Merge branch 'main' of https://huggingface.co/aharshit123456/learn_ddpm

README.md

@@ -1,98 +1,106 @@
-# DDPM Project
-
-This repository contains the implementation of Denoising Diffusion Probabilistic Models (DDPM).
-
-## Table of Contents
-- [Introduction](#introduction)
-- [Installation](#installation)
-- [Usage](#usage)
-- [Contributing](#contributing)
-
-## Introduction
-Denoising Diffusion Probabilistic Models (DDPM) are a class of generative models that learn to generate data by reversing a diffusion process. This repository provides a comprehensive implementation of DDPM.
-
-## Installation
-To install the necessary dependencies, run:
-```bash
-pip install -r requirements.txt
-```
-
-## Usage
-To train the model, use the following command:
-```bash
-python train.py
-```
-To generate samples, use:
-```bash
-python generate.py
-```
-
-## Game
-To understand the model and it's workings, we're working on a cool cute little game where the user is the UNET reverser/diffusion model and is tasked to denoise the images with noise made of grids of lines.
-
-Use [learndiffusion.vercel.app](learndiffusion.vercel.app) to access the primitive version of the game. You can also contribute to the game by checking out at the diffusion_game branch. A new model showcase will also be added such that the model's weights are loaded from the internet, model's files are installed and loaded into a gradio interface for direct use/inference on the vercel. Feel free to make changes for the same, issue is opened.
-
-## Explanations and Mathematics
-- slides from presentation : 
-- notes/explanations : [HERE](slides\notes)
-- a cute lab talk ppt: 
-- plato's allegory : \<link to REPUBLIC>
-
-## Resources
-- Original Paper : https://arxiv.org/pdf/2006.11239
-- Improvement Paper : https://arxiv.org/abs/2102.09672
-- Improvement by OpenAI : https://arxiv.org/pdf/2105.05233
-- Stable Diffusion Paper : https://arxiv.org/abs/2112.10752
-- 
-
-### Papers for background
-- UNET Paper for Biomedical Segmentation
-- Autoencooder
-- Variational Autoencoder
-- Markov Hierarchical VAE
-- Introductory Lectures on Diffusion Process
-
-### Youtube videos and courses
-#### Mathematics
-- Outliers
-- Omar Jahil
-
-#### Pytorch Implementation
-- [Deep Findr](https://www.youtube.com/watch?v=a4Yfz2FxXiY)
-- [Notebook from Deep Findr](https://colab.research.google.com/drive/1sjy9odlSSy0RBVgMTgP7s99NXsqglsUL?usp=sharing)
-
-## Pretrained Weights
-weights from the model can be found in [pretrained_weights](https://drive.google.com/drive/folders/1NiQDI3e67I9FITVnrzNPP2Az0LABRpic?usp=sharing)
-
-For loading the pretrained weights:
-```
-model2 = SimpleUnet()
-model2.load_state_dict(torch.load("/content/drive/MyDrive/Research Work/mlsa/DDPM/model_weights.pth"))
-model2.eval()
-```
-
-For making inferences
-TODO: Errors in the sampling function, boolean errors and etc. Will open issues for solving by others as exercise if needed.
-```
-num_samples = 8  # Number of images to generate
-image_size = (3, 32, 32)  # Example for CIFAR10
-noise = torch.randn(num_samples, *image_size).to("cuda")
-
-model2.to("cuda")
-# Generate images by denoising
-with torch.no_grad():
-    generated_images = model2.sample(noise)
-
-# Save the generated images
-save_image(generated_images, "generated_images.png", nrow=4, normalize=True)
-```
-
-
-## Contributing
-Contributions are welcome! Please open an issue or submit a pull request.
-
-
-## Future Ideas
-- Make the model onnx compatible for training and inferencing on Intel GPUs
-- Build a Stable Diffusion model Text2Img using CLIP implementationnnnn !!!
-- Train the current model for a much larger dataset with more generalizations and nuances
+---
+license: mit
+datasets:
+- uoft-cs/cifar10
+- nyanko7/danbooru2023
+language:
+- en
+---
+# DDPM Project
+
+This repository contains the implementation of Denoising Diffusion Probabilistic Models (DDPM).
+
+## Table of Contents
+- [Introduction](#introduction)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Contributing](#contributing)
+
+## Introduction
+Denoising Diffusion Probabilistic Models (DDPM) are a class of generative models that learn to generate data by reversing a diffusion process. This repository provides a comprehensive implementation of DDPM.
+
+## Installation
+To install the necessary dependencies, run:
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+To train the model, use the following command:
+```bash
+python train.py
+```
+To generate samples, use:
+```bash
+python generate.py
+```
+
+## Game
+To understand the model and it's workings, we're working on a cool cute little game where the user is the UNET reverser/diffusion model and is tasked to denoise the images with noise made of grids of lines.
+
+Use [learndiffusion.vercel.app](learndiffusion.vercel.app) to access the primitive version of the game. You can also contribute to the game by checking out at the diffusion_game branch. A new model showcase will also be added such that the model's weights are loaded from the internet, model's files are installed and loaded into a gradio interface for direct use/inference on the vercel. Feel free to make changes for the same, issue is opened.
+
+## Explanations and Mathematics
+- slides from presentation : 
+- notes/explanations : [HERE](slides\notes)
+- a cute lab talk ppt: 
+- plato's allegory : \<link to REPUBLIC>
+
+## Resources
+- Original Paper : https://arxiv.org/pdf/2006.11239
+- Improvement Paper : https://arxiv.org/abs/2102.09672
+- Improvement by OpenAI : https://arxiv.org/pdf/2105.05233
+- Stable Diffusion Paper : https://arxiv.org/abs/2112.10752
+- 
+
+### Papers for background
+- UNET Paper for Biomedical Segmentation
+- Autoencooder
+- Variational Autoencoder
+- Markov Hierarchical VAE
+- Introductory Lectures on Diffusion Process
+
+### Youtube videos and courses
+#### Mathematics
+- Outliers
+- Omar Jahil
+
+#### Pytorch Implementation
+- [Deep Findr](https://www.youtube.com/watch?v=a4Yfz2FxXiY)
+- [Notebook from Deep Findr](https://colab.research.google.com/drive/1sjy9odlSSy0RBVgMTgP7s99NXsqglsUL?usp=sharing)
+
+## Pretrained Weights
+weights from the model can be found in [pretrained_weights](https://drive.google.com/drive/folders/1NiQDI3e67I9FITVnrzNPP2Az0LABRpic?usp=sharing)
+
+For loading the pretrained weights:
+```
+model2 = SimpleUnet()
+model2.load_state_dict(torch.load("/content/drive/MyDrive/Research Work/mlsa/DDPM/model_weights.pth"))
+model2.eval()
+```
+
+For making inferences
+TODO: Errors in the sampling function, boolean errors and etc. Will open issues for solving by others as exercise if needed.
+```
+num_samples = 8  # Number of images to generate
+image_size = (3, 32, 32)  # Example for CIFAR10
+noise = torch.randn(num_samples, *image_size).to("cuda")
+
+model2.to("cuda")
+# Generate images by denoising
+with torch.no_grad():
+    generated_images = model2.sample(noise)
+
+# Save the generated images
+save_image(generated_images, "generated_images.png", nrow=4, normalize=True)
+```
+
+
+## Contributing
+Contributions are welcome! Please open an issue or submit a pull request.
+
+
+## Future Ideas
+- Make the model onnx compatible for training and inferencing on Intel GPUs
+- Build a Stable Diffusion model Text2Img using CLIP implementationnnnn !!!
+- Train the current model for a much larger dataset with more generalizations and nuances