file mode 100644
index 802ed4ec16f79564907c3f8150a0ab4f9cc1de35..0000000000000000000000000000000000000000
--- a/spaces/GIZ/SDSN-demo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: SDSN Demo
-emoji: 📈
-colorFrom: purple
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/GaenKoki/voicevox/voicevox_engine/utility/mutex_utility.py b/spaces/GaenKoki/voicevox/voicevox_engine/utility/mutex_utility.py
deleted file mode 100644
index 09d8cb9680f71758018bffe82838a763ca46fe31..0000000000000000000000000000000000000000
--- a/spaces/GaenKoki/voicevox/voicevox_engine/utility/mutex_utility.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import threading
-
-
-def mutex_wrapper(lock: threading.Lock):
- def wrap(f):
- def func(*args, **kw):
- lock.acquire()
- try:
- return f(*args, **kw)
- finally:
- lock.release()
-
- return func
-
- return wrap
diff --git a/spaces/Gen-Sim/Gen-Sim/notebooks/dataset_test.py b/spaces/Gen-Sim/Gen-Sim/notebooks/dataset_test.py
deleted file mode 100644
index 191963b2a50622700a123b6b945b88ade1ac1ab2..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/notebooks/dataset_test.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import os
-import sys
-import numpy as np
-import hydra
-
-from cliport.dataset import RavensDataset
-from cliport.utils import utils
-from cliport import tasks
-from cliport.environments.environment import Environment
-
-import torch
-
-
-import matplotlib
-import matplotlib.pyplot as plt
-
-
-
-mode = 'train'
-augment = True
-
-### Uncomment the task you want to generate ###
-# task = 'align-rope'
-# task = 'assembling-kits-seq-seen-colors'
-# task = 'assembling-kits-seq-unseen-colors'
-# task = 'assembling-kits-seq-full'
-# task = 'packing-shapes'
-# task = 'packing-boxes-pairs-seen-colors'
-# task = 'packing-boxes-pairs-unseen-colors'
-# task = 'packing-boxes-pairs-full'
-# task = 'packing-seen-google-objects-seq'
-# task = 'packing-unseen-google-objects-seq'
-# task = 'packing-seen-google-objects-group'
-# task = 'packing-unseen-google-objects-group'
-# task = 'put-block-in-bowl-seen-colors'
-# task = 'put-block-in-bowl-unseen-colors'
-# task = 'put-block-in-bowl-full'
-task = 'align-box-corner'
-# task = 'stack-block-pyramid-seq-unseen-colors'
-# task = 'stack-block-pyramid-seq-full'
-# task = 'separating-piles-seen-colors'
-# task = 'separating-piles-unseen-colors'
-# task = 'separating-piles-full'
-# task = 'towers-of-hanoi-seq-seen-colors'
-# task = 'towers-of-hanoi-seq-unseen-colors'
-# task = 'towers-of-hanoi-seq-full'
-
-### visualization settings
-max_episodes = 1
-max_steps = 100
-
-
-
-root_dir = os.environ['CLIPORT_ROOT']
-config_file = 'train.yaml'
-cfg = utils.load_hydra_config(os.path.join(root_dir, f'cliport/cfg/{config_file}'))
-
-# Override defaults
-cfg['task'] = task
-cfg['mode'] = mode
-cfg['train']['data_augmentation'] = True
-data_dir = os.path.join(root_dir, 'data')
-
-
-
-task = tasks.names[cfg['task']]()
-task.mode = mode
-
-ds = RavensDataset(os.path.join(data_dir, f'{cfg["task"]}-{cfg["mode"]}'), cfg, n_demos=10, augment=augment)
-
-
-
-color_sums = []
-depth_sums = []
-
-total_images = 0
-
-for i in range(0, min(max_episodes, ds.n_episodes)):
- print(f'\n\nEpisode: {i + 1}/{ds.n_episodes}')
- episode, seed = ds.load(i)
-
- total_images += len(episode)-1
-
- total_reward = 0
- for step in range(min(max_steps, len(episode))):
- print(f"\nStep: {step+1}/{len(episode)}")
- obs, act, reward, info = episode[step]
-
- total_reward += reward
- batch = ds[i]
-
- num_images = len(obs['color'])
- fig, axs = plt.subplots(2, num_images+1, figsize=(15, 6))
- for n in range(num_images):
- axs[1, n].imshow(obs['color'][n])
- axs[1, n].set_title(f'Raw RGB {n+1}')
-
- axs[0, n].imshow(obs['depth'][n])
- axs[0, n].set_title(f'Raw Depth {n+1}')
-
- color_sums.append(np.mean(obs['color'][0], axis=(0,1)) / 255.0)
- depth_sums.append(np.mean(obs['depth'][0], axis=(0,1)))
-
- cam_config = None
- if b'camera_info' in info:
- cam_config = ds.get_cam_config(info[b'camera_info'])
-
- img_depth = ds.get_image(obs, cam_config=cam_config)
- img_tensor = torch.from_numpy(img_depth)
- img = np.uint8(img_tensor.detach().cpu().numpy())
- img = img.transpose(1,0,2)
-
- if step < len(episode)-1 and episode[step]:
- batch = ds.process_sample(episode[step], augment=augment)
- else:
- batch = ds.process_goal(episode[step], perturb_params=None)
-
- img_sample = batch['img']
- img_sample = torch.from_numpy(img_sample)
- color = np.uint8(img_sample.detach().cpu().numpy())[:,:,:3]
- color = color.transpose(1,0,2)
- depth = np.array(img_sample.detach().cpu().numpy())[:,:,3]
- depth = depth.transpose(1,0)
-
- axs[0, num_images].imshow(depth)
- axs[0, num_images].set_title('Depth')
-
- axs[1,num_images].imshow(color)
- axs[1,num_images].set_title('RGB + Oracle Pick & Place')
-
- if act and step < len(episode)-1:
- p0 = batch['p0']
- p1 = batch['p1']
- p0_theta = batch['p0_theta']
- p1_theta = batch['p1_theta'] + p0_theta
-
- pick = p0
- place = p1
-
- line_len = 30
- pick0 = (pick[0] + line_len/2.0 * np.sin(p0_theta), pick[1] + line_len/2.0 * np.cos(p0_theta))
- pick1 = (pick[0] - line_len/2.0 * np.sin(p0_theta), pick[1] - line_len/2.0 * np.cos(p0_theta))
- axs[1,num_images].plot((pick1[0], pick0[0]), (pick1[1], pick0[1]), color='r', linewidth=2)
-
- place0 = (place[0] + line_len/2.0 * np.sin(p1_theta), place[1] + line_len/2.0 * np.cos(p1_theta))
- place1 = (place[0] - line_len/2.0 * np.sin(p1_theta), place[1] - line_len/2.0 * np.cos(p1_theta))
- axs[1,num_images].plot((place1[0], place0[0]), (place1[1], place0[1]), color='g', linewidth=2)
-
- c_pick = plt.Circle(pick, 3, color='r', fill=False)
- c_place = plt.Circle(place, 3, color='g', fill=False)
-
- axs[1,num_images].add_patch(c_pick)
- axs[1,num_images].add_patch(c_place)
-
- plt.show()
-
- print(f"Language Goal: {batch['lang_goal']}")
- print(f"Step Reward: {reward}")
- print(f"Total Reward: {total_reward}")
-
- print(f"Done, Total Reward: {total_reward}")
-
-print("\n\nDataset Statistics: ")
-print(f"Color Mean: {np.mean(color_sums, axis=0)}, Std: {np.std(color_sums, axis=0)}")
-print(f"Depth Mean: {np.mean(depth_sums, axis=0)}, Std: {np.std(depth_sums, axis=0)}")
-print(f"Total Image-Action Pairs: {total_images}")
\ No newline at end of file
diff --git a/spaces/GitMylo/bark-voice-cloning/hubert/hubert_manager.py b/spaces/GitMylo/bark-voice-cloning/hubert/hubert_manager.py
deleted file mode 100644
index 857f2af29886fca6eb4df506853f446066af7c04..0000000000000000000000000000000000000000
--- a/spaces/GitMylo/bark-voice-cloning/hubert/hubert_manager.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import os.path
-import shutil
-import urllib.request
-
-import huggingface_hub
-
-
-class HuBERTManager:
- @staticmethod
- def make_sure_hubert_installed(download_url: str = 'https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt', file_name: str = 'hubert.pt'):
- install_dir = os.path.join('data', 'models', 'hubert')
- if not os.path.isdir(install_dir):
- os.makedirs(install_dir, exist_ok=True)
- install_file = os.path.join(install_dir, file_name)
- if not os.path.isfile(install_file):
- print('Downloading HuBERT base model')
- urllib.request.urlretrieve(download_url, install_file)
- print('Downloaded HuBERT')
- return install_file
-
-
- @staticmethod
- def make_sure_tokenizer_installed(model: str = 'quantifier_hubert_base_ls960_14.pth', repo: str = 'GitMylo/bark-voice-cloning', local_file: str = 'tokenizer.pth'):
- install_dir = os.path.join('data', 'models', 'hubert')
- if not os.path.isdir(install_dir):
- os.makedirs(install_dir, exist_ok=True)
- install_file = os.path.join(install_dir, local_file)
- if not os.path.isfile(install_file):
- print('Downloading HuBERT custom tokenizer')
- huggingface_hub.hf_hub_download(repo, model, local_dir=install_dir, local_dir_use_symlinks=False)
- shutil.move(os.path.join(install_dir, model), install_file)
- print('Downloaded tokenizer')
- return install_file
diff --git a/spaces/Godrose0728/Aisound02/text/__init__.py b/spaces/Godrose0728/Aisound02/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/Godrose0728/Aisound02/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
- '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
- Args:
- text: string to convert to a sequence
- cleaner_names: names of the cleaner functions to run the text through
- Returns:
- List of integers corresponding to the symbols in the text
- '''
- _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
- sequence = []
-
- clean_text = _clean_text(text, cleaner_names)
- for symbol in clean_text:
- if symbol not in _symbol_to_id.keys():
- continue
- symbol_id = _symbol_to_id[symbol]
- sequence += [symbol_id]
- return sequence
-
-
-def _clean_text(text, cleaner_names):
- for name in cleaner_names:
- cleaner = getattr(cleaners, name)
- if not cleaner:
- raise Exception('Unknown cleaner: %s' % name)
- text = cleaner(text)
- return text
diff --git a/spaces/Gradio-Blocks/anime-colorization/README.md b/spaces/Gradio-Blocks/anime-colorization/README.md
deleted file mode 100644
index ca77164f07a41a87a016a5fb3d4a1afa7d1923e6..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/anime-colorization/README.md
+++ /dev/null
@@ -1,188 +0,0 @@
----
-title: Anime Colorization
-emoji: 😻
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.0.5
-app_file: app.py
-pinned: false
-license: mit
----
-
-# Pixel Guide Diffusion For Anime Colorization
-
-
-
-Use denoising diffusion probabilistic model to do the anime colorization task.
-
-v1 test result is in branch [v1_result](https://github.com/HighCWu/pixel-guide-diffusion-for-anime-colorization/tree/v1_result).
-
-The dataset is not clean enough and the sketch as the guide is generated using sketch2keras, so the generalization is not good.
-
-In the future, I may try to use only anime portraits as the target images, and look for some more diverse sketch models.
-
-# Introduction and Usage
-
-Pixel Guide Denoising Diffusion Probabilistic Models ( One Channel Guide Version )
-
-This repo is modified from [improved-diffusion](https://github.com/openai/improved-diffusion).
-
-Use [danbooru-sketch-pair-128x](https://www.kaggle.com/wuhecong/danbooru-sketch-pair-128x) as the dataset. Maybe you should move folders in the dataset first to make guide-target pair dataset.
-
-Modify `train_danbooru*.sh`, `test_danbooru*.sh` to meet your needs.
-
-The model is divided into a 32px part and a super-divided part, which can be cascaded during testing to get the final result. But there is no cascade during training.
-
-QQ Group: 1044867291
-
-Discord: https://discord.gg/YwWcAS47qb
-
-# Original README
-
-# improved-diffusion
-
-This is the codebase for [Improved Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2102.09672).
-
-# Usage
-
-This section of the README walks through how to train and sample from a model.
-
-## Installation
-
-Clone this repository and navigate to it in your terminal. Then run:
-
-```
-pip install -e .
-```
-
-This should install the ~~`improved_diffusion`~~ `pixel_guide_diffusion` python package that the scripts depend on.
-
-## Preparing Data
-
-The training code reads images from a directory of image files. In the [datasets](datasets) folder, we have provided instructions/scripts for preparing these directories for ImageNet, LSUN bedrooms, and CIFAR-10.
-
-For creating your own dataset, simply dump all of your images into a directory with ".jpg", ".jpeg", or ".png" extensions. If you wish to train a class-conditional model, name the files like "mylabel1_XXX.jpg", "mylabel2_YYY.jpg", etc., so that the data loader knows that "mylabel1" and "mylabel2" are the labels. Subdirectories will automatically be enumerated as well, so the images can be organized into a recursive structure (although the directory names will be ignored, and the underscore prefixes are used as names).
-
-The images will automatically be scaled and center-cropped by the data-loading pipeline. Simply pass `--data_dir path/to/images` to the training script, and it will take care of the rest.
-
-## Training
-
-To train your model, you should first decide some hyperparameters. We will split up our hyperparameters into three groups: model architecture, diffusion process, and training flags. Here are some reasonable defaults for a baseline:
-
-```
-MODEL_FLAGS="--image_size 64 --num_channels 128 --num_res_blocks 3"
-DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule linear"
-TRAIN_FLAGS="--lr 1e-4 --batch_size 128"
-```
-
-Here are some changes we experiment with, and how to set them in the flags:
-
- * **Learned sigmas:** add `--learn_sigma True` to `MODEL_FLAGS`
- * **Cosine schedule:** change `--noise_schedule linear` to `--noise_schedule cosine`
- * **Reweighted VLB:** add `--use_kl True` to `DIFFUSION_FLAGS` and add `--schedule_sampler loss-second-moment` to `TRAIN_FLAGS`.
- * **Class-conditional:** add `--class_cond True` to `MODEL_FLAGS`.
-
-Once you have setup your hyper-parameters, you can run an experiment like so:
-
-```
-python scripts/image_train.py --data_dir path/to/images $MODEL_FLAGS $DIFFUSION_FLAGS $TRAIN_FLAGS
-```
-
-You may also want to train in a distributed manner. In this case, run the same command with `mpiexec`:
-
-```
-mpiexec -n $NUM_GPUS python scripts/image_train.py --data_dir path/to/images $MODEL_FLAGS $DIFFUSION_FLAGS $TRAIN_FLAGS
-```
-
-When training in a distributed manner, you must manually divide the `--batch_size` argument by the number of ranks. In lieu of distributed training, you may use `--microbatch 16` (or `--microbatch 1` in extreme memory-limited cases) to reduce memory usage.
-
-The logs and saved models will be written to a logging directory determined by the `OPENAI_LOGDIR` environment variable. If it is not set, then a temporary directory will be created in `/tmp`.
-
-## Sampling
-
-The above training script saves checkpoints to `.pt` files in the logging directory. These checkpoints will have names like `ema_0.9999_200000.pt` and `model200000.pt`. You will likely want to sample from the EMA models, since those produce much better samples.
-
-Once you have a path to your model, you can generate a large batch of samples like so:
-
-```
-python scripts/image_sample.py --model_path /path/to/model.pt $MODEL_FLAGS $DIFFUSION_FLAGS
-```
-
-Again, this will save results to a logging directory. Samples are saved as a large `npz` file, where `arr_0` in the file is a large batch of samples.
-
-Just like for training, you can run `image_sample.py` through MPI to use multiple GPUs and machines.
-
-You can change the number of sampling steps using the `--timestep_respacing` argument. For example, `--timestep_respacing 250` uses 250 steps to sample. Passing `--timestep_respacing ddim250` is similar, but uses the uniform stride from the [DDIM paper](https://arxiv.org/abs/2010.02502) rather than our stride.
-
-To sample using [DDIM](https://arxiv.org/abs/2010.02502), pass `--use_ddim True`.
-
-## Models and Hyperparameters
-
-This section includes model checkpoints and run flags for the main models in the paper.
-
-Note that the batch sizes are specified for single-GPU training, even though most of these runs will not naturally fit on a single GPU. To address this, either set `--microbatch` to a small value (e.g. 4) to train on one GPU, or run with MPI and divide `--batch_size` by the number of GPUs.
-
-Unconditional ImageNet-64 with our `L_hybrid` objective and cosine noise schedule [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/imagenet64_uncond_100M_1500K.pt)]:
-
-```bash
-MODEL_FLAGS="--image_size 64 --num_channels 128 --num_res_blocks 3 --learn_sigma True"
-DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine"
-TRAIN_FLAGS="--lr 1e-4 --batch_size 128"
-```
-
-Unconditional CIFAR-10 with our `L_hybrid` objective and cosine noise schedule [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/cifar10_uncond_50M_500K.pt)]:
-
-```bash
-MODEL_FLAGS="--image_size 32 --num_channels 128 --num_res_blocks 3 --learn_sigma True --dropout 0.3"
-DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine"
-TRAIN_FLAGS="--lr 1e-4 --batch_size 128"
-```
-
-Class-conditional ImageNet-64 model (270M parameters, trained for 250K iterations) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/imagenet64_cond_270M_250K.pt)]:
-
-```bash
-MODEL_FLAGS="--image_size 64 --num_channels 192 --num_res_blocks 3 --learn_sigma True --class_cond True"
-DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine --rescale_learned_sigmas False --rescale_timesteps False"
-TRAIN_FLAGS="--lr 3e-4 --batch_size 2048"
-```
-
-Upsampling 256x256 model (280M parameters, trained for 500K iterations) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/upsample_cond_500K.pt)]:
-
-```bash
-MODEL_FLAGS="--num_channels 192 --num_res_blocks 2 --learn_sigma True --class_cond True"
-DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False"
-TRAIN_FLAGS="--lr 3e-4 --batch_size 256"
-```
-
-LSUN bedroom model (lr=1e-4) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/lsun_uncond_100M_1200K_bs128.pt)]:
-
-```bash
-MODEL_FLAGS="--image_size 256 --num_channels 128 --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16"
-DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False"
-TRAIN_FLAGS="--lr 1e-4 --batch_size 128"
-```
-
-LSUN bedroom model (lr=2e-5) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/lsun_uncond_100M_2400K_bs64.pt)]:
-
-```bash
-MODEL_FLAGS="--image_size 256 --num_channels 128 --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16"
-DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False --use_scale_shift_norm False"
-TRAIN_FLAGS="--lr 2e-5 --batch_size 128"
-```
-
-Unconditional ImageNet-64 with the `L_vlb` objective and cosine noise schedule [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/imagenet64_uncond_vlb_100M_1500K.pt)]:
-
-```bash
-MODEL_FLAGS="--image_size 64 --num_channels 128 --num_res_blocks 3 --learn_sigma True"
-DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine"
-TRAIN_FLAGS="--lr 1e-4 --batch_size 128 --schedule_sampler loss-second-moment"
-```
-
-Unconditional CIFAR-10 with the `L_vlb` objective and cosine noise schedule [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/cifar10_uncond_vlb_50M_500K.pt)]:
-
-```bash
-MODEL_FLAGS="--image_size 32 --num_channels 128 --num_res_blocks 3 --learn_sigma True --dropout 0.3"
-DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine"
-TRAIN_FLAGS="--lr 1e-4 --batch_size 128 --schedule_sampler loss-second-moment"
-```
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py
deleted file mode 100644
index 169278e5738b0abd4ae5e99594e4adbaaefa2d96..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py'
-# learning policy
-lr_config = dict(step=[28, 34])
-runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/pseudo_sampler.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/pseudo_sampler.py
deleted file mode 100644
index 2bd81abcdc62debc14772659d7a171f20bf33364..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/pseudo_sampler.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import torch
-
-from ..builder import BBOX_SAMPLERS
-from .base_sampler import BaseSampler
-from .sampling_result import SamplingResult
-
-
-@BBOX_SAMPLERS.register_module()
-class PseudoSampler(BaseSampler):
- """A pseudo sampler that does not do sampling actually."""
-
- def __init__(self, **kwargs):
- pass
-
- def _sample_pos(self, **kwargs):
- """Sample positive samples."""
- raise NotImplementedError
-
- def _sample_neg(self, **kwargs):
- """Sample negative samples."""
- raise NotImplementedError
-
- def sample(self, assign_result, bboxes, gt_bboxes, **kwargs):
- """Directly returns the positive and negative indices of samples.
-
- Args:
- assign_result (:obj:`AssignResult`): Assigned results
- bboxes (torch.Tensor): Bounding boxes
- gt_bboxes (torch.Tensor): Ground truth boxes
-
- Returns:
- :obj:`SamplingResult`: sampler results
- """
- pos_inds = torch.nonzero(
- assign_result.gt_inds > 0, as_tuple=False).squeeze(-1).unique()
- neg_inds = torch.nonzero(
- assign_result.gt_inds == 0, as_tuple=False).squeeze(-1).unique()
- gt_flags = bboxes.new_zeros(bboxes.shape[0], dtype=torch.uint8)
- sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
- assign_result, gt_flags)
- return sampling_result
diff --git a/spaces/HESOAYM/ElviraMulti/modules/shared.py b/spaces/HESOAYM/ElviraMulti/modules/shared.py
deleted file mode 100644
index a9e72580aa7ae48f907e923a09099513570a9ad8..0000000000000000000000000000000000000000
--- a/spaces/HESOAYM/ElviraMulti/modules/shared.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from modules.presets import COMPLETION_URL, BALANCE_API_URL, USAGE_API_URL, API_HOST
-import os
-import queue
-
-class State:
- interrupted = False
- multi_api_key = False
- completion_url = COMPLETION_URL
- balance_api_url = BALANCE_API_URL
- usage_api_url = USAGE_API_URL
-
- def interrupt(self):
- self.interrupted = True
-
- def recover(self):
- self.interrupted = False
-
- def set_api_host(self, api_host):
- self.completion_url = f"https://{api_host}/v1/chat/completions"
- self.balance_api_url = f"https://{api_host}/dashboard/billing/credit_grants"
- self.usage_api_url = f"https://{api_host}/dashboard/billing/usage"
- os.environ["OPENAI_API_BASE"] = f"https://{api_host}/v1"
-
- def reset_api_host(self):
- self.completion_url = COMPLETION_URL
- self.balance_api_url = BALANCE_API_URL
- self.usage_api_url = USAGE_API_URL
- os.environ["OPENAI_API_BASE"] = f"https://{API_HOST}/v1"
- return API_HOST
-
- def reset_all(self):
- self.interrupted = False
- self.completion_url = COMPLETION_URL
-
- def set_api_key_queue(self, api_key_list):
- self.multi_api_key = True
- self.api_key_queue = queue.Queue()
- for api_key in api_key_list:
- self.api_key_queue.put(api_key)
-
- def switching_api_key(self, func):
- if not hasattr(self, "api_key_queue"):
- return func
-
- def wrapped(*args, **kwargs):
- api_key = self.api_key_queue.get()
- args[0].api_key = api_key
- ret = func(*args, **kwargs)
- self.api_key_queue.put(api_key)
- return ret
-
- return wrapped
-
-
-state = State()
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/roberta/wsc/wsc_task.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/roberta/wsc/wsc_task.py
deleted file mode 100644
index 602ea737ed75a33fddf44dd859e999ecfce2730d..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/roberta/wsc/wsc_task.py
+++ /dev/null
@@ -1,401 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import json
-import os
-import tempfile
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.data import (
- Dictionary,
- IdDataset,
- ListDataset,
- NestedDictionaryDataset,
- NumelDataset,
- NumSamplesDataset,
- PadDataset,
- SortDataset,
- data_utils,
- encoders,
-)
-from fairseq.tasks import LegacyFairseqTask, register_task
-
-from . import wsc_utils
-
-
-@register_task("wsc")
-class WSCTask(LegacyFairseqTask):
- """Task to finetune RoBERTa for Winograd Schemas."""
-
- @staticmethod
- def add_args(parser):
- """Add task-specific arguments to the parser."""
- parser.add_argument(
- "data", metavar="DIR", help="path to data directory; we load 挨拶カウンター
-
-
-
-
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/examples/m2m_100/tokenizers/seg_ko.sh b/spaces/ICML2022/OFA/fairseq/examples/m2m_100/tokenizers/seg_ko.sh
deleted file mode 100644
index c523d92634d9b61b97bbcdbfd17dfc33465bfc09..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/m2m_100/tokenizers/seg_ko.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/usr/bin/env bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-SCRIPT=`realpath $0`
-MECAB=`dirname $SCRIPT`/thirdparty/mecab-0.996-ko-0.9.2
-
-export PATH=$PATH:"$MECAB/bin":"$MECAB/lib"
-export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:"$MECAB/lib"
-
-cat - | mecab -O wakati
diff --git a/spaces/ICML2022/OFA/fairseq/examples/noisychannel/rerank_score_bw.py b/spaces/ICML2022/OFA/fairseq/examples/noisychannel/rerank_score_bw.py
deleted file mode 100644
index b0bc913651bd76667e25c214acb70f2bca19e185..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/noisychannel/rerank_score_bw.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import os
-from contextlib import redirect_stdout
-
-from fairseq import options
-from fairseq_cli import generate
-
-from examples.noisychannel import rerank_options, rerank_utils
-
-
-def score_bw(args):
- if args.backwards1:
- scorer1_src = args.target_lang
- scorer1_tgt = args.source_lang
- else:
- scorer1_src = args.source_lang
- scorer1_tgt = args.target_lang
-
- if args.score_model2 is not None:
- if args.backwards2:
- scorer2_src = args.target_lang
- scorer2_tgt = args.source_lang
- else:
- scorer2_src = args.source_lang
- scorer2_tgt = args.target_lang
-
- rerank1_is_gen = (
- args.gen_model == args.score_model1 and args.source_prefix_frac is None
- )
- rerank2_is_gen = (
- args.gen_model == args.score_model2 and args.source_prefix_frac is None
- )
-
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- args.shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- score1_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model1_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards1,
- )
-
- if args.score_model2 is not None:
- score2_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model2_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards2,
- )
-
- if args.right_to_left1:
- rerank_data1 = right_to_left_preprocessed_dir
- elif args.backwards1:
- rerank_data1 = backwards_preprocessed_dir
- else:
- rerank_data1 = left_to_right_preprocessed_dir
-
- gen_param = ["--batch-size", str(128), "--score-reference", "--gen-subset", "train"]
- if not rerank1_is_gen and not os.path.isfile(score1_file):
- print("STEP 4: score the translations for model 1")
-
- model_param1 = [
- "--path",
- args.score_model1,
- "--source-lang",
- scorer1_src,
- "--target-lang",
- scorer1_tgt,
- ]
- gen_model1_param = [rerank_data1] + gen_param + model_param1
-
- gen_parser = options.get_generation_parser()
- input_args = options.parse_args_and_arch(gen_parser, gen_model1_param)
-
- with open(score1_file, "w") as f:
- with redirect_stdout(f):
- generate.main(input_args)
-
- if (
- args.score_model2 is not None
- and not os.path.isfile(score2_file)
- and not rerank2_is_gen
- ):
- print("STEP 4: score the translations for model 2")
-
- if args.right_to_left2:
- rerank_data2 = right_to_left_preprocessed_dir
- elif args.backwards2:
- rerank_data2 = backwards_preprocessed_dir
- else:
- rerank_data2 = left_to_right_preprocessed_dir
-
- model_param2 = [
- "--path",
- args.score_model2,
- "--source-lang",
- scorer2_src,
- "--target-lang",
- scorer2_tgt,
- ]
- gen_model2_param = [rerank_data2] + gen_param + model_param2
-
- gen_parser = options.get_generation_parser()
- input_args = options.parse_args_and_arch(gen_parser, gen_model2_param)
-
- with open(score2_file, "w") as f:
- with redirect_stdout(f):
- generate.main(input_args)
-
-
-def cli_main():
- parser = rerank_options.get_reranking_parser()
- args = options.parse_args_and_arch(parser)
- score_bw(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/data/language_pair_dataset.py b/spaces/ICML2022/OFA/fairseq/fairseq/data/language_pair_dataset.py
deleted file mode 100644
index ff3e14bf14770638524ef6067b558e455dbe5f2b..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/data/language_pair_dataset.py
+++ /dev/null
@@ -1,471 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-
-import numpy as np
-import torch
-from fairseq.data import FairseqDataset, data_utils
-
-
-logger = logging.getLogger(__name__)
-
-
-def collate(
- samples,
- pad_idx,
- eos_idx,
- left_pad_source=True,
- left_pad_target=False,
- input_feeding=True,
- pad_to_length=None,
- pad_to_multiple=1,
-):
- if len(samples) == 0:
- return {}
-
- def merge(key, left_pad, move_eos_to_beginning=False, pad_to_length=None):
- return data_utils.collate_tokens(
- [s[key] for s in samples],
- pad_idx,
- eos_idx,
- left_pad,
- move_eos_to_beginning,
- pad_to_length=pad_to_length,
- pad_to_multiple=pad_to_multiple,
- )
-
- def check_alignment(alignment, src_len, tgt_len):
- if alignment is None or len(alignment) == 0:
- return False
- if (
- alignment[:, 0].max().item() >= src_len - 1
- or alignment[:, 1].max().item() >= tgt_len - 1
- ):
- logger.warning("alignment size mismatch found, skipping alignment!")
- return False
- return True
-
- def compute_alignment_weights(alignments):
- """
- Given a tensor of shape [:, 2] containing the source-target indices
- corresponding to the alignments, a weight vector containing the
- inverse frequency of each target index is computed.
- For e.g. if alignments = [[5, 7], [2, 3], [1, 3], [4, 2]], then
- a tensor containing [1., 0.5, 0.5, 1] should be returned (since target
- index 3 is repeated twice)
- """
- align_tgt = alignments[:, 1]
- _, align_tgt_i, align_tgt_c = torch.unique(
- align_tgt, return_inverse=True, return_counts=True
- )
- align_weights = align_tgt_c[align_tgt_i[np.arange(len(align_tgt))]]
- return 1.0 / align_weights.float()
-
- id = torch.LongTensor([s["id"] for s in samples])
- src_tokens = merge(
- "source",
- left_pad=left_pad_source,
- pad_to_length=pad_to_length["source"] if pad_to_length is not None else None,
- )
- # sort by descending source length
- src_lengths = torch.LongTensor(
- [s["source"].ne(pad_idx).long().sum() for s in samples]
- )
- src_lengths, sort_order = src_lengths.sort(descending=True)
- id = id.index_select(0, sort_order)
- src_tokens = src_tokens.index_select(0, sort_order)
-
- prev_output_tokens = None
- target = None
- if samples[0].get("target", None) is not None:
- target = merge(
- "target",
- left_pad=left_pad_target,
- pad_to_length=pad_to_length["target"]
- if pad_to_length is not None
- else None,
- )
- target = target.index_select(0, sort_order)
- tgt_lengths = torch.LongTensor(
- [s["target"].ne(pad_idx).long().sum() for s in samples]
- ).index_select(0, sort_order)
- ntokens = tgt_lengths.sum().item()
-
- if samples[0].get("prev_output_tokens", None) is not None:
- prev_output_tokens = merge("prev_output_tokens", left_pad=left_pad_target)
- elif input_feeding:
- # we create a shifted version of targets for feeding the
- # previous output token(s) into the next decoder step
- prev_output_tokens = merge(
- "target",
- left_pad=left_pad_target,
- move_eos_to_beginning=True,
- pad_to_length=pad_to_length["target"]
- if pad_to_length is not None
- else None,
- )
- else:
- ntokens = src_lengths.sum().item()
-
- batch = {
- "id": id,
- "nsentences": len(samples),
- "ntokens": ntokens,
- "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths,},
- "target": target,
- }
- if prev_output_tokens is not None:
- batch["net_input"]["prev_output_tokens"] = prev_output_tokens.index_select(
- 0, sort_order
- )
-
- if samples[0].get("alignment", None) is not None:
- bsz, tgt_sz = batch["target"].shape
- src_sz = batch["net_input"]["src_tokens"].shape[1]
-
- offsets = torch.zeros((len(sort_order), 2), dtype=torch.long)
- offsets[:, 1] += torch.arange(len(sort_order), dtype=torch.long) * tgt_sz
- if left_pad_source:
- offsets[:, 0] += src_sz - src_lengths
- if left_pad_target:
- offsets[:, 1] += tgt_sz - tgt_lengths
-
- alignments = [
- alignment + offset
- for align_idx, offset, src_len, tgt_len in zip(
- sort_order, offsets, src_lengths, tgt_lengths
- )
- for alignment in [samples[align_idx]["alignment"].view(-1, 2)]
- if check_alignment(alignment, src_len, tgt_len)
- ]
-
- if len(alignments) > 0:
- alignments = torch.cat(alignments, dim=0)
- align_weights = compute_alignment_weights(alignments)
-
- batch["alignments"] = alignments
- batch["align_weights"] = align_weights
-
- if samples[0].get("constraints", None) is not None:
- # Collate the packed constraints across the samples, padding to
- # the length of the longest sample.
- lens = [sample.get("constraints").size(0) for sample in samples]
- max_len = max(lens)
- constraints = torch.zeros((len(samples), max(lens))).long()
- for i, sample in enumerate(samples):
- constraints[i, 0 : lens[i]] = samples[i].get("constraints")
- batch["constraints"] = constraints.index_select(0, sort_order)
-
- return batch
-
-
-class LanguagePairDataset(FairseqDataset):
- """
- A pair of torch.utils.data.Datasets.
-
- Args:
- src (torch.utils.data.Dataset): source dataset to wrap
- src_sizes (List[int]): source sentence lengths
- src_dict (~fairseq.data.Dictionary): source vocabulary
- tgt (torch.utils.data.Dataset, optional): target dataset to wrap
- tgt_sizes (List[int], optional): target sentence lengths
- tgt_dict (~fairseq.data.Dictionary, optional): target vocabulary
- left_pad_source (bool, optional): pad source tensors on the left side
- (default: True).
- left_pad_target (bool, optional): pad target tensors on the left side
- (default: False).
- shuffle (bool, optional): shuffle dataset elements before batching
- (default: True).
- input_feeding (bool, optional): create a shifted version of the targets
- to be passed into the model for teacher forcing (default: True).
- remove_eos_from_source (bool, optional): if set, removes eos from end
- of source if it's present (default: False).
- append_eos_to_target (bool, optional): if set, appends eos to end of
- target if it's absent (default: False).
- align_dataset (torch.utils.data.Dataset, optional): dataset
- containing alignments.
- constraints (Tensor, optional): 2d tensor with a concatenated, zero-
- delimited list of constraints for each sentence.
- append_bos (bool, optional): if set, appends bos to the beginning of
- source/target sentence.
- num_buckets (int, optional): if set to a value greater than 0, then
- batches will be bucketed into the given number of batch shapes.
- src_lang_id (int, optional): source language ID, if set, the collated batch
- will contain a field 'src_lang_id' in 'net_input' which indicates the
- source language of the samples.
- tgt_lang_id (int, optional): target language ID, if set, the collated batch
- will contain a field 'tgt_lang_id' which indicates the target language
- of the samples.
- """
-
- def __init__(
- self,
- src,
- src_sizes,
- src_dict,
- tgt=None,
- tgt_sizes=None,
- tgt_dict=None,
- left_pad_source=True,
- left_pad_target=False,
- shuffle=True,
- input_feeding=True,
- remove_eos_from_source=False,
- append_eos_to_target=False,
- align_dataset=None,
- constraints=None,
- append_bos=False,
- eos=None,
- num_buckets=0,
- src_lang_id=None,
- tgt_lang_id=None,
- pad_to_multiple=1,
- ):
- if tgt_dict is not None:
- assert src_dict.pad() == tgt_dict.pad()
- assert src_dict.eos() == tgt_dict.eos()
- assert src_dict.unk() == tgt_dict.unk()
- if tgt is not None:
- assert len(src) == len(
- tgt
- ), "Source and target must contain the same number of examples"
- self.src = src
- self.tgt = tgt
- self.src_sizes = np.array(src_sizes)
- self.tgt_sizes = np.array(tgt_sizes) if tgt_sizes is not None else None
- self.sizes = (
- np.vstack((self.src_sizes, self.tgt_sizes)).T
- if self.tgt_sizes is not None
- else self.src_sizes
- )
- self.src_dict = src_dict
- self.tgt_dict = tgt_dict
- self.left_pad_source = left_pad_source
- self.left_pad_target = left_pad_target
- self.shuffle = shuffle
- self.input_feeding = input_feeding
- self.remove_eos_from_source = remove_eos_from_source
- self.append_eos_to_target = append_eos_to_target
- self.align_dataset = align_dataset
- if self.align_dataset is not None:
- assert (
- self.tgt_sizes is not None
- ), "Both source and target needed when alignments are provided"
- self.constraints = constraints
- self.append_bos = append_bos
- self.eos = eos if eos is not None else src_dict.eos()
- self.src_lang_id = src_lang_id
- self.tgt_lang_id = tgt_lang_id
- if num_buckets > 0:
- from fairseq.data import BucketPadLengthDataset
-
- self.src = BucketPadLengthDataset(
- self.src,
- sizes=self.src_sizes,
- num_buckets=num_buckets,
- pad_idx=self.src_dict.pad(),
- left_pad=self.left_pad_source,
- )
- self.src_sizes = self.src.sizes
- logger.info("bucketing source lengths: {}".format(list(self.src.buckets)))
- if self.tgt is not None:
- self.tgt = BucketPadLengthDataset(
- self.tgt,
- sizes=self.tgt_sizes,
- num_buckets=num_buckets,
- pad_idx=self.tgt_dict.pad(),
- left_pad=self.left_pad_target,
- )
- self.tgt_sizes = self.tgt.sizes
- logger.info(
- "bucketing target lengths: {}".format(list(self.tgt.buckets))
- )
-
- # determine bucket sizes using self.num_tokens, which will return
- # the padded lengths (thanks to BucketPadLengthDataset)
- num_tokens = np.vectorize(self.num_tokens, otypes=[np.compat.long])
- self.bucketed_num_tokens = num_tokens(np.arange(len(self.src)))
- self.buckets = [
- (None, num_tokens) for num_tokens in np.unique(self.bucketed_num_tokens)
- ]
- else:
- self.buckets = None
- self.pad_to_multiple = pad_to_multiple
-
- def get_batch_shapes(self):
- return self.buckets
-
- def __getitem__(self, index):
- tgt_item = self.tgt[index] if self.tgt is not None else None
- src_item = self.src[index]
- # Append EOS to end of tgt sentence if it does not have an EOS and remove
- # EOS from end of src sentence if it exists. This is useful when we use
- # use existing datasets for opposite directions i.e., when we want to
- # use tgt_dataset as src_dataset and vice versa
- if self.append_eos_to_target:
- eos = self.tgt_dict.eos() if self.tgt_dict else self.src_dict.eos()
- if self.tgt and self.tgt[index][-1] != eos:
- tgt_item = torch.cat([self.tgt[index], torch.LongTensor([eos])])
-
- if self.append_bos:
- bos = self.tgt_dict.bos() if self.tgt_dict else self.src_dict.bos()
- if self.tgt and self.tgt[index][0] != bos:
- tgt_item = torch.cat([torch.LongTensor([bos]), self.tgt[index]])
-
- bos = self.src_dict.bos()
- if self.src[index][0] != bos:
- src_item = torch.cat([torch.LongTensor([bos]), self.src[index]])
-
- if self.remove_eos_from_source:
- eos = self.src_dict.eos()
- if self.src[index][-1] == eos:
- src_item = self.src[index][:-1]
-
- example = {
- "id": index,
- "source": src_item,
- "target": tgt_item,
- }
- if self.align_dataset is not None:
- example["alignment"] = self.align_dataset[index]
- if self.constraints is not None:
- example["constraints"] = self.constraints[index]
- return example
-
- def __len__(self):
- return len(self.src)
-
- def collater(self, samples, pad_to_length=None):
- """Merge a list of samples to form a mini-batch.
-
- Args:
- samples (List[dict]): samples to collate
- pad_to_length (dict, optional): a dictionary of
- {'source': source_pad_to_length, 'target': target_pad_to_length}
- to indicate the max length to pad to in source and target respectively.
-
- Returns:
- dict: a mini-batch with the following keys:
-
- - `id` (LongTensor): example IDs in the original input order
- - `ntokens` (int): total number of tokens in the batch
- - `net_input` (dict): the input to the Model, containing keys:
-
- - `src_tokens` (LongTensor): a padded 2D Tensor of tokens in
- the source sentence of shape `(bsz, src_len)`. Padding will
- appear on the left if *left_pad_source* is ``True``.
- - `src_lengths` (LongTensor): 1D Tensor of the unpadded
- lengths of each source sentence of shape `(bsz)`
- - `prev_output_tokens` (LongTensor): a padded 2D Tensor of
- tokens in the target sentence, shifted right by one
- position for teacher forcing, of shape `(bsz, tgt_len)`.
- This key will not be present if *input_feeding* is
- ``False``. Padding will appear on the left if
- *left_pad_target* is ``True``.
- - `src_lang_id` (LongTensor): a long Tensor which contains source
- language IDs of each sample in the batch
-
- - `target` (LongTensor): a padded 2D Tensor of tokens in the
- target sentence of shape `(bsz, tgt_len)`. Padding will appear
- on the left if *left_pad_target* is ``True``.
- - `tgt_lang_id` (LongTensor): a long Tensor which contains target language
- IDs of each sample in the batch
- """
- res = collate(
- samples,
- pad_idx=self.src_dict.pad(),
- eos_idx=self.eos,
- left_pad_source=self.left_pad_source,
- left_pad_target=self.left_pad_target,
- input_feeding=self.input_feeding,
- pad_to_length=pad_to_length,
- pad_to_multiple=self.pad_to_multiple,
- )
- if self.src_lang_id is not None or self.tgt_lang_id is not None:
- src_tokens = res["net_input"]["src_tokens"]
- bsz = src_tokens.size(0)
- if self.src_lang_id is not None:
- res["net_input"]["src_lang_id"] = (
- torch.LongTensor([[self.src_lang_id]]).expand(bsz, 1).to(src_tokens)
- )
- if self.tgt_lang_id is not None:
- res["tgt_lang_id"] = (
- torch.LongTensor([[self.tgt_lang_id]]).expand(bsz, 1).to(src_tokens)
- )
- return res
-
- def num_tokens(self, index):
- """Return the number of tokens in a sample. This value is used to
- enforce ``--max-tokens`` during batching."""
- return max(
- self.src_sizes[index],
- self.tgt_sizes[index] if self.tgt_sizes is not None else 0,
- )
-
- def num_tokens_vec(self, indices):
- """Return the number of tokens for a set of positions defined by indices.
- This value is used to enforce ``--max-tokens`` during batching."""
- sizes = self.src_sizes[indices]
- if self.tgt_sizes is not None:
- sizes = np.maximum(sizes, self.tgt_sizes[indices])
- return sizes
-
- def size(self, index):
- """Return an example's size as a float or tuple. This value is used when
- filtering a dataset with ``--max-positions``."""
- return (
- self.src_sizes[index],
- self.tgt_sizes[index] if self.tgt_sizes is not None else 0,
- )
-
- def ordered_indices(self):
- """Return an ordered list of indices. Batches will be constructed based
- on this order."""
- if self.shuffle:
- indices = np.random.permutation(len(self)).astype(np.int64)
- else:
- indices = np.arange(len(self), dtype=np.int64)
- if self.buckets is None:
- # sort by target length, then source length
- if self.tgt_sizes is not None:
- indices = indices[np.argsort(self.tgt_sizes[indices], kind="mergesort")]
- return indices[np.argsort(self.src_sizes[indices], kind="mergesort")]
- else:
- # sort by bucketed_num_tokens, which is:
- # max(padded_src_len, padded_tgt_len)
- return indices[
- np.argsort(self.bucketed_num_tokens[indices], kind="mergesort")
- ]
-
- @property
- def supports_prefetch(self):
- return getattr(self.src, "supports_prefetch", False) and (
- getattr(self.tgt, "supports_prefetch", False) or self.tgt is None
- )
-
- def prefetch(self, indices):
- self.src.prefetch(indices)
- if self.tgt is not None:
- self.tgt.prefetch(indices)
- if self.align_dataset is not None:
- self.align_dataset.prefetch(indices)
-
- def filter_indices_by_size(self, indices, max_sizes):
- """Filter a list of sample indices. Remove those that are longer
- than specified in max_sizes.
-
- Args:
- indices (np.array): original array of sample indices
- max_sizes (int or list[int] or tuple[int]): max sample size,
- can be defined separately for src and tgt (then list or tuple)
-
- Returns:
- np.array: filtered sample array
- list: list of removed indices
- """
- return data_utils.filter_paired_dataset_indices_by_size(
- self.src_sizes, self.tgt_sizes, indices, max_sizes,
- )
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/debug/analyze_overlapping_masks.sh b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/debug/analyze_overlapping_masks.sh
deleted file mode 100644
index 4a4727b0129007d9b0eed3fc25780adb565965a2..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/debug/analyze_overlapping_masks.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-
-BASEDIR="$(dirname $0)"
-
-# paths are valid for mml7
-
-# select images
-#ls /data/inpainting/work/data/train | shuf | head -2000 | xargs -n1 -I{} cp {} /data/inpainting/mask_analysis/src
-
-# generate masks
-#"$BASEDIR/../gen_debug_mask_dataset.py" \
-# "$BASEDIR/../../configs/debug_mask_gen.yaml" \
-# "/data/inpainting/mask_analysis/src" \
-# "/data/inpainting/mask_analysis/generated"
-
-# predict
-#"$BASEDIR/../predict.py" \
-# model.path="simple_pix2pix2_gap_sdpl_novgg_large_b18_ffc075_batch8x15/saved_checkpoint/r.suvorov_2021-04-30_14-41-12_train_simple_pix2pix2_gap_sdpl_novgg_large_b18_ffc075_batch8x15_epoch22-step-574999" \
-# indir="/data/inpainting/mask_analysis/generated" \
-# outdir="/data/inpainting/mask_analysis/predicted" \
-# dataset.img_suffix=.jpg \
-# +out_ext=.jpg
-
-# analyze good and bad samples
-"$BASEDIR/../analyze_errors.py" \
- --only-report \
- --n-jobs 8 \
- "$BASEDIR/../../configs/analyze_mask_errors.yaml" \
- "/data/inpainting/mask_analysis/small/generated" \
- "/data/inpainting/mask_analysis/small/predicted" \
- "/data/inpainting/mask_analysis/small/report"
diff --git a/spaces/JunchuanYu/SegRS/segment_anything/utils/amg.py b/spaces/JunchuanYu/SegRS/segment_anything/utils/amg.py
deleted file mode 100644
index 3a137778e45c464c079658ecb87ec53270e789f7..0000000000000000000000000000000000000000
--- a/spaces/JunchuanYu/SegRS/segment_anything/utils/amg.py
+++ /dev/null
@@ -1,346 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import torch
-
-import math
-from copy import deepcopy
-from itertools import product
-from typing import Any, Dict, Generator, ItemsView, List, Tuple
-
-
-class MaskData:
- """
- A structure for storing masks and their related data in batched format.
- Implements basic filtering and concatenation.
- """
-
- def __init__(self, **kwargs) -> None:
- for v in kwargs.values():
- assert isinstance(
- v, (list, np.ndarray, torch.Tensor)
- ), "MaskData only supports list, numpy arrays, and torch tensors."
- self._stats = dict(**kwargs)
-
- def __setitem__(self, key: str, item: Any) -> None:
- assert isinstance(
- item, (list, np.ndarray, torch.Tensor)
- ), "MaskData only supports list, numpy arrays, and torch tensors."
- self._stats[key] = item
-
- def __delitem__(self, key: str) -> None:
- del self._stats[key]
-
- def __getitem__(self, key: str) -> Any:
- return self._stats[key]
-
- def items(self) -> ItemsView[str, Any]:
- return self._stats.items()
-
- def filter(self, keep: torch.Tensor) -> None:
- for k, v in self._stats.items():
- if v is None:
- self._stats[k] = None
- elif isinstance(v, torch.Tensor):
- self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
- elif isinstance(v, np.ndarray):
- self._stats[k] = v[keep.detach().cpu().numpy()]
- elif isinstance(v, list) and keep.dtype == torch.bool:
- self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
- elif isinstance(v, list):
- self._stats[k] = [v[i] for i in keep]
- else:
- raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
-
- def cat(self, new_stats: "MaskData") -> None:
- for k, v in new_stats.items():
- if k not in self._stats or self._stats[k] is None:
- self._stats[k] = deepcopy(v)
- elif isinstance(v, torch.Tensor):
- self._stats[k] = torch.cat([self._stats[k], v], dim=0)
- elif isinstance(v, np.ndarray):
- self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
- elif isinstance(v, list):
- self._stats[k] = self._stats[k] + deepcopy(v)
- else:
- raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
-
- def to_numpy(self) -> None:
- for k, v in self._stats.items():
- if isinstance(v, torch.Tensor):
- self._stats[k] = v.detach().cpu().numpy()
-
-
-def is_box_near_crop_edge(
- boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0
-) -> torch.Tensor:
- """Filter masks at the edge of a crop, but not at the edge of the original image."""
- crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
- orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
- boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
- near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
- near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
- near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
- return torch.any(near_crop_edge, dim=1)
-
-
-def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
- box_xywh = deepcopy(box_xyxy)
- box_xywh[2] = box_xywh[2] - box_xywh[0]
- box_xywh[3] = box_xywh[3] - box_xywh[1]
- return box_xywh
-
-
-def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
- assert len(args) > 0 and all(
- len(a) == len(args[0]) for a in args
- ), "Batched iteration must have inputs of all the same size."
- n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
- for b in range(n_batches):
- yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
-
-
-def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
- """
- Encodes masks to an uncompressed RLE, in the format expected by
- pycoco tools.
- """
- # Put in fortran order and flatten h,w
- b, h, w = tensor.shape
- tensor = tensor.permute(0, 2, 1).flatten(1)
-
- # Compute change indices
- diff = tensor[:, 1:] ^ tensor[:, :-1]
- change_indices = diff.nonzero()
-
- # Encode run length
- out = []
- for i in range(b):
- cur_idxs = change_indices[change_indices[:, 0] == i, 1]
- cur_idxs = torch.cat(
- [
- torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
- cur_idxs + 1,
- torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
- ]
- )
- btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
- counts = [] if tensor[i, 0] == 0 else [0]
- counts.extend(btw_idxs.detach().cpu().tolist())
- out.append({"size": [h, w], "counts": counts})
- return out
-
-
-def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
- """Compute a binary mask from an uncompressed RLE."""
- h, w = rle["size"]
- mask = np.empty(h * w, dtype=bool)
- idx = 0
- parity = False
- for count in rle["counts"]:
- mask[idx : idx + count] = parity
- idx += count
- parity ^= True
- mask = mask.reshape(w, h)
- return mask.transpose() # Put in C order
-
-
-def area_from_rle(rle: Dict[str, Any]) -> int:
- return sum(rle["counts"][1::2])
-
-
-def calculate_stability_score(
- masks: torch.Tensor, mask_threshold: float, threshold_offset: float
-) -> torch.Tensor:
- """
- Computes the stability score for a batch of masks. The stability
- score is the IoU between the binary masks obtained by thresholding
- the predicted mask logits at high and low values.
- """
- # One mask is always contained inside the other.
- # Save memory by preventing unnecesary cast to torch.int64
- intersections = (
- (masks > (mask_threshold + threshold_offset))
- .sum(-1, dtype=torch.int16)
- .sum(-1, dtype=torch.int32)
- )
- unions = (
- (masks > (mask_threshold - threshold_offset))
- .sum(-1, dtype=torch.int16)
- .sum(-1, dtype=torch.int32)
- )
- return intersections / unions
-
-
-def build_point_grid(n_per_side: int) -> np.ndarray:
- """Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
- offset = 1 / (2 * n_per_side)
- points_one_side = np.linspace(offset, 1 - offset, n_per_side)
- points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
- points_y = np.tile(points_one_side[:, None], (1, n_per_side))
- points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
- return points
-
-
-def build_all_layer_point_grids(
- n_per_side: int, n_layers: int, scale_per_layer: int
-) -> List[np.ndarray]:
- """Generates point grids for all crop layers."""
- points_by_layer = []
- for i in range(n_layers + 1):
- n_points = int(n_per_side / (scale_per_layer**i))
- points_by_layer.append(build_point_grid(n_points))
- return points_by_layer
-
-
-def generate_crop_boxes(
- im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
-) -> Tuple[List[List[int]], List[int]]:
- """
- Generates a list of crop boxes of different sizes. Each layer
- has (2**i)**2 boxes for the ith layer.
- """
- crop_boxes, layer_idxs = [], []
- im_h, im_w = im_size
- short_side = min(im_h, im_w)
-
- # Original image
- crop_boxes.append([0, 0, im_w, im_h])
- layer_idxs.append(0)
-
- def crop_len(orig_len, n_crops, overlap):
- return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
-
- for i_layer in range(n_layers):
- n_crops_per_side = 2 ** (i_layer + 1)
- overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
-
- crop_w = crop_len(im_w, n_crops_per_side, overlap)
- crop_h = crop_len(im_h, n_crops_per_side, overlap)
-
- crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
- crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
-
- # Crops in XYWH format
- for x0, y0 in product(crop_box_x0, crop_box_y0):
- box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
- crop_boxes.append(box)
- layer_idxs.append(i_layer + 1)
-
- return crop_boxes, layer_idxs
-
-
-def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
- x0, y0, _, _ = crop_box
- offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
- # Check if boxes has a channel dimension
- if len(boxes.shape) == 3:
- offset = offset.unsqueeze(1)
- return boxes + offset
-
-
-def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
- x0, y0, _, _ = crop_box
- offset = torch.tensor([[x0, y0]], device=points.device)
- # Check if points has a channel dimension
- if len(points.shape) == 3:
- offset = offset.unsqueeze(1)
- return points + offset
-
-
-def uncrop_masks(
- masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int
-) -> torch.Tensor:
- x0, y0, x1, y1 = crop_box
- if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
- return masks
- # Coordinate transform masks
- pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
- pad = (x0, pad_x - x0, y0, pad_y - y0)
- return torch.nn.functional.pad(masks, pad, value=0)
-
-
-def remove_small_regions(
- mask: np.ndarray, area_thresh: float, mode: str
-) -> Tuple[np.ndarray, bool]:
- """
- Removes small disconnected regions and holes in a mask. Returns the
- mask and an indicator of if the mask has been modified.
- """
- import cv2 # type: ignore
-
- assert mode in ["holes", "islands"]
- correct_holes = mode == "holes"
- working_mask = (correct_holes ^ mask).astype(np.uint8)
- n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
- sizes = stats[:, -1][1:] # Row 0 is background label
- small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
- if len(small_regions) == 0:
- return mask, False
- fill_labels = [0] + small_regions
- if not correct_holes:
- fill_labels = [i for i in range(n_labels) if i not in fill_labels]
- # If every region is below threshold, keep largest
- if len(fill_labels) == 0:
- fill_labels = [int(np.argmax(sizes)) + 1]
- mask = np.isin(regions, fill_labels)
- return mask, True
-
-
-def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
- from pycocotools import mask as mask_utils # type: ignore
-
- h, w = uncompressed_rle["size"]
- rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
- rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json
- return rle
-
-
-def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
- """
- Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
- an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
- """
- # torch.max below raises an error on empty inputs, just skip in this case
- if torch.numel(masks) == 0:
- return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
-
- # Normalize shape to CxHxW
- shape = masks.shape
- h, w = shape[-2:]
- if len(shape) > 2:
- masks = masks.flatten(0, -3)
- else:
- masks = masks.unsqueeze(0)
-
- # Get top and bottom edges
- in_height, _ = torch.max(masks, dim=-1)
- in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
- bottom_edges, _ = torch.max(in_height_coords, dim=-1)
- in_height_coords = in_height_coords + h * (~in_height)
- top_edges, _ = torch.min(in_height_coords, dim=-1)
-
- # Get left and right edges
- in_width, _ = torch.max(masks, dim=-2)
- in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
- right_edges, _ = torch.max(in_width_coords, dim=-1)
- in_width_coords = in_width_coords + w * (~in_width)
- left_edges, _ = torch.min(in_width_coords, dim=-1)
-
- # If the mask is empty the right edge will be to the left of the left edge.
- # Replace these boxes with [0, 0, 0, 0]
- empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
- out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
- out = out * (~empty_filter).unsqueeze(-1)
-
- # Return to original shape
- if len(shape) > 2:
- out = out.reshape(*shape[:-2], 4)
- else:
- out = out[0]
-
- return out
diff --git a/spaces/Justin-Choo/Lemon_WEB_UI/app.py b/spaces/Justin-Choo/Lemon_WEB_UI/app.py
deleted file mode 100644
index ca8bed6e2dffb239c72a70a035ed537ebffe6446..0000000000000000000000000000000000000000
--- a/spaces/Justin-Choo/Lemon_WEB_UI/app.py
+++ /dev/null
@@ -1,149 +0,0 @@
-import os
-from sys import executable as pyexecutable
-import subprocess
-import pathlib
-import gc
-
-def Gitclone(URI:str,ClonePath:str = "") -> int :
- if(ClonePath == "") :
- while True:
- i=subprocess.run([r"git",r"clone",URI])
- if(i.returncode == 0 ):
- del i
- gc.collect()
- return 0
- else :
- del i
- else:
- while True:
- i=subprocess.run([r"git",r"clone",URI,ClonePath])
- if(i.returncode == 0 ):
- del i
- gc.collect()
- return 0
- else :
- del i
-def DownLoad(URI:str,DownloadPath:str,DownLoadFileName:str ) -> int:
- while (True):
- i=subprocess.run([r"aria2c",r"-c",r"-x" ,r"16", r"-s",r"16", r"-k" ,r"1M" ,r"-m",r"0",r"--enable-mmap=false",r"--console-log-level=error",r"-d",DownloadPath,r"-o",DownLoadFileName,URI]);
- if(i.returncode == 0 ):
- del i
- gc.collect()
- return 0
- else :
- del i
-user_home =pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-#clone stable-diffusion-webui repo
-print("cloning stable-diffusion-webui repo")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui.git",str(user_home / r"stable-diffusion-webui"))
-os.chdir(str(user_home / r"stable-diffusion-webui"))
-os.system("git reset --hard 89f9faa63388756314e8a1d96cf86bf5e0663045")
-#
-
-#install extensions
-print("installing extensions")
-Gitclone(r"https://huggingface.co/embed/negative",str(user_home / r"stable-diffusion-webui" / r"embeddings" / r"negative"))
-Gitclone(r"https://huggingface.co/embed/lora",str(user_home / r"stable-diffusion-webui" / r"models" / r"Lora" / r"positive"))
-DownLoad(r"https://huggingface.co/embed/upscale/resolve/main/4x-UltraSharp.pth",str(user_home / r"stable-diffusion-webui" / r"models" / r"ESRGAN") ,r"4x-UltraSharp.pth")
-while True:
- if(subprocess.run([r"wget",r"https://raw.githubusercontent.com/camenduru/stable-diffusion-webui-scripts/main/run_n_times.py",r"-O",str(user_home / r"stable-diffusion-webui" / r"scripts" / r"run_n_times.py")]).returncode == 0):
- break
-Gitclone(r"https://github.com/deforum-art/deforum-for-automatic1111-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"deforum-for-automatic1111-webui" ))
-#Gitclone(r"https://github.com/AlUlkesh/stable-diffusion-webui-images-browser",str(user_home / r"stable-diffusion-webui" / r"extensions"/ r"stable-diffusion-webui-images-browser"))
-Gitclone(r"https://github.com/camenduru/stable-diffusion-webui-huggingface",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-huggingface"))
-Gitclone(r"https://github.com/camenduru/sd-civitai-browser",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-civitai-browser"))
-Gitclone(r"https://github.com/kohya-ss/sd-webui-additional-networks",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks"))
-Gitclone(r"https://github.com/Mikubill/sd-webui-controlnet",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet"))
-Gitclone(r"https://github.com/fkunn1326/openpose-editor",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"openpose-editor"))
-Gitclone(r"https://github.com/jexom/sd-webui-depth-lib",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-depth-lib"))
-Gitclone(r"https://github.com/hnmr293/posex",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"posex"))
-Gitclone(r"https://github.com/nonnonstop/sd-webui-3d-open-pose-editor",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-3d-open-pose-editor"))
-#中文本地化的请解除下一行的注释
-#Gitclone(r"https://github.com/dtlnor/stable-diffusion-webui-localization-zh_CN.git",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-localization-zh_CN"))
-Gitclone(r"https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git" , str(user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-tagcomplete"))
-Gitclone(r"https://github.com/camenduru/sd-webui-tunnels",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-tunnels"))
-Gitclone(r"https://github.com/etherealxx/batchlinks-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"batchlinks-webui"))
-Gitclone(r"https://github.com/catppuccin/stable-diffusion-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-catppuccin"))
-
-#Gitclone(r"https://github.com/KohakuBueleaf/a1111-sd-webui-locon",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-locon" ))
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-rembg"))
-Gitclone(r"https://github.com/ashen-sensored/stable-diffusion-webui-two-shot",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-two-shot"))
-Gitclone(r"https://github.com/camenduru/sd_webui_stealth_pnginfo",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_webui_stealth_pnginfo"))
-
-os.chdir(user_home / r"stable-diffusion-webui")
-
-#download ControlNet models
-print("extensions dolwnload done .\ndownloading ControlNet models")
-dList =[r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_seg_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_ip2p_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_shuffle_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_canny_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1p_sd15_depth_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_inpaint_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_lineart_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_mlsd_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_normalbae_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_openpose_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_scribble_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_seg_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_softedge_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15s2_lineart_anime_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1e_sd15_tile_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_style_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_seg_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_openpose_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_keypose_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_zoedepth_sd15v1.pth"]
-for i in range(0,len(dList)): DownLoad(dList[i],str(user_home / "stable-diffusion-webui" / "extensions" / "sd-webui-controlnet" / "models"),pathlib.Path(dList[i]).name)
-del dList
-
-#download model
-#you can change model download address here
-print("ControlNet models download done.\ndownloading model")
-DownLoad(r"https://huggingface.co/iZELX1/Grapefruit/resolve/main/lemon.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"lemon.safetensors")
-
-#DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.5-pruned.ckpt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anything-v4.5-pruned.ckpt")
-#DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.0.vae.pt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anything-v4.0.vae.pt")
-#DownLoad(r"https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/Counterfeit-V3.0_fp16.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"Counterfeit-V3.0_fp16.safetensors")
-#DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1B_orangemixs.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"AOM3A1B_orangemixs.safetensors")
-#DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"orangemix.vae.pt")
-#DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Baked%20VAE.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"MeinaPastelV5_BakedVAE.safetensors")
-#DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Without%20VAE.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"MeinaPastelV5_WithoutVAE.safetensors")
-#DownLoad(r"https://civitai.com/api/download/models/9474",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"chilloutmix_NiPrunedFp16.safetensors")
-
-DownLoad(r"https://civitai.com/api/download/models/39885",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"Better_light.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/21065",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"LAS.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/39164",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"backlighting.safetensors")
-#strt webui
-
-print("Done\nStarting Webui...")
-os.chdir(user_home / r"stable-diffusion-webui")
-while True:
- ret=subprocess.run([r"python3" ,r"launch.py",r"--precision",r"full",r"--no-half",r"--no-half-vae",r"--enable-insecure-extension-access",r"--medvram",r"--skip-torch-cuda-test",r"--enable-console-prompts",r"--ui-settings-file="+str(pathlib.Path(__file__).parent /r"config.json")])
- if(ret.returncode == 0 ):
- del ret
- gc.collect()
- else :
- del ret
-
-del os ,user_home ,pyexecutable ,subprocess
\ No newline at end of file
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/extract_locale.py b/spaces/Kangarroar/ApplioRVC-Inference/extract_locale.py
deleted file mode 100644
index a4ff5ea3ddd7c612c640544099ab98a861b8fe35..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/extract_locale.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import json
-import re
-
-# Define regular expression patterns
-pattern = r"""i18n\([\s\n\t]*(["'][^"']+["'])[\s\n\t]*\)"""
-
-# Initialize the dictionary to store key-value pairs
-data = {}
-
-
-def process(fn: str):
- global data
- with open(fn, "r", encoding="utf-8") as f:
- contents = f.read()
- matches = re.findall(pattern, contents)
- for key in matches:
- key = eval(key)
- print("extract:", key)
- data[key] = key
-
-
-print("processing infer-web.py")
-process("infer-web.py")
-
-print("processing gui_v0.py")
-process("gui_v0.py")
-
-print("processing gui_v1.py")
-process("gui_v1.py")
-
-# Save as a JSON file
-with open("./i18n/en_US.json", "w", encoding="utf-8") as f:
- json.dump(data, f, ensure_ascii=False, indent=4)
- f.write("\n")
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/lib/uvr5_pack/lib_v5/layers_537227KB.py b/spaces/Kangarroar/ApplioRVC-Inference/lib/uvr5_pack/lib_v5/layers_537227KB.py
deleted file mode 100644
index a38b7bb3ae3136b07eadfc2db445fef4c2de186b..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/lib/uvr5_pack/lib_v5/layers_537227KB.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(Conv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nout,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- bias=False,
- ),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(SeperableConv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nin,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- groups=nin,
- bias=False,
- ),
- nn.Conv2d(nin, nout, kernel_size=1, bias=False),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class Encoder(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
- super(Encoder, self).__init__()
- self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
- def __call__(self, x):
- skip = self.conv1(x)
- h = self.conv2(skip)
-
- return h, skip
-
-
-class Decoder(nn.Module):
- def __init__(
- self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
- ):
- super(Decoder, self).__init__()
- self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.dropout = nn.Dropout2d(0.1) if dropout else None
-
- def __call__(self, x, skip=None):
- x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
- if skip is not None:
- skip = spec_utils.crop_center(skip, x)
- x = torch.cat([x, skip], dim=1)
- h = self.conv(x)
-
- if self.dropout is not None:
- h = self.dropout(h)
-
- return h
-
-
-class ASPPModule(nn.Module):
- def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
- super(ASPPModule, self).__init__()
- self.conv1 = nn.Sequential(
- nn.AdaptiveAvgPool2d((1, None)),
- Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
- )
- self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
- self.conv3 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
- )
- self.conv4 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
- )
- self.conv5 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.conv6 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.conv7 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.bottleneck = nn.Sequential(
- Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
- )
-
- def forward(self, x):
- _, _, h, w = x.size()
- feat1 = F.interpolate(
- self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
- )
- feat2 = self.conv2(x)
- feat3 = self.conv3(x)
- feat4 = self.conv4(x)
- feat5 = self.conv5(x)
- feat6 = self.conv6(x)
- feat7 = self.conv7(x)
- out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
- bottle = self.bottleneck(out)
- return bottle
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/backbones/__init__.py b/spaces/KyanChen/RSPrompter/mmdet/models/backbones/__init__.py
deleted file mode 100644
index e16ff85f7037b36fb2046fcbcd3af523050a6516..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/backbones/__init__.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .csp_darknet import CSPDarknet
-from .cspnext import CSPNeXt
-from .darknet import Darknet
-from .detectors_resnet import DetectoRS_ResNet
-from .detectors_resnext import DetectoRS_ResNeXt
-from .efficientnet import EfficientNet
-from .hourglass import HourglassNet
-from .hrnet import HRNet
-from .mobilenet_v2 import MobileNetV2
-from .pvt import PyramidVisionTransformer, PyramidVisionTransformerV2
-from .regnet import RegNet
-from .res2net import Res2Net
-from .resnest import ResNeSt
-from .resnet import ResNet, ResNetV1d
-from .resnext import ResNeXt
-from .ssd_vgg import SSDVGG
-from .swin import SwinTransformer
-from .trident_resnet import TridentResNet
-
-__all__ = [
- 'RegNet', 'ResNet', 'ResNetV1d', 'ResNeXt', 'SSDVGG', 'HRNet',
- 'MobileNetV2', 'Res2Net', 'HourglassNet', 'DetectoRS_ResNet',
- 'DetectoRS_ResNeXt', 'Darknet', 'ResNeSt', 'TridentResNet', 'CSPDarknet',
- 'SwinTransformer', 'PyramidVisionTransformer',
- 'PyramidVisionTransformerV2', 'EfficientNet', 'CSPNeXt'
-]
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_pack/modules/F0Predictor/PMF0Predictor.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_pack/modules/F0Predictor/PMF0Predictor.py
deleted file mode 100644
index 29b2d78eec2b4de5e617a21120abd5fb5a716ee5..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_pack/modules/F0Predictor/PMF0Predictor.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from lib.infer.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import parselmouth
-import numpy as np
-
-
-class PMF0Predictor(F0Predictor):
- def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
- self.hop_length = hop_length
- self.f0_min = f0_min
- self.f0_max = f0_max
- self.sampling_rate = sampling_rate
-
- def interpolate_f0(self, f0):
- """
- 对F0进行插值处理
- """
-
- data = np.reshape(f0, (f0.size, 1))
-
- vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
- vuv_vector[data > 0.0] = 1.0
- vuv_vector[data <= 0.0] = 0.0
-
- ip_data = data
-
- frame_number = data.size
- last_value = 0.0
- for i in range(frame_number):
- if data[i] <= 0.0:
- j = i + 1
- for j in range(i + 1, frame_number):
- if data[j] > 0.0:
- break
- if j < frame_number - 1:
- if last_value > 0.0:
- step = (data[j] - data[i - 1]) / float(j - i)
- for k in range(i, j):
- ip_data[k] = data[i - 1] + step * (k - i + 1)
- else:
- for k in range(i, j):
- ip_data[k] = data[j]
- else:
- for k in range(i, frame_number):
- ip_data[k] = last_value
- else:
- ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝
- last_value = data[i]
-
- return ip_data[:, 0], vuv_vector[:, 0]
-
- def compute_f0(self, wav, p_len=None):
- x = wav
- if p_len is None:
- p_len = x.shape[0] // self.hop_length
- else:
- assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
- time_step = self.hop_length / self.sampling_rate * 1000
- f0 = (
- parselmouth.Sound(x, self.sampling_rate)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=self.f0_min,
- pitch_ceiling=self.f0_max,
- )
- .selected_array["frequency"]
- )
-
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
- f0, uv = self.interpolate_f0(f0)
- return f0
-
- def compute_f0_uv(self, wav, p_len=None):
- x = wav
- if p_len is None:
- p_len = x.shape[0] // self.hop_length
- else:
- assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
- time_step = self.hop_length / self.sampling_rate * 1000
- f0 = (
- parselmouth.Sound(x, self.sampling_rate)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=self.f0_min,
- pitch_ceiling=self.f0_max,
- )
- .selected_array["frequency"]
- )
-
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
- f0, uv = self.interpolate_f0(f0)
- return f0, uv
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/analyzers/calmar.py b/spaces/Lianjd/stock_dashboard/backtrader/analyzers/calmar.py
deleted file mode 100644
index ec4849285c3fb1ee5ccad6ffd220787108482150..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/analyzers/calmar.py
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program. If not, see
-
- ) : null
-}
diff --git a/spaces/NeuralInternet/Text-Generation_Playground/modules/ui.py b/spaces/NeuralInternet/Text-Generation_Playground/modules/ui.py
deleted file mode 100644
index bb193e35c11b2a3d474ea89e7567206a3343395a..0000000000000000000000000000000000000000
--- a/spaces/NeuralInternet/Text-Generation_Playground/modules/ui.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import gradio as gr
-
-refresh_symbol = '\U0001f504' # 🔄
-
-css = """
-.tabs.svelte-710i53 {
- margin-top: 0
-}
-.py-6 {
- padding-top: 2.5rem
-}
-.dark #refresh-button {
- background-color: #ffffff1f;
-}
-#refresh-button {
- flex: none;
- margin: 0;
- padding: 0;
- min-width: 50px;
- border: none;
- box-shadow: none;
- border-radius: 10px;
- background-color: #0000000d;
-}
-#download-label, #upload-label {
- min-height: 0
-}
-#accordion {
-}
-.dark svg {
- fill: white;
-}
-svg {
- display: unset !important;
- vertical-align: middle !important;
- margin: 5px;
-}
-ol li p, ul li p {
- display: inline-block;
-}
-"""
-
-chat_css = """
-.h-\[40vh\], .wrap.svelte-byatnx.svelte-byatnx.svelte-byatnx {
- height: 66.67vh
-}
-.gradio-container {
- max-width: 800px !important;
- margin-left: auto !important;
- margin-right: auto !important;
-}
-.w-screen {
- width: unset
-}
-div.svelte-362y77>*, div.svelte-362y77>.form>* {
- flex-wrap: nowrap
-}
-/* fixes the API documentation in chat mode */
-.api-docs.svelte-1iguv9h.svelte-1iguv9h.svelte-1iguv9h {
- display: grid;
-}
-.pending.svelte-1ed2p3z {
- opacity: 1;
-}
-"""
-
-class ToolButton(gr.Button, gr.components.FormComponent):
- """Small button with single emoji as text, fits inside gradio forms"""
-
- def __init__(self, **kwargs):
- super().__init__(variant="tool", **kwargs)
-
- def get_block_name(self):
- return "button"
-
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
- def refresh():
- refresh_method()
- args = refreshed_args() if callable(refreshed_args) else refreshed_args
-
- for k, v in args.items():
- setattr(refresh_component, k, v)
-
- return gr.update(**(args or {}))
-
- refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
- refresh_button.click(
- fn=refresh,
- inputs=[],
- outputs=[refresh_component]
- )
- return refresh_button
diff --git a/spaces/NeuroSenko/audio-processing-utils/README.md b/spaces/NeuroSenko/audio-processing-utils/README.md
deleted file mode 100644
index fd87949483922dbe320990a02f82a42b3177cbf5..0000000000000000000000000000000000000000
--- a/spaces/NeuroSenko/audio-processing-utils/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: Audio Processing Utils
-emoji: 📊
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-How to run locally using Windows:
-1. Mare sure you have installed ffmpeg in your system
-2. Clone the repo: `git clone https://huggingface.co/spaces/NeuroSenko/audio-processing-utils`
-3. Run `install.bat`
-4. Run `start.bat`
diff --git a/spaces/NickOrion21/stabilityai-stable-diffusion-2-1/app.py b/spaces/NickOrion21/stabilityai-stable-diffusion-2-1/app.py
deleted file mode 100644
index 0160420876923d89f2ab5fccb9f4d13725e29972..0000000000000000000000000000000000000000
--- a/spaces/NickOrion21/stabilityai-stable-diffusion-2-1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stabilityai/stable-diffusion-2-1").launch()
\ No newline at end of file
diff --git a/spaces/NoCrypt/mikuTTS/app.py b/spaces/NoCrypt/mikuTTS/app.py
deleted file mode 100644
index a4b8011e6198a0466cadf939119c6a60289f82e5..0000000000000000000000000000000000000000
--- a/spaces/NoCrypt/mikuTTS/app.py
+++ /dev/null
@@ -1,311 +0,0 @@
-import asyncio
-import datetime
-import logging
-import os
-import time
-import traceback
-
-import edge_tts
-import gradio as gr
-import librosa
-import torch
-from fairseq import checkpoint_utils
-from huggingface_hub import snapshot_download
-
-
-from config import Config
-from lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from rmvpe import RMVPE
-from vc_infer_pipeline import VC
-
-logging.getLogger("fairseq").setLevel(logging.WARNING)
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-limitation = os.getenv("SYSTEM") == "spaces"
-
-config = Config()
-
-# Edge TTS
-edge_output_filename = "edge_output.mp3"
-tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-tts_voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
-
-# RVC models
-model_root = snapshot_download(repo_id="NoCrypt/miku_RVC", token=os.environ["TOKEN"])
-models = [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
-models.sort()
-
-
-def model_data(model_name):
- # global n_spk, tgt_sr, net_g, vc, cpt, version, index_file
- pth_path = [
- f"{model_root}/{model_name}/{f}"
- for f in os.listdir(f"{model_root}/{model_name}")
- if f.endswith(".pth")
- ][0]
- print(f"Loading {pth_path}")
- cpt = torch.load(pth_path, map_location="cpu")
- tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- if_f0 = cpt.get("f0", 1)
- version = cpt.get("version", "v1")
- if version == "v1":
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- elif version == "v2":
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
- else:
- raise ValueError("Unknown version")
- del net_g.enc_q
- net_g.load_state_dict(cpt["weight"], strict=False)
- print("Model loaded")
- net_g.eval().to(config.device)
- if config.is_half:
- net_g = net_g.half()
- else:
- net_g = net_g.float()
- vc = VC(tgt_sr, config)
- # n_spk = cpt["config"][-3]
-
- index_files = [
- f"{model_root}/{model_name}/{f}"
- for f in os.listdir(f"{model_root}/{model_name}")
- if f.endswith(".index")
- ]
- if len(index_files) == 0:
- print("No index file found")
- index_file = ""
- else:
- index_file = index_files[0]
- print(f"Index file found: {index_file}")
-
- return tgt_sr, net_g, vc, version, index_file, if_f0
-
-
-def load_hubert():
- # global hubert_model
- models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
- ["hubert_base.pt"],
- suffix="",
- )
- hubert_model = models[0]
- hubert_model = hubert_model.to(config.device)
- if config.is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
- return hubert_model.eval()
-
-
-def tts(
- model_name,
- speed,
- tts_text,
- tts_voice,
- f0_up_key,
- f0_method,
- index_rate,
- protect,
- filter_radius=3,
- resample_sr=0,
- rms_mix_rate=0.25,
-):
- print("------------------")
- print(datetime.datetime.now())
- print("tts_text:")
- print(tts_text)
- print(f"tts_voice: {tts_voice}, speed: {speed}")
- print(f"Model name: {model_name}")
- print(f"F0: {f0_method}, Key: {f0_up_key}, Index: {index_rate}, Protect: {protect}")
- try:
- if limitation and len(tts_text) > 1000:
- print("Error: Text too long")
- return (
- f"Text characters should be at most 1000 in this huggingface space, but got {len(tts_text)} characters.",
- None,
- None,
- )
- t0 = time.time()
- if speed >= 0:
- speed_str = f"+{speed}%"
- else:
- speed_str = f"{speed}%"
- asyncio.run(
- edge_tts.Communicate(
- tts_text, "-".join(tts_voice.split("-")[:-1]), rate=speed_str
- ).save(edge_output_filename)
- )
- t1 = time.time()
- edge_time = t1 - t0
- audio, sr = librosa.load(edge_output_filename, sr=16000, mono=True)
- duration = len(audio) / sr
- print(f"Audio duration: {duration}s")
- if limitation and duration >= 200:
- print("Error: Audio too long")
- return (
- f"Audio should be less than 200 seconds in this huggingface space, but got {duration}s.",
- edge_output_filename,
- None,
- )
- f0_up_key = int(f0_up_key)
-
- tgt_sr, net_g, vc, version, index_file, if_f0 = model_data(model_name)
- if f0_method == "rmvpe":
- vc.model_rmvpe = rmvpe_model
- times = [0, 0, 0]
- audio_opt = vc.pipeline(
- hubert_model,
- net_g,
- 0,
- audio,
- edge_output_filename,
- times,
- f0_up_key,
- f0_method,
- index_file,
- # file_big_npy,
- index_rate,
- if_f0,
- filter_radius,
- tgt_sr,
- resample_sr,
- rms_mix_rate,
- version,
- protect,
- None,
- )
- if tgt_sr != resample_sr >= 16000:
- tgt_sr = resample_sr
- info = f"Success. Time: edge-tts: {edge_time}s, npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s"
- print(info)
- return (
- info,
- edge_output_filename,
- (tgt_sr, audio_opt),
- )
- except EOFError:
- info = (
- "It seems that the edge-tts output is not valid. "
- "This may occur when the input text and the speaker do not match. "
- "For example, maybe you entered Japanese (without alphabets) text but chose non-Japanese speaker?"
- )
- print(info)
- return info, None, None
- except:
- info = traceback.format_exc()
- print(info)
- return info, None, None
-
-
-print("Loading hubert model...")
-hubert_model = load_hubert()
-print("Hubert model loaded.")
-
-print("Loading rmvpe model...")
-rmvpe_model = RMVPE("rmvpe.pt", config.is_half, config.device)
-print("rmvpe model loaded.")
-
-initial_md = """
-
-"""
-
-app = gr.Blocks(theme='NoCrypt/miku')
-with app:
- gr.Markdown(initial_md)
- with gr.Row():
- with gr.Column():
- model_name = gr.Dropdown(
- label="Model",
- choices=models,
- value=models[0],
- )
- f0_key_up = gr.Number(
- label="Tune",
- value=6,
- )
- with gr.Column():
- f0_method = gr.Radio(
- label="Pitch extraction method (pm: very fast, low quality, rmvpe: a little slow, high quality)",
- choices=["pm", "rmvpe"], # harvest and crepe is too slow
- value="rmvpe",
- interactive=True,
- )
- index_rate = gr.Slider(
- minimum=0,
- maximum=1,
- label="Index rate",
- value=1,
- interactive=True,
- )
- protect0 = gr.Slider(
- minimum=0,
- maximum=0.5,
- label="Protect",
- value=0.33,
- step=0.01,
- interactive=True,
- )
- with gr.Row():
- with gr.Column():
- tts_voice = gr.Dropdown(
- label="Edge-tts speaker (format: language-Country-Name-Gender), make sure the gender matches the model",
- choices=tts_voices,
- allow_custom_value=False,
- value="ja-JP-NanamiNeural-Female",
- )
- speed = gr.Slider(
- minimum=-100,
- maximum=100,
- label="Speech speed (%)",
- value=0,
- step=10,
- interactive=True,
- )
- tts_text = gr.Textbox(label="Input Text", value="こんにちは、私の名前は初音ミクです!")
- with gr.Column():
- but0 = gr.Button("Convert", variant="primary")
- info_text = gr.Textbox(label="Output info")
- with gr.Column():
- with gr.Accordion("Edge Voice", open=False):
- edge_tts_output = gr.Audio(label="Edge Voice", type="filepath")
- tts_output = gr.Audio(label="Result")
- but0.click(
- tts,
- [
- model_name,
- speed,
- tts_text,
- tts_voice,
- f0_key_up,
- f0_method,
- index_rate,
- protect0,
- ],
- [info_text, edge_tts_output, tts_output],
- )
- with gr.Row():
- examples = gr.Examples(
- examples_per_page=100,
- examples=[
- ["こんにちは、私の名前は初音ミクです!", "ja-JP-NanamiNeural-Female", 6],
- ["Hello there. My name is Hatsune Miku!","en-CA-ClaraNeural-Female", 6],
- ["Halo. Nama saya Hatsune Miku!","id-ID-GadisNeural-Female", 4],
- ["Halo. Jenengku Hatsune Miku!","jv-ID-SitiNeural-Female", 10],
- ],
- inputs=[tts_text, tts_voice, f0_key_up],
- )
-
-app.launch()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/wav2vec/wav2vec2_asr.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/wav2vec/wav2vec2_asr.py
deleted file mode 100644
index eb5d819da5121a243e345b3812292ef0b13ccf98..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/wav2vec/wav2vec2_asr.py
+++ /dev/null
@@ -1,664 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from argparse import Namespace
-import contextlib
-import copy
-import math
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from dataclasses import dataclass, field
-from omegaconf import MISSING, II, open_dict
-from typing import Any, Optional
-
-from fairseq import checkpoint_utils, tasks, utils
-from fairseq.dataclass import FairseqDataclass
-from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-from fairseq.tasks import FairseqTask
-from fairseq.models import (
- BaseFairseqModel,
- FairseqEncoder,
- FairseqEncoderDecoderModel,
- FairseqIncrementalDecoder,
- register_model,
-)
-from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES
-from fairseq.modules import (
- LayerNorm,
- PositionalEmbedding,
- TransformerDecoderLayer,
-)
-
-
-@dataclass
-class Wav2Vec2AsrConfig(FairseqDataclass):
- w2v_path: str = field(
- default=MISSING, metadata={"help": "path to wav2vec 2.0 model"}
- )
- no_pretrained_weights: bool = field(
- default=False, metadata={"help": "if true, does not load pretrained weights"}
- )
- dropout_input: float = field(
- default=0.0,
- metadata={"help": "dropout to apply to the input (after feat extr)"},
- )
- final_dropout: float = field(
- default=0.0,
- metadata={"help": "dropout after transformer and before final projection"},
- )
- dropout: float = field(
- default=0.0, metadata={"help": "dropout probability inside wav2vec 2.0 model"}
- )
- attention_dropout: float = field(
- default=0.0,
- metadata={
- "help": "dropout probability for attention weights inside wav2vec 2.0 model"
- },
- )
- activation_dropout: float = field(
- default=0.0,
- metadata={
- "help": "dropout probability after activation in FFN inside wav2vec 2.0 model"
- },
- )
- conv_feature_layers: Optional[str] = field(
- default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]",
- metadata={
- "help": (
- "string describing convolutional feature extraction "
- "layers in form of a python list that contains "
- "[(dim, kernel_size, stride), ...]"
- ),
- },
- )
- encoder_embed_dim: Optional[int] = field(
- default=768, metadata={"help": "encoder embedding dimension"}
- )
-
- # masking
- apply_mask: bool = field(
- default=False, metadata={"help": "apply masking during fine-tuning"}
- )
- mask_length: int = field(
- default=10, metadata={"help": "repeat the mask indices multiple times"}
- )
- mask_prob: float = field(
- default=0.5,
- metadata={
- "help": "probability of replacing a token with mask (normalized by length)"
- },
- )
- mask_selection: MASKING_DISTRIBUTION_CHOICES = field(
- default="static", metadata={"help": "how to choose masks"}
- )
- mask_other: float = field(
- default=0,
- metadata={
- "help": "secondary mask argument (used for more complex distributions), "
- "see help in compute_mask_indices"
- },
- )
- no_mask_overlap: bool = field(
- default=False, metadata={"help": "whether to allow masks to overlap"}
- )
- mask_min_space: Optional[int] = field(
- default=1,
- metadata={"help": "min space between spans (if no overlap is enabled)"},
- )
-
- # channel masking
- mask_channel_length: int = field(
- default=10, metadata={"help": "length of the mask for features (channels)"}
- )
- mask_channel_prob: float = field(
- default=0.0, metadata={"help": "probability of replacing a feature with 0"}
- )
- mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field(
- default="static",
- metadata={"help": "how to choose mask length for channel masking"},
- )
- mask_channel_other: float = field(
- default=0,
- metadata={
- "help": "secondary mask argument (used for more complex distributions), "
- "see help in compute_mask_indicesh"
- },
- )
- no_mask_channel_overlap: bool = field(
- default=False, metadata={"help": "whether to allow channel masks to overlap"}
- )
- freeze_finetune_updates: int = field(
- default=0, metadata={"help": "dont finetune wav2vec for this many updates"}
- )
- feature_grad_mult: float = field(
- default=0.0, metadata={"help": "reset feature grad mult in wav2vec 2.0 to this"}
- )
- layerdrop: float = field(
- default=0.0, metadata={"help": "probability of dropping a layer in wav2vec 2.0"}
- )
- mask_channel_min_space: Optional[int] = field(
- default=1,
- metadata={"help": "min space between spans (if no overlap is enabled)"},
- )
- mask_channel_before: bool = False
- normalize: bool = II("task.normalize")
- data: str = II("task.data")
- # this holds the loaded wav2vec args
- w2v_args: Any = None
-
-
-@dataclass
-class Wav2Vec2CtcConfig(Wav2Vec2AsrConfig):
- blank_weight: float = 0
- blank_mode: str = "add"
-
-
-@register_model("wav2vec_ctc", dataclass=Wav2Vec2CtcConfig)
-class Wav2VecCtc(BaseFairseqModel):
- def __init__(self, cfg: Wav2Vec2CtcConfig, w2v_encoder: BaseFairseqModel):
- super().__init__()
- self.cfg = cfg
- self.w2v_encoder = w2v_encoder
- self.blank_weight = cfg.blank_weight
- self.blank_mode = cfg.blank_mode
-
- def upgrade_state_dict_named(self, state_dict, name):
- super().upgrade_state_dict_named(state_dict, name)
- return state_dict
-
- @classmethod
- def build_model(cls, cfg: Wav2Vec2CtcConfig, task: FairseqTask):
- """Build a new model instance."""
- w2v_encoder = Wav2VecEncoder(cfg, len(task.target_dictionary))
- return cls(cfg, w2v_encoder)
-
- def get_logits(self, net_output, normalize=False):
- logits = net_output["encoder_out"]
- if self.blank_weight != 0:
- if self.blank_mode == "add":
- logits[..., 0] += self.blank_weight
- elif self.blank_mode == "set":
- logits[..., 0] = self.blank_weight
- else:
- raise Exception(f"invalid blank mode {self.blank_mode}")
-
- if net_output["padding_mask"] is not None and net_output["padding_mask"].any():
- logits[net_output["padding_mask"].T][..., 0] = float("inf")
- logits[net_output["padding_mask"].T][..., 1:] = float("-inf")
-
- if normalize:
- logits = utils.log_softmax(logits.float(), dim=-1)
-
- return logits
-
- def get_normalized_probs(self, net_output, log_probs):
- """Get normalized probabilities (or log probs) from a net's output."""
-
- logits = self.get_logits(net_output)
-
- if log_probs:
- return utils.log_softmax(logits.float(), dim=-1)
- else:
- return utils.softmax(logits.float(), dim=-1)
-
- def forward(self, **kwargs):
- x = self.w2v_encoder(**kwargs)
- return x
-
-
-@dataclass
-class Wav2Vec2Seq2SeqConfig(Wav2Vec2AsrConfig):
- decoder_embed_dim: int = field(
- default=768, metadata={"help": "decoder embedding dimension"}
- )
- decoder_ffn_embed_dim: int = field(
- default=3072, metadata={"help": "decoder embedding dimension for FFN"}
- )
- decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"})
- decoder_layerdrop: float = field(
- default=0.0, metadata={"help": "decoder layerdrop chance"}
- )
- decoder_attention_heads: int = field(
- default=4, metadata={"help": "num decoder attention heads"}
- )
- decoder_learned_pos: bool = field(
- default=False,
- metadata={"help": "use learned positional embeddings in the decoder"},
- )
- decoder_normalize_before: bool = field(
- default=False, metadata={"help": "apply layernorm before each decoder block"}
- )
- no_token_positional_embeddings: bool = field(
- default=False,
- metadata={
- "help": "if set, disables positional embeddings (outside self attention)"
- },
- )
- decoder_dropout: float = field(
- default=0.0, metadata={"help": "dropout probability in the decoder"}
- )
- decoder_attention_dropout: float = field(
- default=0.0,
- metadata={
- "help": "dropout probability for attention weights inside the decoder"
- },
- )
- decoder_activation_dropout: float = field(
- default=0.0,
- metadata={
- "help": "dropout probability after activation in FFN inside the decoder"
- },
- )
- max_target_positions: int = field(
- default=2048, metadata={"help": "max target positions"}
- )
- share_decoder_input_output_embed: bool = field(
- default=False, metadata={"help": "share decoder input and output embeddings"}
- )
- autoregressive: bool = II("task.autoregressive")
-
-
-@register_model("wav2vec_seq2seq", dataclass=Wav2Vec2Seq2SeqConfig)
-class Wav2Vec2Seq2SeqModel(FairseqEncoderDecoderModel):
- def __init__(self, encoder, decoder):
- super().__init__(encoder, decoder)
-
- @classmethod
- def build_model(cls, cfg: Wav2Vec2Seq2SeqConfig, task: FairseqTask):
- """Build a new model instance."""
-
- assert (
- cfg.autoregressive
- ), "Please set task.autoregressive=true for seq2seq asr models"
-
- src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
-
- def build_embedding(dictionary, embed_dim):
- num_embeddings = len(dictionary)
- padding_idx = dictionary.pad()
- emb = Embedding(num_embeddings, embed_dim, padding_idx)
- return emb
-
- decoder_embed_tokens = build_embedding(tgt_dict, cfg.decoder_embed_dim)
-
- encoder = cls.build_encoder(cfg)
- decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens)
-
- return Wav2Vec2Seq2SeqModel(encoder, decoder)
-
- @classmethod
- def build_encoder(cls, cfg: Wav2Vec2AsrConfig):
- return Wav2VecEncoder(cfg)
-
- @classmethod
- def build_decoder(cls, cfg: Wav2Vec2Seq2SeqConfig, tgt_dict, embed_tokens):
- return TransformerDecoder(cfg, tgt_dict, embed_tokens)
-
- def forward(self, **kwargs):
- encoder_out = self.encoder(**kwargs)
- decoder_out = self.decoder(encoder_out=encoder_out, **kwargs)
- return decoder_out
-
- def upgrade_state_dict_named(self, state_dict, name):
- super().upgrade_state_dict_named(state_dict, name)
- return state_dict
-
-
-class Wav2VecEncoder(FairseqEncoder):
- def __init__(self, cfg: Wav2Vec2AsrConfig, output_size=None):
- self.apply_mask = cfg.apply_mask
-
- arg_overrides = {
- "dropout": cfg.dropout,
- "activation_dropout": cfg.activation_dropout,
- "dropout_input": cfg.dropout_input,
- "attention_dropout": cfg.attention_dropout,
- "mask_length": cfg.mask_length,
- "mask_prob": cfg.mask_prob,
- "mask_selection": cfg.mask_selection,
- "mask_other": cfg.mask_other,
- "no_mask_overlap": cfg.no_mask_overlap,
- "mask_channel_length": cfg.mask_channel_length,
- "mask_channel_prob": cfg.mask_channel_prob,
- "mask_channel_before": cfg.mask_channel_before,
- "mask_channel_selection": cfg.mask_channel_selection,
- "mask_channel_other": cfg.mask_channel_other,
- "no_mask_channel_overlap": cfg.no_mask_channel_overlap,
- "encoder_layerdrop": cfg.layerdrop,
- "feature_grad_mult": cfg.feature_grad_mult,
- }
-
- if cfg.w2v_args is None:
- state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides)
- w2v_args = state.get("cfg", None)
- if w2v_args is None:
- w2v_args = convert_namespace_to_omegaconf(state["args"])
- w2v_args.criterion = None
- w2v_args.lr_scheduler = None
- cfg.w2v_args = w2v_args
- else:
- state = None
- w2v_args = cfg.w2v_args
- if isinstance(w2v_args, Namespace):
- cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args)
-
- assert cfg.normalize == w2v_args.task.normalize, (
- "Fine-tuning works best when data normalization is the same. "
- "Please check that --normalize is set or unset for both pre-training and here"
- )
-
- w2v_args.task.data = cfg.data
- task = tasks.setup_task(w2v_args.task)
- model = task.build_model(w2v_args.model)
-
- if state is not None and not cfg.no_pretrained_weights:
- model.load_state_dict(state["model"], strict=True)
-
- model.remove_pretraining_modules()
-
- super().__init__(task.source_dictionary)
-
- d = w2v_args.model.encoder_embed_dim
-
- self.w2v_model = model
-
- self.final_dropout = nn.Dropout(cfg.final_dropout)
- self.freeze_finetune_updates = cfg.freeze_finetune_updates
- self.num_updates = 0
-
- targ_d = None
- self.proj = None
-
- if output_size is not None:
- targ_d = output_size
- elif getattr(cfg, "decoder_embed_dim", d) != d:
- targ_d = cfg.decoder_embed_dim
-
- if targ_d is not None:
- self.proj = Linear(d, targ_d)
-
- def set_num_updates(self, num_updates):
- """Set the number of parameters updates."""
- super().set_num_updates(num_updates)
- self.num_updates = num_updates
-
- def forward(self, source, padding_mask, **kwargs):
-
- w2v_args = {
- "source": source,
- "padding_mask": padding_mask,
- "mask": self.apply_mask and self.training,
- }
-
- ft = self.freeze_finetune_updates <= self.num_updates
-
- with torch.no_grad() if not ft else contextlib.ExitStack():
- res = self.w2v_model.extract_features(**w2v_args)
-
- x = res["x"]
- padding_mask = res["padding_mask"]
-
- # B x T x C -> T x B x C
- x = x.transpose(0, 1)
-
- x = self.final_dropout(x)
-
- if self.proj:
- x = self.proj(x)
-
- return {
- "encoder_out": x, # T x B x C
- "padding_mask": padding_mask, # B x T,
- "layer_results": res["layer_results"],
- }
-
- def forward_torchscript(self, net_input):
- if torch.jit.is_scripting():
- return self.forward(net_input["source"], net_input["padding_mask"])
- else:
- return self.forward_non_torchscript(net_input)
-
- def reorder_encoder_out(self, encoder_out, new_order):
- if encoder_out["encoder_out"] is not None:
- encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
- 1, new_order
- )
- if encoder_out["padding_mask"] is not None:
- encoder_out["padding_mask"] = encoder_out[
- "padding_mask"
- ].index_select(0, new_order)
- return encoder_out
-
- def max_positions(self):
- """Maximum input length supported by the encoder."""
- return None
-
- def upgrade_state_dict_named(self, state_dict, name):
- return state_dict
-
-
-class TransformerDecoder(FairseqIncrementalDecoder):
- """
- Transformer decoder consisting of *args.decoder_layers* layers. Each layer
- is a :class:`TransformerDecoderLayer`.
-
- Args:
- args (argparse.Namespace): parsed command-line arguments
- dictionary (~fairseq.data.Dictionary): decoding dictionary
- embed_tokens (torch.nn.Embedding): output embedding
- no_encoder_attn (bool, optional): whether to attend to encoder outputs
- (default: False).
- """
-
- def __init__(
- self,
- cfg: Wav2Vec2Seq2SeqConfig,
- dictionary,
- embed_tokens,
- no_encoder_attn=False,
- ):
- super().__init__(dictionary)
-
- self.dropout = cfg.decoder_dropout
- self.share_input_output_embed = cfg.share_decoder_input_output_embed
-
- input_embed_dim = embed_tokens.embedding_dim
- embed_dim = cfg.decoder_embed_dim
- self.output_embed_dim = cfg.decoder_embed_dim
-
- self.layerdrop = cfg.decoder_layerdrop
-
- self.padding_idx = embed_tokens.padding_idx
- self.max_target_positions = cfg.max_target_positions
-
- self.embed_tokens = embed_tokens
- self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim
-
- self.project_in_dim = (
- Linear(input_embed_dim, embed_dim, bias=False)
- if embed_dim != input_embed_dim
- else None
- )
-
- self.embed_positions = (
- PositionalEmbedding(
- cfg.max_target_positions,
- embed_dim,
- self.padding_idx,
- learned=cfg.decoder_learned_pos,
- )
- if not cfg.no_token_positional_embeddings
- else None
- )
-
- # TODO: update this when transformer gets converted to dataclass configs
- transformer_cfg = copy.deepcopy(cfg)
- with open_dict(transformer_cfg):
- transformer_cfg.dropout = transformer_cfg.decoder_dropout
- transformer_cfg.attention_dropout = (
- transformer_cfg.decoder_attention_dropout
- )
- transformer_cfg.activation_dropout = (
- transformer_cfg.decoder_activation_dropout
- )
-
- self.layers = nn.ModuleList([])
- self.layers.extend(
- [
- TransformerDecoderLayer(transformer_cfg, no_encoder_attn)
- for _ in range(transformer_cfg.decoder_layers)
- ]
- )
-
- if not self.share_input_output_embed:
- self.embed_out = nn.Parameter(
- torch.Tensor(len(dictionary), self.output_embed_dim)
- )
- nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim ** -0.5)
-
- if transformer_cfg.decoder_normalize_before:
- self.layer_norm = LayerNorm(embed_dim)
- else:
- self.layer_norm = None
-
- def forward(
- self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused
- ):
- """
- Args:
- prev_output_tokens (LongTensor): previous decoder outputs of shape
- `(batch, tgt_len)`, for teacher forcing
- encoder_out (Tensor, optional): output from the encoder, used for
- encoder-side attention
- incremental_state (dict): dictionary used for storing state during
- :ref:`Incremental decoding`
-
- Returns:
- tuple:
- - the decoder's output of shape `(batch, tgt_len, vocab)`
- - a dictionary with any model-specific outputs
- """
- prev_output_tokens = prev_output_tokens.long()
- x, extra = self.extract_features(
- prev_output_tokens, encoder_out, incremental_state
- )
- x = self.output_layer(x)
- return x, extra
-
- def extract_features(
- self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused
- ):
- """
- Similar to *forward* but only return features.
-
- Returns:
- tuple:
- - the decoder's features of shape `(batch, tgt_len, embed_dim)`
- - a dictionary with any model-specific outputs
- """
-
- # embed positions
- positions = (
- self.embed_positions(
- prev_output_tokens, incremental_state=incremental_state
- )
- if self.embed_positions is not None
- else None
- )
-
- if incremental_state is not None:
- prev_output_tokens = prev_output_tokens[:, -1:]
- if positions is not None:
- positions = positions[:, -1:]
-
- # embed tokens and positions
- x = self.embed_scale * self.embed_tokens(prev_output_tokens)
-
- if self.project_in_dim is not None:
- x = self.project_in_dim(x)
-
- if positions is not None:
- x += positions
- x = F.dropout(x, p=self.dropout, training=self.training)
-
- # B x T x C -> T x B x C
- x = x.transpose(0, 1)
- attn = None
-
- inner_states = [x]
-
- # decoder layers
- self_attn_padding_mask = None
- if prev_output_tokens.eq(self.padding_idx).any():
- self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)
- for layer in self.layers:
- dropout_probability = np.random.random()
- if not self.training or (dropout_probability > self.layerdrop):
- x, attn, _ = layer(
- x,
- encoder_out["encoder_out"] if encoder_out is not None else None,
- encoder_out["padding_mask"] if encoder_out is not None else None,
- incremental_state,
- self_attn_mask=self.buffered_future_mask(x)
- if incremental_state is None
- else None,
- self_attn_padding_mask=self_attn_padding_mask
- )
- inner_states.append(x)
-
- if self.layer_norm:
- x = self.layer_norm(x)
-
- # T x B x C -> B x T x C
- x = x.transpose(0, 1)
-
- return x, {"attn": attn, "inner_states": inner_states}
-
- def output_layer(self, features, **kwargs):
- """Project features to the vocabulary size."""
- # project back to size of vocabulary
- if self.share_input_output_embed:
- return F.linear(features, self.embed_tokens.weight)
- else:
- return F.linear(features, self.embed_out)
-
- def max_positions(self):
- """Maximum output length supported by the decoder."""
- if self.embed_positions is None:
- return self.max_target_positions
- return min(self.max_target_positions, self.embed_positions.max_positions)
-
- def buffered_future_mask(self, tensor):
- dim = tensor.size(0)
- if (
- not hasattr(self, "_future_mask")
- or self._future_mask is None
- or self._future_mask.device != tensor.device
- or self._future_mask.size(0) < dim
- ):
- self._future_mask = torch.triu(
- utils.fill_with_neg_inf(tensor.new(dim, dim)), 1
- )
- return self._future_mask[:dim, :dim]
-
- def upgrade_state_dict_named(self, state_dict, name):
- return state_dict
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx):
- m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
- nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
- nn.init.constant_(m.weight[padding_idx], 0)
- return m
-
-
-def Linear(in_features, out_features, bias=True):
- m = nn.Linear(in_features, out_features, bias)
- nn.init.xavier_uniform_(m.weight)
- if bias:
- nn.init.constant_(m.bias, 0.0)
- return m
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_metrics.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_metrics.py
deleted file mode 100644
index 2de6969cf4445bc6cda44dacf6de765ea30d5f5b..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_metrics.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import unittest
-import uuid
-
-from fairseq import metrics
-
-
-class TestMetrics(unittest.TestCase):
- def test_nesting(self):
- with metrics.aggregate() as a:
- metrics.log_scalar("loss", 1)
- with metrics.aggregate() as b:
- metrics.log_scalar("loss", 2)
-
- self.assertEqual(a.get_smoothed_values()["loss"], 1.5)
- self.assertEqual(b.get_smoothed_values()["loss"], 2)
-
- def test_new_root(self):
- with metrics.aggregate() as a:
- metrics.log_scalar("loss", 1)
- with metrics.aggregate(new_root=True) as b:
- metrics.log_scalar("loss", 2)
-
- self.assertEqual(a.get_smoothed_values()["loss"], 1)
- self.assertEqual(b.get_smoothed_values()["loss"], 2)
-
- def test_nested_new_root(self):
- with metrics.aggregate() as layer1:
- metrics.log_scalar("loss", 1)
- with metrics.aggregate(new_root=True) as layer2:
- metrics.log_scalar("loss", 2)
- with metrics.aggregate() as layer3:
- metrics.log_scalar("loss", 3)
- with metrics.aggregate(new_root=True) as layer4:
- metrics.log_scalar("loss", 4)
- metrics.log_scalar("loss", 1.5)
-
- self.assertEqual(layer4.get_smoothed_values()["loss"], 4)
- self.assertEqual(layer3.get_smoothed_values()["loss"], 3)
- self.assertEqual(layer2.get_smoothed_values()["loss"], 2.5)
- self.assertEqual(layer1.get_smoothed_values()["loss"], 1.25)
-
- def test_named(self):
- name = str(uuid.uuid4())
- metrics.reset_meters(name)
-
- with metrics.aggregate(name):
- metrics.log_scalar("loss", 1)
-
- metrics.log_scalar("loss", 3)
-
- with metrics.aggregate(name):
- metrics.log_scalar("loss", 2)
-
- self.assertEqual(metrics.get_smoothed_values(name)["loss"], 1.5)
-
- def test_nested_duplicate_names(self):
- name = str(uuid.uuid4())
- metrics.reset_meters(name)
-
- with metrics.aggregate(name):
- metrics.log_scalar("loss", 1)
- with metrics.aggregate() as other:
- with metrics.aggregate(name):
- metrics.log_scalar("loss", 2)
- metrics.log_scalar("loss", 6)
-
- self.assertEqual(metrics.get_smoothed_values(name)["loss"], 3)
- self.assertEqual(other.get_smoothed_values()["loss"], 2)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/criterions/ctc.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/criterions/ctc.py
deleted file mode 100644
index 10e3618382c86a84466cb4264d62f31537980251..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/criterions/ctc.py
+++ /dev/null
@@ -1,295 +0,0 @@
-# All rights reserved.
-#
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
-
-import math
-from argparse import Namespace
-from dataclasses import dataclass, field
-from omegaconf import II
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from fairseq.data.data_utils import post_process
-from fairseq.tasks import FairseqTask
-from fairseq.logging.meters import safe_round
-
-
-@dataclass
-class CtcCriterionConfig(FairseqDataclass):
- zero_infinity: bool = field(
- default=False,
- metadata={"help": "zero inf loss when source length <= target length"},
- )
- sentence_avg: bool = II("optimization.sentence_avg")
- post_process: str = field(
- default="letter",
- metadata={
- "help": "how to post process predictions into words. can be letter, "
- "wordpiece, BPE symbols, etc. "
- "See fairseq.data.data_utils.post_process() for full list of options"
- },
- )
- wer_kenlm_model: Optional[str] = field(
- default=None,
- metadata={
- "help": "if this is provided, use kenlm to compute wer (along with other wer_* args)"
- },
- )
- wer_lexicon: Optional[str] = field(
- default=None,
- metadata={"help": "lexicon to use with wer_kenlm_model"},
- )
- wer_lm_weight: float = field(
- default=2.0,
- metadata={"help": "lm weight to use with wer_kenlm_model"},
- )
- wer_word_score: float = field(
- default=-1.0,
- metadata={"help": "lm word score to use with wer_kenlm_model"},
- )
-
- wer_args: Optional[str] = field(
- default=None,
- metadata={
- "help": "DEPRECATED: tuple of (wer_kenlm_model, wer_lexicon, wer_lm_weight, wer_word_score)"
- },
- )
-
-
-@register_criterion("ctc", dataclass=CtcCriterionConfig)
-class CtcCriterion(FairseqCriterion):
- def __init__(self, cfg: CtcCriterionConfig, task: FairseqTask):
- super().__init__(task)
- self.blank_idx = (
- task.target_dictionary.index(task.blank_symbol)
- if hasattr(task, "blank_symbol")
- else 0
- )
- self.pad_idx = task.target_dictionary.pad()
- self.eos_idx = task.target_dictionary.eos()
- self.post_process = cfg.post_process
-
- if cfg.wer_args is not None:
- (
- cfg.wer_kenlm_model,
- cfg.wer_lexicon,
- cfg.wer_lm_weight,
- cfg.wer_word_score,
- ) = eval(cfg.wer_args)
-
- if cfg.wer_kenlm_model is not None:
- from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder
-
- dec_args = Namespace()
- dec_args.nbest = 1
- dec_args.criterion = "ctc"
- dec_args.kenlm_model = cfg.wer_kenlm_model
- dec_args.lexicon = cfg.wer_lexicon
- dec_args.beam = 50
- dec_args.beam_size_token = min(50, len(task.target_dictionary))
- dec_args.beam_threshold = min(50, len(task.target_dictionary))
- dec_args.lm_weight = cfg.wer_lm_weight
- dec_args.word_score = cfg.wer_word_score
- dec_args.unk_weight = -math.inf
- dec_args.sil_weight = 0
-
- self.w2l_decoder = W2lKenLMDecoder(dec_args, task.target_dictionary)
- else:
- self.w2l_decoder = None
-
- self.zero_infinity = cfg.zero_infinity
- self.sentence_avg = cfg.sentence_avg
-
- def forward(self, model, sample, reduce=True):
- net_output = model(**sample["net_input"])
- lprobs = model.get_normalized_probs(
- net_output, log_probs=True
- ).contiguous() # (T, B, C) from the encoder
-
- if "src_lengths" in sample["net_input"]:
- input_lengths = sample["net_input"]["src_lengths"]
- else:
- if net_output["padding_mask"] is not None:
- non_padding_mask = ~net_output["padding_mask"]
- input_lengths = non_padding_mask.long().sum(-1)
- else:
- input_lengths = lprobs.new_full(
- (lprobs.size(1),), lprobs.size(0), dtype=torch.long
- )
-
- pad_mask = (sample["target"] != self.pad_idx) & (
- sample["target"] != self.eos_idx
- )
- targets_flat = sample["target"].masked_select(pad_mask)
- if "target_lengths" in sample:
- target_lengths = sample["target_lengths"]
- else:
- target_lengths = pad_mask.sum(-1)
-
- with torch.backends.cudnn.flags(enabled=False):
- loss = F.ctc_loss(
- lprobs,
- targets_flat,
- input_lengths,
- target_lengths,
- blank=self.blank_idx,
- reduction="sum",
- zero_infinity=self.zero_infinity,
- )
-
- ntokens = (
- sample["ntokens"] if "ntokens" in sample else target_lengths.sum().item()
- )
-
- sample_size = sample["target"].size(0) if self.sentence_avg else ntokens
- logging_output = {
- "loss": utils.item(loss.data), # * sample['ntokens'],
- "ntokens": ntokens,
- "nsentences": sample["id"].numel(),
- "sample_size": sample_size,
- }
-
- if not model.training:
- import editdistance
-
- with torch.no_grad():
- lprobs_t = lprobs.transpose(0, 1).float().contiguous().cpu()
-
- c_err = 0
- c_len = 0
- w_errs = 0
- w_len = 0
- wv_errs = 0
- for lp, t, inp_l in zip(
- lprobs_t,
- sample["target_label"]
- if "target_label" in sample
- else sample["target"],
- input_lengths,
- ):
- lp = lp[:inp_l].unsqueeze(0)
-
- decoded = None
- if self.w2l_decoder is not None:
- decoded = self.w2l_decoder.decode(lp)
- if len(decoded) < 1:
- decoded = None
- else:
- decoded = decoded[0]
- if len(decoded) < 1:
- decoded = None
- else:
- decoded = decoded[0]
-
- p = (t != self.task.target_dictionary.pad()) & (
- t != self.task.target_dictionary.eos()
- )
- targ = t[p]
- targ_units = self.task.target_dictionary.string(targ)
- targ_units_arr = targ.tolist()
-
- toks = lp.argmax(dim=-1).unique_consecutive()
- pred_units_arr = toks[toks != self.blank_idx].tolist()
-
- c_err += editdistance.eval(pred_units_arr, targ_units_arr)
- c_len += len(targ_units_arr)
-
- targ_words = post_process(targ_units, self.post_process).split()
-
- pred_units = self.task.target_dictionary.string(pred_units_arr)
- pred_words_raw = post_process(pred_units, self.post_process).split()
-
- if decoded is not None and "words" in decoded:
- pred_words = decoded["words"]
- w_errs += editdistance.eval(pred_words, targ_words)
- wv_errs += editdistance.eval(pred_words_raw, targ_words)
- else:
- dist = editdistance.eval(pred_words_raw, targ_words)
- w_errs += dist
- wv_errs += dist
-
- w_len += len(targ_words)
-
- logging_output["wv_errors"] = wv_errs
- logging_output["w_errors"] = w_errs
- logging_output["w_total"] = w_len
- logging_output["c_errors"] = c_err
- logging_output["c_total"] = c_len
-
- return loss, sample_size, logging_output
-
- @staticmethod
- def reduce_metrics(logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
-
- loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
- ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs))
- nsentences = utils.item(
- sum(log.get("nsentences", 0) for log in logging_outputs)
- )
- sample_size = utils.item(
- sum(log.get("sample_size", 0) for log in logging_outputs)
- )
-
- metrics.log_scalar(
- "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
- )
- metrics.log_scalar("ntokens", ntokens)
- metrics.log_scalar("nsentences", nsentences)
- if sample_size != ntokens:
- metrics.log_scalar(
- "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
- )
-
- c_errors = sum(log.get("c_errors", 0) for log in logging_outputs)
- metrics.log_scalar("_c_errors", c_errors)
- c_total = sum(log.get("c_total", 0) for log in logging_outputs)
- metrics.log_scalar("_c_total", c_total)
- w_errors = sum(log.get("w_errors", 0) for log in logging_outputs)
- metrics.log_scalar("_w_errors", w_errors)
- wv_errors = sum(log.get("wv_errors", 0) for log in logging_outputs)
- metrics.log_scalar("_wv_errors", wv_errors)
- w_total = sum(log.get("w_total", 0) for log in logging_outputs)
- metrics.log_scalar("_w_total", w_total)
-
- if c_total > 0:
- metrics.log_derived(
- "uer",
- lambda meters: safe_round(
- meters["_c_errors"].sum * 100.0 / meters["_c_total"].sum, 3
- )
- if meters["_c_total"].sum > 0
- else float("nan"),
- )
- if w_total > 0:
- metrics.log_derived(
- "wer",
- lambda meters: safe_round(
- meters["_w_errors"].sum * 100.0 / meters["_w_total"].sum, 3
- )
- if meters["_w_total"].sum > 0
- else float("nan"),
- )
- metrics.log_derived(
- "raw_wer",
- lambda meters: safe_round(
- meters["_wv_errors"].sum * 100.0 / meters["_w_total"].sum, 3
- )
- if meters["_w_total"].sum > 0
- else float("nan"),
- )
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/encoders/space_tokenizer.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/encoders/space_tokenizer.py
deleted file mode 100644
index 925ad41b7c1aee6738c63938c36bd3ee16dca812..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/encoders/space_tokenizer.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import re
-
-from fairseq.data.encoders import register_tokenizer
-from fairseq.dataclass import FairseqDataclass
-
-
-@register_tokenizer("space", dataclass=FairseqDataclass)
-class SpaceTokenizer(object):
- def __init__(self, *unused):
- self.space_tok = re.compile(r"\s+")
-
- def encode(self, x: str) -> str:
- return self.space_tok.sub(" ", x)
-
- def decode(self, x: str) -> str:
- return x
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/tasks/speech_to_text.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/tasks/speech_to_text.py
deleted file mode 100644
index 06e292103ef898d607eb23441ce840de1fc800a1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/tasks/speech_to_text.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from pathlib import Path
-from argparse import Namespace
-
-from fairseq.data import Dictionary, encoders
-from fairseq.data.audio.speech_to_text_dataset import (
- S2TDataConfig,
- SpeechToTextDataset,
- SpeechToTextDatasetCreator,
- get_features_or_waveform
-)
-from fairseq.tasks import LegacyFairseqTask, register_task
-
-
-logger = logging.getLogger(__name__)
-
-
-@register_task("speech_to_text")
-class SpeechToTextTask(LegacyFairseqTask):
- @classmethod
- def add_args(cls, parser):
- parser.add_argument("data", help="manifest root path")
- parser.add_argument(
- "--config-yaml",
- type=str,
- default="config.yaml",
- help="Configuration YAML filename (under manifest root)",
- )
- parser.add_argument(
- "--max-source-positions",
- default=6000,
- type=int,
- metavar="N",
- help="max number of tokens in the source sequence",
- )
- parser.add_argument(
- "--max-target-positions",
- default=1024,
- type=int,
- metavar="N",
- help="max number of tokens in the target sequence",
- )
-
- def __init__(self, args, tgt_dict):
- super().__init__(args)
- self.tgt_dict = tgt_dict
- self.data_cfg = S2TDataConfig(Path(args.data) / args.config_yaml)
- self.speaker_to_id = self._get_speaker_to_id()
-
- def _get_speaker_to_id(self):
- speaker_to_id = None
- speaker_set_filename = self.data_cfg.config.get("speaker_set_filename")
- if speaker_set_filename is not None:
- speaker_set_path = Path(self.args.data) / speaker_set_filename
- with open(speaker_set_path) as f:
- speaker_to_id = {r.strip(): i for i, r in enumerate(f)}
- return speaker_to_id
-
- @classmethod
- def setup_task(cls, args, **kwargs):
- data_cfg = S2TDataConfig(Path(args.data) / args.config_yaml)
- dict_path = Path(args.data) / data_cfg.vocab_filename
- if not dict_path.is_file():
- raise FileNotFoundError(f"Dict not found: {dict_path.as_posix()}")
- tgt_dict = Dictionary.load(dict_path.as_posix())
- logger.info(
- f"dictionary size ({data_cfg.vocab_filename}): " f"{len(tgt_dict):,}"
- )
-
- if getattr(args, "train_subset", None) is not None:
- if not all(s.startswith("train") for s in args.train_subset.split(",")):
- raise ValueError('Train splits should be named like "train*".')
- return cls(args, tgt_dict)
-
- def build_criterion(self, args):
- from fairseq import criterions
-
- if self.data_cfg.prepend_tgt_lang_tag and args.ignore_prefix_size != 1:
- raise ValueError(
- 'Please set "--ignore-prefix-size 1" since '
- "target language ID token is prepended as BOS."
- )
- return criterions.build_criterion(args, self)
-
- def load_dataset(self, split, epoch=1, combine=False, **kwargs):
- is_train_split = split.startswith("train")
- pre_tokenizer = self.build_tokenizer(self.args)
- bpe_tokenizer = self.build_bpe(self.args)
- self.datasets[split] = SpeechToTextDatasetCreator.from_tsv(
- self.args.data,
- self.data_cfg,
- split,
- self.tgt_dict,
- pre_tokenizer,
- bpe_tokenizer,
- is_train_split=is_train_split,
- epoch=epoch,
- seed=self.args.seed,
- speaker_to_id=self.speaker_to_id
- )
-
- @property
- def target_dictionary(self):
- return self.tgt_dict
-
- @property
- def source_dictionary(self):
- return None
-
- def max_positions(self):
- return self.args.max_source_positions, self.args.max_target_positions
-
- def build_model(self, args):
- args.input_feat_per_channel = self.data_cfg.input_feat_per_channel
- args.input_channels = self.data_cfg.input_channels
- args.speaker_to_id = self.speaker_to_id
- return super(SpeechToTextTask, self).build_model(args)
-
- def build_generator(
- self,
- models,
- args,
- seq_gen_cls=None,
- extra_gen_cls_kwargs=None,
- ):
- if self.data_cfg.prepend_tgt_lang_tag and args.prefix_size != 1:
- raise ValueError(
- 'Please set "--prefix-size 1" since '
- "target language ID token is prepended as BOS."
- )
- lang_token_ids = {
- i
- for s, i in self.tgt_dict.indices.items()
- if SpeechToTextDataset.is_lang_tag(s)
- }
-
- if extra_gen_cls_kwargs is None:
- extra_gen_cls_kwargs = {}
- extra_gen_cls_kwargs["symbols_to_strip_from_output"] = lang_token_ids
- return super().build_generator(
- models, args, seq_gen_cls=None,
- extra_gen_cls_kwargs=extra_gen_cls_kwargs
- )
-
- def build_tokenizer(self, args):
- logger.info(f"pre-tokenizer: {self.data_cfg.pre_tokenizer}")
- return encoders.build_tokenizer(Namespace(**self.data_cfg.pre_tokenizer))
-
- def build_bpe(self, args):
- logger.info(f"tokenizer: {self.data_cfg.bpe_tokenizer}")
- return encoders.build_bpe(Namespace(**self.data_cfg.bpe_tokenizer))
-
- def get_interactive_tokens_and_lengths(self, lines, encode_fn):
- n_frames = [get_features_or_waveform(p).shape[0] for p in lines]
- return lines, n_frames
-
- def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs):
- return SpeechToTextDataset(
- "interactive", False, self.data_cfg, src_tokens, src_lengths
- )
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/benchmark/dummy_model.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/benchmark/dummy_model.py
deleted file mode 100644
index ff26e4fe655d8e8d7f9942c4bd3df7cd267405fb..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/benchmark/dummy_model.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq.data import Dictionary
-from fairseq.models import (
- FairseqDecoder,
- FairseqLanguageModel,
- register_model,
- register_model_architecture,
-)
-
-
-@register_model("dummy_model")
-class DummyModel(FairseqLanguageModel):
- def __init__(self, args, encoder):
- super().__init__(encoder)
- self.args = args
-
- @staticmethod
- def add_args(parser):
- parser.add_argument("--num-layers", type=int, default=24)
- parser.add_argument("--embed-dim", type=int, default=1024)
-
- @classmethod
- def build_model(cls, args, task):
- encoder = DummyEncoder(
- num_embed=len(task.target_dictionary),
- embed_dim=args.embed_dim,
- num_layers=args.num_layers,
- )
- return cls(args, encoder)
-
- def forward(self, src_tokens, masked_tokens=None, **kwargs):
- return self.decoder(src_tokens, masked_tokens=masked_tokens)
-
-
-class DummyEncoder(FairseqDecoder):
- def __init__(self, num_embed=50000, embed_dim=1024, num_layers=24):
- super().__init__(Dictionary())
- self.embed = nn.Embedding(
- num_embeddings=num_embed, embedding_dim=embed_dim, padding_idx=0
- )
- self.layers_a = nn.ModuleList(
- [
- nn.Sequential(
- nn.LayerNorm(embed_dim),
- nn.Linear(embed_dim, 3 * embed_dim), # q, k, v input projection
- nn.Linear(3 * embed_dim, embed_dim), # skip self-attention
- nn.Linear(embed_dim, embed_dim), # output projection
- nn.Dropout(),
- )
- for i in range(num_layers)
- ]
- )
- self.layers_b = nn.ModuleList(
- [
- nn.Sequential(
- nn.LayerNorm(embed_dim),
- nn.Linear(embed_dim, 4 * embed_dim), # FFN
- nn.ReLU(),
- nn.Linear(4 * embed_dim, embed_dim), # FFN
- nn.Dropout(0.1),
- )
- for i in range(num_layers)
- ]
- )
- self.out_proj = nn.Linear(embed_dim, num_embed)
-
- def forward(self, tokens, masked_tokens=None):
- x = self.embed(tokens)
- for layer_a, layer_b in zip(self.layers_a, self.layers_b):
- x = x + layer_a(x)
- x = x + layer_b(x)
- x = self.out_proj(x)
- if masked_tokens is not None:
- x = x[masked_tokens]
- return (x,)
-
- def max_positions(self):
- return 1024
-
- def get_normalized_probs(self, net_output, log_probs, sample=None):
- logits = net_output[0].float()
- if log_probs:
- return F.log_softmax(logits, dim=-1)
- else:
- return F.softmax(logits, dim=-1)
-
-
-@register_model_architecture("dummy_model", "dummy_model")
-def base_architecture(args):
- pass
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/logging/meters.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/logging/meters.py
deleted file mode 100644
index 2100b1fa0b2704b1c585f59e9349655bba0cc9e6..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/logging/meters.py
+++ /dev/null
@@ -1,323 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import bisect
-import time
-from collections import OrderedDict
-from typing import Dict, Optional
-
-
-try:
- import torch
-
- def type_as(a, b):
- if torch.is_tensor(a) and torch.is_tensor(b):
- return a.to(b)
- else:
- return a
-
-
-except ImportError:
- torch = None
-
- def type_as(a, b):
- return a
-
-
-try:
- import numpy as np
-except ImportError:
- np = None
-
-
-class Meter(object):
- """Base class for Meters."""
-
- def __init__(self):
- pass
-
- def state_dict(self):
- return {}
-
- def load_state_dict(self, state_dict):
- pass
-
- def reset(self):
- raise NotImplementedError
-
- @property
- def smoothed_value(self) -> float:
- """Smoothed value used for logging."""
- raise NotImplementedError
-
-
-def safe_round(number, ndigits):
- if hasattr(number, "__round__"):
- return round(number, ndigits)
- elif torch is not None and torch.is_tensor(number) and number.numel() == 1:
- return safe_round(number.item(), ndigits)
- elif np is not None and np.ndim(number) == 0 and hasattr(number, "item"):
- return safe_round(number.item(), ndigits)
- else:
- return number
-
-
-class AverageMeter(Meter):
- """Computes and stores the average and current value"""
-
- def __init__(self, round: Optional[int] = None):
- self.round = round
- self.reset()
-
- def reset(self):
- self.val = None # most recent update
- self.sum = 0 # sum from all updates
- self.count = 0 # total n from all updates
-
- def update(self, val, n=1):
- if val is not None:
- self.val = val
- if n > 0:
- self.sum = type_as(self.sum, val) + (val * n)
- self.count = type_as(self.count, n) + n
-
- def state_dict(self):
- return {
- "val": self.val,
- "sum": self.sum,
- "count": self.count,
- "round": self.round,
- }
-
- def load_state_dict(self, state_dict):
- self.val = state_dict["val"]
- self.sum = state_dict["sum"]
- self.count = state_dict["count"]
- self.round = state_dict.get("round", None)
-
- @property
- def avg(self):
- return self.sum / self.count if self.count > 0 else self.val
-
- @property
- def smoothed_value(self) -> float:
- val = self.avg
- if self.round is not None and val is not None:
- val = safe_round(val, self.round)
- return val
-
-
-class SumMeter(Meter):
- """Computes and stores the sum"""
-
- def __init__(self, round: Optional[int] = None):
- self.round = round
- self.reset()
-
- def reset(self):
- self.sum = 0 # sum from all updates
-
- def update(self, val):
- if val is not None:
- self.sum = type_as(self.sum, val) + val
-
- def state_dict(self):
- return {
- "sum": self.sum,
- "round": self.round,
- }
-
- def load_state_dict(self, state_dict):
- self.sum = state_dict["sum"]
- self.round = state_dict.get("round", None)
-
- @property
- def smoothed_value(self) -> float:
- val = self.sum
- if self.round is not None and val is not None:
- val = safe_round(val, self.round)
- return val
-
-
-class TimeMeter(Meter):
- """Computes the average occurrence of some event per second"""
-
- def __init__(
- self,
- init: int = 0,
- n: int = 0,
- round: Optional[int] = None,
- ):
- self.round = round
- self.reset(init, n)
-
- def reset(self, init=0, n=0):
- self.init = init
- self.start = time.perf_counter()
- self.n = n
- self.i = 0
-
- def update(self, val=1):
- self.n = type_as(self.n, val) + val
- self.i += 1
-
- def state_dict(self):
- return {
- "init": self.elapsed_time,
- "n": self.n,
- "round": self.round,
- }
-
- def load_state_dict(self, state_dict):
- if "start" in state_dict:
- # backwards compatibility for old state_dicts
- self.reset(init=state_dict["init"])
- else:
- self.reset(init=state_dict["init"], n=state_dict["n"])
- self.round = state_dict.get("round", None)
-
- @property
- def avg(self):
- return self.n / self.elapsed_time
-
- @property
- def elapsed_time(self):
- return self.init + (time.perf_counter() - self.start)
-
- @property
- def smoothed_value(self) -> float:
- val = self.avg
- if self.round is not None and val is not None:
- val = safe_round(val, self.round)
- return val
-
-
-class StopwatchMeter(Meter):
- """Computes the sum/avg duration of some event in seconds"""
-
- def __init__(self, round: Optional[int] = None):
- self.round = round
- self.sum = 0
- self.n = 0
- self.start_time = None
-
- def start(self):
- self.start_time = time.perf_counter()
-
- def stop(self, n=1, prehook=None):
- if self.start_time is not None:
- if prehook is not None:
- prehook()
- delta = time.perf_counter() - self.start_time
- self.sum = self.sum + delta
- self.n = type_as(self.n, n) + n
-
- def reset(self):
- self.sum = 0 # cumulative time during which stopwatch was active
- self.n = 0 # total n across all start/stop
- self.start()
-
- def state_dict(self):
- return {
- "sum": self.sum,
- "n": self.n,
- "round": self.round,
- }
-
- def load_state_dict(self, state_dict):
- self.sum = state_dict["sum"]
- self.n = state_dict["n"]
- self.start_time = None
- self.round = state_dict.get("round", None)
-
- @property
- def avg(self):
- return self.sum / self.n if self.n > 0 else self.sum
-
- @property
- def elapsed_time(self):
- if self.start_time is None:
- return 0.0
- return time.perf_counter() - self.start_time
-
- @property
- def smoothed_value(self) -> float:
- val = self.avg if self.sum > 0 else self.elapsed_time
- if self.round is not None and val is not None:
- val = safe_round(val, self.round)
- return val
-
-
-class MetersDict(OrderedDict):
- """A sorted dictionary of :class:`Meters`.
-
- Meters are sorted according to a priority that is given when the
- meter is first added to the dictionary.
- """
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.priorities = []
-
- def __setitem__(self, key, value):
- assert key not in self, "MetersDict doesn't support reassignment"
- priority, value = value
- bisect.insort(self.priorities, (priority, len(self.priorities), key))
- super().__setitem__(key, value)
- for _, _, key in self.priorities: # reorder dict to match priorities
- self.move_to_end(key)
-
- def add_meter(self, key, meter, priority):
- self.__setitem__(key, (priority, meter))
-
- def state_dict(self):
- return [
- (pri, key, self[key].__class__.__name__, self[key].state_dict())
- for pri, _, key in self.priorities
- # can't serialize DerivedMeter instances
- if not isinstance(self[key], MetersDict._DerivedMeter)
- ]
-
- def load_state_dict(self, state_dict):
- self.clear()
- self.priorities.clear()
- for pri, key, meter_cls, meter_state in state_dict:
- meter = globals()[meter_cls]()
- meter.load_state_dict(meter_state)
- self.add_meter(key, meter, pri)
-
- def get_smoothed_value(self, key: str) -> float:
- """Get a single smoothed value."""
- meter = self[key]
- if isinstance(meter, MetersDict._DerivedMeter):
- return meter.fn(self)
- else:
- return meter.smoothed_value
-
- def get_smoothed_values(self) -> Dict[str, float]:
- """Get all smoothed values."""
- return OrderedDict(
- [
- (key, self.get_smoothed_value(key))
- for key in self.keys()
- if not key.startswith("_")
- ]
- )
-
- def reset(self):
- """Reset Meter instances."""
- for meter in self.values():
- if isinstance(meter, MetersDict._DerivedMeter):
- continue
- meter.reset()
-
- class _DerivedMeter(Meter):
- """A Meter whose values are derived from other Meters."""
-
- def __init__(self, fn):
- self.fn = fn
-
- def reset(self):
- pass
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/fconv_lm.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/fconv_lm.py
deleted file mode 100644
index 4b243d6669cb57880353b45a01843ec22010fb5f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/fconv_lm.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from fairseq import utils
-from fairseq.models import (
- FairseqLanguageModel,
- register_model,
- register_model_architecture,
-)
-from fairseq.models.fconv import FConvDecoder
-from fairseq.utils import safe_hasattr
-
-
-@register_model("fconv_lm")
-class FConvLanguageModel(FairseqLanguageModel):
- def __init__(self, decoder):
- super().__init__(decoder)
-
- @staticmethod
- def add_args(parser):
- """Add model-specific arguments to the parser."""
- parser.add_argument(
- "--dropout", type=float, metavar="D", help="dropout probability"
- )
- parser.add_argument(
- "--decoder-embed-dim",
- type=int,
- metavar="N",
- help="decoder embedding dimension",
- )
- parser.add_argument(
- "--decoder-layers",
- type=str,
- metavar="EXPR",
- help="decoder layers [(dim, kernel_size), ...]",
- )
- parser.add_argument(
- "--decoder-out-embed-dim",
- type=int,
- metavar="N",
- help="decoder output embedding dimension",
- )
- parser.add_argument(
- "--adaptive-softmax-cutoff",
- metavar="EXPR",
- help="comma separated list of adaptive softmax cutoff points. "
- "Must be used with adaptive_loss criterion",
- )
- parser.add_argument(
- "--adaptive-softmax-dropout",
- type=float,
- metavar="D",
- help="sets adaptive softmax dropout for the tail projections",
- )
- parser.add_argument(
- "--decoder-attention",
- type=str,
- metavar="EXPR",
- help="decoder attention [True, ...]",
- )
-
- @classmethod
- def build_model(cls, args, task):
- """Build a new model instance."""
- # make sure all arguments are present in older models
- base_lm_architecture(args)
-
- if safe_hasattr(args, "max_target_positions") and not safe_hasattr(
- args, "tokens_per_sample"
- ):
- args.tokens_per_sample = args.max_target_positions
-
- decoder = FConvDecoder(
- dictionary=task.target_dictionary,
- embed_dim=args.decoder_embed_dim,
- convolutions=eval(args.decoder_layers),
- out_embed_dim=args.decoder_embed_dim,
- attention=eval(args.decoder_attention),
- dropout=args.dropout,
- max_positions=args.tokens_per_sample,
- share_embed=False,
- positional_embeddings=False,
- adaptive_softmax_cutoff=(
- utils.eval_str_list(args.adaptive_softmax_cutoff, type=int)
- if args.criterion == "adaptive_loss"
- else None
- ),
- adaptive_softmax_dropout=args.adaptive_softmax_dropout,
- )
- return FConvLanguageModel(decoder)
-
-
-@register_model_architecture("fconv_lm", "fconv_lm")
-def base_lm_architecture(args):
- args.dropout = getattr(args, "dropout", 0.1)
- args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128)
- args.decoder_layers = getattr(args, "decoder_layers", "[(1268, 4)] * 13")
- args.decoder_attention = getattr(args, "decoder_attention", "False")
- args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
- args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-
-
-@register_model_architecture("fconv_lm", "fconv_lm_dauphin_wikitext103")
-def fconv_lm_dauphin_wikitext103(args):
- layers = "[(850, 6)] * 3"
- layers += " + [(850, 1)] * 1"
- layers += " + [(850, 5)] * 4"
- layers += " + [(850, 1)] * 1"
- layers += " + [(850, 4)] * 3"
- layers += " + [(1024, 4)] * 1"
- layers += " + [(2048, 4)] * 1"
- args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 280)
- args.decoder_layers = getattr(args, "decoder_layers", layers)
- args.decoder_attention = getattr(args, "decoder_attention", "False")
- args.adaptive_softmax_cutoff = getattr(
- args, "adaptive_softmax_cutoff", "10000,20000,200000"
- )
- base_lm_architecture(args)
-
-
-@register_model_architecture("fconv_lm", "fconv_lm_dauphin_gbw")
-def fconv_lm_dauphin_gbw(args):
- layers = "[(512, 5)]"
- layers += " + [(128, 1, 0), (128, 5, 0), (512, 1, 3)] * 3"
- layers += " + [(512, 1, 0), (512, 5, 0), (1024, 1, 3)] * 3"
- layers += " + [(1024, 1, 0), (1024, 5, 0), (2048, 1, 3)] * 6"
- layers += " + [(1024, 1, 0), (1024, 5, 0), (4096, 1, 3)]"
- args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128)
- args.decoder_layers = getattr(args, "decoder_layers", layers)
- args.decoder_attention = getattr(args, "decoder_attention", "False")
- args.adaptive_softmax_cutoff = getattr(
- args, "adaptive_softmax_cutoff", "10000,50000,200000"
- )
- base_lm_architecture(args)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/conv_tbc.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/conv_tbc.py
deleted file mode 100644
index 65e17ec94f7e595cb657b3d2daaa1052a95d0677..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/conv_tbc.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from torch import nn
-from torch.nn.modules.utils import _single
-from torch import Tensor
-
-
-class ConvTBC(torch.nn.Module):
- """1D convolution over an input of shape (time x batch x channel)
-
- The implementation uses gemm to perform the convolution. This implementation
- is faster than cuDNN for small kernel sizes.
- """
-
- def __init__(self, in_channels, out_channels, kernel_size, padding=0):
- super(ConvTBC, self).__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.kernel_size = _single(kernel_size)
- self.padding = _single(padding)
-
- self.weight = torch.nn.Parameter(
- torch.Tensor(self.kernel_size[0], in_channels, out_channels)
- )
- self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
-
- self.reset_parameters()
-
- def reset_parameters(self):
- nn.init.xavier_normal_(self.weight)
- nn.init.zeros_(self.bias)
-
- def conv_tbc(self, input: Tensor):
- return torch.conv_tbc(
- input.contiguous(), self.weight, self.bias, self.padding[0]
- )
-
- def forward(self, input: Tensor):
- return self.conv_tbc(input)
-
- def __repr__(self):
- s = (
- "{name}({in_channels}, {out_channels}, kernel_size={kernel_size}"
- ", padding={padding}"
- )
- if self.bias is None:
- s += ", bias=False"
- s += ")"
- return s.format(name=self.__class__.__name__, **self.__dict__)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py
deleted file mode 100644
index 73c3c8ea3435d6050401c45e737e4ecf5662825c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass, field
-from typing import Optional, List
-from omegaconf import II
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
-
-
-@dataclass
-class PolynomialDecayLRScheduleConfig(FairseqDataclass):
- warmup_updates: int = field(
- default=0,
- metadata={"help": "warmup the learning rate linearly for the first N updates"},
- )
- warmup_ratio: float = field(
- default=0,
- metadata={"help": "warmup ratio"},
- )
- force_anneal: Optional[int] = field(
- default=None,
- metadata={"help": "force annealing at specified epoch"},
- )
- end_learning_rate: float = field(
- default=0.0,
- metadata={"help": "learning rate to decay to"},
- )
- power: float = field(
- default=1.0,
- metadata={"help": "decay exponent"},
- )
- total_num_update: Optional[float] = field(
- default=1000000,
- metadata={"help": "total number of updates over which to decay learning rate"},
- )
- lr: List[float] = II("optimization.lr")
-
-
-@register_lr_scheduler("polynomial_decay", dataclass=PolynomialDecayLRScheduleConfig)
-class PolynomialDecayLRSchedule(FairseqLRScheduler):
- """Decay the LR on a fixed schedule."""
-
- def __init__(self, cfg: PolynomialDecayLRScheduleConfig, optimizer):
- super().__init__(cfg, optimizer)
-
- assert cfg.total_num_update > 0
- # set defaults
- cfg.warmup_updates = getattr(cfg, 'warmup_updates', 0) or 0
-
- self.lr = cfg.lr[0]
- self.warmup_updates = cfg.warmup_updates
- if self.warmup_updates > 0:
- self.warmup_factor = 1.0 / self.warmup_updates
- else:
- self.warmup_factor = 1
- self.end_learning_rate = cfg.end_learning_rate
- self.total_num_update = cfg.total_num_update
- self.power = cfg.power
- self.optimizer.set_lr(self.warmup_factor * self.lr)
-
- def get_next_lr(self, epoch):
- lrs = self.cfg.lr
- if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal:
- # use fixed LR schedule
- next_lr = lrs[min(epoch, len(lrs) - 1)]
- else:
- # annneal based on lr_shrink
- next_lr = self.optimizer.get_lr()
- return next_lr
-
- def step_begin_epoch(self, epoch):
- """Update the learning rate at the beginning of the given epoch."""
- self.lr = self.get_next_lr(epoch)
- self.optimizer.set_lr(self.warmup_factor * self.lr)
- return self.optimizer.get_lr()
-
- def step_update(self, num_updates):
- """Update the learning rate after each update."""
- if self.warmup_updates > 0 and num_updates <= self.warmup_updates:
- self.warmup_factor = num_updates / float(self.warmup_updates)
- lr = self.warmup_factor * self.lr
- elif num_updates >= self.total_num_update:
- lr = self.end_learning_rate
- else:
- warmup = self.warmup_updates
- lr_range = self.lr - self.end_learning_rate
- pct_remaining = 1 - (num_updates - warmup) / (self.total_num_update - warmup)
- lr = lr_range * pct_remaining ** (self.power) + self.end_learning_rate
- self.optimizer.set_lr(lr)
- return self.optimizer.get_lr()
-
- def reinit(self, total_num_update, num_updates):
- # only enable this when set warmup_ratio
- if self.cfg.warmup_ratio <= 0:
- return
- # re init this according to the real number of updates
- self.total_num_update = total_num_update
- self.warmup_updates = int(self.total_num_update * self.cfg.warmup_ratio)
- if num_updates > 0:
- self.warmup_factor = min(1.0, num_updates / float(self.warmup_updates))
- self.step_update(num_updates)
- else:
- self.warmup_factor = 1.0 / self.warmup_updates
- self.optimizer.set_lr(self.warmup_factor * self.lr)
- print('Total steps {}, warmup steps {}, warmup_factor {}'.format(self.total_num_update, self.warmup_updates,
- self.warmup_factor))
\ No newline at end of file
diff --git a/spaces/OIUGLK/bingo/src/components/welcome-screen.tsx b/spaces/OIUGLK/bingo/src/components/welcome-screen.tsx
deleted file mode 100644
index f7449fcbb6c621875e235db98f2790bf7894fb0a..0000000000000000000000000000000000000000
--- a/spaces/OIUGLK/bingo/src/components/welcome-screen.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-import { useBing } from '@/lib/hooks/use-bing'
-
-const exampleMessages = [
- {
- heading: '🧐 提出复杂问题',
- message: `我可以为我挑剔的只吃橙色食物的孩子做什么饭?`
- },
- {
- heading: '🙌 获取更好的答案',
- message: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?'
- },
- {
- heading: '🎨 获得创意灵感',
- message: `以海盗的口吻写一首关于外太空鳄鱼的俳句`
- }
-]
-
-export function WelcomeScreen({ setInput }: Pick
- 
- }
- } catch (e) {
- }
- return
- },
- p({ children }) {
- return
- {children}
- }, - code({ node, inline, className, children, ...props }) { - if (children.length) { - if (children[0] == '▍') { - return ( - ▍ - ) - } - - children[0] = (children[0] as string).replace('`▍`', '▍') - } - - const match = /language-(\w+)/.exec(className || '') - - if (inline) { - return ( -
- {children}
-
- )
- }
-
- return (
-
- {message.author === 'bot' &&
-
- {exampleMessages.map(example => (
-
- ))}
-
- )
-}
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp
deleted file mode 100644
index c843487b5fa4e8077dd27402ec99009266ddda8d..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp
+++ /dev/null
@@ -1,39 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#include "box_iou_rotated.h"
-#include "box_iou_rotated_utils.h"
-
-namespace detectron2 {
-
-template Text Localization
", unsafe_allow_html=True - ) - - vis_processor = load_processor("blip_image_eval").build(image_size=384) - text_processor = load_processor("blip_caption") - - tokenizer = init_bert_tokenizer() - - instructions = "Try the provided image and text or use your own ones." - file = st.file_uploader(instructions) - - query = st.text_input( - "Try a different input.", "A girl playing with her dog on the beach." - ) - - submit_button = st.button("Submit") - - col1, col2 = st.columns(2) - - if file: - raw_img = Image.open(file).convert("RGB") - else: - raw_img = load_demo_image() - - col1.header("Image") - w, h = raw_img.size - scaling_factor = 720 / w - resized_image = raw_img.resize((int(w * scaling_factor), int(h * scaling_factor))) - col1.image(resized_image, use_column_width=True) - - col2.header("GradCam") - - if submit_button: - if model_type.startswith("BLIP"): - blip_type = model_type.split("_")[1] - model = load_blip_itm_model(device, model_type=blip_type) - - img = vis_processor(raw_img).unsqueeze(0).to(device) - qry = text_processor(query) - - qry_tok = tokenizer(qry, return_tensors="pt").to(device) - - norm_img = np.float32(resized_image) / 255 - - gradcam, _ = compute_gradcam(model, img, qry, qry_tok, block_num=layer_num) - - avg_gradcam = getAttMap(norm_img, gradcam[0][1], blur=True) - col2.image(avg_gradcam, use_column_width=True, clamp=True) - - num_cols = 4.0 - num_tokens = len(qry_tok.input_ids[0]) - 2 - - num_rows = int(math.ceil(num_tokens / num_cols)) - - gradcam_iter = iter(gradcam[0][2:-1]) - token_id_iter = iter(qry_tok.input_ids[0][1:-1]) - - for _ in range(num_rows): - with st.container(): - for col in st.columns(int(num_cols)): - token_id = next(token_id_iter, None) - if not token_id: - break - gradcam_img = next(gradcam_iter) - - word = tokenizer.decode([token_id]) - gradcam_todraw = getAttMap(norm_img, gradcam_img, blur=True) - - new_title = ( - '{}
'.format( - word - ) - ) - col.markdown(new_title, unsafe_allow_html=True) - # st.image(image, channels="BGR") - col.image(gradcam_todraw, use_column_width=True, clamp=True) diff --git a/spaces/SerdarHelli/Pix2Pix3D/app.py b/spaces/SerdarHelli/Pix2Pix3D/app.py deleted file mode 100644 index b45d5bd9c3c9afb43cd03b401f0de22a1cf61f50..0000000000000000000000000000000000000000 --- a/spaces/SerdarHelli/Pix2Pix3D/app.py +++ /dev/null @@ -1,328 +0,0 @@ -import sys -import os - -os.system("git clone https://github.com/dunbar12138/pix2pix3D.git") -sys.path.append("pix2pix3D") - -from typing import List, Optional, Tuple, Union -import dnnlib -import numpy as np -import PIL.Image -import torch -from tqdm import tqdm - -import legacy -from camera_utils import LookAtPoseSampler -from huggingface_hub import hf_hub_download -from matplotlib import pyplot as plt -from pathlib import Path -import gradio as gr -from training.utils import color_mask, color_list -import plotly.graph_objects as go -from tqdm import tqdm -import imageio -import trimesh -import mcubes -import copy - -import pickle -import numpy as np -import torch -import dnnlib -from torch_utils import misc -from legacy import * -import io - -os.environ["PYOPENGL_PLATFORM"] = "egl" - - -def get_sigma_field_np(nerf, styles, resolution=512, block_resolution=64): - # return numpy array of forwarded sigma value - # bound = (nerf.rendering_kwargs['ray_end'] - nerf.rendering_kwargs['ray_start']) * 0.5 - bound = nerf.rendering_kwargs['box_warp'] * 0.5 - X = torch.linspace(-bound, bound, resolution).split(block_resolution) - - sigma_np = np.zeros([resolution, resolution, resolution], dtype=np.float32) - - for xi, xs in enumerate(X): - for yi, ys in enumerate(X): - for zi, zs in enumerate(X): - xx, yy, zz = torch.meshgrid(xs, ys, zs) - pts = torch.stack([xx, yy, zz], dim=-1).unsqueeze(0).to(styles.device) # B, H, H, H, C - block_shape = [1, len(xs), len(ys), len(zs)] - out = nerf.sample_mixed(pts.reshape(1,-1,3), None, ws=styles, noise_mode='const') - feat_out, sigma_out = out['rgb'], out['sigma'] - sigma_np[xi * block_resolution: xi * block_resolution + len(xs), \ - yi * block_resolution: yi * block_resolution + len(ys), \ - zi * block_resolution: zi * block_resolution + len(zs)] = sigma_out.reshape(block_shape[1:]).detach().cpu().numpy() - # print(feat_out.shape) - - return sigma_np, bound - - -def extract_geometry(nerf, styles, resolution, threshold): - - # print('threshold: {}'.format(threshold)) - u, bound = get_sigma_field_np(nerf, styles, resolution) - vertices, faces = mcubes.marching_cubes(u, threshold) - # vertices, faces, normals, values = skimage.measure.marching_cubes( - # u, level=10 - # ) - b_min_np = np.array([-bound, -bound, -bound]) - b_max_np = np.array([ bound, bound, bound]) - - vertices = vertices / (resolution - 1.0) * (b_max_np - b_min_np)[None, :] + b_min_np[None, :] - return vertices.astype('float32'), faces -def render_video(G, ws, intrinsics, num_frames = 120, pitch_range = 0.25, yaw_range = 0.35, neural_rendering_resolution = 128, device='cuda'): - frames, frames_label = [], [] - - for frame_idx in tqdm(range(num_frames)): - cam2world_pose = LookAtPoseSampler.sample(3.14/2 + yaw_range * np.sin(2 * 3.14 * frame_idx / num_frames), - 3.14/2 -0.05 + pitch_range * np.cos(2 * 3.14 * frame_idx / num_frames), - torch.tensor(G.rendering_kwargs['avg_camera_pivot'], device=device), radius=G.rendering_kwargs['avg_camera_radius'], device=device) - pose = torch.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1) - with torch.no_grad(): - # out = G(z, pose, {'mask': batch['mask'].unsqueeze(0).to(device), 'pose': torch.tensor(batch['pose']).unsqueeze(0).to(device)}) - out = G.synthesis(ws, pose, noise_mode='const', neural_rendering_resolution=neural_rendering_resolution) - frames.append(((out['image'].cpu().numpy()[0] + 1) * 127.5).clip(0, 255).astype(np.uint8).transpose(1, 2, 0)) - frames_label.append(color_mask(torch.argmax(out['semantic'], dim=1).cpu().numpy()[0]).astype(np.uint8)) - - return frames, frames_label - -def return_plot_go(mesh_trimesh): - x=np.asarray(mesh_trimesh.vertices).T[0] - y=np.asarray(mesh_trimesh.vertices).T[1] - z=np.asarray(mesh_trimesh.vertices).T[2] - - i=np.asarray(mesh_trimesh.faces).T[0] - j=np.asarray(mesh_trimesh.faces).T[1] - k=np.asarray(mesh_trimesh.faces).T[2] - fig = go.Figure(go.Mesh3d(x=x, y=y, z=z, - i=i, j=j, k=k, - vertexcolor=np.asarray(mesh_trimesh.visual.vertex_colors) , - lighting=dict(ambient=0.5, - diffuse=1, - fresnel=4, - specular=0.5, - roughness=0.05, - facenormalsepsilon=0, - vertexnormalsepsilon=0), - lightposition=dict(x=100, - y=100, - z=1000))) - return fig - - - -network_cat=hf_hub_download("SerdarHelli/pix2pix3d_seg2cat", filename="pix2pix3d_seg2cat.pkl",revision="main") - -models={"seg2cat":network_cat - } - -device='cuda' if torch.cuda.is_available() else 'cpu' -outdir="./" - -class CPU_Unpickler(pickle.Unpickler): - def find_class(self, module, name): - if module == 'torch.storage' and name == '_load_from_bytes': - return lambda b: torch.load(io.BytesIO(b), map_location='cpu') - return super().find_class(module, name) - -def load_network_pkl_cpu(f, force_fp16=False): - data = CPU_Unpickler(f).load() - - # Legacy TensorFlow pickle => convert. - if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data): - tf_G, tf_D, tf_Gs = data - G = convert_tf_generator(tf_G) - D = convert_tf_discriminator(tf_D) - G_ema = convert_tf_generator(tf_Gs) - data = dict(G=G, D=D, G_ema=G_ema) - - # Add missing fields. - if 'training_set_kwargs' not in data: - data['training_set_kwargs'] = None - if 'augment_pipe' not in data: - data['augment_pipe'] = None - - # Validate contents. - assert isinstance(data['G'], torch.nn.Module) - assert isinstance(data['D'], torch.nn.Module) - assert isinstance(data['G_ema'], torch.nn.Module) - assert isinstance(data['training_set_kwargs'], (dict, type(None))) - assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None))) - - # Force FP16. - if force_fp16: - for key in ['G', 'D', 'G_ema']: - old = data[key] - kwargs = copy.deepcopy(old.init_kwargs) - fp16_kwargs = kwargs.get('synthesis_kwargs', kwargs) - fp16_kwargs.num_fp16_res = 4 - fp16_kwargs.conv_clamp = 256 - if kwargs != old.init_kwargs: - new = type(old)(**kwargs).eval().requires_grad_(False) - misc.copy_params_and_buffers(old, new, require_all=True) - data[key] = new - return data - -color_list = [[255, 255, 255], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] - -def colormap2labelmap(color_img): - im_base = np.zeros((color_img.shape[0], color_img.shape[1])) - for idx, color in enumerate(color_list): - - k1=((color_img == np.asarray(color))[:,:,0])*1 - k2=((color_img == np.asarray(color))[:,:,1])*1 - k3=((color_img == np.asarray(color))[:,:,2])*1 - k=((k1*k2*k3)==1) - - im_base[k] = idx - return im_base - - -def checklabelmap(img): - labels=np.unique(img) - for idx,label in enumerate(labels): - img[img==label]=idx - return img - -def get_all(cfg,input,truncation_psi,mesh_resolution,random_seed,fps,num_frames): - - network=models[cfg] - - if device=="cpu": - with dnnlib.util.open_url(network) as f: - G = load_network_pkl_cpu(f)['G_ema'].eval().to(device) - else: - with dnnlib.util.open_url(network) as f: - G = legacy.load_network_pkl(f)['G_ema'].eval().to(device) - - if cfg == 'seg2cat' or cfg == 'seg2face': - neural_rendering_resolution = 128 - data_type = 'seg' - # Initialize pose sampler. - forward_cam2world_pose = LookAtPoseSampler.sample(3.14/2, 3.14/2, torch.tensor(G.rendering_kwargs['avg_camera_pivot'], device=device), - radius=G.rendering_kwargs['avg_camera_radius'], device=device) - focal_length = 4.2647 # shapenet has higher FOV - intrinsics = torch.tensor([[focal_length, 0, 0.5], [0, focal_length, 0.5], [0, 0, 1]], device=device) - forward_pose = torch.cat([forward_cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1) - elif cfg == 'edge2car': - neural_rendering_resolution = 64 - data_type= 'edge' - else: - print('Invalid cfg') - - save_dir = Path(outdir) - - - if isinstance(input,str): - input_label =np.asarray( PIL.Image.open(input)) - else: - input_label=np.asarray(input) - - input_label=colormap2labelmap(input_label) - input_label=checklabelmap(input_label) - input_label = np.asarray(input_label).astype(np.uint8) - input_label = torch.from_numpy(input_label).unsqueeze(0).unsqueeze(0).to(device) - input_pose = forward_pose.to(device) - - # Generate videos - z = torch.from_numpy(np.random.RandomState(int(random_seed)).randn(1, G.z_dim).astype('float32')).to(device) - - with torch.no_grad(): - ws = G.mapping(z, input_pose, {'mask': input_label, 'pose': input_pose}) - out = G.synthesis(ws, input_pose, noise_mode='const', neural_rendering_resolution=neural_rendering_resolution) - - image_color = ((out['image'][0].permute(1, 2, 0).cpu().numpy().clip(-1, 1) + 1) * 127.5).astype(np.uint8) - image_seg = color_mask(torch.argmax(out['semantic'][0], dim=0).cpu().numpy()).astype(np.uint8) - mesh_trimesh = trimesh.Trimesh(*extract_geometry(G, ws, resolution=mesh_resolution, threshold=50.)) - - verts_np = np.array(mesh_trimesh.vertices) - colors = torch.zeros((verts_np.shape[0], 3), device=device) - semantic_colors = torch.zeros((verts_np.shape[0], 6), device=device) - samples_color = torch.tensor(verts_np, device=device).unsqueeze(0).float() - - head = 0 - max_batch = 10000000 - with tqdm(total = verts_np.shape[0]) as pbar: - with torch.no_grad(): - while head < verts_np.shape[0]: - torch.manual_seed(0) - out = G.sample_mixed(samples_color[:, head:head+max_batch], None, ws, truncation_psi=truncation_psi, noise_mode='const') - # sigma = out['sigma'] - colors[head:head+max_batch, :] = out['rgb'][0,:,:3] - seg = out['rgb'][0, :, 32:32+6] - semantic_colors[head:head+max_batch, :] = seg - # semantics[:, head:head+max_batch] = out['semantic'] - head += max_batch - pbar.update(max_batch) - - semantic_colors = torch.tensor(color_list,device=device)[torch.argmax(semantic_colors, dim=-1)] - - mesh_trimesh.visual.vertex_colors = semantic_colors.cpu().numpy().astype(np.uint8) - frames, frames_label = render_video(G, ws, intrinsics, num_frames = num_frames, pitch_range = 0.25, yaw_range = 0.35, neural_rendering_resolution=neural_rendering_resolution, device=device) - - # Save the video - video=os.path.join(save_dir ,f'{cfg}_color.mp4') - video_label=os.path.join(save_dir,f'{cfg}_label.mp4') - imageio.mimsave(video, frames, fps=fps) - imageio.mimsave(video_label, frames_label, fps=fps), - fig_mesh=return_plot_go(mesh_trimesh) - return fig_mesh,image_color,image_seg,video,video_label - -title="3D-aware Conditional Image Synthesis" -desc=f''' - - [Arxiv: "3D-aware Conditional Image Synthesis".](https://arxiv.org/abs/2302.08509) - - [Project Page.](https://www.cs.cmu.edu/~pix2pix3D/) - - [For the official implementation.](https://github.com/dunbar12138/pix2pix3D) - - ### Future Work based on interest - - Adding new models for new type objects - - New Customization - - - It is running on {device} - The process can take long time.Especially ,To generate videos and the time of process depends the number of frames,Mesh Resolution and current compiler device. - -''' -demo_inputs=[ - gr.Dropdown(choices=["seg2cat"],label="Choose Model",value="seg2cat"), - gr.Image(type="filepath",shape=(512, 512),label="Mask"), - gr.Slider( minimum=0, maximum=2,label='Truncation PSI',value=1), - gr.Slider( minimum=32, maximum=512,label='Mesh Resolution',value=32), - gr.Slider( minimum=0, maximum=2**16,label='Seed',value=128), - gr.Slider( minimum=10, maximum=120,label='FPS',value=30), - gr.Slider( minimum=10, maximum=120,label='The Number of Frames',value=30), - -] -demo_outputs=[ - gr.Plot(label="Generated Mesh"), - gr.Image(type="pil",shape=(256,256),label="Generated Image"), - gr.Image(type="pil",shape=(256,256),label="Generated LabelMap"), - gr.Video(label="Generated Video ") , - gr.Video(label="Generated Label Video ") - -] -examples = [ - ["seg2cat", "img.png", 1, 32, 128, 30, 30], - ["seg2cat", "img2.png", 1, 32, 128, 30, 30], - ["seg2cat", "img3.png", 1, 32, 128, 30, 30], - -] - - -demo_app = gr.Interface( - fn=get_all, - inputs=demo_inputs, - outputs=demo_outputs, - cache_examples=True, - title=title, - theme="huggingface", - description=desc, - examples=examples, -) -demo_app.launch(debug=True, enable_queue=True) diff --git a/spaces/SkalskiP/MetaCLIP/app.py b/spaces/SkalskiP/MetaCLIP/app.py deleted file mode 100644 index 7c4d3fd948f0e4c213846e45ad160edfb2849501..0000000000000000000000000000000000000000 --- a/spaces/SkalskiP/MetaCLIP/app.py +++ /dev/null @@ -1,63 +0,0 @@ -from typing import List - -import gradio as gr -import numpy as np -import torch -from transformers import CLIPProcessor, CLIPModel - -IMAGENET_CLASSES_FILE = "imagenet-classes.txt" -EXAMPLES = ["dog.jpeg", "car.png"] - -MARKDOWN = """ -# Zero-Shot Image Classification with MetaCLIP - -This is the demo for a zero-shot image classification model based on -[MetaCLIP](https://github.com/facebookresearch/MetaCLIP), described in the paper -[Demystifying CLIP Data](https://arxiv.org/abs/2309.16671) that formalizes CLIP data -curation as a simple algorithm. -""" - - -def load_text_lines(file_path: str) -> List[str]: - with open(file_path, 'r') as file: - lines = file.readlines() - return [line.rstrip() for line in lines] - - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -model = CLIPModel.from_pretrained("facebook/metaclip-b32-400m").to(device) -processor = CLIPProcessor.from_pretrained("facebook/metaclip-b32-400m") -imagenet_classes = load_text_lines(IMAGENET_CLASSES_FILE) - - -def classify_image(input_image) -> str: - inputs = processor( - text=imagenet_classes, - images=input_image, - return_tensors="pt", - padding=True).to(device) - outputs = model(**inputs) - probs = outputs.logits_per_image.softmax(dim=1) - class_index = np.argmax(probs.detach().cpu().numpy()) - return imagenet_classes[class_index] - - -with gr.Blocks() as demo: - gr.Markdown(MARKDOWN) - with gr.Row(): - image = gr.Image(image_mode='RGB', type='pil') - output_text = gr.Textbox(label="Output") - submit_button = gr.Button("Submit") - - submit_button.click(classify_image, inputs=[image], outputs=output_text) - - gr.Examples( - examples=EXAMPLES, - fn=classify_image, - inputs=[image], - outputs=[output_text], - cache_examples=True, - run_on_click=True - ) - -demo.launch(debug=False) diff --git a/spaces/Sumit7864/Image-Enhancer/docs/ncnn_conversion.md b/spaces/Sumit7864/Image-Enhancer/docs/ncnn_conversion.md deleted file mode 100644 index e1785cd079ccbb6f0a5ddefe24f63bfe81ce9b21..0000000000000000000000000000000000000000 --- a/spaces/Sumit7864/Image-Enhancer/docs/ncnn_conversion.md +++ /dev/null @@ -1,11 +0,0 @@ -# Instructions on converting to NCNN models - -1. Convert to onnx model with `scripts/pytorch2onnx.py`. Remember to modify codes accordingly -1. Convert onnx model to ncnn model - 1. `cd ncnn-master\ncnn\build\tools\onnx` - 1. `onnx2ncnn.exe realesrgan-x4.onnx realesrgan-x4-raw.param realesrgan-x4-raw.bin` -1. Optimize ncnn model - 1. fp16 mode - 1. `cd ncnn-master\ncnn\build\tools` - 1. `ncnnoptimize.exe realesrgan-x4-raw.param realesrgan-x4-raw.bin realesrgan-x4.param realesrgan-x4.bin 1` -1. Modify the blob name in `realesrgan-x4.param`: `data` and `output` diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/pt_inputhooks/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/pt_inputhooks/__init__.py deleted file mode 100644 index 9043f15e86b7e9c73cef1387acd07100444e0e24..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/pt_inputhooks/__init__.py +++ /dev/null @@ -1,138 +0,0 @@ -import importlib -import os - -aliases = { - 'qt4': 'qt', - 'gtk2': 'gtk', -} - -backends = [ - "qt", - "qt5", - "qt6", - "gtk", - "gtk2", - "gtk3", - "gtk4", - "tk", - "wx", - "pyglet", - "glut", - "osx", - "asyncio", -] - -registered = {} - -def register(name, inputhook): - """Register the function *inputhook* as an event loop integration.""" - registered[name] = inputhook - - -class UnknownBackend(KeyError): - def __init__(self, name): - self.name = name - - def __str__(self): - return ("No event loop integration for {!r}. " - "Supported event loops are: {}").format(self.name, - ', '.join(backends + sorted(registered))) - - -def set_qt_api(gui): - """Sets the `QT_API` environment variable if it isn't already set.""" - - qt_api = os.environ.get("QT_API", None) - - from IPython.external.qt_loaders import ( - QT_API_PYQT, - QT_API_PYQT5, - QT_API_PYQT6, - QT_API_PYSIDE, - QT_API_PYSIDE2, - QT_API_PYSIDE6, - QT_API_PYQTv1, - loaded_api, - ) - - loaded = loaded_api() - - qt_env2gui = { - QT_API_PYSIDE: "qt4", - QT_API_PYQTv1: "qt4", - QT_API_PYQT: "qt4", - QT_API_PYSIDE2: "qt5", - QT_API_PYQT5: "qt5", - QT_API_PYSIDE6: "qt6", - QT_API_PYQT6: "qt6", - } - if loaded is not None and gui != "qt": - if qt_env2gui[loaded] != gui: - print( - f"Cannot switch Qt versions for this session; will use {qt_env2gui[loaded]}." - ) - return qt_env2gui[loaded] - - if qt_api is not None and gui != "qt": - if qt_env2gui[qt_api] != gui: - print( - f'Request for "{gui}" will be ignored because `QT_API` ' - f'environment variable is set to "{qt_api}"' - ) - return qt_env2gui[qt_api] - else: - if gui == "qt5": - try: - import PyQt5 # noqa - - os.environ["QT_API"] = "pyqt5" - except ImportError: - try: - import PySide2 # noqa - - os.environ["QT_API"] = "pyside2" - except ImportError: - os.environ["QT_API"] = "pyqt5" - elif gui == "qt6": - try: - import PyQt6 # noqa - - os.environ["QT_API"] = "pyqt6" - except ImportError: - try: - import PySide6 # noqa - - os.environ["QT_API"] = "pyside6" - except ImportError: - os.environ["QT_API"] = "pyqt6" - elif gui == "qt": - # Don't set QT_API; let IPython logic choose the version. - if "QT_API" in os.environ.keys(): - del os.environ["QT_API"] - else: - print(f'Unrecognized Qt version: {gui}. Should be "qt5", "qt6", or "qt".') - return - - # Import it now so we can figure out which version it is. - from IPython.external.qt_for_kernel import QT_API - - return qt_env2gui[QT_API] - - -def get_inputhook_name_and_func(gui): - if gui in registered: - return gui, registered[gui] - - if gui not in backends: - raise UnknownBackend(gui) - - if gui in aliases: - return get_inputhook_name_and_func(aliases[gui]) - - gui_mod = gui - if gui.startswith("qt"): - gui = set_qt_api(gui) - gui_mod = "qt" - - mod = importlib.import_module("IPython.terminal.pt_inputhooks." + gui_mod) - return gui, mod.inputhook diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageFilter.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageFilter.py deleted file mode 100644 index 63d6dcf5cec1576148e0cd972081c6efdccf8e71..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageFilter.py +++ /dev/null @@ -1,549 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# standard filters -# -# History: -# 1995-11-27 fl Created -# 2002-06-08 fl Added rank and mode filters -# 2003-09-15 fl Fixed rank calculation in rank filter; added expand call -# -# Copyright (c) 1997-2003 by Secret Labs AB. -# Copyright (c) 1995-2002 by Fredrik Lundh. -# -# See the README file for information on usage and redistribution. -# -import functools - - -class Filter: - pass - - -class MultibandFilter(Filter): - pass - - -class BuiltinFilter(MultibandFilter): - def filter(self, image): - if image.mode == "P": - msg = "cannot filter palette images" - raise ValueError(msg) - return image.filter(*self.filterargs) - - -class Kernel(BuiltinFilter): - """ - Create a convolution kernel. The current version only - supports 3x3 and 5x5 integer and floating point kernels. - - In the current version, kernels can only be applied to - "L" and "RGB" images. - - :param size: Kernel size, given as (width, height). In the current - version, this must be (3,3) or (5,5). - :param kernel: A sequence containing kernel weights. - :param scale: Scale factor. If given, the result for each pixel is - divided by this value. The default is the sum of the - kernel weights. - :param offset: Offset. If given, this value is added to the result, - after it has been divided by the scale factor. - """ - - name = "Kernel" - - def __init__(self, size, kernel, scale=None, offset=0): - if scale is None: - # default scale is sum of kernel - scale = functools.reduce(lambda a, b: a + b, kernel) - if size[0] * size[1] != len(kernel): - msg = "not enough coefficients in kernel" - raise ValueError(msg) - self.filterargs = size, scale, offset, kernel - - -class RankFilter(Filter): - """ - Create a rank filter. The rank filter sorts all pixels in - a window of the given size, and returns the ``rank``'th value. - - :param size: The kernel size, in pixels. - :param rank: What pixel value to pick. Use 0 for a min filter, - ``size * size / 2`` for a median filter, ``size * size - 1`` - for a max filter, etc. - """ - - name = "Rank" - - def __init__(self, size, rank): - self.size = size - self.rank = rank - - def filter(self, image): - if image.mode == "P": - msg = "cannot filter palette images" - raise ValueError(msg) - image = image.expand(self.size // 2, self.size // 2) - return image.rankfilter(self.size, self.rank) - - -class MedianFilter(RankFilter): - """ - Create a median filter. Picks the median pixel value in a window with the - given size. - - :param size: The kernel size, in pixels. - """ - - name = "Median" - - def __init__(self, size=3): - self.size = size - self.rank = size * size // 2 - - -class MinFilter(RankFilter): - """ - Create a min filter. Picks the lowest pixel value in a window with the - given size. - - :param size: The kernel size, in pixels. - """ - - name = "Min" - - def __init__(self, size=3): - self.size = size - self.rank = 0 - - -class MaxFilter(RankFilter): - """ - Create a max filter. Picks the largest pixel value in a window with the - given size. - - :param size: The kernel size, in pixels. - """ - - name = "Max" - - def __init__(self, size=3): - self.size = size - self.rank = size * size - 1 - - -class ModeFilter(Filter): - """ - Create a mode filter. Picks the most frequent pixel value in a box with the - given size. Pixel values that occur only once or twice are ignored; if no - pixel value occurs more than twice, the original pixel value is preserved. - - :param size: The kernel size, in pixels. - """ - - name = "Mode" - - def __init__(self, size=3): - self.size = size - - def filter(self, image): - return image.modefilter(self.size) - - -class GaussianBlur(MultibandFilter): - """Blurs the image with a sequence of extended box filters, which - approximates a Gaussian kernel. For details on accuracy see -
- {children}
-
- Pediatric Pneumonia Chest X-Ray Predictor. Zakia Salod. 2022.
" - - -image = gr.inputs.Image(shape=(512, 512)) -label = gr.outputs.Label() -examples = [ - ['person1_virus_6.jpeg'], - ['NORMAL2-IM-0285-0001.jpeg'], - ['person82_bacteria_404.jpeg'], - ['NORMAL2-IM-0373-0001.jpeg'], - ['person1618_virus_2805.jpeg'], - ['NORMAL2-IM-0381-0001.jpeg'], - ['person159_bacteria_747.jpeg'], - ['NORMAL2-IM-0222-0001.jpeg'], - ] -interpretation = 'default' -enable_queue = True - - - - -iface = gr.Interface( - fn=predict, - title=title, - description=description, - article=article, - inputs=image, - outputs=label, - theme="default", - examples=examples, - interpretation=interpretation, - enable_queue=enable_queue -) - - - -iface.launch(inline=False) diff --git a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/snowflake/overview.md b/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/snowflake/overview.md deleted file mode 100644 index 57de66c45bcf4765dd33ebae93299a2b9eaa1d73..0000000000000000000000000000000000000000 --- a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/snowflake/overview.md +++ /dev/null @@ -1,48 +0,0 @@ ---- -title: Overview ---- -# Snowflake Ingestion Guide: Overview - -## What You Will Get Out of This Guide - -This guide will help you set up the Snowflake connector to begin ingesting metadata into DataHub. - -Upon completing this guide, you will have a recurring ingestion pipeline that will extract metadata from Snowflake and load it into DataHub. This will include to following Snowflake asset types: - -* Databases -* Schemas -* Tables -* External Tables -* Views -* Materialized Views - -The pipeline will also extract: - -* **Usage statistics** to help you understand recent query activity (available if using Snowflake Enterprise edition or above) -* **Table- and Column-level lineage** to automatically define interdependencies between datasets and columns (available if using Snowflake Enterprise edition or above) -* **Table-level profile statistics** to help you understand the shape of the data - -:::caution -You will NOT have extracted Stages, Snowpipes, Streams, Tasks, Procedures from Snowflake, as the connector does not support ingesting these assets yet. -::: - -### Caveats - -By default, DataHub only profiles datasets that have changed in the past 1 day. This can be changed in the YAML editor by setting the value of `profile_if_updated_since_days` to something greater than 1. - -Additionally, DataHub only extracts usage and lineage information based on operations performed in the last 1 day. This can be changed by setting a custom value for `start_time` and `end_time` in the YAML editor. - -*To learn more about setting these advanced values, check out the [Snowflake Ingestion Source](https://datahubproject.io/docs/generated/ingestion/sources/snowflake/#module-snowflake).* - -## Next Steps - -If that all sounds like what you're looking for, navigate to the [next page](setup.md), where we'll talk about prerequisites. - -## Advanced Guides and Reference - -If you want to ingest metadata from Snowflake using the DataHub CLI, check out the following resources: - -* Learn about CLI Ingestion in the [Introduction to Metadata Ingestion](../../../metadata-ingestion/README.md) -* [Snowflake Ingestion Source](https://datahubproject.io/docs/generated/ingestion/sources/snowflake/#module-snowflake) - -*Need more help? Join the conversation in [Slack](http://slack.datahubproject.io)!* \ No newline at end of file diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/image/photometric.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/image/photometric.py deleted file mode 100644 index 5085d012019c0cbf56f66f421a378278c1a058ae..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/image/photometric.py +++ /dev/null @@ -1,428 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import cv2 -import numpy as np - -from ..utils import is_tuple_of -from .colorspace import bgr2gray, gray2bgr - - -def imnormalize(img, mean, std, to_rgb=True): - """Normalize an image with mean and std. - - Args: - img (ndarray): Image to be normalized. - mean (ndarray): The mean to be used for normalize. - std (ndarray): The std to be used for normalize. - to_rgb (bool): Whether to convert to rgb. - - Returns: - ndarray: The normalized image. - """ - img = img.copy().astype(np.float32) - return imnormalize_(img, mean, std, to_rgb) - - -def imnormalize_(img, mean, std, to_rgb=True): - """Inplace normalize an image with mean and std. - - Args: - img (ndarray): Image to be normalized. - mean (ndarray): The mean to be used for normalize. - std (ndarray): The std to be used for normalize. - to_rgb (bool): Whether to convert to rgb. - - Returns: - ndarray: The normalized image. - """ - # cv2 inplace normalization does not accept uint8 - assert img.dtype != np.uint8 - mean = np.float64(mean.reshape(1, -1)) - stdinv = 1 / np.float64(std.reshape(1, -1)) - if to_rgb: - cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) # inplace - cv2.subtract(img, mean, img) # inplace - cv2.multiply(img, stdinv, img) # inplace - return img - - -def imdenormalize(img, mean, std, to_bgr=True): - assert img.dtype != np.uint8 - mean = mean.reshape(1, -1).astype(np.float64) - std = std.reshape(1, -1).astype(np.float64) - img = cv2.multiply(img, std) # make a copy - cv2.add(img, mean, img) # inplace - if to_bgr: - cv2.cvtColor(img, cv2.COLOR_RGB2BGR, img) # inplace - return img - - -def iminvert(img): - """Invert (negate) an image. - - Args: - img (ndarray): Image to be inverted. - - Returns: - ndarray: The inverted image. - """ - return np.full_like(img, 255) - img - - -def solarize(img, thr=128): - """Solarize an image (invert all pixel values above a threshold) - - Args: - img (ndarray): Image to be solarized. - thr (int): Threshold for solarizing (0 - 255). - - Returns: - ndarray: The solarized image. - """ - img = np.where(img < thr, img, 255 - img) - return img - - -def posterize(img, bits): - """Posterize an image (reduce the number of bits for each color channel) - - Args: - img (ndarray): Image to be posterized. - bits (int): Number of bits (1 to 8) to use for posterizing. - - Returns: - ndarray: The posterized image. - """ - shift = 8 - bits - img = np.left_shift(np.right_shift(img, shift), shift) - return img - - -def adjust_color(img, alpha=1, beta=None, gamma=0): - r"""It blends the source image and its gray image: - - .. math:: - output = img * alpha + gray\_img * beta + gamma - - Args: - img (ndarray): The input source image. - alpha (int | float): Weight for the source image. Default 1. - beta (int | float): Weight for the converted gray image. - If None, it's assigned the value (1 - `alpha`). - gamma (int | float): Scalar added to each sum. - Same as :func:`cv2.addWeighted`. Default 0. - - Returns: - ndarray: Colored image which has the same size and dtype as input. - """ - gray_img = bgr2gray(img) - gray_img = np.tile(gray_img[..., None], [1, 1, 3]) - if beta is None: - beta = 1 - alpha - colored_img = cv2.addWeighted(img, alpha, gray_img, beta, gamma) - if not colored_img.dtype == np.uint8: - # Note when the dtype of `img` is not the default `np.uint8` - # (e.g. np.float32), the value in `colored_img` got from cv2 - # is not guaranteed to be in range [0, 255], so here clip - # is needed. - colored_img = np.clip(colored_img, 0, 255) - return colored_img - - -def imequalize(img): - """Equalize the image histogram. - - This function applies a non-linear mapping to the input image, - in order to create a uniform distribution of grayscale values - in the output image. - - Args: - img (ndarray): Image to be equalized. - - Returns: - ndarray: The equalized image. - """ - - def _scale_channel(im, c): - """Scale the data in the corresponding channel.""" - im = im[:, :, c] - # Compute the histogram of the image channel. - histo = np.histogram(im, 256, (0, 255))[0] - # For computing the step, filter out the nonzeros. - nonzero_histo = histo[histo > 0] - step = (np.sum(nonzero_histo) - nonzero_histo[-1]) // 255 - if not step: - lut = np.array(range(256)) - else: - # Compute the cumulative sum, shifted by step // 2 - # and then normalized by step. - lut = (np.cumsum(histo) + (step // 2)) // step - # Shift lut, prepending with 0. - lut = np.concatenate([[0], lut[:-1]], 0) - # handle potential integer overflow - lut[lut > 255] = 255 - # If step is zero, return the original image. - # Otherwise, index from lut. - return np.where(np.equal(step, 0), im, lut[im]) - - # Scales each channel independently and then stacks - # the result. - s1 = _scale_channel(img, 0) - s2 = _scale_channel(img, 1) - s3 = _scale_channel(img, 2) - equalized_img = np.stack([s1, s2, s3], axis=-1) - return equalized_img.astype(img.dtype) - - -def adjust_brightness(img, factor=1.): - """Adjust image brightness. - - This function controls the brightness of an image. An - enhancement factor of 0.0 gives a black image. - A factor of 1.0 gives the original image. This function - blends the source image and the degenerated black image: - - .. math:: - output = img * factor + degenerated * (1 - factor) - - Args: - img (ndarray): Image to be brightened. - factor (float): A value controls the enhancement. - Factor 1.0 returns the original image, lower - factors mean less color (brightness, contrast, - etc), and higher values more. Default 1. - - Returns: - ndarray: The brightened image. - """ - degenerated = np.zeros_like(img) - # Note manually convert the dtype to np.float32, to - # achieve as close results as PIL.ImageEnhance.Brightness. - # Set beta=1-factor, and gamma=0 - brightened_img = cv2.addWeighted( - img.astype(np.float32), factor, degenerated.astype(np.float32), - 1 - factor, 0) - brightened_img = np.clip(brightened_img, 0, 255) - return brightened_img.astype(img.dtype) - - -def adjust_contrast(img, factor=1.): - """Adjust image contrast. - - This function controls the contrast of an image. An - enhancement factor of 0.0 gives a solid grey - image. A factor of 1.0 gives the original image. It - blends the source image and the degenerated mean image: - - .. math:: - output = img * factor + degenerated * (1 - factor) - - Args: - img (ndarray): Image to be contrasted. BGR order. - factor (float): Same as :func:`mmcv.adjust_brightness`. - - Returns: - ndarray: The contrasted image. - """ - gray_img = bgr2gray(img) - hist = np.histogram(gray_img, 256, (0, 255))[0] - mean = round(np.sum(gray_img) / np.sum(hist)) - degenerated = (np.ones_like(img[..., 0]) * mean).astype(img.dtype) - degenerated = gray2bgr(degenerated) - contrasted_img = cv2.addWeighted( - img.astype(np.float32), factor, degenerated.astype(np.float32), - 1 - factor, 0) - contrasted_img = np.clip(contrasted_img, 0, 255) - return contrasted_img.astype(img.dtype) - - -def auto_contrast(img, cutoff=0): - """Auto adjust image contrast. - - This function maximize (normalize) image contrast by first removing cutoff - percent of the lightest and darkest pixels from the histogram and remapping - the image so that the darkest pixel becomes black (0), and the lightest - becomes white (255). - - Args: - img (ndarray): Image to be contrasted. BGR order. - cutoff (int | float | tuple): The cutoff percent of the lightest and - darkest pixels to be removed. If given as tuple, it shall be - (low, high). Otherwise, the single value will be used for both. - Defaults to 0. - - Returns: - ndarray: The contrasted image. - """ - - def _auto_contrast_channel(im, c, cutoff): - im = im[:, :, c] - # Compute the histogram of the image channel. - histo = np.histogram(im, 256, (0, 255))[0] - # Remove cut-off percent pixels from histo - histo_sum = np.cumsum(histo) - cut_low = histo_sum[-1] * cutoff[0] // 100 - cut_high = histo_sum[-1] - histo_sum[-1] * cutoff[1] // 100 - histo_sum = np.clip(histo_sum, cut_low, cut_high) - cut_low - histo = np.concatenate([[histo_sum[0]], np.diff(histo_sum)], 0) - - # Compute mapping - low, high = np.nonzero(histo)[0][0], np.nonzero(histo)[0][-1] - # If all the values have been cut off, return the origin img - if low >= high: - return im - scale = 255.0 / (high - low) - offset = -low * scale - lut = np.array(range(256)) - lut = lut * scale + offset - lut = np.clip(lut, 0, 255) - return lut[im] - - if isinstance(cutoff, (int, float)): - cutoff = (cutoff, cutoff) - else: - assert isinstance(cutoff, tuple), 'cutoff must be of type int, ' \ - f'float or tuple, but got {type(cutoff)} instead.' - # Auto adjusts contrast for each channel independently and then stacks - # the result. - s1 = _auto_contrast_channel(img, 0, cutoff) - s2 = _auto_contrast_channel(img, 1, cutoff) - s3 = _auto_contrast_channel(img, 2, cutoff) - contrasted_img = np.stack([s1, s2, s3], axis=-1) - return contrasted_img.astype(img.dtype) - - -def adjust_sharpness(img, factor=1., kernel=None): - """Adjust image sharpness. - - This function controls the sharpness of an image. An - enhancement factor of 0.0 gives a blurred image. A - factor of 1.0 gives the original image. And a factor - of 2.0 gives a sharpened image. It blends the source - image and the degenerated mean image: - - .. math:: - output = img * factor + degenerated * (1 - factor) - - Args: - img (ndarray): Image to be sharpened. BGR order. - factor (float): Same as :func:`mmcv.adjust_brightness`. - kernel (np.ndarray, optional): Filter kernel to be applied on the img - to obtain the degenerated img. Defaults to None. - - Note: - No value sanity check is enforced on the kernel set by users. So with - an inappropriate kernel, the ``adjust_sharpness`` may fail to perform - the function its name indicates but end up performing whatever - transform determined by the kernel. - - Returns: - ndarray: The sharpened image. - """ - - if kernel is None: - # adopted from PIL.ImageFilter.SMOOTH - kernel = np.array([[1., 1., 1.], [1., 5., 1.], [1., 1., 1.]]) / 13 - assert isinstance(kernel, np.ndarray), \ - f'kernel must be of type np.ndarray, but got {type(kernel)} instead.' - assert kernel.ndim == 2, \ - f'kernel must have a dimension of 2, but got {kernel.ndim} instead.' - - degenerated = cv2.filter2D(img, -1, kernel) - sharpened_img = cv2.addWeighted( - img.astype(np.float32), factor, degenerated.astype(np.float32), - 1 - factor, 0) - sharpened_img = np.clip(sharpened_img, 0, 255) - return sharpened_img.astype(img.dtype) - - -def adjust_lighting(img, eigval, eigvec, alphastd=0.1, to_rgb=True): - """AlexNet-style PCA jitter. - - This data augmentation is proposed in `ImageNet Classification with Deep - Convolutional Neural Networks -{"Best Model with Summarization Results"}
', unsafe_allow_html=True) - st.write("\nBest model name: ",x) -# st.write("\nBest model Score: ",c) - - st.write("Best Model Rouge Scores: ") - st.write("Rouge 1 Score: ",c["rouge1"]) - st.write("Rouge 2 Score: ",c["rouge2"]) - st.write("Rouge L Score: ",c["rougeL"]) - st.write("Rouge LSum Score: ",c["rougeLsum"]) - - st.write("\nOriginal Data first 50 characters: ", v) - st.write("\nBest Model Result: ",b) - - -# print("between summarizer analysis") - st.markdown(f'{"Summarization Results for Model 1"}
', unsafe_allow_html=True) -# st.write("Summarization Results for Model 1") - st.write("Model name: facebook/bart-large-cnn") - st.write("Rouge Scores: ") - st.write("Rouge 1 Score: ",analysis["facebook/bart-large-cnn"]["Score"]["rouge1"]) - st.write("Rouge 2 Score: ",analysis["facebook/bart-large-cnn"]["Score"]["rouge2"]) - st.write("Rouge L Score: ",analysis["facebook/bart-large-cnn"]["Score"]["rougeL"]) - st.write(f"Rouge LSum Score: ",analysis["facebook/bart-large-cnn"]["Score"]["rougeLsum"]) - st.write("Result: ", analysis["facebook/bart-large-cnn"]["Result"]) - - st.markdown(f'{"Summarization Results for Model 2"}
', unsafe_allow_html=True) -# st.write("Summarization Results for Model 2") - st.write("Model name: tuner007/pegasus_summarizer") - st.write("Rouge Scores: ") - st.write("Rouge 1 Score: ",analysis["tuner007/pegasus_summarizer"]["Score"]["rouge1"]) - st.write("Rouge 2 Score: ",analysis["tuner007/pegasus_summarizer"]["Score"]["rouge2"]) - st.write("Rouge L Score: ",analysis["tuner007/pegasus_summarizer"]["Score"]["rougeL"]) - st.write("Rouge LSum Score: ",analysis["tuner007/pegasus_summarizer"]["Score"]["rougeLsum"]) - st.write("Result: ", analysis["tuner007/pegasus_summarizer"]["Result"][0]) - - - - st.markdown(f'{"Summarization Results for Model 3"}
', unsafe_allow_html=True) -# st.write("Summarization Results for Model 3") - st.write("Model name: sshleifer/distilbart-cnn-12-6") - st.write("Rouge Scores: ") - st.write("Rouge 1 Score: ",analysis["sshleifer/distilbart-cnn-12-6"]["Score"]["rouge1"]) - st.write("Rouge 2 Score: ",analysis["sshleifer/distilbart-cnn-12-6"]["Score"]["rouge2"]) - st.write("Rouge L Score: ",analysis["sshleifer/distilbart-cnn-12-6"]["Score"]["rougeL"]) - st.write("Rouge LSum Score: ",analysis["sshleifer/distilbart-cnn-12-6"]["Score"]["rougeLsum"]) - - st.write("Result: ", analysis["sshleifer/distilbart-cnn-12-6"]["Result"]) - - - - -#OBJECT DETECTION - -def yolo_tiny(name): - image = read_image(name) - - model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny') - image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny") - - inputs = image_processor(images=image, return_tensors="pt") - outputs = model(**inputs) - - # model predicts bounding boxes and corresponding COCO classes - logits = outputs.logits - bboxes = outputs.pred_boxes - - - # print results - target_sizes = torch.tensor([image.shape[::-1][:2]]) - - results = image_processor.post_process_object_detection(outputs, threshold=0.7, target_sizes=target_sizes)[0] - - label_ = [] - bboxes = [] - - for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): - box = [round(i, 2) for i in box.tolist()] - print( - f"Detected {model.config.id2label[label.item()]} with confidence " - f"{round(score.item(), 3)} at location {box}" - ) - - label_.append(model.config.id2label[label.item()]) - bboxes.append(np.asarray(box,dtype="int")) - bboxes = torch.tensor(bboxes, dtype=torch.int) - - img=draw_bounding_boxes(image, bboxes,labels = label_, width=3) - img = torchvision.transforms.ToPILImage()(img) - return img -# img.show() - - - -def resnet_101(name): - image = read_image(name) - processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101") - model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101") - - inputs = processor(images=image, return_tensors="pt") - outputs = model(**inputs) - - # convert outputs (bounding boxes and class logits) to COCO API - # let's only keep detections with score > 0.9 - target_sizes = torch.tensor([image.shape[::-1][:2]]) - results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0] - label_ = [] - bboxes = [] - for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): - box = [round(i, 2) for i in box.tolist()] - print( - f"Detected {model.config.id2label[label.item()]} with confidence " - f"{round(score.item(), 3)} at location {box}") - label_.append(model.config.id2label[label.item()]) - bboxes.append(np.asarray(box,dtype="int")) - bboxes = torch.tensor(bboxes, dtype=torch.int) - - - bboxes = torch.tensor(bboxes, dtype=torch.int) - - img=draw_bounding_boxes(image, bboxes,labels = label_, width=3) - img = torchvision.transforms.ToPILImage()(img) - return img - - - - - -def resnet_50(name): - image = read_image(name) - processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") - model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") - - inputs = processor(images=image, return_tensors="pt") - outputs = model(**inputs) - - # convert outputs (bounding boxes and class logits) to COCO API - # let's only keep detections with score > 0.9 - target_sizes = torch.tensor([image.shape[::-1][:2]]) - results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0] - label_ = [] - bboxes = [] - for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): - box = [round(i, 2) for i in box.tolist()] - print( - f"Detected {model.config.id2label[label.item()]} with confidence " - f"{round(score.item(), 3)} at location {box}" - ) - label_.append(model.config.id2label[label.item()]) - bboxes.append(np.asarray(box,dtype="int")) - bboxes = torch.tensor(bboxes, dtype=torch.int) - - bboxes = torch.tensor(bboxes, dtype=torch.int) - - img=draw_bounding_boxes(image, bboxes,labels = label_, width=3) - img = torchvision.transforms.ToPILImage()(img) - return img - - - -def object_detection(): -# st.write("Upload your image: ") -# uploaded_files = "" -# uploaded_files = st.file_uploader("Choose a image file", accept_multiple_files=True) - - option = st.selectbox( - 'What image you want for analysis?', - ("Choose an image for object detection analysis from the options below:",'Cat and Dog', '2 lazy cats chilling on a couch', 'An astronaut riding wild horse')) - - if option == 'Cat and Dog' or option == '2 lazy cats chilling on a couch' or option == 'An astronaut riding wild horse': - st.write('You selected:', option) - - if option == 'Cat and Dog': - name = "cat_dog.jpg" - st.image("cat_dog.jpg") - - if option == '2 lazy cats chilling on a couch': - name = "cat_remote.jpg" - st.image("cat_remote.jpg") - - if option == 'An astronaut riding wild horse': - name = "astronaut_rides_horse.png" - st.image("astronaut_rides_horse.png") - - if st.button("Done"): - # global file_data -# st.write("filename:", uploaded_files) -# for uploaded_file in uploaded_files: - # print("here") - # file_data = open(uploaded_file.name).read() - st.write("filename:", name) -# name = uploaded_file.name - st.image([yolo_tiny(name),resnet_101(name),resnet_50(name)],caption=["hustvl/yolos-tiny","facebook/detr-resnet-101","facebook/detr-resnet-50"]) - - -def task_categorization_model_predictions(): - st.image("./panelup.png") - - # st.title("Text Analysis App") - - data = "" - - classifier = pipeline("zero-shot-classification",model="facebook/bart-large-mnli") - - global check - - st.markdown(f'{"Write down below the description of your AI application in few sentences:"}
', unsafe_allow_html=True) - - prompt = st.text_input(" ") - - st.write("") - st.write("") - - if prompt != "": - # sbert_saved_model = torch.load("Sbert_saved_model", map_location=torch.device('cpu')).to("cpu") - # model = sbert_saved_model.to("cpu") - # tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2") - # pipe = TextClassificationPipeline(model= model, tokenizer=tokenizer, return_all_scores=True) - # # outputs a list of dicts like [[{'label': 'NEGATIVE', 'score': 0.0001223755971295759}, {'label': 'POSITIVE', 'score': 0.9998776316642761}]] - - # # prompt = ["What is the the best ai for putting text report into data table?","How can I generate car sales agreement with ai model?","AI model to detect burglar on 48 hours of cctv video footage","I need Ai model help me with rewriting 50 financial statements emails into one summary report ?","I need a model for extracting person from an image"] - # # responses = pipe(prompt) - - - # models_list = pd.read_csv("models.csv") - # # st.write(get_top_3(prompt)) - - # top_cat, top_models = get_top_3(prompt) - # # prompt = input("Enter your AI task idea:") - # # top_cats,cat_to_models = get_models(prompt) - - # # top_models = cat_to_models[top_cats[0]] - - # top_cat = " " + top_cat[0].upper() + top_cat[1:] - - - - st.markdown(f'{"Recognized AI Domain: "}
', unsafe_allow_html=True) - - domains = ["Computer Vision Task","Natural Language Processing Problem","Audio Operations Problem","Tabular Data Task","Reinforcement Learning Problem","Time Series Forecasting Problem"] - - - - #st.write(classifier(prompt, domains)) - domain = classifier(prompt, domains)["labels"][0] - - st.markdown(f'{domain}
', unsafe_allow_html=True) - # st.write("Recommended AI Domain Type: ",top_cat) - check = 0 - if st.button("This seems accurate"): - check = 1 - if st.button("Show me other likely category recommendations:"): - if domain == "Tabular Data Problem": - if st.button("Computer Vision Task"): - domain = "Computer Vision Task" - check = 1 - if st.button("Natural Language Processing Problem"): - domain = "Natural Language Processing Problem" - check = 1 - if st.button("Multimodal AI Model"): - domain = "Multimodal AI Model" - check = 1 - if st.button("Audio Operations Problem"): - domain = "Audio Operations Problem" - check = 1 - # if st.button("Tabular Data Task"): - # domain = "Tabular Data Task" - if st.button("Reinforcement Learning Problem"): - domain = "Reinforcement Learning Problem" - check = 1 - if st.button("Time Series Forecasting Problem"): - domain = "Time Series Forecasting Problem" - check = 1 - - - if domain == "Computer Vision Task": - # if st.button("Computer Vision Task"): - # domain = "Computer Vision Task" - if st.button("Natural Language Processing Problem"): - domain = "Natural Language Processing Problem" - check = 1 - - if st.button("Multimodal AI Model"): - domain = "Multimodal AI Model" - check = 1 - - if st.button("Audio Operations Problem"): - domain = "Audio Operations Problem" - check = 1 - if st.button("Tabular Data Task"): - domain = "Tabular Data Task" - check = 1 - if st.button("Reinforcement Learning Problem"): - domain = "Reinforcement Learning Problem" - check = 1 - if st.button("Time Series Forecasting Problem"): - domain = "Time Series Forecasting Problem" - check = 1 - - - if domain == "Natural Language Processing Problem": - if st.button("Computer Vision Task"): - domain = "Computer Vision Task" - check = 1 - # if st.button("Natural Language Processing Problem"): - # domain = "Natural Language Processing Problem" - if st.button("Multimodal AI Model"): - domain = "multimodal" - check = 1 - if st.button("Audio Operations Problem"): - domain = "Audio Operations Problem" - check = 1 - if st.button("Tabular Data Task"): - domain = "Tabular Data Task" - check = 1 - if st.button("Reinforcement Learning Problem"): - domain = "Reinforcement Learning Problem" - check = 1 - if st.button("Time Series Forecasting Problem"): - domain = "Time Series Forecasting Problem" - check = 1 - - - if domain == "Multimodal AI Model": - if st.button("Computer Vision Task"): - domain = "Computer Vision Task" - check = 1 - if st.button("Natural Language Processing Problem"): - domain = "Natural Language Processing Problem" - check = 1 - # if st.button("Multimodal AI Model"): - # domain = "Multimodal AI Model" - if st.button("Audio Operations Problem"): - domain = "Audio Operations Problem" - check = 1 - if st.button("Tabular Data Task"): - domain = "Tabular Data Task" - check = 1 - if st.button("Reinforcement Learning Problem"): - domain = "Reinforcement Learning Problem" - check = 1 - if st.button("Time Series Forecasting Problem"): - domain = "Time Series Forecasting Problem" - check = 1 - - - if domain == "audio": - if st.button("Computer Vision Task"): - domain = "Computer Vision Task" - check = 1 - if st.button("Natural Language Processing Problem"): - domain = "Natural Language Processing Problem" - check = 1 - if st.button("Multimodal AI Model"): - domain = "Multimodal AI Model" - check = 1 - # if st.button("Audio Operations Problem"): - # domain = "Audio Operations Problem" - if st.button("Tabular Data Task"): - domain = "Tabular Data Task" - check = 1 - if st.button("Reinforcement Learning Problem"): - domain = "Reinforcement Learning Problem" - check = 1 - if st.button("Time Series Forecasting Problem"): - domain = "Time Series Forecasting Problem" - check = 1 - - - if domain == "reinforcement-learning": - if st.button("Computer Vision Task"): - domain = "Computer Vision Task" - check = 1 - if st.button("Natural Language Processing Problem"): - domain = "Natural Language Processing Problem" - check = 1 - if st.button("Multimodal AI Model"): - domain = "multimodal" - check = 1 - if st.button("Audio Operations Problem"): - domain = "Audio Operations Problem" - check = 1 - if st.button("Tabular Data Task"): - domain = "Tabular Data Task" - check = 1 - # if st.button("Reinforcement Learning Problem"): - # domain = "Reinforcement Learning Problem" - if st.button("Time Series Forecasting Problem"): - domain = "Time Series Forecasting Problem" - check = 1 - - if domain == "Time Series Forecasting": - if st.button("Computer Vision Task"): - domain = "Computer Vision Task" - check = 1 - if st.button("Natural Language Processing Problem"): - domain = "Natural Language Processing Problem" - check = 1 - if st.button("Multimodal AI Model"): - domain = "Multimodal AI Model" - check = 1 - if st.button("Audio Operations Problem"): - domain = "Audio Operations Problem" - check = 1 - if st.button("Tabular Data Task"): - domain = "Tabular Data Task" - check = 1 - if st.button("Reinforcement Learning Problem"): - domain = "Reinforcement Learning Problem" - check = 1 - # if st.button("Time Series Forecasting Problem"): - # domain = "Time Series Forecasting Problem" - - # st.write("Recommended Models for category: ",top_cats[0], " are:",top_models) - - # st.write("Recommended Task category: ",top_models[0]) - - - - knowledge_base_tasks = {"Computer Vision Task":['depth-estimation', 'image-classification', 'image-segmentation', - 'image-to-image', 'object-detection', 'video-classification', - 'unconditional-image-generation', 'zero-shot-image-classification'],"Natural Language Processing Problem":[ - 'conversational', 'fill-mask', 'question-answering', - 'sentence-similarity', 'summarization', 'table-question-answering', - 'text-classification', 'text-generation', 'token-classification', - 'translation', 'zero-shot-classification'],"Audio Operations Problem":["audio-classification","audio-to-audio","automatic-speech-recognition", - "text-to-speech"],"Tabular Data Task":["tabular-classification","tabular-regression"],"others":["document-question-answering", - "feature-extraction","image-to-text","text-to-image","text-to-video","visual-question-answering"], - "Reinforcement Learning Problem":["reinforcement-learning"],"time-series-forecasting":["time-series-forecasting"]} - - # st.write(check) - # st.write(domain) - if check == 1: - - category = classifier(prompt, knowledge_base_tasks[domain])["labels"][0] - - - st.markdown(f'{"Recognized sub category in Domain: "+domain}
', unsafe_allow_html=True) - - st.markdown(f'{category}
', unsafe_allow_html=True) - - - top_models = get_top_3(category) - #st.write(top_models) - st.markdown(f'{"The best models selected for this domain:"}
', unsafe_allow_html=True) - - - st.markdown(f'{"1- "+top_models[0]}
', unsafe_allow_html=True) - - st.image("./buttons1.png") - - # if st.button("Show more"): - - st.markdown(f'{"2- "+top_models[1]}
', unsafe_allow_html=True) - st.image("./buttons1.png") - - - st.markdown(f'{"3- "+top_models[2]}
', unsafe_allow_html=True) - st.image("./buttons1.png") - - - - - - - - - - -page_names_to_funcs = { - "Pick the best Model for your AI app":task_categorization_model_predictions, - "Compare Object Detection Performance": object_detection, - "Compare Document Summarization Performance": text_summarization -} - -demo_name = st.sidebar.selectbox("Pick the best model for your next AI task or compare model performance if to advance your builds", page_names_to_funcs.keys()) -page_names_to_funcs[demo_name]() - - - -# st.write("Recommended Most Popular Model for category ",top_cat, " is:",top_models[0]) -# if st.button("Show more"): -# for i in range(1,len(top_models)): -# st.write("Model#",str(i+1),top_models[i]) - - -# data = prompt - -# # print("before len data") - -# if len(data) != 0: -# # print("after len data") -# st.write("Recommended Task category: ",top_cats[0]) -# st.write("Recommended Most Popular Model for category ",top_cats[0], " is:",top_models[0]) -# if st.button("Show more"): -# for i in range(1,len(top_models)): -# st.write("Model#",str(i+1),top_models[i]) - -# st.write("Upload your file: ") -# uploaded_files = "" -# uploaded_files = st.file_uploader("Choose a text file", accept_multiple_files=True) -# if st.button("Done"): -# global file_data -# st.write("filename:", uploaded_files) -# for uploaded_file in uploaded_files: -# # print("here") -# file_data = open(uploaded_file.name,encoding="utf8").read() -# st.write("filename:", uploaded_file.name) -# # st.write(file_data[:500]) -# # print("before summarizer") -# print(file_data[:500]) -# analysis = summarizer(models = top_models, data = file_data[:500]) -# # print("between summarizer analysis") - -# z,x,c,v,b = best_model(analysis,file_data[:500]) -# st.write("Best model for Task: ",z) -# st.write("\nBest model name: ",x) -# st.write("\nBest model Score: ",c) -# st.write("\nOriginal Data first 500 characters: ", v) -# st.write("\nBest Model Result: ",b) -# st.success(result) diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/optimizedSD/optimized_txt2img.py b/spaces/awaawawawa/iurf7irfuyytruyyugb/optimizedSD/optimized_txt2img.py deleted file mode 100644 index 5022cac9f59183556811526400d585d8e82831d4..0000000000000000000000000000000000000000 --- a/spaces/awaawawawa/iurf7irfuyytruyyugb/optimizedSD/optimized_txt2img.py +++ /dev/null @@ -1,347 +0,0 @@ -import argparse, os, re -import torch -import numpy as np -from random import randint -from omegaconf import OmegaConf -from PIL import Image -from tqdm import tqdm, trange -from itertools import islice -from einops import rearrange -from torchvision.utils import make_grid -import time -from pytorch_lightning import seed_everything -from torch import autocast -from contextlib import contextmanager, nullcontext -from ldmlib.util import instantiate_from_config -from optimUtils import split_weighted_subprompts, logger -from transformers import logging -# from samplers import CompVisDenoiser -logging.set_verbosity_error() - - -def chunk(it, size): - it = iter(it) - return iter(lambda: tuple(islice(it, size)), ()) - - -def load_model_from_config(ckpt, verbose=False): - print(f"Loading model from {ckpt}") - pl_sd = torch.load(ckpt, map_location="cpu") - if "global_step" in pl_sd: - print(f"Global Step: {pl_sd['global_step']}") - sd = pl_sd["state_dict"] - return sd - - -config = "optimizedSD/v1-inference.yaml" -DEFAULT_CKPT = "models/ldm/stable-diffusion-v1/model.ckpt" - -parser = argparse.ArgumentParser() - -parser.add_argument( - "--prompt", type=str, nargs="?", default="a painting of a virus monster playing guitar", help="the prompt to render" -) -parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2img-samples") -parser.add_argument( - "--skip_grid", - action="store_true", - help="do not save a grid, only individual samples. Helpful when evaluating lots of samples", -) -parser.add_argument( - "--skip_save", - action="store_true", - help="do not save individual samples. For speed measurements.", -) -parser.add_argument( - "--ddim_steps", - type=int, - default=50, - help="number of ddim sampling steps", -) - -parser.add_argument( - "--fixed_code", - action="store_true", - help="if enabled, uses the same starting code across samples ", -) -parser.add_argument( - "--ddim_eta", - type=float, - default=0.0, - help="ddim eta (eta=0.0 corresponds to deterministic sampling", -) -parser.add_argument( - "--n_iter", - type=int, - default=1, - help="sample this often", -) -parser.add_argument( - "--H", - type=int, - default=512, - help="image height, in pixel space", -) -parser.add_argument( - "--W", - type=int, - default=512, - help="image width, in pixel space", -) -parser.add_argument( - "--C", - type=int, - default=4, - help="latent channels", -) -parser.add_argument( - "--f", - type=int, - default=8, - help="downsampling factor", -) -parser.add_argument( - "--n_samples", - type=int, - default=5, - help="how many samples to produce for each given prompt. A.k.a. batch size", -) -parser.add_argument( - "--n_rows", - type=int, - default=0, - help="rows in the grid (default: n_samples)", -) -parser.add_argument( - "--scale", - type=float, - default=7.5, - help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", -) -parser.add_argument( - "--device", - type=str, - default="cuda", - help="specify GPU (cuda/cuda:0/cuda:1/...)", -) -parser.add_argument( - "--from-file", - type=str, - help="if specified, load prompts from this file", -) -parser.add_argument( - "--seed", - type=int, - default=None, - help="the seed (for reproducible sampling)", -) -parser.add_argument( - "--unet_bs", - type=int, - default=1, - help="Slightly reduces inference time at the expense of high VRAM (value > 1 not recommended )", -) -parser.add_argument( - "--turbo", - action="store_true", - help="Reduces inference time on the expense of 1GB VRAM", -) -parser.add_argument( - "--precision", - type=str, - help="evaluate at this precision", - choices=["full", "autocast"], - default="autocast" -) -parser.add_argument( - "--format", - type=str, - help="output image format", - choices=["jpg", "png"], - default="png", -) -parser.add_argument( - "--sampler", - type=str, - help="sampler", - choices=["ddim", "plms","heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"], - default="plms", -) -parser.add_argument( - "--ckpt", - type=str, - help="path to checkpoint of model", - default=DEFAULT_CKPT, -) -opt = parser.parse_args() - -tic = time.time() -os.makedirs(opt.outdir, exist_ok=True) -outpath = opt.outdir -grid_count = len(os.listdir(outpath)) - 1 - -if opt.seed == None: - opt.seed = randint(0, 1000000) -seed_everything(opt.seed) - -# Logging -logger(vars(opt), log_csv = "logs/txt2img_logs.csv") - -sd = load_model_from_config(f"{opt.ckpt}") -li, lo = [], [] -for key, value in sd.items(): - sp = key.split(".") - if (sp[0]) == "model": - if "input_blocks" in sp: - li.append(key) - elif "middle_block" in sp: - li.append(key) - elif "time_embed" in sp: - li.append(key) - else: - lo.append(key) -for key in li: - sd["model1." + key[6:]] = sd.pop(key) -for key in lo: - sd["model2." + key[6:]] = sd.pop(key) - -config = OmegaConf.load(f"{config}") - -model = instantiate_from_config(config.modelUNet) -_, _ = model.load_state_dict(sd, strict=False) -model.eval() -model.unet_bs = opt.unet_bs -model.cdevice = opt.device -model.turbo = opt.turbo - -modelCS = instantiate_from_config(config.modelCondStage) -_, _ = modelCS.load_state_dict(sd, strict=False) -modelCS.eval() -modelCS.cond_stage_model.device = opt.device - -modelFS = instantiate_from_config(config.modelFirstStage) -_, _ = modelFS.load_state_dict(sd, strict=False) -modelFS.eval() -del sd - -if opt.device != "cpu" and opt.precision == "autocast": - model.half() - modelCS.half() - -start_code = None -if opt.fixed_code: - start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=opt.device) - - -batch_size = opt.n_samples -n_rows = opt.n_rows if opt.n_rows > 0 else batch_size -if not opt.from_file: - assert opt.prompt is not None - prompt = opt.prompt - print(f"Using prompt: {prompt}") - data = [batch_size * [prompt]] - -else: - print(f"reading prompts from {opt.from_file}") - with open(opt.from_file, "r") as f: - text = f.read() - print(f"Using prompt: {text.strip()}") - data = text.splitlines() - data = batch_size * list(data) - data = list(chunk(sorted(data), batch_size)) - - -if opt.precision == "autocast" and opt.device != "cpu": - precision_scope = autocast -else: - precision_scope = nullcontext - -seeds = "" -with torch.no_grad(): - - all_samples = list() - for n in trange(opt.n_iter, desc="Sampling"): - for prompts in tqdm(data, desc="data"): - - sample_path = os.path.join(outpath, "_".join(re.split(":| ", prompts[0])))[:150] - os.makedirs(sample_path, exist_ok=True) - base_count = len(os.listdir(sample_path)) - - with precision_scope("cuda"): - modelCS.to(opt.device) - uc = None - if opt.scale != 1.0: - uc = modelCS.get_learned_conditioning(batch_size * [""]) - if isinstance(prompts, tuple): - prompts = list(prompts) - - subprompts, weights = split_weighted_subprompts(prompts[0]) - if len(subprompts) > 1: - c = torch.zeros_like(uc) - totalWeight = sum(weights) - # normalize each "sub prompt" and add it - for i in range(len(subprompts)): - weight = weights[i] - # if not skip_normalize: - weight = weight / totalWeight - c = torch.add(c, modelCS.get_learned_conditioning(subprompts[i]), alpha=weight) - else: - c = modelCS.get_learned_conditioning(prompts) - - shape = [opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f] - - if opt.device != "cpu": - mem = torch.cuda.memory_allocated() / 1e6 - modelCS.to("cpu") - while torch.cuda.memory_allocated() / 1e6 >= mem: - time.sleep(1) - - samples_ddim = model.sample( - S=opt.ddim_steps, - conditioning=c, - seed=opt.seed, - shape=shape, - verbose=False, - unconditional_guidance_scale=opt.scale, - unconditional_conditioning=uc, - eta=opt.ddim_eta, - x_T=start_code, - sampler = opt.sampler, - ) - - modelFS.to(opt.device) - - print(samples_ddim.shape) - print("saving images") - for i in range(batch_size): - - x_samples_ddim = modelFS.decode_first_stage(samples_ddim[i].unsqueeze(0)) - x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) - x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c") - Image.fromarray(x_sample.astype(np.uint8)).save( - os.path.join(sample_path, "seed_" + str(opt.seed) + "_" + f"{base_count:05}.{opt.format}") - ) - seeds += str(opt.seed) + "," - opt.seed += 1 - base_count += 1 - - if opt.device != "cpu": - mem = torch.cuda.memory_allocated() / 1e6 - modelFS.to("cpu") - while torch.cuda.memory_allocated() / 1e6 >= mem: - time.sleep(1) - del samples_ddim - print("memory_final = ", torch.cuda.memory_allocated() / 1e6) - -toc = time.time() - -time_taken = (toc - tic) / 60.0 - -print( - ( - "Samples finished in {0:.2f} minutes and exported to " - + sample_path - + "\n Seeds used = " - + seeds[:-1] - ).format(time_taken) -) diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/sd_internal/__init__.py b/spaces/awaawawawa/iurf7irfuyytruyyugb/sd_internal/__init__.py deleted file mode 100644 index f2a9901f080ef6a837dccaee1548ccf9b661dba3..0000000000000000000000000000000000000000 --- a/spaces/awaawawawa/iurf7irfuyytruyyugb/sd_internal/__init__.py +++ /dev/null @@ -1,107 +0,0 @@ -import json - -class Request: - session_id: str = "session" - prompt: str = "" - negative_prompt: str = "" - init_image: str = None # base64 - mask: str = None # base64 - num_outputs: int = 1 - num_inference_steps: int = 50 - guidance_scale: float = 7.5 - width: int = 512 - height: int = 512 - seed: int = 42 - prompt_strength: float = 0.8 - sampler: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms" - # allow_nsfw: bool = False - precision: str = "autocast" # or "full" - save_to_disk_path: str = None - turbo: bool = True - use_cpu: bool = False - use_full_precision: bool = False - use_face_correction: str = None # or "GFPGANv1.3" - use_upscale: str = None # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B" - use_stable_diffusion_model: str = "sd-v1-4" - show_only_filtered_image: bool = False - output_format: str = "jpeg" # or "png" - - stream_progress_updates: bool = False - stream_image_progress: bool = False - - def json(self): - return { - "session_id": self.session_id, - "prompt": self.prompt, - "negative_prompt": self.negative_prompt, - "num_outputs": self.num_outputs, - "num_inference_steps": self.num_inference_steps, - "guidance_scale": self.guidance_scale, - "width": self.width, - "height": self.height, - "seed": self.seed, - "prompt_strength": self.prompt_strength, - "sampler": self.sampler, - "use_face_correction": self.use_face_correction, - "use_upscale": self.use_upscale, - "use_stable_diffusion_model": self.use_stable_diffusion_model, - "output_format": self.output_format, - } - - def to_string(self): - return f''' - session_id: {self.session_id} - prompt: {self.prompt} - negative_prompt: {self.negative_prompt} - seed: {self.seed} - num_inference_steps: {self.num_inference_steps} - sampler: {self.sampler} - guidance_scale: {self.guidance_scale} - w: {self.width} - h: {self.height} - precision: {self.precision} - save_to_disk_path: {self.save_to_disk_path} - turbo: {self.turbo} - use_cpu: {self.use_cpu} - use_full_precision: {self.use_full_precision} - use_face_correction: {self.use_face_correction} - use_upscale: {self.use_upscale} - use_stable_diffusion_model: {self.use_stable_diffusion_model} - show_only_filtered_image: {self.show_only_filtered_image} - output_format: {self.output_format} - - stream_progress_updates: {self.stream_progress_updates} - stream_image_progress: {self.stream_image_progress}''' - -class Image: - data: str # base64 - seed: int - is_nsfw: bool - path_abs: str = None - - def __init__(self, data, seed): - self.data = data - self.seed = seed - - def json(self): - return { - "data": self.data, - "seed": self.seed, - "path_abs": self.path_abs, - } - -class Response: - request: Request - images: list - - def json(self): - res = { - "status": 'succeeded', - "request": self.request.json(), - "output": [], - } - - for image in self.images: - res["output"].append(image.json()) - - return res diff --git a/spaces/awacke1/Flan-Upvote-Downvote-Human-Feedback/app.py b/spaces/awacke1/Flan-Upvote-Downvote-Human-Feedback/app.py deleted file mode 100644 index 30185ee08762c0396d36597ed525ba30149f03a4..0000000000000000000000000000000000000000 --- a/spaces/awacke1/Flan-Upvote-Downvote-Human-Feedback/app.py +++ /dev/null @@ -1,77 +0,0 @@ - -import os -import asyncio -from concurrent.futures import ThreadPoolExecutor -import requests -import gradio as gr - - -examples = [ - ["Please answer to the following question. Who is going to be the next Ballon d'or?"], - ["Q: Can Barack Obama have a conversation with George Washington? Give the rationale before answering."], - ["Summarize the following text: Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and was rushed to the hospital. Since she was diagnosed with a brain injury, the doctor told Peter to stay besides her until she gets well. Therefore, Peter stayed with her at the hospital for 3 days without leaving."], - ["Please answer the following question: What is the boiling point of water?"], - ["Answer the following question by detailing your reasoning: Are Pokemons alive?"], - ["Translate to German: How old are you?"], - ["Generate a cooking recipe to make bolognese pasta:"], - ["Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"], - ["Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?"], - ["Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch and bought 6 more, how many apples do they have?"], - ["""Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? -A: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11. -Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?"""], -] -title = "Flan UL2 vs Flan T5 XXL" -description = "This demo compares [Flan-T5-xxl](https://huggingface.co/google/flan-t5-xxl) and [Flan-UL2](https://huggingface.co/google/flan-ul2). Learn more about these models in their model card!" - - - -MAX_NEW_TOKENS = 256 -TOKEN = os.environ.get("API_TOKEN", None) -URLS = [ - "https://api-inference.huggingface.co/models/google/flan-ul2", - "https://api-inference.huggingface.co/models/google/flan-t5-xxl", -] - - -def fetch(session, text, api_url): - model = api_url.split("/")[-1] - response = session.post(api_url, json={"inputs": text, "parameters": {"max_new_tokens": MAX_NEW_TOKENS}}) - if response.status_code != 200: - return model, None - return model, response.json() - - - -async def inference(text): - with ThreadPoolExecutor(max_workers=2) as executor: - with requests.Session() as session: - session.headers = {"Authorization": f"Bearer {TOKEN}"} - # Initialize the event loop - loop = asyncio.get_event_loop() - tasks = [ - loop.run_in_executor( - executor, fetch, *(session, text, url) # Allows us to pass in multiple arguments to `fetch` - ) - for url in URLS - ] - - # Initializes the tasks to run and awaits their results - responses = [None, None] - for (model, response) in await asyncio.gather(*tasks): - if response is not None: - if model == "flan-ul2": - responses[0] = response[0]["generated_text"] - elif model == "flan-t5-xxl": - responses[1] = response[0]["generated_text"] - return responses - - -io = gr.Interface( - inference, - gr.Textbox(lines=3), - outputs=[gr.Textbox(lines=3, label="Flan T5-UL2"), gr.Textbox(lines=3, label="Flan T5-XXL")], - description=description, - examples=examples, -) -io.launch() \ No newline at end of file diff --git a/spaces/awacke1/HTML5-Aframe-Lsystems/style.css b/spaces/awacke1/HTML5-Aframe-Lsystems/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/awacke1/HTML5-Aframe-Lsystems/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/awacke1/NSFW_text_classifier/app.py b/spaces/awacke1/NSFW_text_classifier/app.py deleted file mode 100644 index 549bc3c792207511958f3c0290d1f5440b5adb4f..0000000000000000000000000000000000000000 --- a/spaces/awacke1/NSFW_text_classifier/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/michellejieli/NSFW_text_classifier").launch() \ No newline at end of file diff --git a/spaces/awacke1/Spinning.Model-1-10/README.md b/spaces/awacke1/Spinning.Model-1-10/README.md deleted file mode 100644 index 915030822758f3e50982108a4b0cb845bc954f7d..0000000000000000000000000000000000000000 --- a/spaces/awacke1/Spinning.Model-1-10/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Spinning.Model 1 10 -emoji: 📊 -colorFrom: indigo -colorTo: red -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/aymm/Task-Exploration-Hate-Speech/posts/conclusion.py b/spaces/aymm/Task-Exploration-Hate-Speech/posts/conclusion.py deleted file mode 100644 index 75b2fab5572b23de0bc101c1e4ecacbf4f4b9f99..0000000000000000000000000000000000000000 --- a/spaces/aymm/Task-Exploration-Hate-Speech/posts/conclusion.py +++ /dev/null @@ -1,18 +0,0 @@ -import streamlit as st - -title = "Key Takeaways" -description = "The key takeaways from this exploration" -date = "2022-01-26" -thumbnail = "images/huggingface_logo.png" - -def run_article(): - st.markdown(""" - # Conclusion - - Here are some of the main ideas we have conveyed in this exploration: - - Defining hate speech is hard and changes depending on your context and goals. - - Capturing a snapshot of what you've defined to be hate speech in a dataset is hard. - - Models learn lots of different things based on the data it sees, and that can include things you didn't intend for them to learn. - - Action items? - """) diff --git a/spaces/banana-projects/web3d/node_modules/three/src/math/interpolants/QuaternionLinearInterpolant.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/math/interpolants/QuaternionLinearInterpolant.d.ts deleted file mode 100644 index 4b9f19601f1ae2e9ab0b79bad35db0839656dfc1..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/src/math/interpolants/QuaternionLinearInterpolant.d.ts +++ /dev/null @@ -1,12 +0,0 @@ -import { Interpolant } from '../Interpolant'; - -export class QuaternionLinearInterpolant extends Interpolant { - constructor( - parameterPositions: any, - samplesValues: any, - sampleSize: number, - resultBuffer?: any - ); - - interpolate_(i1: number, t0: number, t: number, t1: number): any; -} diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/lights_lambert_vertex.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/lights_lambert_vertex.glsl.js deleted file mode 100644 index e503a878d7310b0404e5902b74cfce45ccbcca10..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/lights_lambert_vertex.glsl.js +++ /dev/null @@ -1,119 +0,0 @@ -export default /* glsl */` -vec3 diffuse = vec3( 1.0 ); - -GeometricContext geometry; -geometry.position = mvPosition.xyz; -geometry.normal = normalize( transformedNormal ); -geometry.viewDir = normalize( -mvPosition.xyz ); - -GeometricContext backGeometry; -backGeometry.position = geometry.position; -backGeometry.normal = -geometry.normal; -backGeometry.viewDir = geometry.viewDir; - -vLightFront = vec3( 0.0 ); -vIndirectFront = vec3( 0.0 ); - -#ifdef DOUBLE_SIDED - vLightBack = vec3( 0.0 ); - vIndirectBack = vec3( 0.0 ); -#endif - -IncidentLight directLight; -float dotNL; -vec3 directLightColor_Diffuse; - -#if NUM_POINT_LIGHTS > 0 - - #pragma unroll_loop - for ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) { - - getPointDirectLightIrradiance( pointLights[ i ], geometry, directLight ); - - dotNL = dot( geometry.normal, directLight.direction ); - directLightColor_Diffuse = PI * directLight.color; - - vLightFront += saturate( dotNL ) * directLightColor_Diffuse; - - #ifdef DOUBLE_SIDED - - vLightBack += saturate( -dotNL ) * directLightColor_Diffuse; - - #endif - - } - -#endif - -#if NUM_SPOT_LIGHTS > 0 - - #pragma unroll_loop - for ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) { - - getSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight ); - - dotNL = dot( geometry.normal, directLight.direction ); - directLightColor_Diffuse = PI * directLight.color; - - vLightFront += saturate( dotNL ) * directLightColor_Diffuse; - - #ifdef DOUBLE_SIDED - - vLightBack += saturate( -dotNL ) * directLightColor_Diffuse; - - #endif - } - -#endif - -/* -#if NUM_RECT_AREA_LIGHTS > 0 - - for ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) { - - // TODO (abelnation): implement - - } - -#endif -*/ - -#if NUM_DIR_LIGHTS > 0 - - #pragma unroll_loop - for ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) { - - getDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight ); - - dotNL = dot( geometry.normal, directLight.direction ); - directLightColor_Diffuse = PI * directLight.color; - - vLightFront += saturate( dotNL ) * directLightColor_Diffuse; - - #ifdef DOUBLE_SIDED - - vLightBack += saturate( -dotNL ) * directLightColor_Diffuse; - - #endif - - } - -#endif - -#if NUM_HEMI_LIGHTS > 0 - - #pragma unroll_loop - for ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) { - - vIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry ); - - #ifdef DOUBLE_SIDED - - vIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry ); - - #endif - - } - -#endif -`; diff --git a/spaces/bankholdup/stylegan_petbreeder/e4e/metrics/LEC.py b/spaces/bankholdup/stylegan_petbreeder/e4e/metrics/LEC.py deleted file mode 100644 index 3eef2d2f00a4d757a56b6e845a8fde16aab306ab..0000000000000000000000000000000000000000 --- a/spaces/bankholdup/stylegan_petbreeder/e4e/metrics/LEC.py +++ /dev/null @@ -1,134 +0,0 @@ -import sys -import argparse -import torch -import numpy as np -from torch.utils.data import DataLoader - -sys.path.append(".") -sys.path.append("..") - -from configs import data_configs -from datasets.images_dataset import ImagesDataset -from utils.model_utils import setup_model - - -class LEC: - def __init__(self, net, is_cars=False): - """ - Latent Editing Consistency metric as proposed in the main paper. - :param net: e4e model loaded over the pSp framework. - :param is_cars: An indication as to whether or not to crop the middle of the StyleGAN's output images. - """ - self.net = net - self.is_cars = is_cars - - def _encode(self, images): - """ - Encodes the given images into StyleGAN's latent space. - :param images: Tensor of shape NxCxHxW representing the images to be encoded. - :return: Tensor of shape NxKx512 representing the latent space embeddings of the given image (in W(K, *) space). - """ - codes = self.net.encoder(images) - assert codes.ndim == 3, f"Invalid latent codes shape, should be NxKx512 but is {codes.shape}" - # normalize with respect to the center of an average face - if self.net.opts.start_from_latent_avg: - codes = codes + self.net.latent_avg.repeat(codes.shape[0], 1, 1) - return codes - - def _generate(self, codes): - """ - Generate the StyleGAN2 images of the given codes - :param codes: Tensor of shape NxKx512 representing the StyleGAN's latent codes (in W(K, *) space). - :return: Tensor of shape NxCxHxW representing the generated images. - """ - images, _ = self.net.decoder([codes], input_is_latent=True, randomize_noise=False, return_latents=True) - images = self.net.face_pool(images) - if self.is_cars: - images = images[:, :, 32:224, :] - return images - - @staticmethod - def _filter_outliers(arr): - arr = np.array(arr) - - lo = np.percentile(arr, 1, interpolation="lower") - hi = np.percentile(arr, 99, interpolation="higher") - return np.extract( - np.logical_and(lo <= arr, arr <= hi), arr - ) - - def calculate_metric(self, data_loader, edit_function, inverse_edit_function): - """ - Calculate the LEC metric score. - :param data_loader: An iterable that returns a tuple of (images, _), similar to the training data loader. - :param edit_function: A function that receives latent codes and performs a semantically meaningful edit in the - latent space. - :param inverse_edit_function: A function that receives latent codes and performs the inverse edit of the - `edit_function` parameter. - :return: The LEC metric score. - """ - distances = [] - with torch.no_grad(): - for batch in data_loader: - x, _ = batch - inputs = x.to(device).float() - - codes = self._encode(inputs) - edited_codes = edit_function(codes) - edited_image = self._generate(edited_codes) - edited_image_inversion_codes = self._encode(edited_image) - inverse_edit_codes = inverse_edit_function(edited_image_inversion_codes) - - dist = (codes - inverse_edit_codes).norm(2, dim=(1, 2)).mean() - distances.append(dist.to("cpu").numpy()) - - distances = self._filter_outliers(distances) - return distances.mean() - - -if __name__ == "__main__": - device = "cuda" - - parser = argparse.ArgumentParser(description="LEC metric calculator") - - parser.add_argument("--batch", type=int, default=8, help="batch size for the models") - parser.add_argument("--images_dir", type=str, default=None, - help="Path to the images directory on which we calculate the LEC score") - parser.add_argument("ckpt", metavar="CHECKPOINT", help="path to the model checkpoints") - - args = parser.parse_args() - print(args) - - net, opts = setup_model(args.ckpt, device) - dataset_args = data_configs.DATASETS[opts.dataset_type] - transforms_dict = dataset_args['transforms'](opts).get_transforms() - - images_directory = dataset_args['test_source_root'] if args.images_dir is None else args.images_dir - test_dataset = ImagesDataset(source_root=images_directory, - target_root=images_directory, - source_transform=transforms_dict['transform_source'], - target_transform=transforms_dict['transform_test'], - opts=opts) - - data_loader = DataLoader(test_dataset, - batch_size=args.batch, - shuffle=False, - num_workers=2, - drop_last=True) - - print(f'dataset length: {len(test_dataset)}') - - # In the following example, we are using an InterfaceGAN based editing to calculate the LEC metric. - # Change the provided example according to your domain and needs. - direction = torch.load('../editings/interfacegan_directions/age.pt').to(device) - - def edit_func_example(codes): - return codes + 3 * direction - - - def inverse_edit_func_example(codes): - return codes - 3 * direction - - lec = LEC(net, is_cars='car' in opts.dataset_type) - result = lec.calculate_metric(data_loader, edit_func_example, inverse_edit_func_example) - print(f"LEC: {result}") diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/inference_gfpgan_20220326225636.py b/spaces/beihai/GFPGAN-V1.3-whole-image/.history/inference_gfpgan_20220326225636.py deleted file mode 100644 index ac814a50ead170ab69f32c1714bd445c6c5baf17..0000000000000000000000000000000000000000 --- a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/inference_gfpgan_20220326225636.py +++ /dev/null @@ -1,116 +0,0 @@ -import argparse -import cv2 -import glob -import numpy as np -import os -import torch -from basicsr.utils import imwrite - -from gfpgan import GFPGANer - - -def main(): - """Inference demo for GFPGAN. - """ - parser = argparse.ArgumentParser() - parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image') - parser.add_argument('--arch', type=str, default='clean', help='The GFPGAN architecture. Option: clean | original') - parser.add_argument('--channel', type=int, default=2, help='Channel multiplier for large networks of StyleGAN2') - parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANv1.3.pth') - parser.add_argument('--bg_upsampler', type=str, default='realesrgan', help='background upsampler') - parser.add_argument( - '--bg_tile', type=int, default=400, help='Tile size for background sampler, 0 for no tile during testing') - parser.add_argument('--test_path', type=str, default='inputs/whole_imgs', help='Input folder') - parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces') - parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face') - parser.add_argument('--aligned', action='store_true', help='Input are aligned faces') - parser.add_argument('--paste_back', action='store_false', help='Paste the restored faces back to images') - parser.add_argument('--save_root', type=str, default='results', help='Path to save root') - parser.add_argument( - '--ext', - type=str, - default='auto', - help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs') - args = parser.parse_args() - - args = parser.parse_args() - if args.test_path.endswith('/'): - args.test_path = args.test_path[:-1] - os.makedirs(args.save_root, exist_ok=True) - - # background upsampler - if args.bg_upsampler == 'realesrgan': - if not torch.cuda.is_available(): # CPU - import warnings - warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' - 'If you really want to use it, please modify the corresponding codes.') - bg_upsampler = None - else: - from basicsr.archs.rrdbnet_arch import RRDBNet - from realesrgan import RealESRGANer - model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) - bg_upsampler = RealESRGANer( - scale=2, - model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth', - model=model, - tile=args.bg_tile, - tile_pad=10, - pre_pad=0, - half=True) # need to set False in CPU mode - else: - bg_upsampler = None - # set up GFPGAN restorer - restorer = GFPGANer( - model_path=args.model_path, - upscale=args.upscale, - arch=args.arch, - channel_multiplier=args.channel, - bg_upsampler=bg_upsampler) - - img_list = sorted(glob.glob(os.path.join(args.test_path, '*'))) - for img_path in img_list: - # read image - img_name = os.path.basename(img_path) - print(f'Processing {img_name} ...') - basename, ext = os.path.splitext(img_name) - input_img = cv2.imread(img_path, cv2.IMREAD_COLOR) - - # restore faces and background if necessary - cropped_faces, restored_faces, restored_img = restorer.enhance( - input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back) - - # save faces - for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)): - # save cropped face - save_crop_path = os.path.join(args.save_root, 'cropped_faces', f'{basename}_{idx:02d}.png') - imwrite(cropped_face, save_crop_path) - # save restored face - if args.suffix is not None: - save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png' - else: - save_face_name = f'{basename}_{idx:02d}.png' - save_restore_path = os.path.join(args.save_root, 'restored_faces', save_face_name) - imwrite(restored_face, save_restore_path) - # save comparison image - cmp_img = np.concatenate((cropped_face, restored_face), axis=1) - imwrite(cmp_img, os.path.join(args.save_root, 'cmp', f'{basename}_{idx:02d}.png')) - - # save restored img - if restored_img is not None: - if args.ext == 'auto': - extension = ext[1:] - else: - extension = args.ext - - if args.suffix is not None: - save_restore_path = os.path.join(args.save_root, 'restored_imgs', - f'{basename}_{args.suffix}.{extension}') - else: - save_restore_path = os.path.join(args.save_root, 'restored_imgs', f'{basename}.{extension}') - imwrite(restored_img, save_restore_path) - - print(f'Results are in the [{args.save_root}] folder.') - - -if __name__ == '__main__': - main() diff --git a/spaces/beki/pii-anonymizer/app.py b/spaces/beki/pii-anonymizer/app.py deleted file mode 100644 index 20d29fec1557d098add26dd5fc9e39ea6ac9d784..0000000000000000000000000000000000000000 --- a/spaces/beki/pii-anonymizer/app.py +++ /dev/null @@ -1,212 +0,0 @@ - -"""Streamlit app for Presidio + Privy-trained PII models.""" - -import spacy -from spacy_recognizer import CustomSpacyRecognizer -from presidio_analyzer.nlp_engine import NlpEngineProvider -from presidio_anonymizer import AnonymizerEngine -from presidio_analyzer import AnalyzerEngine, RecognizerRegistry -import pandas as pd -from annotated_text import annotated_text -from json import JSONEncoder -import json -import warnings -import streamlit as st -import os -os.environ["TOKENIZERS_PARALLELISM"] = "false" -warnings.filterwarnings('ignore') -# from flair_recognizer import FlairRecognizer - -# Helper methods -@st.cache(allow_output_mutation=True) -def analyzer_engine(): - """Return AnalyzerEngine.""" - - spacy_recognizer = CustomSpacyRecognizer() - - configuration = { - "nlp_engine_name": "spacy", - "models": [ - {"lang_code": "en", "model_name": "en_spacy_pii_distilbert"}], - } - - # Create NLP engine based on configuration - provider = NlpEngineProvider(nlp_configuration=configuration) - nlp_engine = provider.create_engine() - - registry = RecognizerRegistry() - # add rule-based recognizers - registry.load_predefined_recognizers(nlp_engine=nlp_engine) - registry.add_recognizer(spacy_recognizer) - # remove the nlp engine we passed, to use custom label mappings - registry.remove_recognizer("SpacyRecognizer") - - analyzer = AnalyzerEngine(nlp_engine=nlp_engine, - registry=registry, supported_languages=["en"]) - - # uncomment for flair-based NLP recognizer - # flair_recognizer = FlairRecognizer() - # registry.load_predefined_recognizers() - # registry.add_recognizer(flair_recognizer) - # analyzer = AnalyzerEngine(registry=registry, supported_languages=["en"]) - return analyzer - - -@st.cache(allow_output_mutation=True) -def anonymizer_engine(): - """Return AnonymizerEngine.""" - return AnonymizerEngine() - - -def get_supported_entities(): - """Return supported entities from the Analyzer Engine.""" - return analyzer_engine().get_supported_entities() - - -def analyze(**kwargs): - """Analyze input using Analyzer engine and input arguments (kwargs).""" - if "entities" not in kwargs or "All" in kwargs["entities"]: - kwargs["entities"] = None - return analyzer_engine().analyze(**kwargs) - - -def anonymize(text, analyze_results): - """Anonymize identified input using Presidio Abonymizer.""" - if not text: - return - res = anonymizer_engine().anonymize(text, analyze_results) - return res.text - - -def annotate(text, st_analyze_results, st_entities): - tokens = [] - # sort by start index - results = sorted(st_analyze_results, key=lambda x: x.start) - for i, res in enumerate(results): - if i == 0: - tokens.append(text[:res.start]) - - # append entity text and entity type - tokens.append((text[res.start: res.end], res.entity_type)) - - # if another entity coming i.e. we're not at the last results element, add text up to next entity - if i != len(results) - 1: - tokens.append(text[res.end:results[i+1].start]) - # if no more entities coming, add all remaining text - else: - tokens.append(text[res.end:]) - return tokens - - -st.set_page_config(page_title="Privy + Presidio demo (English)", layout="wide") - -# Side bar -st.sidebar.markdown( - """ -Detect and anonymize PII in text using an [NLP model](https://huggingface.co/beki/en_spacy_pii_distilbert) trained on protocol traces (JSON, SQL, XML etc.) generated by -[Privy](https://github.com/pixie-io/pixie/tree/main/src/datagen/pii/privy) and rule-based classifiers from [Presidio](https://aka.ms/presidio). -""" -) - -st_entities = st.sidebar.multiselect( - label="Which entities to look for?", - options=get_supported_entities(), - default=list(get_supported_entities()), -) - -st_threshold = st.sidebar.slider( - label="Acceptance threshold", min_value=0.0, max_value=1.0, value=0.35 -) - -st_return_decision_process = st.sidebar.checkbox( - "Add analysis explanations in json") - -st.sidebar.info( - "Privy is an open source framework for synthetic data generation in protocol trace formats (json, sql, html etc). Presidio is an open source framework for PII detection and anonymization. " - "For more info visit [privy](https://github.com/pixie-io/pixie/tree/main/src/datagen/pii/privy) and [aka.ms/presidio](https://aka.ms/presidio)" -) - - -# Main panel -analyzer_load_state = st.info( - "Starting Presidio analyzer and loading Privy-trained PII model...") -engine = analyzer_engine() -analyzer_load_state.empty() - - -st_text = st.text_area( - label="Type in some text", - value="SELECT shipping FROM users WHERE shipping = '201 Thayer St Providence RI 02912'" - "\n\n" - "{user: Willie Porter, ip: 192.168.2.80, email: willie@gmail.com}", - height=200, -) - -button = st.button("Detect PII") - -if 'first_load' not in st.session_state: - st.session_state['first_load'] = True - -# After -st.subheader("Analyzed") -with st.spinner("Analyzing..."): - if button or st.session_state.first_load: - st_analyze_results = analyze( - text=st_text, - entities=st_entities, - language="en", - score_threshold=st_threshold, - return_decision_process=st_return_decision_process, - ) - annotated_tokens = annotate(st_text, st_analyze_results, st_entities) - # annotated_tokens - annotated_text(*annotated_tokens) -# vertical space -st.text("") - -st.subheader("Anonymized") - -with st.spinner("Anonymizing..."): - if button or st.session_state.first_load: - st_anonymize_results = anonymize(st_text, st_analyze_results) - st_anonymize_results - - -# table result -st.subheader("Detailed Findings") -if st_analyze_results: - res_dicts = [r.to_dict() for r in st_analyze_results] - for d in res_dicts: - d['Value'] = st_text[d['start']:d['end']] - df = pd.DataFrame.from_records(res_dicts) - df = df[["entity_type", "Value", "score", "start", "end"]].rename( - { - "entity_type": "Entity type", - "start": "Start", - "end": "End", - "score": "Confidence", - }, - axis=1, - ) - - st.dataframe(df, width=1000) -else: - st.text("No findings") - -st.session_state['first_load'] = True - -# json result - - -class ToDictListEncoder(JSONEncoder): - """Encode dict to json.""" - - def default(self, o): - """Encode to JSON using to_dict.""" - if o: - return o.to_dict() - return [] - - -if st_return_decision_process: - st.json(json.dumps(st_analyze_results, cls=ToDictListEncoder)) diff --git a/spaces/bguberfain/Detic/detic/data/custom_dataset_dataloader.py b/spaces/bguberfain/Detic/detic/data/custom_dataset_dataloader.py deleted file mode 100644 index 8f8d6817704026796d2c2f457fe2624800693267..0000000000000000000000000000000000000000 --- a/spaces/bguberfain/Detic/detic/data/custom_dataset_dataloader.py +++ /dev/null @@ -1,331 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# Part of the code is from https://github.com/xingyizhou/UniDet/blob/master/projects/UniDet/unidet/data/multi_dataset_dataloader.py (Apache-2.0 License) -import copy -import logging -import numpy as np -import operator -import torch -import torch.utils.data -import json -from detectron2.utils.comm import get_world_size -from detectron2.utils.logger import _log_api_usage, log_first_n - -from detectron2.config import configurable -from detectron2.data import samplers -from torch.utils.data.sampler import BatchSampler, Sampler -from detectron2.data.common import DatasetFromList, MapDataset -from detectron2.data.dataset_mapper import DatasetMapper -from detectron2.data.build import get_detection_dataset_dicts, build_batch_data_loader -from detectron2.data.samplers import TrainingSampler, RepeatFactorTrainingSampler -from detectron2.data.build import worker_init_reset_seed, print_instances_class_histogram -from detectron2.data.build import filter_images_with_only_crowd_annotations -from detectron2.data.build import filter_images_with_few_keypoints -from detectron2.data.build import check_metadata_consistency -from detectron2.data.catalog import MetadataCatalog, DatasetCatalog -from detectron2.utils import comm -import itertools -import math -from collections import defaultdict -from typing import Optional - - -def _custom_train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None): - sampler_name = cfg.DATALOADER.SAMPLER_TRAIN - if 'MultiDataset' in sampler_name: - dataset_dicts = get_detection_dataset_dicts_with_source( - cfg.DATASETS.TRAIN, - filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, - min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE - if cfg.MODEL.KEYPOINT_ON else 0, - proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, - ) - else: - dataset_dicts = get_detection_dataset_dicts( - cfg.DATASETS.TRAIN, - filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, - min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE - if cfg.MODEL.KEYPOINT_ON else 0, - proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, - ) - - if mapper is None: - mapper = DatasetMapper(cfg, True) - - if sampler is not None: - pass - elif sampler_name == "TrainingSampler": - sampler = TrainingSampler(len(dataset)) - elif sampler_name == "MultiDatasetSampler": - sampler = MultiDatasetSampler( - dataset_dicts, - dataset_ratio = cfg.DATALOADER.DATASET_RATIO, - use_rfs = cfg.DATALOADER.USE_RFS, - dataset_ann = cfg.DATALOADER.DATASET_ANN, - repeat_threshold = cfg.DATALOADER.REPEAT_THRESHOLD, - ) - elif sampler_name == "RepeatFactorTrainingSampler": - repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( - dataset_dicts, cfg.DATALOADER.REPEAT_THRESHOLD - ) - sampler = RepeatFactorTrainingSampler(repeat_factors) - else: - raise ValueError("Unknown training sampler: {}".format(sampler_name)) - - return { - "dataset": dataset_dicts, - "sampler": sampler, - "mapper": mapper, - "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, - "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING, - "num_workers": cfg.DATALOADER.NUM_WORKERS, - 'multi_dataset_grouping': cfg.DATALOADER.MULTI_DATASET_GROUPING, - 'use_diff_bs_size': cfg.DATALOADER.USE_DIFF_BS_SIZE, - 'dataset_bs': cfg.DATALOADER.DATASET_BS, - 'num_datasets': len(cfg.DATASETS.TRAIN) - } - - -@configurable(from_config=_custom_train_loader_from_config) -def build_custom_train_loader( - dataset, *, mapper, sampler, - total_batch_size=16, - aspect_ratio_grouping=True, - num_workers=0, - num_datasets=1, - multi_dataset_grouping=False, - use_diff_bs_size=False, - dataset_bs=[] - ): - """ - Modified from detectron2.data.build.build_custom_train_loader, but supports - different samplers - """ - if isinstance(dataset, list): - dataset = DatasetFromList(dataset, copy=False) - if mapper is not None: - dataset = MapDataset(dataset, mapper) - if sampler is None: - sampler = TrainingSampler(len(dataset)) - assert isinstance(sampler, torch.utils.data.sampler.Sampler) - if multi_dataset_grouping: - return build_multi_dataset_batch_data_loader( - use_diff_bs_size, - dataset_bs, - dataset, - sampler, - total_batch_size, - num_datasets=num_datasets, - num_workers=num_workers, - ) - else: - return build_batch_data_loader( - dataset, - sampler, - total_batch_size, - aspect_ratio_grouping=aspect_ratio_grouping, - num_workers=num_workers, - ) - - -def build_multi_dataset_batch_data_loader( - use_diff_bs_size, dataset_bs, - dataset, sampler, total_batch_size, num_datasets, num_workers=0 -): - """ - """ - world_size = get_world_size() - assert ( - total_batch_size > 0 and total_batch_size % world_size == 0 - ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format( - total_batch_size, world_size - ) - - batch_size = total_batch_size // world_size - data_loader = torch.utils.data.DataLoader( - dataset, - sampler=sampler, - num_workers=num_workers, - batch_sampler=None, - collate_fn=operator.itemgetter(0), # don't batch, but yield individual elements - worker_init_fn=worker_init_reset_seed, - ) # yield individual mapped dict - if use_diff_bs_size: - return DIFFMDAspectRatioGroupedDataset( - data_loader, dataset_bs, num_datasets) - else: - return MDAspectRatioGroupedDataset( - data_loader, batch_size, num_datasets) - - -def get_detection_dataset_dicts_with_source( - dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None -): - assert len(dataset_names) - dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names] - for dataset_name, dicts in zip(dataset_names, dataset_dicts): - assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) - - for source_id, (dataset_name, dicts) in \ - enumerate(zip(dataset_names, dataset_dicts)): - assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) - for d in dicts: - d['dataset_source'] = source_id - - if "annotations" in dicts[0]: - try: - class_names = MetadataCatalog.get(dataset_name).thing_classes - check_metadata_consistency("thing_classes", dataset_name) - print_instances_class_histogram(dicts, class_names) - except AttributeError: # class names are not available for this dataset - pass - - assert proposal_files is None - - dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) - - has_instances = "annotations" in dataset_dicts[0] - if filter_empty and has_instances: - dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts) - if min_keypoints > 0 and has_instances: - dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints) - - return dataset_dicts - - -class MultiDatasetSampler(Sampler): - def __init__( - self, - dataset_dicts, - dataset_ratio, - use_rfs, - dataset_ann, - repeat_threshold=0.001, - seed: Optional[int] = None, - ): - """ - """ - sizes = [0 for _ in range(len(dataset_ratio))] - for d in dataset_dicts: - sizes[d['dataset_source']] += 1 - print('dataset sizes', sizes) - self.sizes = sizes - assert len(dataset_ratio) == len(sizes), \ - 'length of dataset ratio {} should be equal to number if dataset {}'.format( - len(dataset_ratio), len(sizes) - ) - if seed is None: - seed = comm.shared_random_seed() - self._seed = int(seed) - self._rank = comm.get_rank() - self._world_size = comm.get_world_size() - - self.dataset_ids = torch.tensor( - [d['dataset_source'] for d in dataset_dicts], dtype=torch.long) - - dataset_weight = [torch.ones(s) * max(sizes) / s * r / sum(dataset_ratio) \ - for i, (r, s) in enumerate(zip(dataset_ratio, sizes))] - dataset_weight = torch.cat(dataset_weight) - - rfs_factors = [] - st = 0 - for i, s in enumerate(sizes): - if use_rfs[i]: - if dataset_ann[i] == 'box': - rfs_func = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency - else: - rfs_func = repeat_factors_from_tag_frequency - rfs_factor = rfs_func( - dataset_dicts[st: st + s], - repeat_thresh=repeat_threshold) - rfs_factor = rfs_factor * (s / rfs_factor.sum()) - else: - rfs_factor = torch.ones(s) - rfs_factors.append(rfs_factor) - st = st + s - rfs_factors = torch.cat(rfs_factors) - - self.weights = dataset_weight * rfs_factors - self.sample_epoch_size = len(self.weights) - - def __iter__(self): - start = self._rank - yield from itertools.islice( - self._infinite_indices(), start, None, self._world_size) - - - def _infinite_indices(self): - g = torch.Generator() - g.manual_seed(self._seed) - while True: - ids = torch.multinomial( - self.weights, self.sample_epoch_size, generator=g, - replacement=True) - nums = [(self.dataset_ids[ids] == i).sum().int().item() \ - for i in range(len(self.sizes))] - yield from ids - - -class MDAspectRatioGroupedDataset(torch.utils.data.IterableDataset): - def __init__(self, dataset, batch_size, num_datasets): - """ - """ - self.dataset = dataset - self.batch_size = batch_size - self._buckets = [[] for _ in range(2 * num_datasets)] - - def __iter__(self): - for d in self.dataset: - w, h = d["width"], d["height"] - aspect_ratio_bucket_id = 0 if w > h else 1 - bucket_id = d['dataset_source'] * 2 + aspect_ratio_bucket_id - bucket = self._buckets[bucket_id] - bucket.append(d) - if len(bucket) == self.batch_size: - yield bucket[:] - del bucket[:] - - -class DIFFMDAspectRatioGroupedDataset(torch.utils.data.IterableDataset): - def __init__(self, dataset, batch_sizes, num_datasets): - """ - """ - self.dataset = dataset - self.batch_sizes = batch_sizes - self._buckets = [[] for _ in range(2 * num_datasets)] - - def __iter__(self): - for d in self.dataset: - w, h = d["width"], d["height"] - aspect_ratio_bucket_id = 0 if w > h else 1 - bucket_id = d['dataset_source'] * 2 + aspect_ratio_bucket_id - bucket = self._buckets[bucket_id] - bucket.append(d) - if len(bucket) == self.batch_sizes[d['dataset_source']]: - yield bucket[:] - del bucket[:] - - -def repeat_factors_from_tag_frequency(dataset_dicts, repeat_thresh): - """ - """ - category_freq = defaultdict(int) - for dataset_dict in dataset_dicts: - cat_ids = dataset_dict['pos_category_ids'] - for cat_id in cat_ids: - category_freq[cat_id] += 1 - num_images = len(dataset_dicts) - for k, v in category_freq.items(): - category_freq[k] = v / num_images - - category_rep = { - cat_id: max(1.0, math.sqrt(repeat_thresh / cat_freq)) - for cat_id, cat_freq in category_freq.items() - } - - rep_factors = [] - for dataset_dict in dataset_dicts: - cat_ids = dataset_dict['pos_category_ids'] - rep_factor = max({category_rep[cat_id] for cat_id in cat_ids}, default=1.0) - rep_factors.append(rep_factor) - - return torch.tensor(rep_factors, dtype=torch.float32) \ No newline at end of file diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/deepbooru.py b/spaces/bigjoker/stable-diffusion-webui/modules/deepbooru.py deleted file mode 100644 index 122fce7f569dbd28f9c6d83af874bb3efed34a5e..0000000000000000000000000000000000000000 --- a/spaces/bigjoker/stable-diffusion-webui/modules/deepbooru.py +++ /dev/null @@ -1,99 +0,0 @@ -import os -import re - -import torch -from PIL import Image -import numpy as np - -from modules import modelloader, paths, deepbooru_model, devices, images, shared - -re_special = re.compile(r'([\\()])') - - -class DeepDanbooru: - def __init__(self): - self.model = None - - def load(self): - if self.model is not None: - return - - files = modelloader.load_models( - model_path=os.path.join(paths.models_path, "torch_deepdanbooru"), - model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt', - ext_filter=[".pt"], - download_name='model-resnet_custom_v3.pt', - ) - - self.model = deepbooru_model.DeepDanbooruModel() - self.model.load_state_dict(torch.load(files[0], map_location="cpu")) - - self.model.eval() - self.model.to(devices.cpu, devices.dtype) - - def start(self): - self.load() - self.model.to(devices.device) - - def stop(self): - if not shared.opts.interrogate_keep_models_in_memory: - self.model.to(devices.cpu) - devices.torch_gc() - - def tag(self, pil_image): - self.start() - res = self.tag_multi(pil_image) - self.stop() - - return res - - def tag_multi(self, pil_image, force_disable_ranks=False): - threshold = shared.opts.interrogate_deepbooru_score_threshold - use_spaces = shared.opts.deepbooru_use_spaces - use_escape = shared.opts.deepbooru_escape - alpha_sort = shared.opts.deepbooru_sort_alpha - include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks - - pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512) - a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255 - - with torch.no_grad(), devices.autocast(): - x = torch.from_numpy(a).to(devices.device) - y = self.model(x)[0].detach().cpu().numpy() - - probability_dict = {} - - for tag, probability in zip(self.model.tags, y): - if probability < threshold: - continue - - if tag.startswith("rating:"): - continue - - probability_dict[tag] = probability - - if alpha_sort: - tags = sorted(probability_dict) - else: - tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])] - - res = [] - - filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")]) - - for tag in [x for x in tags if x not in filtertags]: - probability = probability_dict[tag] - tag_outformat = tag - if use_spaces: - tag_outformat = tag_outformat.replace('_', ' ') - if use_escape: - tag_outformat = re.sub(re_special, r'\\\1', tag_outformat) - if include_ranks: - tag_outformat = f"({tag_outformat}:{probability:.3f})" - - res.append(tag_outformat) - - return ", ".join(res) - - -model = DeepDanbooru() diff --git a/spaces/billusanda007/Resume-Ranker/README.md b/spaces/billusanda007/Resume-Ranker/README.md deleted file mode 100644 index 26904e60e7ff1a27c4dd296d55e868bb06d36d27..0000000000000000000000000000000000000000 --- a/spaces/billusanda007/Resume-Ranker/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Resume Ranker -emoji: 📊 -colorFrom: pink -colorTo: gray -sdk: streamlit -sdk_version: 1.25.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/birsardar/stable-diffusion-mat-outpainting-primer/dataset_tool.py b/spaces/birsardar/stable-diffusion-mat-outpainting-primer/dataset_tool.py deleted file mode 100644 index c59e6292891c3896722965020af7c60056729f2d..0000000000000000000000000000000000000000 --- a/spaces/birsardar/stable-diffusion-mat-outpainting-primer/dataset_tool.py +++ /dev/null @@ -1,444 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -import functools -import io -import json -import os -import pickle -import sys -import tarfile -import gzip -import zipfile -from pathlib import Path -from typing import Callable, Optional, Tuple, Union - -import click -import numpy as np -import PIL.Image -from tqdm import tqdm - -#---------------------------------------------------------------------------- - -def error(msg): - print('Error: ' + msg) - sys.exit(1) - -#---------------------------------------------------------------------------- - -def maybe_min(a: int, b: Optional[int]) -> int: - if b is not None: - return min(a, b) - return a - -#---------------------------------------------------------------------------- - -def file_ext(name: Union[str, Path]) -> str: - return str(name).split('.')[-1] - -#---------------------------------------------------------------------------- - -def is_image_ext(fname: Union[str, Path]) -> bool: - ext = file_ext(fname).lower() - return f'.{ext}' in PIL.Image.EXTENSION # type: ignore - -#---------------------------------------------------------------------------- - -def open_image_folder(source_dir, *, max_images: Optional[int]): - input_images = [str(f) for f in sorted(Path(source_dir).rglob('*')) if is_image_ext(f) and os.path.isfile(f)] - - # Load labels. - labels = {} - meta_fname = os.path.join(source_dir, 'dataset.json') - if os.path.isfile(meta_fname): - with open(meta_fname, 'r') as file: - labels = json.load(file)['labels'] - if labels is not None: - labels = { x[0]: x[1] for x in labels } - else: - labels = {} - - max_idx = maybe_min(len(input_images), max_images) - - def iterate_images(): - for idx, fname in enumerate(input_images): - arch_fname = os.path.relpath(fname, source_dir) - arch_fname = arch_fname.replace('\\', '/') - img = np.array(PIL.Image.open(fname)) - yield dict(img=img, label=labels.get(arch_fname)) - if idx >= max_idx-1: - break - return max_idx, iterate_images() - -#---------------------------------------------------------------------------- - -def open_image_zip(source, *, max_images: Optional[int]): - with zipfile.ZipFile(source, mode='r') as z: - input_images = [str(f) for f in sorted(z.namelist()) if is_image_ext(f)] - - # Load labels. - labels = {} - if 'dataset.json' in z.namelist(): - with z.open('dataset.json', 'r') as file: - labels = json.load(file)['labels'] - if labels is not None: - labels = { x[0]: x[1] for x in labels } - else: - labels = {} - - max_idx = maybe_min(len(input_images), max_images) - - def iterate_images(): - with zipfile.ZipFile(source, mode='r') as z: - for idx, fname in enumerate(input_images): - with z.open(fname, 'r') as file: - img = PIL.Image.open(file) # type: ignore - img = np.array(img) - yield dict(img=img, label=labels.get(fname)) - if idx >= max_idx-1: - break - return max_idx, iterate_images() - -#---------------------------------------------------------------------------- - -def open_lmdb(lmdb_dir: str, *, max_images: Optional[int]): - import cv2 # pip install opencv-python - import lmdb # pip install lmdb # pylint: disable=import-error - - with lmdb.open(lmdb_dir, readonly=True, lock=False).begin(write=False) as txn: - max_idx = maybe_min(txn.stat()['entries'], max_images) - - def iterate_images(): - with lmdb.open(lmdb_dir, readonly=True, lock=False).begin(write=False) as txn: - for idx, (_key, value) in enumerate(txn.cursor()): - try: - try: - img = cv2.imdecode(np.frombuffer(value, dtype=np.uint8), 1) - if img is None: - raise IOError('cv2.imdecode failed') - img = img[:, :, ::-1] # BGR => RGB - except IOError: - img = np.array(PIL.Image.open(io.BytesIO(value))) - yield dict(img=img, label=None) - if idx >= max_idx-1: - break - except: - print(sys.exc_info()[1]) - - return max_idx, iterate_images() - -#---------------------------------------------------------------------------- - -def open_cifar10(tarball: str, *, max_images: Optional[int]): - images = [] - labels = [] - - with tarfile.open(tarball, 'r:gz') as tar: - for batch in range(1, 6): - member = tar.getmember(f'cifar-10-batches-py/data_batch_{batch}') - with tar.extractfile(member) as file: - data = pickle.load(file, encoding='latin1') - images.append(data['data'].reshape(-1, 3, 32, 32)) - labels.append(data['labels']) - - images = np.concatenate(images) - labels = np.concatenate(labels) - images = images.transpose([0, 2, 3, 1]) # NCHW -> NHWC - assert images.shape == (50000, 32, 32, 3) and images.dtype == np.uint8 - assert labels.shape == (50000,) and labels.dtype in [np.int32, np.int64] - assert np.min(images) == 0 and np.max(images) == 255 - assert np.min(labels) == 0 and np.max(labels) == 9 - - max_idx = maybe_min(len(images), max_images) - - def iterate_images(): - for idx, img in enumerate(images): - yield dict(img=img, label=int(labels[idx])) - if idx >= max_idx-1: - break - - return max_idx, iterate_images() - -#---------------------------------------------------------------------------- - -def open_mnist(images_gz: str, *, max_images: Optional[int]): - labels_gz = images_gz.replace('-images-idx3-ubyte.gz', '-labels-idx1-ubyte.gz') - assert labels_gz != images_gz - images = [] - labels = [] - - with gzip.open(images_gz, 'rb') as f: - images = np.frombuffer(f.read(), np.uint8, offset=16) - with gzip.open(labels_gz, 'rb') as f: - labels = np.frombuffer(f.read(), np.uint8, offset=8) - - images = images.reshape(-1, 28, 28) - images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0) - assert images.shape == (60000, 32, 32) and images.dtype == np.uint8 - assert labels.shape == (60000,) and labels.dtype == np.uint8 - assert np.min(images) == 0 and np.max(images) == 255 - assert np.min(labels) == 0 and np.max(labels) == 9 - - max_idx = maybe_min(len(images), max_images) - - def iterate_images(): - for idx, img in enumerate(images): - yield dict(img=img, label=int(labels[idx])) - if idx >= max_idx-1: - break - - return max_idx, iterate_images() - -#---------------------------------------------------------------------------- - -def make_transform( - transform: Optional[str], - output_width: Optional[int], - output_height: Optional[int], - resize_filter: str -) -> Callable[[np.ndarray], Optional[np.ndarray]]: - resample = { 'box': PIL.Image.BOX, 'lanczos': PIL.Image.LANCZOS }[resize_filter] - def scale(width, height, img): - w = img.shape[1] - h = img.shape[0] - if width == w and height == h: - return img - img = PIL.Image.fromarray(img) - ww = width if width is not None else w - hh = height if height is not None else h - img = img.resize((ww, hh), resample) - return np.array(img) - - def center_crop(width, height, img): - crop = np.min(img.shape[:2]) - img = img[(img.shape[0] - crop) // 2 : (img.shape[0] + crop) // 2, (img.shape[1] - crop) // 2 : (img.shape[1] + crop) // 2] - img = PIL.Image.fromarray(img, 'RGB') - img = img.resize((width, height), resample) - return np.array(img) - - def center_crop_wide(width, height, img): - ch = int(np.round(width * img.shape[0] / img.shape[1])) - if img.shape[1] < width or ch < height: - return None - - img = img[(img.shape[0] - ch) // 2 : (img.shape[0] + ch) // 2] - img = PIL.Image.fromarray(img, 'RGB') - img = img.resize((width, height), resample) - img = np.array(img) - - canvas = np.zeros([width, width, 3], dtype=np.uint8) - canvas[(width - height) // 2 : (width + height) // 2, :] = img - return canvas - - if transform is None: - return functools.partial(scale, output_width, output_height) - if transform == 'center-crop': - if (output_width is None) or (output_height is None): - error ('must specify --width and --height when using ' + transform + 'transform') - return functools.partial(center_crop, output_width, output_height) - if transform == 'center-crop-wide': - if (output_width is None) or (output_height is None): - error ('must specify --width and --height when using ' + transform + ' transform') - return functools.partial(center_crop_wide, output_width, output_height) - assert False, 'unknown transform' - -#---------------------------------------------------------------------------- - -def open_dataset(source, *, max_images: Optional[int]): - if os.path.isdir(source): - if source.rstrip('/').endswith('_lmdb'): - return open_lmdb(source, max_images=max_images) - else: - return open_image_folder(source, max_images=max_images) - elif os.path.isfile(source): - if os.path.basename(source) == 'cifar-10-python.tar.gz': - return open_cifar10(source, max_images=max_images) - elif os.path.basename(source) == 'train-images-idx3-ubyte.gz': - return open_mnist(source, max_images=max_images) - elif file_ext(source) == 'zip': - return open_image_zip(source, max_images=max_images) - else: - assert False, 'unknown archive type' - else: - error(f'Missing input file or directory: {source}') - -#---------------------------------------------------------------------------- - -def open_dest(dest: str) -> Tuple[str, Callable[[str, Union[bytes, str]], None], Callable[[], None]]: - dest_ext = file_ext(dest) - - if dest_ext == 'zip': - if os.path.dirname(dest) != '': - os.makedirs(os.path.dirname(dest), exist_ok=True) - zf = zipfile.ZipFile(file=dest, mode='w', compression=zipfile.ZIP_STORED) - def zip_write_bytes(fname: str, data: Union[bytes, str]): - zf.writestr(fname, data) - return '', zip_write_bytes, zf.close - else: - # If the output folder already exists, check that is is - # empty. - # - # Note: creating the output directory is not strictly - # necessary as folder_write_bytes() also mkdirs, but it's better - # to give an error message earlier in case the dest folder - # somehow cannot be created. - if os.path.isdir(dest) and len(os.listdir(dest)) != 0: - error('--dest folder must be empty') - os.makedirs(dest, exist_ok=True) - - def folder_write_bytes(fname: str, data: Union[bytes, str]): - os.makedirs(os.path.dirname(fname), exist_ok=True) - with open(fname, 'wb') as fout: - if isinstance(data, str): - data = data.encode('utf8') - fout.write(data) - return dest, folder_write_bytes, lambda: None - -#---------------------------------------------------------------------------- - -@click.command() -@click.pass_context -@click.option('--source', help='Directory or archive name for input dataset', required=True, metavar='PATH') -@click.option('--dest', help='Output directory or archive name for output dataset', required=True, metavar='PATH') -@click.option('--max-images', help='Output only up to `max-images` images', type=int, default=None) -@click.option('--resize-filter', help='Filter to use when resizing images for output resolution', type=click.Choice(['box', 'lanczos']), default='lanczos', show_default=True) -@click.option('--transform', help='Input crop/resize mode', type=click.Choice(['center-crop', 'center-crop-wide'])) -@click.option('--width', help='Output width', type=int) -@click.option('--height', help='Output height', type=int) -def convert_dataset( - ctx: click.Context, - source: str, - dest: str, - max_images: Optional[int], - transform: Optional[str], - resize_filter: str, - width: Optional[int], - height: Optional[int] -): - """Convert an image dataset into a dataset archive usable with StyleGAN2 ADA PyTorch. - - The input dataset format is guessed from the --source argument: - - \b - --source *_lmdb/ Load LSUN dataset - --source cifar-10-python.tar.gz Load CIFAR-10 dataset - --source train-images-idx3-ubyte.gz Load MNIST dataset - --source path/ Recursively load all images from path/ - --source dataset.zip Recursively load all images from dataset.zip - - Specifying the output format and path: - - \b - --dest /path/to/dir Save output files under /path/to/dir - --dest /path/to/dataset.zip Save output files into /path/to/dataset.zip - - The output dataset format can be either an image folder or an uncompressed zip archive. - Zip archives makes it easier to move datasets around file servers and clusters, and may - offer better training performance on network file systems. - - Images within the dataset archive will be stored as uncompressed PNG. - Uncompresed PNGs can be efficiently decoded in the training loop. - - Class labels are stored in a file called 'dataset.json' that is stored at the - dataset root folder. This file has the following structure: - - \b - { - "labels": [ - ["00000/img00000000.png",6], - ["00000/img00000001.png",9], - ... repeated for every image in the datase - ["00049/img00049999.png",1] - ] - } - - If the 'dataset.json' file cannot be found, the dataset is interpreted as - not containing class labels. - - Image scale/crop and resolution requirements: - - Output images must be square-shaped and they must all have the same power-of-two - dimensions. - - To scale arbitrary input image size to a specific width and height, use the - --width and --height options. Output resolution will be either the original - input resolution (if --width/--height was not specified) or the one specified with - --width/height. - - Use the --transform=center-crop or --transform=center-crop-wide options to apply a - center crop transform on the input image. These options should be used with the - --width and --height options. For example: - - \b - python dataset_tool.py --source LSUN/raw/cat_lmdb --dest /tmp/lsun_cat \\ - --transform=center-crop-wide --width 512 --height=384 - """ - - PIL.Image.init() # type: ignore - - if dest == '': - ctx.fail('--dest output filename or directory must not be an empty string') - - num_files, input_iter = open_dataset(source, max_images=max_images) - archive_root_dir, save_bytes, close_dest = open_dest(dest) - - transform_image = make_transform(transform, width, height, resize_filter) - - dataset_attrs = None - - labels = [] - for idx, image in tqdm(enumerate(input_iter), total=num_files): - idx_str = f'{idx:08d}' - archive_fname = f'{idx_str[:5]}/img{idx_str}.png' - - # Apply crop and resize. - img = transform_image(image['img']) - - # Transform may drop images. - if img is None: - continue - - # Error check to require uniform image attributes across - # the whole dataset. - channels = img.shape[2] if img.ndim == 3 else 1 - cur_image_attrs = { - 'width': img.shape[1], - 'height': img.shape[0], - 'channels': channels - } - if dataset_attrs is None: - dataset_attrs = cur_image_attrs - width = dataset_attrs['width'] - height = dataset_attrs['height'] - if width != height: - error(f'Image dimensions after scale and crop are required to be square. Got {width}x{height}') - if dataset_attrs['channels'] not in [1, 3]: - error('Input images must be stored as RGB or grayscale') - if width != 2 ** int(np.floor(np.log2(width))): - error('Image width/height after scale and crop are required to be power-of-two') - elif dataset_attrs != cur_image_attrs: - err = [f' dataset {k}/cur image {k}: {dataset_attrs[k]}/{cur_image_attrs[k]}' for k in dataset_attrs.keys()] - error(f'Image {archive_fname} attributes must be equal across all images of the dataset. Got:\n' + '\n'.join(err)) - - # Save the image as an uncompressed PNG. - img = PIL.Image.fromarray(img, { 1: 'L', 3: 'RGB' }[channels]) - image_bits = io.BytesIO() - img.save(image_bits, format='png', compress_level=0, optimize=False) - save_bytes(os.path.join(archive_root_dir, archive_fname), image_bits.getbuffer()) - labels.append([archive_fname, image['label']] if image['label'] is not None else None) - - metadata = { - 'labels': labels if all(x is not None for x in labels) else None - } - save_bytes(os.path.join(archive_root_dir, 'dataset.json'), json.dumps(metadata)) - close_dest() - -#---------------------------------------------------------------------------- - -if __name__ == "__main__": - convert_dataset() # pylint: disable=no-value-for-parameter diff --git a/spaces/blmdsydm/faster-whisper-webui/src/conversion/hf_converter.py b/spaces/blmdsydm/faster-whisper-webui/src/conversion/hf_converter.py deleted file mode 100644 index 6da4f0fd672d63b099f21d0498ba4001d23356f7..0000000000000000000000000000000000000000 --- a/spaces/blmdsydm/faster-whisper-webui/src/conversion/hf_converter.py +++ /dev/null @@ -1,67 +0,0 @@ -# https://github.com/bayartsogt-ya/whisper-multiple-hf-datasets - -from copy import deepcopy -import torch - -WHISPER_MAPPING = { - "layers": "blocks", - "fc1": "mlp.0", - "fc2": "mlp.2", - "final_layer_norm": "mlp_ln", - "layers": "blocks", - ".self_attn.q_proj": ".attn.query", - ".self_attn.k_proj": ".attn.key", - ".self_attn.v_proj": ".attn.value", - ".self_attn_layer_norm": ".attn_ln", - ".self_attn.out_proj": ".attn.out", - ".encoder_attn.q_proj": ".cross_attn.query", - ".encoder_attn.k_proj": ".cross_attn.key", - ".encoder_attn.v_proj": ".cross_attn.value", - ".encoder_attn_layer_norm": ".cross_attn_ln", - ".encoder_attn.out_proj": ".cross_attn.out", - "decoder.layer_norm.": "decoder.ln.", - "encoder.layer_norm.": "encoder.ln_post.", - "embed_tokens": "token_embedding", - "encoder.embed_positions.weight": "encoder.positional_embedding", - "decoder.embed_positions.weight": "decoder.positional_embedding", - "layer_norm": "ln_post", -} - - -def rename_keys(s_dict): - keys = list(s_dict.keys()) - for key in keys: - new_key = key - for k, v in WHISPER_MAPPING.items(): - if k in key: - new_key = new_key.replace(k, v) - - print(f"{key} -> {new_key}") - - s_dict[new_key] = s_dict.pop(key) - return s_dict - - -def convert_hf_whisper(hf_model_name_or_path: str, whisper_state_path: str): - from transformers import WhisperForConditionalGeneration - transformer_model = WhisperForConditionalGeneration.from_pretrained(hf_model_name_or_path) - config = transformer_model.config - - # first build dims - dims = { - 'n_mels': config.num_mel_bins, - 'n_vocab': config.vocab_size, - 'n_audio_ctx': config.max_source_positions, - 'n_audio_state': config.d_model, - 'n_audio_head': config.encoder_attention_heads, - 'n_audio_layer': config.encoder_layers, - 'n_text_ctx': config.max_target_positions, - 'n_text_state': config.d_model, - 'n_text_head': config.decoder_attention_heads, - 'n_text_layer': config.decoder_layers - } - - state_dict = deepcopy(transformer_model.model.state_dict()) - state_dict = rename_keys(state_dict) - - torch.save({"dims": dims, "model_state_dict": state_dict}, whisper_state_path) \ No newline at end of file diff --git a/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/model/utils.py b/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/model/utils.py deleted file mode 100644 index 93df40bee97dee744b7585dba1fe9a3f26d3e9b5..0000000000000000000000000000000000000000 --- a/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/model/utils.py +++ /dev/null @@ -1,44 +0,0 @@ -""" from https://github.com/jaywalnut310/glow-tts """ - -import torch - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(int(max_length), dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def fix_len_compatibility(length, num_downsamplings_in_unet=2): - while True: - if length % (2**num_downsamplings_in_unet) == 0: - return length - length += 1 - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def generate_path(duration, mask): - device = duration.device - - b, t_x, t_y = mask.shape - cum_duration = torch.cumsum(duration, 1) - path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - torch.nn.functional.pad(path, convert_pad_shape([[0, 0], - [1, 0], [0, 0]]))[:, :-1] - path = path * mask - return path - - -def duration_loss(logw, logw_, lengths): - loss = torch.sum((logw - logw_)**2) / torch.sum(lengths) - return loss diff --git a/spaces/bradarrML/stablediffusion-infinity/js/w2ui.min.js b/spaces/bradarrML/stablediffusion-infinity/js/w2ui.min.js deleted file mode 100644 index ae849e5012ea6583f8d4f83151d94ad270c6bf4e..0000000000000000000000000000000000000000 --- a/spaces/bradarrML/stablediffusion-infinity/js/w2ui.min.js +++ /dev/null @@ -1,486 +0,0 @@ -/* w2ui 2.0.x (nightly) (10/10/2022, 1:43:34 PM) (c) http://w2ui.com, vitmalina@gmail.com */ -class w2event{constructor(e,t){Object.assign(this,{type:t.type??null,detail:t,owner:e,target:t.target??null,phase:t.phase??"before",object:t.object??null,execute:null,isStopped:!1,isCancelled:!1,onComplete:null,listeners:[]}),delete t.type,delete t.target,delete t.object,this.complete=new Promise((e,t)=>{this._resolve=e,this._reject=t}),this.complete.catch(()=>{})}finish(e){e&&w2utils.extend(this.detail,e),this.phase="after",this.owner.trigger.call(this.owner,this)}done(e){this.listeners.push(e)}preventDefault(){this._reject(),this.isCancelled=!0}stopPropagation(){this.isStopped=!0}}class w2base{constructor(e){if(this.activeEvents=[],this.listeners=[],void 0!==e){if(!w2utils.checkName(e))return;w2ui[e]=this}this.debug=!1}on(e,r){return(e="string"==typeof e?e.split(/[,\s]+/):[e]).forEach(e=>{var t,i,s,l="string"==typeof e?e:e.type+":"+e.execute+"."+e.scope;"string"==typeof e&&([i,t]=e.split("."),[i,s]=i.replace(":complete",":after").replace(":done",":after").split(":"),e={type:i,execute:s??"before",scope:t}),(e=w2utils.extend({type:null,execute:"before",onComplete:null},e)).type?r?(Array.isArray(this.listeners)||(this.listeners=[]),this.listeners.push({name:l,edata:e,handler:r}),this.debug&&console.log("w2base: add event",{name:l,edata:e,handler:r})):console.log("ERROR: You must specify event handler function when calling .on() method of "+this.name):console.log("ERROR: You must specify event type when calling .on() method of "+this.name)}),this}off(e,r){return(e="string"==typeof e?e.split(/[,\s]+/):[e]).forEach(i=>{var e,t,s,l="string"==typeof i?i:i.type+":"+i.execute+"."+i.scope;if("string"==typeof i&&([t,e]=i.split("."),[t,s]=t.replace(":complete",":after").replace(":done",":after").split(":"),i={type:t||"*",execute:s||"",scope:e||""}),(i=w2utils.extend({type:null,execute:null,onComplete:null},i)).type||i.scope){r=r||null;let t=0;this.listeners=this.listeners.filter(e=>"*"!==i.type&&i.type!==e.edata.type||""!==i.execute&&i.execute!==e.edata.execute||""!==i.scope&&i.scope!==e.edata.scope||null!=i.handler&&i.handler!==e.edata.handler||(t++,!1)),this.debug&&console.log(`w2base: remove event (${t})`,{name:l,edata:i,handler:r})}else console.log("ERROR: You must specify event type when calling .off() method of "+this.name)}),this}trigger(e,i){if(1==arguments.length?i=e:(i.type=e,i.target=i.target??this),w2utils.isPlainObject(i)&&"after"==i.phase){if(!(i=this.activeEvents.find(e=>e.type==i.type&&e.target==i.target)))return void console.log(`ERROR: Cannot find even handler for "${i.type}" on "${i.target}".`);console.log("NOTICE: This syntax \"edata.trigger({ phase: 'after' })\" is outdated. 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- top: ${n}px;
- width: ${parseInt(s.width)}px;
- height: ${parseInt(s.height)}px;
- transition: ${s.speed}s
- `;t=``,query("body").append(t),query("#w2ui-popup")[0]._w2popup={self:this,created:new Promise(e=>{this._promCreated=e}),opened:new Promise(e=>{this._promOpened=e}),closing:new Promise(e=>{this._promClosing=e}),closed:new Promise(e=>{this._promClosed=e})},a=`${s.title?"":"top: 0px !important;"} `+(s.buttons?"":"bottom: 0px !important;"),t=`
-
- ${e}
-
-
-
-
- `,query("#w2ui-popup").html(t),s.title&&query("#w2ui-popup .w2ui-popup-title").append(w2utils.lang(s.title)),s.buttons&&query("#w2ui-popup .w2ui-popup-buttons").append(s.buttons),s.body&&query("#w2ui-popup .w2ui-popup-body").append(s.body),setTimeout(()=>{query("#w2ui-popup").css("transition",s.speed+"s").removeClass("w2ui-anim-open"),w2utils.bindEvents("#w2ui-popup .w2ui-eaction",this),query("#w2ui-popup").find(".w2ui-popup-body").show(),this._promCreated()},1),clearTimeout(this._timer),this._timer=setTimeout(()=>{this.status="open",l.setFocus(s.focus),r.finish(),this._promOpened(),query("#w2ui-popup").removeClass("animating")},1e3*s.speed)}else{if(!0===(r=this.trigger("open",{target:"popup",present:!0})).isCancelled)return;this.status="opening",null!=e&&(e.maximized||e.width==s.width&&e.height==s.height||this.resize(s.width,s.height),s.prevSize=s.width+"px:"+s.height+"px",s.maximized=e.maximized);n=query("#w2ui-popup 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e&&(s=s.filter("#"+e)),Object.assign(i,{width:parseInt(query(s).css("width")),height:parseInt(query(s).css("height")),title:query(s).find("[rel=title]").html(),body:query(s).find("[rel=body]").html(),buttons:query(s).find("[rel=buttons]").html(),style:query(s).find("[rel=body]").get(0).style.cssText}),this.open(i)}action(e,t){let i=this.options.actions[e];i instanceof Object&&i.onClick&&(i=i.onClick);e=this.trigger("action",{action:e,target:"popup",self:this,originalEvent:t,value:this.input?this.input.value:null});!0!==e.isCancelled&&("function"==typeof i&&i.call(this,t),e.finish())}keydown(e){var t;this.options&&!this.options.keyboard||!0!==(t=this.trigger("keydown",{target:"popup",originalEvent:e})).isCancelled&&(27===e.keyCode&&(e.preventDefault(),0==query("#w2ui-popup .w2ui-message").length&&(this.options.cancelAction?this.action(this.options.cancelAction):this.close())),t.finish())}close(e){let t=this.trigger("close",{target:"popup"});var i;!0!==t.isCancelled&&(i=()=>{query("#w2ui-popup").remove(),this.options._last_focus&&0
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- ${e}
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-
`:`${l.label}
-
-
-
-
-
`),w2utils.extend(l,{title:w2utils.lang(t??"Notification"),showClose:!1,modal:!0,cancelAction:"cancel"}),w2utils.normButtons(l,{ok:"Ok",cancel:"Cancel"}),(s=0
-
",i<2&&(r+='')}return r=(r=(r+="")+`
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- `;for(let i=0;i';for(let t=0;t
-
`}r+="
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`}initControls(a){let n,o=this;var e=a.options;let h=w2utils.parseColor(e.color||a.tmp.initColor),d=(null==h&&(h={r:140,g:150,b:160,a:1}),w2utils.rgb2hsv(h));!0===e.advanced&&this.tabClick(2,a.name),u(d,!0,!0),query(a.box).find("input").off(".w2color").on("change.w2color",e=>{e=query(e.target);let t=parseFloat(e.val());var i=parseFloat(e.attr("max")),i=(isNaN(t)&&(t=0,e.val(0)),1
-
-
- ${"string"==typeof l.html?l.html:""}
-
-
-
- ${w2utils.lang("Loading...")}
-
- `;u=u||[],null==e&&(e=h.items),Array.isArray(e)||(e=[]);let c=0,t=null,i="",p=(!d&&h.search&&(i+=`
- `,e.forEach(e=>e.hidden=!1)),!d&&h.topHTML&&(i+=``),`
- ${i}
- "}refreshIndex(e){var t,i,e=Tooltip.active[e.replace(/[\s\.#]/g,"_")];e&&(e.displayed||this.show(e.name),t=query(e.box).find(".w2ui-overlay-body").get(0),i=query(e.box).find(".w2ui-menu-search, .w2ui-menu-top").get(0),query(e.box).find(".w2ui-menu-item.w2ui-selected").removeClass("w2ui-selected"),(e=query(e.box).find(`.w2ui-menu-item[index="${e.selected}"]`).addClass("w2ui-selected").get(0))&&(e.offsetTop+e.clientHeight>t.clientHeight+t.scrollTop&&e.scrollIntoView({behavior:"smooth",block:"start",inline:"start"}),e.offsetTop
- ${w2utils.lang(s.options.msgNoItems)}
-
`),query(s.box).find(".w2ui-no-items").show()))}applyFilter(r,e,n){let a=0;var t=Tooltip.active[r.replace(/[\s\.#]/g,"_")];let o=t.options;if(!1!==o.filter){null==e&&(e=t.options.items),null==n&&(n=["INPUT","TEXTAREA"].includes(t.anchor.tagName)?t.anchor.value:"");let l=[];return o.selected&&(Array.isArray(o.selected)?l=o.selected.map(e=>e?.id??e):o.selected?.id&&(l=[o.selected.id])),e.forEach(e=>{let t="",i="";-1!==["is","begins","begins with"].indexOf(o.match)&&(t="^"),-1!==["is","ends","ends with"].indexOf(o.match)&&(i="$");try{new RegExp(t+n+i,"i").test(e.text)||"..."===e.text?e.hidden=!1:e.hidden=!0}catch(e){}var s;o.hideSelected&&l.includes(e.id)&&(e.hidden=!0),Array.isArray(e.items)&&0
-
-
-
-
-
-
-
- ${w2utils.settings.fullmonths[r-1]}, ${e}
-
-
- ${o}
- `,p=new Date(e+`/${r}/1`);t=p.getDay();"M"==w2utils.settings.weekStarts&&a--,0
- ${g}
-
`,p=new Date(p.getTime()+864e5)}return c+="",l.btnNow&&(t=w2utils.lang("Today"+("datetime"==l.type?" & Now":"")),c+=`${t}
`),{html:c,month:r,year:e}}getYearHTML(){let t="",i="";for(let e=0;e${e}
`;return`
-
`}getHourHTML(l){(l=l??{}).format||(l.format=w2utils.settings.timeFormat);var r=-1${t}
- ${i}
-
-
`}}getMinHTML(i,s){null==i&&(i=0),(s=s??{}).format||(s.format=w2utils.settings.timeFormat);var l=-1${w2utils.lang("Select Hour")}
-
-
- ${l.btnNow?`${a[0]}
- ${a[1]}
- ${a[2]}
- ${w2utils.lang("Now")}
`:""}
-
-
`}}inRange(i,s,e){let l=!1;if("date"===s.type){var r=w2utils.isDate(i,s.format,!0);if(r){if(s.start||s.end){var n="string"==typeof s.start?s.start:query(s.start).val(),a="string"==typeof s.end?s.end:query(s.end).val();let e=w2utils.isDate(n,s.format,!0),t=w2utils.isDate(a,s.format,!0);n=new Date(r);e=e||n,t=t||n,n>=e&&n<=t&&(l=!0)}else l=!0;Array.isArray(s.blockDates)&&s.blockDates.includes(i)&&(l=!1),Array.isArray(s.blockWeekdays)&&s.blockWeekdays.includes(r.getDay())&&(l=!1)}}else if("time"===s.type)if(s.start||s.end){a=this.str2min(i);let e=this.str2min(s.start),t=this.str2min(s.end);e=e||a,t=t||a,a>=e&&a<=t&&(l=!0)}else l=!0;else"datetime"!==s.type||(n=w2utils.isDateTime(i,s.format,!0))&&(r=s.format.split("|").map(e=>e.trim()),e?(a=w2utils.formatDate(n,r[0]),i=w2utils.extend({},s,{type:"date",format:r[0]}),this.inRange(a,i)&&(l=!0)):(e=w2utils.formatTime(n,r[1]),a={type:"time",format:r[1],start:s.startTime,end:s.endTime},this.inRange(e,a)&&(l=!0)));return l}str2min(e){var t;return"string"!=typeof e||2!==(t=e.split(":")).length?null:(t[0]=parseInt(t[0]),t[1]=parseInt(t[1]),-1!==e.indexOf("pm")&&12!==t[0]&&(t[0]+=12),e.includes("am")&&12==t[0]&&(t[0]=0),60*t[0]+t[1])}min2str(e,t){let i="";1440<=e&&(e%=1440),e<0&&(e=1440+e);var s=Math.floor(e/60),e=(e%60<10?"0":"")+e%60;return t=t||w2utils.settings.timeFormat,i=-1!==t.indexOf("h24")?s+":"+e:(s<=12?s:s-12)+":"+e+" "+(12<=s?"pm":"am")}}let w2tooltip=new Tooltip,w2menu=new MenuTooltip,w2color=new ColorTooltip,w2date=new DateTooltip;class w2toolbar extends w2base{constructor(e){super(e.name),this.box=null,this.name=null,this.routeData={},this.items=[],this.right="",this.tooltip="top|left",this.onClick=null,this.onMouseDown=null,this.onMouseUp=null,this.onMouseEnter=null,this.onMouseLeave=null,this.onRender=null,this.onRefresh=null,this.onResize=null,this.onDestroy=null,this.item_template={id:null,type:"button",text:null,html:"",tooltip:null,count:null,hidden:!1,disabled:!1,checked:!1,icon:null,route:null,arrow:null,style:null,group:null,items:null,selected:null,color:null,overlay:{anchorClass:""},onClick:null,onRefresh:null},this.last={badge:{}};var t=e.items;delete e.items,Object.assign(this,e),Array.isArray(t)&&this.add(t,!0),e.items=t,"string"==typeof this.box&&(this.box=query(this.box).get(0)),this.box&&this.render(this.box)}add(e,t){this.insert(null,e,t)}insert(r,e,n){(e=Array.isArray(e)?e:[e]).forEach((e,t,i)=>{"string"==typeof e&&(e=i[t]={id:e,text:e});var l,s=["button","check","radio","drop","menu","menu-radio","menu-check","color","text-color","html","break","spacer","new-line"];if(s.includes(String(e.type)))if(null!=e.id||["break","spacer","new-line"].includes(e.type)){if(null==e.type)console.log('ERROR: The parameter "type" is required but not supplied.',e);else if(w2utils.checkUniqueId(e.id,this.items,"toolbar",this.name)){let s=w2utils.extend({},this.item_template,e);"menu-check"==s.type?(Array.isArray(s.selected)||(s.selected=[]),Array.isArray(s.items)&&s.items.forEach(e=>{(e="string"==typeof e?i[t]={id:e,text:e}:e).checked&&!s.selected.includes(e.id)&&s.selected.push(e.id),!e.checked&&s.selected.includes(e.id)&&(e.checked=!0),null==e.checked&&(e.checked=!1)})):"menu-radio"==s.type&&Array.isArray(s.items)&&s.items.forEach((e,t,i)=>{(e="string"==typeof e?i[t]={id:e,text:e}:e).checked&&null==s.selected?s.selected=e.id:e.checked=!1,e.checked||s.selected!=e.id||(e.checked=!0),null==e.checked&&(e.checked=!1)}),null==r?this.items.push(s):(l=this.get(r,!0),this.items=this.items.slice(0,l).concat([s],this.items.slice(l))),s.line=s.line??1,!0!==n&&this.refresh(s.id)}}else console.log('ERROR: The parameter "id" is required but not supplied.',e);else console.log('ERROR: The parameter "type" should be one of the following:',s,`, but ${e.type} is supplied.`,e)}),!0!==n&&this.resize()}remove(){let i=0;return Array.from(arguments).forEach(e=>{var t=this.get(e);t&&-1==String(e).indexOf(":")&&(i++,query(this.box).find("#tb_"+this.name+"_item_"+w2utils.escapeId(t.id)).remove(),null!=(e=this.get(t.id,!0))&&this.items.splice(e,1))}),this.resize(),i}set(e,t){var i=this.get(e);return null!=i&&(Object.assign(i,t),this.refresh(String(e).split(":")[0]),!0)}get(e,i){if(0===arguments.length){var t=[];for(let e=0;e${w2utils.lang("Select Minute")}
-
-
- ${s.btnNow?`${n[0]}
- ${n[1]}
- ${n[2]}
- ${w2utils.lang("Now")}
`:""}
-
-
- `),r.line=i)}return query(this.box).attr("name",this.name).addClass("w2ui-reset w2ui-toolbar").html(t),0
-
-
-
- ${this.right[i-1]??""}
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`:"")),"text-color"==i.type&&(s=''+(i.text?w2utils.lang(i.text):"Aa")+"");case"menu":case"menu-check":case"menu-radio":case"button":case"check":case"radio":case"drop":var r=!0===i.arrow||!1!==i.arrow&&["menu","menu-radio","menu-check","drop","color","text-color"].includes(i.type);e=`
-
- ${t}
- ${""!=s?`
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- ${w2utils.lang(s)}
- ${null!=i.count?w2utils.stripSpaces(`
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- `):""}
- ${r?'':""}
-
`:""}
-
-
-
`;break;case"spacer":e=`
-
`;break;case"html":e=`
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-
`}return e}tooltipShow(t){if(null!=this.tooltip){var i=query(this.box).find("#tb_"+this.name+"_item_"+w2utils.escapeId(t)).get(0),t=this.get(t),s=this.tooltip;let e=t.tooltip;"function"==typeof e&&(e=e.call(this,t)),["menu","menu-radio","menu-check","drop","color","text-color"].includes(t.type)&&1==t.checked||w2tooltip.show({anchor:i,name:this.name+"-tooltip",html:e,position:s})}}tooltipHide(e){null!=this.tooltip&&w2tooltip.hide(this.name+"-tooltip")}menuClick(t){if(t.item&&!t.item.disabled){var i=this.trigger(!0!==t.remove?"click":"remove",{target:t.item.id+":"+t.subItem.id,item:t.item,subItem:t.subItem,originalEvent:t.originalEvent});if(!0!==i.isCancelled){let l=t.subItem,r=this.get(t.item.id),e=r.items;if("function"==typeof e&&(e=r.items()),"menu"==r.type&&(r.selected=l.id),"menu-radio"==r.type&&(r.selected=l.id,Array.isArray(e)&&e.forEach(e=>{!0===e.checked&&delete e.checked,Array.isArray(e.items)&&e.items.forEach(e=>{!0===e.checked&&delete e.checked})}),l.checked=!0),"menu-check"==r.type)if(Array.isArray(r.selected)||(r.selected=[]),null==l.group){var n=r.selected.indexOf(l.id);-1==n?(r.selected.push(l.id),l.checked=!0):(r.selected.splice(n,1),l.checked=!1)}else if(!1!==l.group){let i=[];n=r.selected.indexOf(l.id);let s=e=>{e.forEach(e=>{var t;e.group===l.group&&-1!=(t=r.selected.indexOf(e.id))&&(e.id!=l.id&&i.push(e.id),r.selected.splice(t,1)),Array.isArray(e.items)&&s(e.items)})};s(e),-1==n&&(r.selected.push(l.id),l.checked=!0)}if("string"==typeof l.route){let t=""!==l.route?String("/"+l.route).replace(/\/{2,}/g,"/"):"";var s=w2utils.parseRoute(t);if(0
- ${t.groupShowHide&&t.collapsible?`${!t.hidden&&t.expanded?w2utils.lang("Hide"):w2utils.lang("Show")}`:""} ${e}
-
-
-
`,h.flat&&(i=`
-
-
-
`);let e="";var n=null!=t.count?`
- ${t.count}
-
`:"",a=(!0===t.collapsible&&(e=``),w2utils.lang("function"==typeof t.text?t.text.call(h,t):t.text)),o=["w2ui-node","w2ui-level-"+r,"w2ui-eaction"];t.selected&&o.push("w2ui-selected"),t.disabled&&o.push("w2ui-disabled"),t.class&&o.push(t.class),i=`
-
- ${h.handle.html?`
- `,h.flat&&(i=`
-
- ${"function"==typeof h.handle.html?h.handle.html.call(h,t):h.handle.html}
-
`:""}
-
- ${e} ${l} ${n}
-
- ${a}
-
-
- `)}return i}}}}mouseAction(e,t,i,s,l){var r=this.get(i),n=w2utils.lang("function"==typeof r.text?r.text.call(this,r):r.text)+(r.count||0===r.count?' - '+r.count+"":""),e=this.trigger("mouse"+e,{target:i,node:r,tooltip:n,originalEvent:s});"tooltip"==l&&this.tooltip(t,n,i),"handle"==l&&this.handleTooltip(t,i),e.finish()}tooltip(e,t,i){e=query(e).find(".w2ui-node-data");""!==t?w2tooltip.show({anchor:e.get(0),name:this.name+"_tooltip",html:t,position:"right|left"}):w2tooltip.hide(this.name+"_tooltip")}handleTooltip(e,t){let i=this.handle.tooltip;""!==(i="function"==typeof i?i(t):i)&&null!=t?w2tooltip.show({anchor:e,name:this.name+"_tooltip",html:i,position:"top|bottom"}):w2tooltip.hide(this.name+"_tooltip")}showPlus(e,t){query(e).find("span:nth-child(1)").css("color",t)}resize(){var e,t=Date.now(),i=this.trigger("resize",{target:this.name});if(!0!==i.isCancelled)return e=query(this.box).get(0).getBoundingClientRect(),query(this.box).css("overflow","hidden"),query(this.box).find(":scope > div").css({width:e.width+"px",height:e.height+"px"}),i.finish(),Date.now()-t}destroy(){var e=this.trigger("destroy",{target:this.name});!0!==e.isCancelled&&(0${l}
-
-
`),`
-
- ${w2utils.lang(t)+i}
-
`}refresh(e){var t=Date.now(),i=("up"==this.flow?query(this.box).addClass("w2ui-tabs-up"):query(this.box).removeClass("w2ui-tabs-up"),this.trigger("refresh",{target:null!=e?e:this.name,object:this.get(e)}));if(!0!==i.isCancelled){if(null==e)for(let e=0;e
-
-
- `,query(this.box).attr("name",this.name).addClass("w2ui-reset w2ui-tabs").html(e),0${this.right}
-
- ${e.label}
- ${w2utils.lang(l)}
- ${Array.isArray(s.value)?""+r:`${n}`}
-
-
`}).then(e=>{query(e.detail.overlay.box).find("#remove").on("click",()=>{this.searchData.splice(""+t,1),this.reload(),this.localSearch(),w2tooltip.hide(this.name+"-search-props")})})}searchSuggest(e,t,i){clearTimeout(this.last.kbd_timer),clearTimeout(this.last.overlay_timer),this.searchShowFields(!0),this.searchClose(),!0===t?w2tooltip.hide(this.name+"-search-suggest"):0
- ${w2utils.lang(-1!=t?"Update Search":"Save New Search")}
-
-
`,buttons:`
-
-
- `}).open(async i=>{query(i.detail.box).find("input, button").eq(0).val(e),await i.complete,query(i.detail.box).find("#grid-search-cancel").on("click",()=>{this.message()}),query(i.detail.box).find("#grid-search-save").on("click",()=>{var e=query(i.detail.box).find(".w2ui-message .search-name").val();this.searchSelected&&-1!=t?Object.assign(this.savedSearches[t],{id:e,text:e,logic:this.last.logic,data:w2utils.clone(this.searchData)}):this.savedSearches.push({id:e,text:e,icon:"w2ui-icon-search",remove:!0,logic:this.last.logic,data:this.searchData}),this.cacheSave("searches",this.savedSearches.map(e=>w2utils.clone(e,{exclude:["remove","icon"]}))),this.message(),(this.searchSelected?(this.searchSelected.text=e,query(this.box).find(`#grid_${this.name}_search_name .name-text`)):(this.searchSelected={text:e,logic:this.last.logic,data:w2utils.clone(this.searchData)},query(i.detail.box).find(`#grid_${this.name}_search_all`).val(" ").prop("readOnly",!0),query(i.detail.box).find(`#grid_${this.name}_search_name`).show().find(".name-text"))).html(e),s.finish({name:e})}),query(i.detail.box).find("input, button").off(".message").on("keydown.message",e=>{var t=String(query(i.detail.box).find(".w2ui-message-body input").val()).trim();13==e.keyCode&&""!=t&&query(i.detail.box).find("#grid-search-save").trigger("click"),27==e.keyCode&&this.message()}).eq(0).on("input.message",e=>{var t=query(i.detail.box).closest(".w2ui-message").find("#grid-search-save");""===String(query(i.detail.box).val()).trim()?t.prop("disabled",!0):t.prop("disabled",!1)}).get(0).focus()})}cache(e){if(w2utils.hasLocalStorage&&this.useLocalStorage)try{var t=JSON.parse(localStorage.w2ui||"{}");return t[this.stateId||this.name]??={},t[this.stateId||this.name][e]}catch(e){}return null}cacheSave(e,t){if(w2utils.hasLocalStorage&&this.useLocalStorage)try{var i=JSON.parse(localStorage.w2ui||"{}");return i[this.stateId||this.name]??={},i[this.stateId||this.name][e]=t,localStorage.w2ui=JSON.stringify(i),!0}catch(e){delete localStorage.w2ui}return!1}searchReset(e){var t=[];let i=!1;for(let e=0;e
-
`+h.text)),(l=e.find("div.w2ui-input").get(0)).innerText="object"!=typeof t?t:"",null!=c?w2utils.setCursorPosition(l,l.innerText.length):w2utils.setCursorPosition(l,0,l.innerText.length)):(e.addClass("w2ui-editable").html(w2utils.stripSpaces(``+h.text)),l=e.find("input").get(0),"number"==h.type&&(t=w2utils.formatNumber(t)),"date"==h.type&&(t=w2utils.formatDate(w2utils.isDate(t,h.format,!0)||new Date,h.format)),l.value="object"!=typeof t?t:"",y=e=>{var t=this.last._edit?.escKey;let i=!1;var s=query(l).data("tooltipName");s&&null!=w2tooltip.get(s[0])?.selected&&(i=!0),!this.last.inEditMode||t||!r.includes(h.type)||e.detail.overlay.anchor?.id!=this.last._edit.input?.id&&"list"!=h.type||(this.editChange(),this.editDone(void 0,void 0,{keyCode:i?13:0}))},new w2field(w2utils.extend({},h,{el:l,selected:t,onSelect:y,onHide:y})),null==c&&l&&l.select()),Object.assign(this.last._edit,{input:l,edit:h}),query(l).off(".w2ui-editable").on("blur.w2ui-editable",e=>{var t,i;this.last.inEditMode&&(t=this.last._edit.edit.type,i=query(l).data("tooltipName"),r.includes(t)&&i||(this.editChange(l,o,u,e),this.editDone()))}).on("mousedown.w2ui-editable",e=>{e.stopPropagation()}).on("click.w2ui-editable",e=>{w.call(l,e)}).on("paste.w2ui-editable",e=>{e.preventDefault();e=e.clipboardData.getData("text/plain");document.execCommand("insertHTML",!1,e)}).on("keyup.w2ui-editable",e=>{w.call(l,e)}).on("keydown.w2ui-editable",i=>{switch(i.keyCode){case 8:"list"!=h.type||l._w2field||i.preventDefault();break;case 9:case 13:i.preventDefault();break;case 27:var e=query(l).data("tooltipName");e&&0'+e[0]+'
'+e[1]+'
${this.msgEmpty}
-
-
- x
-
-
-
-
-
- `,this.toolbar.items.push({id:"w2ui-search",type:"html",html:t,onRefresh:async e=>{await e.complete;e=query(this.box).find(`#grid_${this.name}_search_all`);w2utils.bindEvents(query(this.box).find(`#grid_${this.name}_search_all, .w2ui-action`),this),e.on("change",e=>{this.liveSearch||(this.search(this.last.field,e.target.value),this.searchSuggest(!0,!0,this))}).on("blur",()=>{this.last.liveText=""}).on("keyup",e=>{var t=e.target.value;this.liveSearch&&this.last.liveText!=t&&(this.last.liveText=t,this.search(this.last.field,t)),40==e.keyCode&&this.searchSuggest(!0)})}})),Array.isArray(e)&&(t=e.map(e=>e.id),this.show.toolbarAdd&&!t.includes(this.buttons.add.id)&&this.toolbar.items.push(w2utils.extend({},this.buttons.add)),this.show.toolbarEdit&&!t.includes(this.buttons.edit.id)&&this.toolbar.items.push(w2utils.extend({},this.buttons.edit)),this.show.toolbarDelete&&!t.includes(this.buttons.delete.id)&&this.toolbar.items.push(w2utils.extend({},this.buttons.delete)),this.show.toolbarSave&&!t.includes(this.buttons.save.id)&&((this.show.toolbarAdd||this.show.toolbarDelete||this.show.toolbarEdit)&&this.toolbar.items.push({type:"break",id:"w2ui-break2"}),this.toolbar.items.push(w2utils.extend({},this.buttons.save)))),this.toolbar.items.push(...e),this.toolbar.on("click",e=>{var i=this.trigger("toolbar",{target:e.target,originalEvent:e});if(!0!==i.isCancelled){let t;switch(e.detail.item.id){case"w2ui-reload":if(!0===(t=this.trigger("reload",{target:this.name})).isCancelled)return!1;this.reload(),t.finish();break;case"w2ui-column-on-off":e.detail.subItem?(s=e.detail.subItem.id,["w2ui-stateSave","w2ui-stateReset"].includes(s)?this[s.substring(5)]():"w2ui-skip"!=s&&this.columnOnOff(e,e.detail.subItem.id)):(this.initColumnOnOff(),setTimeout(()=>{query(`#w2overlay-${this.name}_toolbar-drop .w2ui-grid-skip`).off(".w2ui-grid").on("click.w2ui-grid",e=>{e.stopPropagation()}).on("keypress",e=>{13==e.keyCode&&(this.skip(e.target.value),this.toolbar.click("w2ui-column-on-off"))})},100));break;case"w2ui-add":if(!0===(t=this.trigger("add",{target:this.name,recid:null})).isCancelled)return!1;t.finish();break;case"w2ui-edit":{var s=this.getSelection();let e=null;if(1==s.length&&(e=s[0]),!0===(t=this.trigger("edit",{target:this.name,recid:e})).isCancelled)return!1;t.finish();break}case"w2ui-delete":this.delete();break;case"w2ui-save":this.save()}i.finish()}}),this.toolbar.on("refresh",e=>{if("w2ui-search"==e.target){let e=this.searchData;setTimeout(()=>{this.searchInitInput(this.last.field,1==e.length?e[0].value:null)},1)}}))}initResize(){let r=this;query(this.box).find(".w2ui-resizer").off(".grid-col-resize").on("click.grid-col-resize",function(e){e.stopPropagation?e.stopPropagation():e.cancelBubble=!0,e.preventDefault&&e.preventDefault()}).on("mousedown.grid-col-resize",function(e){e=e||window.event,r.last.colResizing=!0,r.last.tmp={x:e.screenX,y:e.screenY,gx:e.screenX,gy:e.screenY,col:parseInt(query(this).attr("name"))},r.last.tmp.tds=query(r.box).find("#grid_"+r.name+'_body table tr:first-child td[col="'+r.last.tmp.col+'"]'),e.stopPropagation?e.stopPropagation():e.cancelBubble=!0,e.preventDefault&&e.preventDefault();for(let e=0;e| ${w2utils.lang(s.label)||""} | -${l} | -`;let e;switch(s.type){case"text":case"alphanumeric":case"hex":case"color":case"list":case"combo":case"enum":e="width: 250px;",-1!=["hex","color"].indexOf(s.type)&&(e="width: 90px;"),i+=``;break;case"int":case"float":case"money":case"currency":case"percent":case"date":case"time":case"datetime":e="width: 90px;","datetime"==s.type&&(e="width: 140px;"),i+=` - `;break;case"select":i+=``}i+=s.text+" |
| - - | -- - - | -|
`+` `+(o||" ")+"
${o||" "}
`+"#
'+`
'+(a||" ")+"
"}columnTooltipShow(e,t){var i=query(this.box).find("#grid_"+this.name+"_column_"+e),e=this.columns[e],s=this.columnTooltip;w2tooltip.show({name:this.name+"-column-tooltip",anchor:i.get(0),html:e.tooltip,position:s})}columnTooltipHide(e,t){w2tooltip.hide(this.name+"-column-tooltip")}getRecordsHTML(){let e=this.records.length;var t="object"!=typeof this.url?this.url:this.url.get,t=((e=0==this.searchData.length||t?e:this.last.searchIds.length)>this.vs_start?this.last.show_extra=this.vs_extra:this.last.show_extra=this.vs_start,query(this.box).find(`#grid_${this.name}_records`));let i=Math.floor((t.get(0)?.clientHeight||0)/this.recordHeight)+this.last.show_extra+1;(!this.fixedBody||i>e)&&(i=e);var s=this.getRecordHTML(-1,0);let l="| ';null!=t&&(i=this.getCellHTML(parseInt(t),s,!1)),query(e).after(i)}),g.each(e=>{var t=query(e).parent().attr("index");let i=' | ';null!=t&&(i=this.getCellHTML(parseInt(t),s,!0)),query(e).after(i)}));if(r>this.last.colEnd)for(let s=this.last.colEnd+1;s<=r;s++)this.columns[s]&&(this.columns[s].frozen||this.columns[s].hidden)||(t.before(this.getColumnCellHTML(s)),m.each(e=>{var t=query(e).parent().attr("index");let i=' | ';null!=t&&(i=this.getCellHTML(parseInt(t),s,!1)),query(e).before(i)}),y.each(e=>{var t=query(e).parent().attr("index")||-1,t=this.getCellHTML(parseInt(t),s,!0);query(e).before(t)}));this.last.colStart=l,this.last.colEnd=r}else{this.last.colStart=l,this.last.colEnd=r;var o=this.getColumnsHTML(),w=this.getRecordsHTML(),c=this.getSummaryHTML(),p=a.find(`#grid_${this.name}_columns`);let e=a.find(`#grid_${this.name}_records`);var b=a.find(`#grid_${this.name}_frecords`);let t=a.find(`#grid_${this.name}_summary`);p.find("tbody").html(o[1]),b.html(w[0]),e.prepend(w[1]),null!=c&&t.html(c[1]),setTimeout(()=>{e.find(":scope > table").filter(":not(table:first-child)").remove(),t[0]&&(t[0].scrollLeft=this.last.scrollLeft)},1)}this.resizeRecords()}let v=this.records.length;if(v>this.total&&-1!==this.total&&(v=this.total),0!==(v=0==this.searchData.length||i?v:this.last.searchIds.length)&&0!==d.length&&0!==d.prop("clientHeight")){v>this.vs_start?this.last.show_extra=this.vs_extra:this.last.show_extra=this.vs_start;let e=Math.round(d.prop("scrollTop")/this.recordHeight+1),t=e+(Math.round(d.prop("clientHeight")/this.recordHeight)-1);if(e>v&&(e=v),t>=v-1&&(t=v),query(this.box).find("#grid_"+this.name+"_footer .w2ui-footer-right").html((this.show.statusRange?w2utils.formatNumber(this.offset+e)+"-"+w2utils.formatNumber(this.offset+t)+(-1!=this.total?" "+w2utils.lang("of")+" "+w2utils.formatNumber(this.total):""):"")+(i&&this.show.statusBuffered?" ("+w2utils.lang("buffered")+" "+w2utils.formatNumber(v)+(0 | |||||||||||
| '),this.show.selectColumn&&(o+=' | '),this.show.expandColumn&&(o+=' | '),h+=' | ',this.show.orderColumn&&(h+=' | ');for(let e=0;e | ',h+=' | '}else{var c="object"!=typeof this.url?this.url:this.url.get;if(!0!==a){if(0 | |||||||
| "+(!0!==a?this.getLineHTML(n,u):"")+" | "),this.show.selectColumn&&(o+=''+(!0===a||u.w2ui&&!0===u.w2ui.hideCheckBox?"":' | "),this.show.expandColumn){let e="";e=u.w2ui&&!0===u.w2ui.expanded?"-":"+",!u.w2ui||"none"!=u.w2ui.expanded&&Array.isArray(u.w2ui.children)&&u.w2ui.children.length||(e=""),u.w2ui&&"spinner"==u.w2ui.expanded&&(e=''),o+=''+(!0!==a?` ${e} `:"")+" | "}h+='',this.show.orderColumn&&(h+=' | '+(!0!==a?' | ");let s=0,l=0;for(;;){let e=1;var p,f=this.columns[s];if(null==f)break;if(f.hidden)s++,0',h+=' | '}return o+=" | "+v+(_&&w2utils.stripTags(v)?_:"")+" | ",v=-1===i&&!0===l?'":v}clipboardCopy(e,t,i){var s=(i?this.summary:this.records)[e],l=this.columns[t];let r=l?this.parseField(s,l.field):"";"function"==typeof l.clipboardCopy&&(r=l.clipboardCopy(s,{self:this,index:e,colIndex:t,summary:!!i})),query(this.box).find("#grid_"+this.name+"_focus").text(r).get(0).select(),document.execCommand("copy")}showBubble(s,l,r){var n=this.columns[l].info;if(n){let i="";var a=this.records[s],e=query(this.box).find(`${r?".w2ui-grid-summary":""} #grid_${this.name}_data_${s}_${l} .w2ui-info`);if(this.last.bubbleEl&&w2tooltip.hide(this.name+"-bubble"),this.last.bubbleEl=e,null==n.fields){n.fields=[];for(let e=0;e | ';else{let e=this.getColumn(h[0]),t=(e=null==e?{field:h[0],caption:h[0]}:e)?this.parseField(a,e.field):"";1 | ||||
| "+e.text+" | "+((0===t?"0":t)||"")+" |
| "+d+" | "+((0===t?"0":t)||"")+" |
`;break}case"radio":{i="",null==h.options.items&&null!=h.html.items&&(h.options.items=h.html.items);let t=h.options.items;0<(t=Array.isArray(t)?t:[]).length&&(t=w2utils.normMenu.call(this,t,h));for(let e=0;e
`;break}case"select":{i=`";break}case"textarea":i=``;break;case"toggle":i=` -
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'+(h&&h.html&&h.html.html?h.html.html:"")+"
";break;case"html":case"empty":i=h&&h.html?(h.html.html||"")+(h.html.text||""):""}if(""!==t&&(l!=h.html.page||r!=h.html.column||h.html.group&&t!=h.html.group)&&(s[l][r]+="\n \n ",t=""),h.html.group&&t!=h.html.group){let e="";h.html.groupCollapsible&&(e=''),n+='\n \n
"),this.tabs.tabs)for(let e=0;e"+e+w2utils.lang(h.html.group)+'
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- ${"map"==e.type?`
- ${e.html.key.text||""}
- `:""}
-
- ${e.html.value.text||""}
-
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-
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- ${w2utils.lang("Modified")}:
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- ${this.options.prefix}
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-
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- margin-bottom: 0px;
- margin-left: ${n["margin-left"]};
- margin-right: ${n["margin-right"]};
- width: ${w2utils.getSize(this.el,"width")-parseInt(n["margin-left"],10)-parseInt(n["margin-right"],10)}px;
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-
-
-
-
-
-
- 
-
-
- Ghibli Diffusion -This is the fine-tuned Stable Diffusion model trained on images from modern anime feature films from Studio Ghibli. Use the tokens ghibli style in your prompts for the effect.
-
-
-
\ No newline at end of file
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/smb.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/smb.py
deleted file mode 100644
index 9892f469d563fec7041a2abc68416a19fd96888c..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/smb.py
+++ /dev/null
@@ -1,309 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module contains SMBFileSystem class responsible for handling access to
-Windows Samba network shares by using package smbprotocol
-"""
-
-import datetime
-import uuid
-from stat import S_ISDIR, S_ISLNK
-
-import smbclient
-
-from .. import AbstractFileSystem
-from ..utils import infer_storage_options
-
-# ! pylint: disable=bad-continuation
-
-
-class SMBFileSystem(AbstractFileSystem):
- """Allow reading and writing to Windows and Samba network shares.
-
- When using `fsspec.open()` for getting a file-like object the URI
- should be specified as this format:
- ``smb://workgroup;user:password@server:port/share/folder/file.csv``.
-
- Example::
-
- >>> import fsspec
- >>> with fsspec.open(
- ... 'smb://myuser:mypassword@myserver.com/' 'share/folder/file.csv'
- ... ) as smbfile:
- ... df = pd.read_csv(smbfile, sep='|', header=None)
-
- Note that you need to pass in a valid hostname or IP address for the host
- component of the URL. Do not use the Windows/NetBIOS machine name for the
- host component.
-
- The first component of the path in the URL points to the name of the shared
- folder. Subsequent path components will point to the directory/folder/file.
-
- The URL components ``workgroup`` , ``user``, ``password`` and ``port`` may be
- optional.
-
- .. note::
-
- For working this source require `smbprotocol`_ to be installed, e.g.::
-
- $ pip install smbprotocol
- # or
- # pip install smbprotocol[kerberos]
-
- .. _smbprotocol: https://github.com/jborean93/smbprotocol#requirements
-
- Note: if using this with the ``open`` or ``open_files``, with full URLs,
- there is no way to tell if a path is relative, so all paths are assumed
- to be absolute.
- """
-
- protocol = "smb"
-
- # pylint: disable=too-many-arguments
- def __init__(
- self,
- host,
- port=None,
- username=None,
- password=None,
- timeout=60,
- encrypt=None,
- share_access=None,
- **kwargs,
- ):
- """
- You can use _get_kwargs_from_urls to get some kwargs from
- a reasonable SMB url.
-
- Authentication will be anonymous or integrated if username/password are not
- given.
-
- Parameters
- ----------
- host: str
- The remote server name/ip to connect to
- port: int
- Port to connect with. Usually 445, sometimes 139.
- username: str or None
- Username to connect with. Required if Kerberos auth is not being used.
- password: str or None
- User's password on the server, if using username
- timeout: int
- Connection timeout in seconds
- encrypt: bool
- Whether to force encryption or not, once this has been set to True
- the session cannot be changed back to False.
- share_access: str or None
- Specifies the default access applied to file open operations
- performed with this file system object.
- This affects whether other processes can concurrently open a handle
- to the same file.
-
- - None (the default): exclusively locks the file until closed.
- - 'r': Allow other handles to be opened with read access.
- - 'w': Allow other handles to be opened with write access.
- - 'd': Allow other handles to be opened with delete access.
- """
- super(SMBFileSystem, self).__init__(**kwargs)
- self.host = host
- self.port = port
- self.username = username
- self.password = password
- self.timeout = timeout
- self.encrypt = encrypt
- self.temppath = kwargs.pop("temppath", "")
- self.share_access = share_access
- self._connect()
-
- def _connect(self):
- smbclient.register_session(
- self.host,
- username=self.username,
- password=self.password,
- port=445 if self.port is None else self.port,
- encrypt=self.encrypt,
- connection_timeout=self.timeout,
- )
-
- @classmethod
- def _strip_protocol(cls, path):
- return infer_storage_options(path)["path"]
-
- @staticmethod
- def _get_kwargs_from_urls(path):
- # smb://workgroup;user:password@host:port/share/folder/file.csv
- out = infer_storage_options(path)
- out.pop("path", None)
- out.pop("protocol", None)
- return out
-
- def mkdir(self, path, create_parents=True, **kwargs):
- wpath = _as_unc_path(self.host, path)
- if create_parents:
- smbclient.makedirs(wpath, exist_ok=False, **kwargs)
- else:
- smbclient.mkdir(wpath, **kwargs)
-
- def makedirs(self, path, exist_ok=False):
- if _share_has_path(path):
- wpath = _as_unc_path(self.host, path)
- smbclient.makedirs(wpath, exist_ok=exist_ok)
-
- def rmdir(self, path):
- if _share_has_path(path):
- wpath = _as_unc_path(self.host, path)
- smbclient.rmdir(wpath)
-
- def info(self, path, **kwargs):
- wpath = _as_unc_path(self.host, path)
- stats = smbclient.stat(wpath, **kwargs)
- if S_ISDIR(stats.st_mode):
- stype = "directory"
- elif S_ISLNK(stats.st_mode):
- stype = "link"
- else:
- stype = "file"
- res = {
- "name": path + "/" if stype == "directory" else path,
- "size": stats.st_size,
- "type": stype,
- "uid": stats.st_uid,
- "gid": stats.st_gid,
- "time": stats.st_atime,
- "mtime": stats.st_mtime,
- }
- return res
-
- def created(self, path):
- """Return the created timestamp of a file as a datetime.datetime"""
- wpath = _as_unc_path(self.host, path)
- stats = smbclient.stat(wpath)
- return datetime.datetime.utcfromtimestamp(stats.st_ctime)
-
- def modified(self, path):
- """Return the modified timestamp of a file as a datetime.datetime"""
- wpath = _as_unc_path(self.host, path)
- stats = smbclient.stat(wpath)
- return datetime.datetime.utcfromtimestamp(stats.st_mtime)
-
- def ls(self, path, detail=True, **kwargs):
- unc = _as_unc_path(self.host, path)
- listed = smbclient.listdir(unc, **kwargs)
- dirs = ["/".join([path.rstrip("/"), p]) for p in listed]
- if detail:
- dirs = [self.info(d) for d in dirs]
- return dirs
-
- # pylint: disable=too-many-arguments
- def _open(
- self,
- path,
- mode="rb",
- block_size=-1,
- autocommit=True,
- cache_options=None,
- **kwargs,
- ):
- """
- block_size: int or None
- If 0, no buffering, 1, line buffering, >1, buffer that many bytes
-
- Notes
- -----
- By specifying 'share_access' in 'kwargs' it is possible to override the
- default shared access setting applied in the constructor of this object.
- """
- bls = block_size if block_size is not None and block_size >= 0 else -1
- wpath = _as_unc_path(self.host, path)
- share_access = kwargs.pop("share_access", self.share_access)
- if "w" in mode and autocommit is False:
- temp = _as_temp_path(self.host, path, self.temppath)
- return SMBFileOpener(wpath, temp, mode, block_size=bls, **kwargs)
- return smbclient.open_file(
- wpath, mode, buffering=bls, share_access=share_access, **kwargs
- )
-
- def copy(self, path1, path2, **kwargs):
- """Copy within two locations in the same filesystem"""
- wpath1 = _as_unc_path(self.host, path1)
- wpath2 = _as_unc_path(self.host, path2)
- smbclient.copyfile(wpath1, wpath2, **kwargs)
-
- def _rm(self, path):
- if _share_has_path(path):
- wpath = _as_unc_path(self.host, path)
- stats = smbclient.stat(wpath)
- if S_ISDIR(stats.st_mode):
- smbclient.rmdir(wpath)
- else:
- smbclient.remove(wpath)
-
- def mv(self, path1, path2, **kwargs):
- wpath1 = _as_unc_path(self.host, path1)
- wpath2 = _as_unc_path(self.host, path2)
- smbclient.rename(wpath1, wpath2, **kwargs)
-
-
-def _as_unc_path(host, path):
- rpath = path.replace("/", "\\")
- unc = "\\\\{}{}".format(host, rpath)
- return unc
-
-
-def _as_temp_path(host, path, temppath):
- share = path.split("/")[1]
- temp_file = "/{}{}/{}".format(share, temppath, uuid.uuid4())
- unc = _as_unc_path(host, temp_file)
- return unc
-
-
-def _share_has_path(path):
- parts = path.count("/")
- if path.endswith("/"):
- return parts > 2
- return parts > 1
-
-
-class SMBFileOpener(object):
- """writes to remote temporary file, move on commit"""
-
- def __init__(self, path, temp, mode, block_size=-1, **kwargs):
- self.path = path
- self.temp = temp
- self.mode = mode
- self.block_size = block_size
- self.kwargs = kwargs
- self.smbfile = None
- self._incontext = False
- self._open()
-
- def _open(self):
- if self.smbfile is None or self.smbfile.closed:
- self.smbfile = smbclient.open_file(
- self.temp, self.mode, buffering=self.block_size, **self.kwargs
- )
-
- def commit(self):
- """Move temp file to definitive on success."""
- # TODO: use transaction support in SMB protocol
- smbclient.replace(self.temp, self.path)
-
- def discard(self):
- """Remove the temp file on failure."""
- smbclient.remove(self.temp)
-
- def __fspath__(self):
- return self.path
-
- def __iter__(self):
- return self.smbfile.__iter__()
-
- def __getattr__(self, item):
- return getattr(self.smbfile, item)
-
- def __enter__(self):
- self._incontext = True
- return self.smbfile.__enter__()
-
- def __exit__(self, exc_type, exc_value, traceback):
- self._incontext = False
- self.smbfile.__exit__(exc_type, exc_value, traceback)
diff --git a/spaces/chuanenlin/foodnet/README.md b/spaces/chuanenlin/foodnet/README.md
deleted file mode 100644
index 0305f7062b850cc2a41bcab85e591ffc080be6b1..0000000000000000000000000000000000000000
--- a/spaces/chuanenlin/foodnet/README.md
+++ /dev/null
@@ -1,130 +0,0 @@
----
-title: FoodNet
-emoji: 🍔
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-app_file: foodnet.py
-
----
-
-# 24-679 FoodNet Project
-
-## Authors
-
-David Chuan-En Lin: chuanenl@cs.cmu.edu
-
-Mitch Fogelson: mfogelso@andrew.cmu.edu
-
-Sunny Yang: yundiy@andrew.cmu.edu
-
-Shihao Xu: shihaoxu@andrew.cmu.edu
-
-## TODO
-
-### Must Have
-
-1. Cooking method (How to do this?) (TBD)
-2. Ingredients -> Recipe (Recipe Querey?) (Mitch)
-3. Cuisine Meta Data (Where to get) (TBD)
-4. Deployment on the cloud -> (David)
-
-### Like to have
-
-1. Images related ->
-
- * [Google Image Search API](https://pypi.org/project/Google-Images-Search/)
- * [OpenAI Clip](https://openai.com/api/)
-
-2. User Studies
-
-### Moonshot
-
-1. Recipe Masking Prediction
-2.
-
-## Description
-
-We wanted to help students and households in the Pittsburgh to reduce their food waste. We developed a model that suggests recipes based on current leftovers availible.
-
-* Model -> Facebook's [FastText](https://radimrehurek.com/gensim/models/fasttext.html)
-* Dataset -> [Simplified 1M+ Recipes](https://github.com/schmidtdominik/RecipeNet)
- * [Dominick Schmidt Blog](https://dominikschmidt.xyz/simplified-recipes-1M/#dataset-sources)
-
-## Try WebApp
-
-https://huggingface.co/spaces/chuanenlin/foodnet
-
-## Quick Start
-
-1. Clone repository
-
-```
-git clone git@github.com:chuanenlin/foodnet.git
-```
-
-2. Move into repository
-
-```
-cd foodnet
-```
-
-(**Optional** Create conda environment)
-
-3. Install gdown
-
-```
-pip install gdown
-```
-
-4. Download models
-
-```
-gdown https://drive.google.com/drive/folders/1LlQpd45E71dSfC8FgvIhJjQjqxnlBC9j -O ./models --folder
-```
-
-5. Download datasets (Optional)
-
-```
-gdown https://drive.google.com/drive/folders/18aA3BFKqzkqNz5L4N5vN6bFnp8Ch2CQV -O ./data --folder
-```
-
-6. Install Dependencies
-
-```
-pip install -r requirements.txt
-```
-
-7. Run code
-
-```
-streamlit run foodnet.py
-```
-
-## Args
-
-Train new model
-
-```
-streamlit run foodnet.py -d/--dataset ['/PATH/TO/DATASET'] -t/--train True
-```
-
-Load alternative model
-
-```
-streamlit run foodnet.py --model ['/PATH/TO/MODEL']
-```
-
-## Requirements
-
-* python>=3.6
-* gensim>=4.0.x
-* streamlit
-* gdown
-* nltk
-* pickle
-* matplotlib
-
-## References
-
-TODO
diff --git a/spaces/cncn102/bingo1/src/pages/api/sydney.ts b/spaces/cncn102/bingo1/src/pages/api/sydney.ts
deleted file mode 100644
index 8bd7074bc72bd2803e4acf89d3814908893ff044..0000000000000000000000000000000000000000
--- a/spaces/cncn102/bingo1/src/pages/api/sydney.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-import { NextApiRequest, NextApiResponse } from 'next'
-import { WebSocket, debug } from '@/lib/isomorphic'
-import { BingWebBot } from '@/lib/bots/bing'
-import { websocketUtils } from '@/lib/bots/bing/utils'
-import { WatchDog, createHeaders } from '@/lib/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- const conversationContext = req.body
- const headers = createHeaders(req.cookies)
- const id = headers['x-forwarded-for']
-
- debug(id, headers)
- res.setHeader('Content-Type', 'text/stream; charset=UTF-8')
-
- const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', {
- headers: {
- ...headers,
- 'accept-language': 'zh-CN,zh;q=0.9',
- 'cache-control': 'no-cache',
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- pragma: 'no-cache',
- }
- })
-
- const closeDog = new WatchDog()
- const timeoutDog = new WatchDog()
- ws.onmessage = (event) => {
- timeoutDog.watch(() => {
- debug(id, 'timeout')
- ws.send(websocketUtils.packMessage({ type: 6 }))
- }, 3000)
- closeDog.watch(() => {
- debug(id, 'timeout close')
- ws.close()
- }, 20000)
- res.write(event.data)
- if (/\{"type":([367])\}/.test(String(event.data))) {
- const type = parseInt(RegExp.$1, 10)
- debug(id, 'connection type', type)
- if (type === 3) {
- ws.close()
- } else {
- ws.send(websocketUtils.packMessage({ type }))
- }
- }
- }
-
- ws.onclose = () => {
- timeoutDog.reset()
- closeDog.reset()
- debug(id, 'ws close')
- res.end()
- }
-
- await new Promise((resolve) => ws.onopen = resolve)
- ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 }))
- ws.send(websocketUtils.packMessage({ type: 6 }))
- ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!)))
- req.socket.once('close', () => {
- debug(id, 'connection close')
- ws.close()
- if (!res.closed) {
- res.end()
- }
- })
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cbs_jpeg.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cbs_jpeg.c
deleted file mode 100644
index 5921d624a183311cd5952f9912758d6f6b78260a..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cbs_jpeg.c
+++ /dev/null
@@ -1,444 +0,0 @@
-/*
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "cbs.h"
-#include "cbs_internal.h"
-#include "cbs_jpeg.h"
-
-
-#define HEADER(name) do { \
- ff_cbs_trace_header(ctx, name); \
- } while (0)
-
-#define CHECK(call) do { \
- err = (call); \
- if (err < 0) \
- return err; \
- } while (0)
-
-#define SUBSCRIPTS(subs, ...) (subs > 0 ? ((int[subs + 1]){ subs, __VA_ARGS__ }) : NULL)
-
-#define u(width, name, range_min, range_max) \
- xu(width, name, range_min, range_max, 0, )
-#define us(width, name, sub, range_min, range_max) \
- xu(width, name, range_min, range_max, 1, sub)
-
-
-#define READ
-#define READWRITE read
-#define RWContext GetBitContext
-#define FUNC(name) cbs_jpeg_read_ ## name
-
-#define xu(width, name, range_min, range_max, subs, ...) do { \
- uint32_t value; \
- CHECK(ff_cbs_read_unsigned(ctx, rw, width, #name, \
- SUBSCRIPTS(subs, __VA_ARGS__), \
- &value, range_min, range_max)); \
- current->name = value; \
- } while (0)
-
-#include "cbs_jpeg_syntax_template.c"
-
-#undef READ
-#undef READWRITE
-#undef RWContext
-#undef FUNC
-#undef xu
-
-#define WRITE
-#define READWRITE write
-#define RWContext PutBitContext
-#define FUNC(name) cbs_jpeg_write_ ## name
-
-#define xu(width, name, range_min, range_max, subs, ...) do { \
- uint32_t value = current->name; \
- CHECK(ff_cbs_write_unsigned(ctx, rw, width, #name, \
- SUBSCRIPTS(subs, __VA_ARGS__), \
- value, range_min, range_max)); \
- } while (0)
-
-
-#include "cbs_jpeg_syntax_template.c"
-
-#undef WRITE
-#undef READWRITE
-#undef RWContext
-#undef FUNC
-#undef xu
-
-
-static int cbs_jpeg_split_fragment(CodedBitstreamContext *ctx,
- CodedBitstreamFragment *frag,
- int header)
-{
- AVBufferRef *data_ref;
- uint8_t *data;
- size_t data_size;
- int start, end, marker, next_start, next_marker;
- int err, i, j, length;
-
- if (frag->data_size < 4) {
- // Definitely too short to be meaningful.
- return AVERROR_INVALIDDATA;
- }
-
- for (i = 0; i + 1 < frag->data_size && frag->data[i] != 0xff; i++);
- if (i > 0) {
- av_log(ctx->log_ctx, AV_LOG_WARNING, "Discarding %d bytes at "
- "beginning of image.\n", i);
- }
- for (++i; i + 1 < frag->data_size && frag->data[i] == 0xff; i++);
- if (i + 1 >= frag->data_size && frag->data[i]) {
- av_log(ctx->log_ctx, AV_LOG_ERROR, "Invalid JPEG image: "
- "no SOI marker found.\n");
- return AVERROR_INVALIDDATA;
- }
- marker = frag->data[i];
- if (marker != JPEG_MARKER_SOI) {
- av_log(ctx->log_ctx, AV_LOG_ERROR, "Invalid JPEG image: first "
- "marker is %02x, should be SOI.\n", marker);
- return AVERROR_INVALIDDATA;
- }
- for (++i; i + 1 < frag->data_size && frag->data[i] == 0xff; i++);
- if (i + 1 >= frag->data_size) {
- av_log(ctx->log_ctx, AV_LOG_ERROR, "Invalid JPEG image: "
- "no image content found.\n");
- return AVERROR_INVALIDDATA;
- }
- marker = frag->data[i];
- start = i + 1;
-
- do {
- if (marker == JPEG_MARKER_EOI) {
- break;
- } else if (marker == JPEG_MARKER_SOS) {
- next_marker = -1;
- end = start;
- for (i = start; i + 1 < frag->data_size; i++) {
- if (frag->data[i] != 0xff)
- continue;
- end = i;
- for (++i; i + 1 < frag->data_size &&
- frag->data[i] == 0xff; i++);
- if (i + 1 < frag->data_size) {
- if (frag->data[i] == 0x00)
- continue;
- next_marker = frag->data[i];
- next_start = i + 1;
- }
- break;
- }
- } else {
- i = start;
- if (i + 2 > frag->data_size) {
- av_log(ctx->log_ctx, AV_LOG_ERROR, "Invalid JPEG image: "
- "truncated at %02x marker.\n", marker);
- return AVERROR_INVALIDDATA;
- }
- length = AV_RB16(frag->data + i);
- if (i + length > frag->data_size) {
- av_log(ctx->log_ctx, AV_LOG_ERROR, "Invalid JPEG image: "
- "truncated at %02x marker segment.\n", marker);
- return AVERROR_INVALIDDATA;
- }
- end = start + length;
-
- i = end;
- if (frag->data[i] != 0xff) {
- next_marker = -1;
- } else {
- for (++i; i + 1 < frag->data_size &&
- frag->data[i] == 0xff; i++);
- if (i + 1 >= frag->data_size) {
- next_marker = -1;
- } else {
- next_marker = frag->data[i];
- next_start = i + 1;
- }
- }
- }
-
- if (marker == JPEG_MARKER_SOS) {
- length = AV_RB16(frag->data + start);
-
- if (length > end - start)
- return AVERROR_INVALIDDATA;
-
- data_ref = NULL;
- data = av_malloc(end - start +
- AV_INPUT_BUFFER_PADDING_SIZE);
- if (!data)
- return AVERROR(ENOMEM);
-
- memcpy(data, frag->data + start, length);
- for (i = start + length, j = length; i < end; i++, j++) {
- if (frag->data[i] == 0xff) {
- while (frag->data[i] == 0xff)
- ++i;
- data[j] = 0xff;
- } else {
- data[j] = frag->data[i];
- }
- }
- data_size = j;
-
- memset(data + data_size, 0, AV_INPUT_BUFFER_PADDING_SIZE);
-
- } else {
- data = frag->data + start;
- data_size = end - start;
- data_ref = frag->data_ref;
- }
-
- err = ff_cbs_append_unit_data(frag, marker,
- data, data_size, data_ref);
- if (err < 0)
- return err;
-
- marker = next_marker;
- start = next_start;
- } while (next_marker != -1);
-
- return 0;
-}
-
-static int cbs_jpeg_read_unit(CodedBitstreamContext *ctx,
- CodedBitstreamUnit *unit)
-{
- GetBitContext gbc;
- int err;
-
- err = init_get_bits(&gbc, unit->data, 8 * unit->data_size);
- if (err < 0)
- return err;
-
- err = ff_cbs_alloc_unit_content(ctx, unit);
- if (err < 0)
- return err;
-
- if (unit->type >= JPEG_MARKER_SOF0 &&
- unit->type <= JPEG_MARKER_SOF3) {
- err = cbs_jpeg_read_frame_header(ctx, &gbc, unit->content);
- if (err < 0)
- return err;
-
- } else if (unit->type >= JPEG_MARKER_APPN &&
- unit->type <= JPEG_MARKER_APPN + 15) {
- err = cbs_jpeg_read_application_data(ctx, &gbc, unit->content);
- if (err < 0)
- return err;
-
- } else if (unit->type == JPEG_MARKER_SOS) {
- JPEGRawScan *scan = unit->content;
- int pos;
-
- err = cbs_jpeg_read_scan_header(ctx, &gbc, &scan->header);
- if (err < 0)
- return err;
-
- pos = get_bits_count(&gbc);
- av_assert0(pos % 8 == 0);
- if (pos > 0) {
- scan->data_size = unit->data_size - pos / 8;
- scan->data_ref = av_buffer_ref(unit->data_ref);
- if (!scan->data_ref)
- return AVERROR(ENOMEM);
- scan->data = unit->data + pos / 8;
- }
-
- } else {
- switch (unit->type) {
-#define SEGMENT(marker, func) \
- case JPEG_MARKER_ ## marker: \
- { \
- err = cbs_jpeg_read_ ## func(ctx, &gbc, unit->content); \
- if (err < 0) \
- return err; \
- } \
- break
- SEGMENT(DQT, dqt);
- SEGMENT(DHT, dht);
- SEGMENT(COM, comment);
-#undef SEGMENT
- default:
- return AVERROR(ENOSYS);
- }
- }
-
- return 0;
-}
-
-static int cbs_jpeg_write_scan(CodedBitstreamContext *ctx,
- CodedBitstreamUnit *unit,
- PutBitContext *pbc)
-{
- JPEGRawScan *scan = unit->content;
- int err;
-
- err = cbs_jpeg_write_scan_header(ctx, pbc, &scan->header);
- if (err < 0)
- return err;
-
- if (scan->data) {
- if (scan->data_size * 8 > put_bits_left(pbc))
- return AVERROR(ENOSPC);
-
- av_assert0(put_bits_count(pbc) % 8 == 0);
-
- flush_put_bits(pbc);
-
- memcpy(put_bits_ptr(pbc), scan->data, scan->data_size);
- skip_put_bytes(pbc, scan->data_size);
- }
-
- return 0;
-}
-
-static int cbs_jpeg_write_segment(CodedBitstreamContext *ctx,
- CodedBitstreamUnit *unit,
- PutBitContext *pbc)
-{
- int err;
-
- if (unit->type >= JPEG_MARKER_SOF0 &&
- unit->type <= JPEG_MARKER_SOF3) {
- err = cbs_jpeg_write_frame_header(ctx, pbc, unit->content);
- } else if (unit->type >= JPEG_MARKER_APPN &&
- unit->type <= JPEG_MARKER_APPN + 15) {
- err = cbs_jpeg_write_application_data(ctx, pbc, unit->content);
- } else {
- switch (unit->type) {
-#define SEGMENT(marker, func) \
- case JPEG_MARKER_ ## marker: \
- err = cbs_jpeg_write_ ## func(ctx, pbc, unit->content); \
- break;
- SEGMENT(DQT, dqt);
- SEGMENT(DHT, dht);
- SEGMENT(COM, comment);
- default:
- return AVERROR_PATCHWELCOME;
- }
- }
-
- return err;
-}
-
-static int cbs_jpeg_write_unit(CodedBitstreamContext *ctx,
- CodedBitstreamUnit *unit,
- PutBitContext *pbc)
-{
- if (unit->type == JPEG_MARKER_SOS)
- return cbs_jpeg_write_scan (ctx, unit, pbc);
- else
- return cbs_jpeg_write_segment(ctx, unit, pbc);
-}
-
-static int cbs_jpeg_assemble_fragment(CodedBitstreamContext *ctx,
- CodedBitstreamFragment *frag)
-{
- const CodedBitstreamUnit *unit;
- uint8_t *data;
- size_t size, dp, sp;
- int i;
-
- size = 4; // SOI + EOI.
- for (i = 0; i < frag->nb_units; i++) {
- unit = &frag->units[i];
- size += 2 + unit->data_size;
- if (unit->type == JPEG_MARKER_SOS) {
- for (sp = 0; sp < unit->data_size; sp++) {
- if (unit->data[sp] == 0xff)
- ++size;
- }
- }
- }
-
- frag->data_ref = av_buffer_alloc(size + AV_INPUT_BUFFER_PADDING_SIZE);
- if (!frag->data_ref)
- return AVERROR(ENOMEM);
- data = frag->data_ref->data;
-
- dp = 0;
-
- data[dp++] = 0xff;
- data[dp++] = JPEG_MARKER_SOI;
-
- for (i = 0; i < frag->nb_units; i++) {
- unit = &frag->units[i];
-
- data[dp++] = 0xff;
- data[dp++] = unit->type;
-
- if (unit->type != JPEG_MARKER_SOS) {
- memcpy(data + dp, unit->data, unit->data_size);
- dp += unit->data_size;
- } else {
- sp = AV_RB16(unit->data);
- av_assert0(sp <= unit->data_size);
- memcpy(data + dp, unit->data, sp);
- dp += sp;
-
- for (; sp < unit->data_size; sp++) {
- if (unit->data[sp] == 0xff) {
- data[dp++] = 0xff;
- data[dp++] = 0x00;
- } else {
- data[dp++] = unit->data[sp];
- }
- }
- }
- }
-
- data[dp++] = 0xff;
- data[dp++] = JPEG_MARKER_EOI;
-
- av_assert0(dp == size);
-
- memset(data + size, 0, AV_INPUT_BUFFER_PADDING_SIZE);
- frag->data = data;
- frag->data_size = size;
-
- return 0;
-}
-
-static const CodedBitstreamUnitTypeDescriptor cbs_jpeg_unit_types[] = {
- CBS_UNIT_RANGE_POD(JPEG_MARKER_SOF0, JPEG_MARKER_SOF3, JPEGRawFrameHeader),
-
- CBS_UNIT_RANGE_INTERNAL_REF(JPEG_MARKER_APPN, JPEG_MARKER_APPN + 15,
- JPEGRawApplicationData, Ap),
-
- CBS_UNIT_TYPE_INTERNAL_REF(JPEG_MARKER_SOS, JPEGRawScan, data),
-
- CBS_UNIT_TYPE_POD(JPEG_MARKER_DQT, JPEGRawQuantisationTableSpecification),
- CBS_UNIT_TYPE_POD(JPEG_MARKER_DHT, JPEGRawHuffmanTableSpecification),
-
- CBS_UNIT_TYPE_INTERNAL_REF(JPEG_MARKER_COM, JPEGRawComment, Cm),
-
- CBS_UNIT_TYPE_END_OF_LIST
-};
-
-const CodedBitstreamType ff_cbs_type_jpeg = {
- .codec_id = AV_CODEC_ID_MJPEG,
-
- .unit_types = cbs_jpeg_unit_types,
-
- .split_fragment = &cbs_jpeg_split_fragment,
- .read_unit = &cbs_jpeg_read_unit,
- .write_unit = &cbs_jpeg_write_unit,
- .assemble_fragment = &cbs_jpeg_assemble_fragment,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/imx_dump_header_bsf.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/imx_dump_header_bsf.c
deleted file mode 100644
index 241415a0ef0b1fc946b1f609b7031bf0810325d3..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/imx_dump_header_bsf.c
+++ /dev/null
@@ -1,76 +0,0 @@
-/*
- * imx dump header bitstream filter
- * Copyright (c) 2007 Baptiste Coudurier
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * imx dump header bitstream filter
- * modifies bitstream to fit in mov and be decoded by final cut pro decoder
- */
-
-#include "bsf.h"
-#include "bsf_internal.h"
-#include "bytestream.h"
-
-
-static int imx_dump_header(AVBSFContext *ctx, AVPacket *out)
-{
- /* MXF essence element key */
- static const uint8_t imx_header[16] = { 0x06,0x0e,0x2b,0x34,0x01,0x02,0x01,0x01,0x0d,0x01,0x03,0x01,0x05,0x01,0x01,0x00 };
-
- AVPacket *in;
- int ret = 0;
- uint8_t *out_buf;
-
- ret = ff_bsf_get_packet(ctx, &in);
- if (ret < 0)
- return ret;
-
- ret = av_new_packet(out, in->size + 20);
- if (ret < 0)
- goto fail;
-
- out_buf = out->data;
-
- bytestream_put_buffer(&out_buf, imx_header, 16);
- bytestream_put_byte(&out_buf, 0x83); /* KLV BER long form */
- bytestream_put_be24(&out_buf, in->size);
- bytestream_put_buffer(&out_buf, in->data, in->size);
-
- ret = av_packet_copy_props(out, in);
- if (ret < 0)
- goto fail;
-
-fail:
- if (ret < 0)
- av_packet_unref(out);
- av_packet_free(&in);
- return ret;
-}
-
-static const enum AVCodecID codec_ids[] = {
- AV_CODEC_ID_MPEG2VIDEO, AV_CODEC_ID_NONE,
-};
-
-const FFBitStreamFilter ff_imx_dump_header_bsf = {
- .p.name = "imxdump",
- .p.codec_ids = codec_ids,
- .filter = imx_dump_header,
-};
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Air India Ticket Download Made Simple How to Get Your E-Ticket Online.md b/spaces/congsaPfin/Manga-OCR/logs/Air India Ticket Download Made Simple How to Get Your E-Ticket Online.md
deleted file mode 100644
index 9f16eff76e586e27ddcaae294567c0aac5acf768..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Air India Ticket Download Made Simple How to Get Your E-Ticket Online.md
+++ /dev/null
@@ -1,157 +0,0 @@
--
How to Download Air India Flight Ticket Online
-Are you planning to travel with Air India, the flag carrier of India and one of the largest airlines in the country? If yes, then you might be wondering how to download your flight ticket online and what are the benefits of doing so. In this article, we will guide you through the process of booking, downloading, printing, and managing your flight ticket with Air India. We will also tell you about the other services and benefits that Air India offers to its passengers. So, let's get started!
-air india ticket download
Download File >>> https://urlca.com/2uOfiI
-
What is Air India?
-Air India is the national airline of India and a member of Star Alliance, the world's largest airline network. It operates flights to over 100 domestic and international destinations across Asia, Europe, North America, Africa, and Australia. It has a fleet of more than 170 aircraft, including Boeing 787 Dreamliners, Airbus A320neo, and Boeing 777s. It also has a subsidiary called Air India Express, which operates low-cost flights to the Middle East and Southeast Asia.
-Why do you need to download your flight ticket?
-A flight ticket is a document that confirms your reservation and allows you to board the plane. Nowadays, most airlines offer e-tickets, which are electronic versions of flight tickets that you can access on your smartphone or computer. E-tickets have many advantages over paper tickets, such as:
--
-
To board an Air India flight, you need to have an e-ticket and a valid photo ID. You can also print a copy of your e-ticket if you prefer, but it is not mandatory.
-How to book your flight ticket with Air India?
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-A: You can check the status of your flight with Air India through their website www.airindia.in/ flight-status.htm or their app. You will have to enter your flight number and date of departure. You can also sign up for SMS or email alerts to get updates on your flight status.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/BEYBLADE BURST App Battle Your Friends Online and Offline.md b/spaces/congsaPfin/Manga-OCR/logs/BEYBLADE BURST App Battle Your Friends Online and Offline.md deleted file mode 100644 index 7e1d241449a0782269e705481023de95ca170c17..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/BEYBLADE BURST App Battle Your Friends Online and Offline.md +++ /dev/null @@ -1,88 +0,0 @@ - -
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-The Beyblade Burst App is a video game developed by Hasbro that is based on the popular anime and toy series, Beyblade Burst. The app allows you to scan QR codes included with Hasbro Beyblade products, or create your own custom tops using virtual parts. You can then battle your tops against other players from over 90 countries worldwide, either online or offline, in various modes and arenas. You can also create or join a Battle League with your friends, where you can compete in multi-round tournaments for the title of top Blader. The app also features Bluetooth enabled controls that let you control your tops with your device, as well as RC Bluetooth enabled tops that you can buy separately and control with the app. The app is constantly updated with new features, modes, tops, and events, so you will never run out of things to do.
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-The Beyblade Burst App is available for free on both Android and iOS devices. You can download it from the Google Play Store or the App Store, depending on your device. The app requires access to your device's camera to scan QR codes, so make sure you enable it in your device's privacy settings. The app also requires an internet connection for some features, such as multiplayer online battles, friends leaderboard, and progress restore. The app works with select Android devices (Android 4.4+), Samsung Galaxy S4+, Samsung Galaxy Note 3+, iPod Touch 5, iPhone 4S+, iPhone 6+, iPad 2+, iPad Mini+, iPad Air+, iPad Pro+. You can check beyblade.hasbro.com for more details and device compatibility.
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-How to scan and customize your Beyblade Burst tops
-One of the coolest features of the Beyblade Burst App is that you can scan QR codes included with Hasbro Beyblade products and unlock them in the app. To do this, simply tap on the Scan icon on the main menu, point your device's camera at the QR code on the product or packaging, and wait for it to be scanned. You will then see a confirmation message that shows you the name and image of the top you scanned. You can then add the top to your collection and customize it with different parts, colors, and stickers. You can also create your own custom tops from scratch by choosing from over 1000 parts available in the app. To do this, tap on the Create icon on the main menu, and then select the parts you want for your top. You can also tap on the Random icon to generate a random top. You can then name your top, save it, and use it in battles.
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-Once you have your tops ready, you can start battling with other players from around the world. There are two ways to do this: online or offline. To battle online, tap on the Battle icon on the main menu, and then choose Online Battle. You can then select the mode you want to play: Ranked, Friendly, or Battle League. In Ranked mode, you can battle against random opponents and earn points and rewards based on your performance. In Friendly mode, you can battle against your friends or other players you have added as friends in the app. In Battle League mode, you can create or join a league with your friends or other players and compete in multi-round tournaments for the title of top Blader. To battle offline, tap on the Battle icon on the main menu, and then choose Offline Battle. You can then select the mode you want to play: Exhibition, Tournament, or Scan & Battle. In Exhibition mode, you can battle against AI opponents or another player using the same device. In Tournament mode, you can create or join a tournament with up to 8 players using the same device. In Scan & Battle mode, you can scan a Beyblade Burst product and battle against it using your device.
-How to create and join a Battle League with your friends
-One of the most fun features of the Beyblade Burst App is that you can create or join a Battle League with your friends or other players and compete in multi-round tournaments for the title of top Blader. To do this, tap on the Battle icon on the main menu, and then choose Online Battle. Then, tap on the Battle League icon and select Create League or Join League. If you create a league, you can name it, set the rules, invite your friends or other players, and start playing. If you join a league, you can search for an existing league by name or code, or browse through the list of available leagues. Once you join a league, you can see the schedule of matches, the standings of players, and the rewards for winning. You can also chat with other players in the league and send them friend requests.
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-How to level up your Beyblade Burst tops and unlock new parts
-As you play with your Beyblade Burst tops in the app, you will earn experience points (XP) that will help you level up your tops and unlock new parts. Each top has a maximum level of 10, and each level unlocks a new part that you can use to customize your top. You can see the level and XP of your top by tapping on it in your collection. You can also see the parts that are unlocked or locked by tapping on them in the customization screen. To level up your tops faster, you can use energy orbs that you can earn by playing battles or completing missions. Energy orbs are consumable items that give you extra XP when you use them on your tops.
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-Another cool feature of the Beyblade Burst App is that you can use different launch techniques and avatar attacks to give yourself an edge in battles. Launch techniques are special ways of launching your top that affect its speed, power, stamina, and direction. You can choose from four launch techniques: Standard Launch (normal launch), Power Launch (increased power), Speed Launch (increased speed), and Trick Launch (random direction). To use a launch technique, swipe on the screen when launching your top and align it with the launch meter at the bottom of the screen. Avatar attacks are special moves that unleash your top's avatar and deal massive damage to your opponent's top. You can activate an avatar attack by filling up your avatar meter at the top of the screen by hitting your opponent's top or dodging their attacks. To use an avatar attack, tap on the screen when your avatar meter is full and swipe in the direction of your opponent's top. You can also use different avatar attacks depending on the mode you are playing: Turbo Slingshock, HyperSphere, or Speedstorm. Each mode has its own unique avatar attacks that you can unlock by scanning QR codes or leveling up your tops.
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How to control RC Bluetooth enabled Beyblade Burst tops
-One of the most advanced features of the Beyblade Burst App is that you can control RC Bluetooth enabled Beyblade Burst tops with your device. These are special tops that you can buy separately and pair with your device via Bluetooth. You can then use your device as a remote control to steer your top in the arena, change its direction, speed, and power, and activate avatar attacks. To use this feature, you need to have an RC Bluetooth enabled top, a compatible device, and a Beyblade Burst Beystadium. You also need to download the Beyblade Burst RC app from the Google Play Store or the App Store, depending on your device. The app will guide you through the steps of pairing your top and device, and then you can start playing. You can also switch between RC mode and normal mode by tapping on the RC icon on the main menu.
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-The Beyblade Burst App is a fun and exciting game that lets you experience the thrill of Beyblade Burst on your device. You can scan, create, customize, and battle with your own digital Beyblade Burst tops, and compete with other players from around the world in various modes and arenas. You can also create or join a Battle League with your friends, and control RC Bluetooth enabled tops with your device. The app is free to download and play, and is constantly updated with new features, modes, tops, and events. The app is also compatible with most Android and iOS devices, and works with select Hasbro Beyblade products.
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-What are the supported devices for Beyblade Burst App?
-The Beyblade Burst App works with select Android devices (Android 4.4+), Samsung Galaxy S4+, Samsung Galaxy Note 3+, iPod Touch 5, iPhone 4S+, iPhone 6+, iPad 2+, iPad Mini+, iPad Air+, iPad Pro+. You can check beyblade.hasbro.com for more details and device compatibility.
-Do I need an internet connection to play Beyblade Burst App?
-You need an internet connection for some features of the app, such as multiplayer online battles, friends leaderboard, and progress restore. However, you can still play offline battles, scan QR codes, customize tops, and control RC Bluetooth enabled tops without an internet connection.
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-Beycoins are the currency of the app that you can use to buy new parts, colors, stickers, avatars, and energy orbs. Energy orbs are consumable items that give you extra XP when you use them on your tops. You can get more Beycoins and energy orbs by playing battles, completing missions, watching ads, or buying them with real money.
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-Turbo Slingshock and HyperSphere are two different modes that you can play in the app. Turbo Slingshock mode is based on the Beyblade Burst Turbo season of the anime series, where you can use special rails in the arena to boost your speed and power. HyperSphere mode is based on the Beyblade Burst Rise season of the anime series, where you can use special ramps in the arena to jump into the air and perform aerial attacks.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Dinosaur Hunter 3D MOD APK Explore the Jurassic World with Amazing Graphics and Sound Effects.md b/spaces/congsaPfin/Manga-OCR/logs/Dinosaur Hunter 3D MOD APK Explore the Jurassic World with Amazing Graphics and Sound Effects.md deleted file mode 100644 index 0c2d2d3885fb7f713f79ebce72abd022d30df973..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Dinosaur Hunter 3D MOD APK Explore the Jurassic World with Amazing Graphics and Sound Effects.md +++ /dev/null @@ -1,104 +0,0 @@ - -
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Gangstar New York: How to Download and Play the Open Alpha on PC
-Are you looking for a new and exciting game to play on your PC? Do you want to experience the thrill of being a gangster in a near-future New York City? If so, you should check out Gangstar New York, a free-to-play third-person shooter game that lets you compete with other players in a citywide crime competition. In this article, we will tell you what Gangstar New York is, why you should play it, and how to download and play it on your PC. We will also give you some tips and tricks for playing the game effectively. So, let's get started!
-What is Gangstar New York?
-Gangstar New York is a multiplayer online game developed by Gameloft that is currently in open alpha stage. The game is set in a near-future New York City that is under the surveillance of Aurora Inc., a massive tech conglomerate that uses an algorithmic network to stop crimes before they start. However, Aurora Inc. also turns off the cameras for 20-minute intervals when they need plausible deniability to test "experimental technological initiatives" on the citizens.
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During these intervals, 20 aspiring gangsters compete in a citywide crime competition to score as much cash as possible. The winner of each round is crowned Gangstar, and is showered with fame and accolades. The game features an action-packed PVPVE open world where you can explore, parkour, zipline, jetpack, steal, have aerial battles and race around New York. You can also customize your character with different skins inspired by various gangster typologies.
-Why you should play Gangstar New York?
-Gangstar New York is a game that offers a lot of fun and excitement for anyone who loves shooting games, open world games, or gangster games. Here are some of the reasons why you should play Gangstar New York:
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So, if you are looking for a game that will keep you entertained and challenged for hours, Gangstar New York is the game for you!
-How to download and play Gangstar New York on PC?
-Now that you know what Gangstar New York is and why you should play it, you might be wondering how to download and play it on your PC. Well, don't worry, because we have got you covered. Here are the steps to download and play Gangstar New York on PC:
-Step 1: Go to the Steam store page of Gangstar New York
-The first thing you need to do is to go to the Steam store page of Gangstar New York. You can do this by clicking on this link: [Gangstar New York on Steam]. Alternatively, you can open your Steam client, go to the Store tab, and search for "Gangstar New York" in the search bar.
-Once you are on the store page, you will see some information about the game, such as its description, screenshots, videos, reviews, and system requirements. You will also see a green button that says "Play Game". Before you click on that button, we recommend that you add the game to your wishlist by clicking on the "+WISHLIST" button below it. This way, you will be notified of any updates or news about the game in the future.
-Step 2: Click on the "Play Game" button
-After you have added the game to your wishlist, you can click on the "Play Game" button to start downloading and installing the game on your PC. You will see a pop-up window that asks you to confirm your choice. Click on the "Yes" button to proceed.
-The game will then be added to your Steam library under the "Games" tab. You will see a progress bar that shows you how much of the game has been downloaded and installed. The game size is about 4 GB, so depending on your internet speed and PC performance, it might take some time to complete. You can check the estimated time remaining by hovering over the progress bar.
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Step 3: Launch the game and create your account
-Once the game has been downloaded and installed, you can launch it from your Steam library by double-clicking on its name or right-clicking on it and selecting "Play". You will see a splash screen that shows the game's logo and some loading messages.
-After a few seconds, you will be taken to the main menu of the game. Here, you will see some options such as "Play", "Settings", "Credits", and "Quit". Before you click on "Play", you need to create your Gangstar account by clicking on the "Create Account" button at the bottom right corner of the screen.
-You will then be asked to enter your email address, password, username, and country. Make sure that you enter valid and secure information that you can remember later. You will also need to agree to the terms of service and privacy policy of the game by checking the boxes below. After that, click on the "Create Account" button again to confirm your registration.
-Step 4: Customize your character and join a match
-Congratulations! You have successfully created your Gangstar account and are ready to play the game. The next thing you need to do is to customize your character by choosing a skin from various options. You can do this by clicking on the "Customize" button at the top right corner of the screen.
-You will then see a preview of your character and a list of skins that you can select from. Some skins are unlocked by default, while others require cash or gems to unlock. Cash and gems are the in-game currencies that you can earn by playing the game or buying them with real money. You can also change your skin during the game by finding and using a skin changer device.
-After you have chosen your skin, you can join a match by clicking on the "Play" button at the top left corner of the screen. You will then see a list of available matches that you can join, or you can create your own match by clicking on the "Create Match" button. Each match can have up to 20 players and lasts for 20 minutes. You can also choose the map, mode, and difficulty of the match.
-Once you have joined or created a match, you will be taken to the lobby where you can see the other players and chat with them. You can also invite your friends to join your match by clicking on the "Invite Friends" button at the bottom left corner of the screen. When the match is ready to start, you will see a countdown timer and a "Ready" button. Click on the "Ready" button to confirm that you are ready to play.
-Tips and tricks for playing Gangstar New York on PC
-Now that you know how to download and play Gangstar New York on PC, you might be wondering how to play it well and win the game. Well, don't worry, because we have some tips and tricks for you that will help you improve your skills and performance in the game. Here are some of them:
-Tip 1: Use the map and the mini-map to navigate the city
-One of the most important things to do in Gangstar New York is to use the map and the mini-map to navigate the city and find your way around. The map is a large-scale view of the entire city that shows you all the locations, objectives, and enemies in the game. You can access the map by pressing the "M" key on your keyboard or clicking on the map icon at the top right corner of the screen.
-The mini-map is a small-scale view of your immediate surroundings that shows you your position, direction, and nearby points of interest. You can see the mini-map at the bottom right corner of the screen. You can also zoom in and out of the mini-map by using the mouse wheel or clicking on the plus and minus buttons.
-Using the map and the mini-map will help you plan your route, avoid danger, locate targets, and complete objectives. You can also mark locations on the map by right-clicking on them or pressing the "F" key on your keyboard. This will create a waypoint that will guide you to the marked location.
-Tip 2: Collect cash and weapons from various sources
-Another important thing to do in Gangstar New York is to collect cash and weapons from various sources. Cash is the main currency in the game that you can use to buy weapons, skins, and other items. You can also use cash to score points and win the game. Weapons are the tools that you can use to fight, defend, and survive in the game. You can also use weapons to score points and win the game.
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Collecting cash and weapons from various sources will help you increase your arsenal, improve your equipment, and boost your score.
-Tip 3: Adapt to the experiments and use them to your advantage
-One of the most unique and exciting features of Gangstar New York is the experiments that Aurora Inc. conducts on the city during each match. These experiments are random events that affect the game parameters, such as gravity, weather, physics, lighting, sound, and more. Some examples of experiments are lunar gravity, electric floor, heavy fog, silent night, zombie apocalypse, and more.
-These experiments can have positive or negative effects on your gameplay, depending on how you adapt to them and use them to your advantage. For example, lunar gravity can make you jump higher and farther, Betraying other players can help you gain cash faster, obtain items easier, and eliminate enemies better. -
Working with or against other players will help you create your own style of gameplay, make your own decisions, and face your own consequences.
-Conclusion
-Gangstar New York is a free-to-play third-person shooter game that lets you compete with other players in a citywide crime competition in a near-future New York City. The game features an action-packed PVPVE open world, a level playing field, and dynamic experiments. You can download and play the game on your PC by following the steps we have provided in this article. You can also improve your skills and performance in the game by following the tips and tricks we have shared in this article. So, what are you waiting for? Download Gangstar New York today and become the next Gangstar!
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-Here are some frequently asked questions and answers about Gangstar New York:
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Yes, Gangstar New York is also available on Android and iOS devices. You can download the game from the Google Play Store or the App Store respectively.
-Gangstar New York is primarily a multiplayer game that requires an internet connection to play. However, you can also play the game in single-player mode by creating a private match and playing alone or with bots.
-The open alpha stage of Gangstar New York is expected to last for several months, during which the developers will collect feedback, fix bugs, and add new features to the game. The exact date of the end of the open alpha stage is not yet announced.
-You can report a bug or a problem in Gangstar New York by using the in-game feedback system or by contacting the customer support team via email or social media. You can find more information about how to report a bug or a problem on the official website of the game.
-You can get more cash or gems in Gangstar New York by playing the game regularly, completing objectives, killing enemies, looting buildings, stealing vehicles, and winning matches. You can also get more cash or gems by buying them with real money through the in-game store.
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-One of the best features of Special Forces Group 2 1.6 Mod Apk is that it gives you access to unlimited money and weapons. You can buy any weapon or skin you want from the shop without spending a dime. You can choose from over 150 different weapons, such as pistols, rifles, shotguns, snipers, machine guns, rocket launchers, and more. You can also customize your weapons with various attachments, such as scopes, silencers, lasers, and more. You can also change your skin from over 80 different options, such as soldiers, terrorists, zombies, robots, and more.
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The disadvantages of playing the modded game
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-A2: No, Special Forces Group 2 1.6 Mod Apk may not be compatible with some Android devices or versions. You should check the minimum requirements of the game before downloading and installing it on your device.
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-A3: Yes, you can play Special Forces Group 2 1.6 Mod Apk online with other players via Wi-Fi router or internet connection. However, you may face some difficulties or errors while playing online with the modded game. You may also get banned from online servers if you are detected using the modded game.
-Q4: How can I update Special Forces Group 2 1.6 Mod Apk to the latest version?
-A4: You can update Special Forces Group 2 1.6 Mod Apk to the latest version by downloading and installing the new mod apk file from the same source where you downloaded the previous one. You should also delete the old mod apk file from your device before installing the new one.
-Q5: Where can I find more modded games like Special Forces Group 2 1.6 Mod Apk?
-A5: You can find more modded games like Special Forces Group 2 1.6 Mod Apk on various websites and blogs that offer modded games for Android devices. You can also search for them on Google or other search engines.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Install New State Mobile on Your Android Phone APK and OBB Download Guide.md b/spaces/congsaPfin/Manga-OCR/logs/Install New State Mobile on Your Android Phone APK and OBB Download Guide.md deleted file mode 100644 index 0e7ce98e37f90060e522ef743598d6e8dda74a3f..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Install New State Mobile on Your Android Phone APK and OBB Download Guide.md +++ /dev/null @@ -1,151 +0,0 @@ -
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-How to Download New State Mobile APK and OBB Files-New State Mobile is a new battle royale game developed by Krafton, the makers of PUBG. It is set in a futuristic world where players can use drones, combat rolls, ballistic shields, and other advanced technologies to survive. If you are a fan of PUBG or battle royale games in general, you might want to try out New State Mobile as soon as possible. -new state mobile download apk obbDownload File >>> https://urlca.com/2uOb1Z - But how can you download New State Mobile APK and OBB files on your Android device? In this article, we will show you two ways to do it: from Google Play Store or from third-party sources. We will also explain the benefits and risks of downloading New State Mobile APK and OBB files, as well as some tips to make sure you have a smooth gaming experience. -What is New State Mobile?-New State Mobile is a new mobile game that is based on PUBG but with a twist. It is set in 2051, where anarchy and chaos have taken over the world. Players have to fight against each other in an 8x8 km map with various terrains and landmarks. The game features realistic graphics, dynamic gunplay, diverse vehicles, and customizable weapons. -New State Mobile also introduces some new elements to the battle royale genre, such as drones, combat rolls, ballistic shields, deployable bunkers, fire support systems, and more. These features allow players to create their own strategies and tactics to survive. The game also has a global illumination technology that enhances the lighting and shadows in the game. -Why Download New State Mobile APK and OBB Files?-New State Mobile APK and OBB files are the files that contain the game data and resources. APK stands for Android Package Kit, which is the file format used to distribute and install applications on Android devices. OBB stands for Opaque Binary Blob, which is a file format used to store large amounts of data that are not required to run the application. -Downloading New State Mobile APK and OBB files can have some benefits and risks, depending on where you download them from. Let's take a look at some of them. -How to download PUBG New State APK and OBB files Benefits of Downloading New State Mobile APK and OBB Files-
Risks of Downloading New State Mobile APK and OBB Files-
How to Download New State Mobile APK and OBB Files from Google Play Store?-If you want to download New State Mobile APK and OBB files from Google Play Store, you will have to wait for the official release date of the game. The game is currently in the pre-registration stage, which means that you can sign up for it and get notified when it is available for download. Here are the steps to download New State Mobile APK and OBB files from Google Play Store: -Step 1: Check Your Device Compatibility-New State Mobile is a high-end game that requires a powerful device to run smoothly. According to the official website of the game, the minimum requirements for New State Mobile are as follows: -
You can check your device specifications by going to Settings > About Phone on your device. If your device meets the minimum requirements, you can proceed to the next step. If not, you may have to upgrade your device or look for other options to play the game. -Step 2: Pre-Register for New State Mobile on Google Play Store-To pre-register for New State Mobile on Google Play Store, you will need a Google account and an internet connection. You can follow these steps to pre-register for New State Mobile on Google Play Store: -
Step 3: Wait for the Official Release Date-After you pre-register for New State Mobile on Google Play Store, you will have to wait for the official release date of the game. The developers have not announced the exact release date yet, but they have said that it will be in 2023. You can check the official website of the game or follow their social media accounts to get the latest news and updates about the game. -When the game is released, you will get a notification on your device that says "New State Mobile is ready to play". You can tap on the notification to open Google Play Store and download the game. The game will automatically download and install the APK and OBB files on your device. You can then launch the game and enjoy it. How to Download New State Mobile APK and OBB Files from Third-Party Sources?-If you want to download New State Mobile APK and OBB files from third-party sources, you will have to find a reliable and trustworthy source that offers the latest and compatible files for your device. You will also have to enable unknown sources on your device to allow the installation of files from outside Google Play Store. Here are the steps to download New State Mobile APK and OBB files from third-party sources: -Step 1: Enable Unknown Sources on Your Device-To enable unknown sources on your device, you will need to go to Settings > Security > Unknown Sources on your device. You will see a toggle switch that says "Allow installation of apps from unknown sources". You will have to turn it on and confirm your choice. This will allow you to install APK and OBB files from third-party sources on your device. -However, you should be careful when enabling unknown sources, as it can expose your device to malware and viruses. You should only download files from trusted and verified sources and scan them with a reliable antivirus software before installing them. You should also disable unknown sources after installing the files to prevent any unwanted installations in the future. -Step 2: Find a Reliable Third-Party Source for New State Mobile APK and OBB Files-To find a reliable third-party source for New State Mobile APK and OBB files, you will have to do some research and check the reviews and ratings of the source. You should also check the compatibility and quality of the files before downloading them. You should avoid any sources that ask for your personal information, such as your email, phone number, or credit card details. -Some Examples of Reliable Third-Party Sources for New State Mobile APK and OBB Files-Here are some examples of reliable third-party sources for New State Mobile APK and OBB files that you can try: -
Step 3: Download and Install New State Mobile APK and OBB Files on Your Device-After you find a reliable third-party source for New State Mobile APK and OBB files, you can download and install them on your device. Here are the steps to download and install New State Mobile APK and OBB files on your device: -
How to Extract New State Mobile OBB File?-To extract New State Mobile OBB file, you will need a file manager app that can handle ZIP files, such as ES File Explorer, ZArchiver, or RAR. You can download any of these apps from Google Play Store. Here are the steps to extract New State Mobile OBB file: -
Conclusion-New State Mobile is a new battle royale game that offers a futuristic twist to the genre. It is developed by Krafton, the makers of PUBG, and features realistic graphics, dynamic gunplay, diverse vehicles, and customizable weapons. It also introduces some new elements, such as drones, combat rolls, ballistic shields, deployable bunkers, fire support systems, and more. -If you want to play New State Mobile on your Android device, you can download it from Google Play Store or from third-party sources. However, you should be aware of the benefits and risks of downloading New State Mobile APK and OBB files from different sources. You should also follow the steps carefully to ensure that you have a smooth gaming experience. -We hope that this article has helped you learn how to download New State Mobile APK and OBB files on your Android device. If you have any questions or feedback, please feel free to leave a comment below. Happy gaming! -FAQs-
- - \ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Join the Frozen Fun with Online Games - Featuring Elsa Anna Olaf and More!.md b/spaces/congsaPfin/Manga-OCR/logs/Join the Frozen Fun with Online Games - Featuring Elsa Anna Olaf and More!.md deleted file mode 100644 index d008f02ebe36b4c329e776db1e7ab38ddf880d4d..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Join the Frozen Fun with Online Games - Featuring Elsa Anna Olaf and More!.md +++ /dev/null @@ -1,102 +0,0 @@ - - Frozen Game Online: How to Have Fun with Your Favorite Disney Characters-Do you love the Disney movie Frozen and its sequel Frozen 2? Do you want to join Anna, Elsa, Olaf, Kristoff, and Sven on their magical adventures in the kingdom of Arendelle? If you answered yes, then you will be happy to know that you can play Frozen games online for free on your computer, tablet, or smartphone. In this article, we will tell you everything you need to know about Frozen games online, including what they are, why you should play them, and how to play them. Let's get started! -What are Frozen games?-Frozen games are online games that are inspired by the Frozen movies and feature the characters, settings, and themes from the story. You can find a variety of Frozen games online, such as dress up and makeover games, adventure and puzzle games, cooking and baking games, coloring and painting games, and more. These games are designed to entertain and educate you, as well as to let you express your creativity and imagination. You can play Frozen games online alone or with your friends and family, and have a lot of fun with your favorite Disney characters. -frozen game onlineDownload File ❤ https://urlca.com/2uO6Nl - The Frozen story and the main characters-The Frozen story is about two sisters, Anna and Elsa, who live in the kingdom of Arendelle. Elsa has the power to create ice and snow, but she struggles to control it and fears hurting her sister. When Elsa accidentally unleashes an eternal winter on the kingdom, she runs away to the mountains. Anna sets out to find her sister and bring her back, along with her friends Olaf, a snowman who loves summer; Kristoff, an ice harvester; and Sven, his loyal reindeer. Along the way, they face many dangers and discover the true meaning of love. -In Frozen 2, Anna and Elsa embark on a new adventure to discover the origin of Elsa's powers and save Arendelle from a mysterious threat. They travel to an enchanted forest where they meet new allies and enemies, such as the Northuldra people, the elemental spirits of fire, water, earth, and air, and a mysterious voice that calls to Elsa. They also learn more about their parents' past and their own destiny. -The types of Frozen games you can play online-There are many types of Frozen games you can play online, depending on your interests and preferences. Here are some examples: -Dress up and makeover games-If you love fashion and beauty, you can play dress up and makeover games with Anna and Elsa. You can choose from different outfits, hairstyles, accessories, makeup, and more to create your own look for the princesses. You can also mix and match different styles and colors to create unique combinations. You can also dress up other characters from the Frozen movies, such as Olaf, Kristoff, Sven, or even Hans. -Adventure and puzzle games-If you love action and challenges, you can play adventure and puzzle games with Anna and Elsa. You can join them on their quests to save Arendelle from different threats, such as snow monsters, evil princes, or dark magic. You can also help them solve puzzles and riddles along the way. You can also play adventure and puzzle games with other characters from the Frozen movies, such as Olaf, Kristoff, Sven, or even Marshmallow. -Cooking and baking games-If you love cooking and baking, you can play cooking and baking games with Anna and Elsa. You can learn how to make delicious dishes and desserts, such as chocolate cake, ice cream, pizza, soup, sandwiches, and more. You can also decorate your creations with different toppings, frosting, sprinkles, and more. You can also play cooking and baking games with other characters from the Frozen movies, such as Olaf, Kristoff, Sven, or even Oaken. -Coloring and painting games-If you love coloring and painting, you can play coloring and painting games with Anna and Elsa. You can choose from different images of the Frozen characters and scenes, and use different colors and tools to fill them in. You can also draw your own pictures and add stickers, stamps, glitter, and more. You can also play coloring and painting games with other characters from the Frozen movies, such as Olaf, Kristoff, Sven, or even Bruni. -Why should you play Frozen games online?-There are many reasons why you should play Frozen games online. Here are some of them: -Frozen game online free They are free and easy to access-You don't need to pay anything or download anything to play Frozen games online. All you need is a device that can connect to the internet and a browser that supports Flash or HTML5. You can play Frozen games online anytime and anywhere you want, as long as you have a stable internet connection. -They are fun and educational-You can have a lot of fun playing Frozen games online, as they offer a variety of gameplay options and challenges. You can also learn new skills and knowledge while playing Frozen games online, such as vocabulary, spelling, math, logic, memory, creativity, and more. You can also improve your hand-eye coordination, reaction time, problem-solving, and decision-making skills while playing Frozen games online. -They are suitable for all ages and preferences-You can find Frozen games online that are suitable for all ages and preferences. Whether you are a kid or an adult, a boy or a girl, a fan or a newcomer of the Frozen movies, you can find Frozen games online that match your interests and tastes. You can also choose from different levels of difficulty and complexity while playing Frozen games online. -How to play Frozen games online?-Playing Frozen games online is very easy and simple. Here are the steps you need to follow: -Choose a reliable website that offers Frozen games-The first step is to choose a reliable website that offers Frozen games online. There are many websites that claim to offer Frozen games online, but not all of them are safe and trustworthy. Some of them may contain viruses, malware, pop-ups, ads, or inappropriate content that may harm your device or your privacy. To avoid these risks, you should choose a website that has a good reputation, positive reviews, and high ratings from other users. You should also check the website's terms of service, privacy policy, and contact information before playing any game. -Browse through the categories and select a game you like-The next step is to browse through the categories and select a game you like. You can find different categories of Frozen games online on the website's homepage or menu bar. Some of the common categories are dress up and makeover games, adventure and puzzle games, cooking and baking games, coloring and painting games, and more. You can also use the search bar or the filters to find a specific game you are looking for. Once you find a game you like, click on it to open it in a new tab or window. -Follow the instructions and enjoy the game-The last step is to follow the instructions and enjoy the game. Most of the Frozen games online have simple and clear instructions that you can read or listen to before or during the game. You can also see the controls and the objectives of the game on the screen. You can use your mouse, keyboard, touchpad, or touchscreen to play the game, depending on your device. You can also pause, resume, restart, or quit the game at any time. You can also share your results, feedback, or suggestions with other players or the website. -Conclusion-Frozen games online are a great way to have fun with your favorite Disney characters. You can play a variety of Frozen games online for free, such as dress up and makeover games, adventure and puzzle games, cooking and baking games, coloring and painting games, and more. You can also learn new skills and knowledge while playing Frozen games online, such as vocabulary, spelling, math, logic, memory, creativity, and more. You can also play Frozen games online with your friends and family, and have a lot of fun together. -If you want to play Frozen games online, all you need to do is choose a reliable website that offers Frozen games, browse through the categories and select a game you like, follow the instructions and enjoy the game. It's that easy and simple! -So what are you waiting for? Go ahead and play Frozen games online now and have a blast with Anna, Elsa, Olaf, Kristoff, Sven, and the rest of the Frozen gang! -FAQs-Here are some frequently asked questions about Frozen games online: -Q: Are Frozen games online safe for kids?-A: Yes, Frozen games online are safe for kids, as long as they play them on a reliable website that does not contain any viruses, malware, pop-ups, ads, or inappropriate content. You should also supervise your kids while they play Frozen games online and limit their screen time. -Q: Can I play Frozen games online without internet connection?-A: No, you cannot play Frozen games online without internet connection. You need to have a stable internet connection to access and play Frozen games online. However, some websites may allow you to download or save some Frozen games offline so that you can play them later without internet connection. -Q: Can I play Frozen games online on any device?-A: Yes, you can play Frozen games online on any device that can connect to the internet and has a browser that supports Flash or HTML5. You can play Frozen games online on your computer, tablet, or smartphone. However, some Frozen games online may have different features or quality depending on the device you use. You should also make sure that your device has enough storage space and battery life to play Frozen games online. -Q: Can I play Frozen games online with other players?-A: Yes, you can play Frozen games online with other players, depending on the game you choose. Some Frozen games online are multiplayer games, which means that you can play them with other players from around the world. You can also play Frozen games online with your friends and family, by sharing the same device or using different devices. You can also chat, compete, or cooperate with other players while playing Frozen games online. -Q: Can I create my own Frozen games online?-A: Yes, you can create your own Frozen games online, if you have the skills and tools to do so. You can use different software, platforms, or websites to create your own Frozen games online, such as Scratch, Unity, GameMaker, or Disney Create. You can also share your own Frozen games online with other players and get feedback and ratings from them. 401be4b1e0- - \ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/K-Lite Codec Pack The Ultimate Solution for Windows 10 64-bit Media Playback.md b/spaces/congsaPfin/Manga-OCR/logs/K-Lite Codec Pack The Ultimate Solution for Windows 10 64-bit Media Playback.md deleted file mode 100644 index 3b6f1c28a633787565943a6af97cec1eb77842a0..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/K-Lite Codec Pack The Ultimate Solution for Windows 10 64-bit Media Playback.md +++ /dev/null @@ -1,103 +0,0 @@ - - K-Lite Codec Pack 64-bit Windows 10 Free Download: Everything You Need to Know-If you are looking for a way to play all kinds of audio and video files on your Windows 10 computer, you might have heard of K-Lite Codec Pack. But what is it exactly and how can you download and install it for free? In this article, we will answer these questions and more. We will also show you how to use K-Lite Codec Pack 64-bit Windows 10 to enjoy your media files without any hassle. -k-lite codec pack 64-bit windows 10 free downloadDownload Zip » https://urlca.com/2uO7yM - What is K-Lite Codec Pack?-Before we explain what K-Lite Codec Pack is, let's first understand what codecs are and why you need them. Codecs are software components that encode and decode (compress and decompress) audio and video data. They are essential for playing different formats of media files on your computer. -However, not all codecs are installed by default on your Windows system. Some codecs are proprietary or licensed by certain companies or organizations. This means that you might not be able to play some media files unless you have the right codec installed. -This is where K-Lite Codec Pack comes in handy. It is a collection of codecs and related tools that can help you play almost any audio and video file on your computer. It also includes some media players, such as Media Player Classic Home Cinema (MPC-HC), that can enhance your viewing experience. -Some of the features and benefits of K-Lite Codec Pack are: -
How to Download and Install K-Lite Codec Pack 64-bit Windows 10 for Free?-Now that you know what K-Lite Codec Pack is and what it can do for you, you might be wondering how to get it on your computer. Here are the steps to follow: -
How to Use K-Lite Codec Pack 64-bit Windows 10?-Once you have installed K-Lite Codec Pack 64-bit Windows 10, you can start playing your audio and video files with ease. Here are some tips on how to use it: -
Conclusion-K-Lite Codec Pack 64-bit Windows 10 is a great software that can help you play all kinds of audio and video files on your computer. It is free, easy to use, and compatible with Windows 10 and other versions of Windows. It also offers many features and benefits that can enhance your media playback experience. If you are looking for a reliable and comprehensive codec pack for your Windows system, you should definitely give K-Lite Codec Pack a try. -If you found this article helpful, please share it with your friends and family who might also be interested in K-Lite Codec Pack 64-bit Windows 10 free download. And if you have any questions or feedback about K-Lite Codec Pack, feel free to leave a comment below. We would love to hear from you! -FAQs-Q1: Is K-Lite Codec Pack safe to use?-A1: Yes, K-Lite Codec Pack is safe to use, as long as you download it from the official website or a trusted mirror site. It does not contain any harmful or malicious software, such as viruses, trojans, spyware, or adware. However, you should always be careful when downloading and installing any software from the internet, and scan your files with a reputable antivirus program before running them. -k-lite codec pack full windows 10 64-bit download Q2: What is the difference between K-Lite Codec Pack Basic, Standard, Full, Mega, and Update?-A2: The difference between the versions of K-Lite Codec Pack is mainly the number and type of components that they include. Here is a brief overview of each version: -
Q3: Does K-Lite Codec Pack work with Windows Media Player?-A3: Yes, K-Lite Codec Pack works with Windows Media Player (WMP), as well as other media players that use DirectShow filters. However, some formats may not be supported by WMP due to its limitations or restrictions. For example, WMP does not support subtitles or DVD playback by default. In such cases, you may need to use another media player that comes with K-Lite Codec Pack, such as MPC-HC or VLC. -Q4: How can I uninstall K-Lite Codec Pack from my computer?-A4: To uninstall K-Lite Codec Pack from your computer, you can use the uninstaller that comes with the software. You can find it in the Start menu under "K-Lite Codec Pack" or in the Control Panel under "Programs and Features". You can also run the installer again and choose the option to uninstall. Follow the instructions on the screen and remove all the components that you want to uninstall. -Q5: Where can I get more information and support for K-Lite Codec Pack?-A5: If you want to learn more about K-Lite Codec Pack or need help with using it, you can visit the official website at https://codecguide.com/. There you can find more details about each component, download links, guides, tips, FAQs, forums, contact information, and more. 401be4b1e0- - \ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Red Ball 7 Mod APK A Fun and Challenging Ride Through the Dark World.md b/spaces/congsaPfin/Manga-OCR/logs/Red Ball 7 Mod APK A Fun and Challenging Ride Through the Dark World.md deleted file mode 100644 index 765078b15e43b491fbf589a60e2e81f513268929..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Red Ball 7 Mod APK A Fun and Challenging Ride Through the Dark World.md +++ /dev/null @@ -1,115 +0,0 @@ - - Red Ball 7 Mod APK: A Fun and Adventure Arcade Game-If you are looking for a fun and adventure arcade game that will keep you entertained for hours, you should try Red Ball 7 mod APK. This is a modified version of the original Red Ball 7 game that allows you to access all the premium features for free. In this article, we will tell you everything you need to know about Red Ball 7 mod APK, including what it is, how to download and install it, what are the alternatives, and some FAQs. -red ball 7 mod apkDOWNLOAD ✒ ✒ ✒ https://urlca.com/2uObEK - What is Red Ball 7?-Red Ball 7 is a free game and one of the best in the world. It is a fun and adventure arcade game that challenges you to take a ride with the red ball through the dark world and try to avoid obstacles. You have to catch the stars for score and run faster with the ball. Mind the stars and the obstacles on the road, because the dumb way to die is to touch one of the monsters there! -The gameplay of Red Ball 7-The gameplay of Red Ball 7 is simple but addictive. You have to control the red ball with your finger or mouse and make it jump, dodge, and run until you reach the finish line for every level. There are six levels packs with 106 levels in total, each with different challenges and difficulties. You have to collect as many stars as possible along the way, as they will increase your score and unlock new balls. You also have to avoid falling into pits, spikes, traps, and enemies, as they will make you lose a life. You have three lives in each level, so be careful! -The features of Red Ball 7-Red Ball 7 has many features that make it a fun and adventure arcade game. Some of them are: -
What is a mod APK?-A mod APK is a modified version of an original Android application package (APK) file that has been altered by someone other than the developer. A mod APK usually offers some advantages over the original app, such as unlocking premium features, removing ads, adding cheats, or enhancing performance. -The benefits of installing mod APK files-Installing mod APK files can have some benefits for Android users who want to enjoy more features and options in their apps and games. Some of these benefits are: -red ball 7 game free download
The risks of installing mod APK files-Installing mod APK files can also have some risks for Android users who want to be careful about their security and privacy. Some of these risks are: -
How to download and install Red Ball 7 mod APK?-If you want to download and install Red Ball 7 mod APK on your Android device, you have to follow these steps: -The steps to download and install Red Ball 7 mod APK-
The screenshots of Red Ball 7 mod APK-Here are some screenshots of Red Ball 7 mod APK that show how it looks like and what it offers: -What are the alternatives to Red Ball 7 mod APK?-If you are looking for some other arcade games that you can try instead of or along with Red Ball 7 mod APK, here are some suggestions: -Some other arcade games that you can try-
FAQs about Red Ball 7 mod APK-Here are some frequently asked questions about Red Ball 7 mod APK that you might have: -
Red Ball 7 mod APK is generally safe to download and install if you get it from a trusted site that scans the file for malware and viruses. However, you should always be careful when installing apps from unknown sources and check the permissions they require. You should also backup your data before installing any mod APK file. -Red Ball 7 mod APK is not legal to use as it violates the intellectual property rights of the original developer. It also breaches the terms and conditions of the original app and may result in bans or penalties if detected. Therefore, you should use Red Ball 7 mod APK at your own risk and responsibility. -No, Red Ball 7 mod APK does not require root access to work on your Android device. You just need to enable the installation of apps from unknown sources in your device settings and follow the steps mentioned above. -You can play Red Ball 7 mod APK both online and offline. However, if you want to access the leaderboards and achievements, you need to have an internet connection. You can also play with your friends online by sharing your scores and challenges. -Yes, you can update Red Ball 7 mod APK to the latest version if you download it from a site that offers regular updates. However, you may lose some of the mod features or face compatibility issues if you update the app. Therefore, you should always backup your data before updating any mod APK file. -Conclusion-Red Ball 7 mod APK is a fun and adventure arcade game that allows you to enjoy all the premium features of the original game for free. You can download and install it easily from a trusted site and play it on your Android device. However, you should also be aware of the risks and legal issues involved in using mod APK files and use them at your own discretion. We hope this article has helped you learn more about Red Ball 7 mod APK and how to use it. If you have any questions or feedback, please feel free to leave a comment below. 401be4b1e0- - \ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/The Ultimate Guide to WhatsApp Business Clone APK 2022 Features Benefits and Risks.md b/spaces/congsaPfin/Manga-OCR/logs/The Ultimate Guide to WhatsApp Business Clone APK 2022 Features Benefits and Risks.md deleted file mode 100644 index 251c79e12225d6f816596c2ec316a24183f9c42b..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/The Ultimate Guide to WhatsApp Business Clone APK 2022 Features Benefits and Risks.md +++ /dev/null @@ -1,91 +0,0 @@ - - WhatsApp Business Clone APK 2022: What You Need to Know-WhatsApp is one of the most popular messaging apps in the world, with over 2 billion users. It allows you to send text messages, voice messages, photos, videos, documents, and more to your contacts. But did you know that there is also a version of WhatsApp designed for businesses? And did you know that there is a way to use multiple WhatsApp accounts on one device? In this article, we will tell you everything you need to know about WhatsApp Business and WhatsApp Business Clone APK in 2022. -whatsapp business clone apk 2022Download ——— https://urlca.com/2uO6WR - What is WhatsApp Business?-WhatsApp Business is a separate app from WhatsApp that is designed to help small and medium businesses communicate with their customers and clients. It was launched in 2018 and is available for Android and iOS devices. You can use it to create a business profile, showcase your products and services, send automated messages, and more. -Features of WhatsApp Business-Some of the features that WhatsApp Business offers are: -
Benefits of WhatsApp Business-Some of the benefits that WhatsApp Business can bring to your business are: -
What is WhatsApp Business Clone APK?-WhatsApp Business Clone APK is a modified version of WhatsApp Business that allows you to use multiple WhatsApp accounts on one device. It is not an official app from WhatsApp, but rather a third-party app that is created by developers who modify the original app. It is also known as GBWhatsApp Business or FMWhatsApp Business. -How to Download and Install WhatsApp Business Clone APK-To download and install WhatsApp Business Clone APK on your device, you need to follow these steps: -whatsapp business mod apk latest version 2023
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