--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 library_name: onnx tags: - stable-diffusion - text-to-image - diffusion - webgpu - browser-ai - onnx - zhare-ai - client-side - privacy-preserving pipeline_tag: text-to-image inference: false widget: - text: "A beautiful sunset over mountains, digital art style" example_title: "Mountain Sunset" - text: "A futuristic cityscape with flying cars at night, cyberpunk" example_title: "Cyberpunk City" - text: "A serene lake surrounded by autumn trees, oil painting" example_title: "Autumn Lake" - text: "Portrait of a wise elderly person, studio lighting, photorealistic" example_title: "Portrait" model-index: - name: sd-1-5-webgpu results: - task: type: text-to-image name: Text-to-Image Generation dataset: name: Browser Performance Benchmark type: webgpu-inference metrics: - type: generation-time value: 3-45 name: Generation Time (seconds) config: 512x512, 20 steps, various hardware - type: memory-usage value: 4-6 name: VRAM Usage (GB) config: WebGPU acceleration - type: model-size value: 3.5 name: Total Model Size (GB) config: All ONNX components ---
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# Stable Diffusion 1.5 WebGPU by Zhare-AI
![License](https://img.shields.io/badge/License-CreativeML_OpenRAIL--M-blue.svg) ![WebGPU](https://img.shields.io/badge/WebGPU-Ready-green) ![Privacy](https://img.shields.io/badge/Privacy-First-purple) ![Production](https://img.shields.io/badge/Production-Ready-brightgreen) **Privacy-preserving text-to-image generation in your browser with WebGPU acceleration**
This is a browser-optimized implementation of Stable Diffusion v1.5, specifically converted and optimized for client-side deployment using WebGPU acceleration. Developed by **Zhare-AI**, this model enables high-quality image generation directly in web browsers without requiring server infrastructure, ensuring complete user privacy and data sovereignty.
Zhare-AI - Democratizing AI

Democratizing AI through distributed computing and privacy-preserving technology

## 🌟 Key Features - 🌐 **Fully Client-Side**: Complete image generation in the browser, no data leaves your device - ⚑ **WebGPU Accelerated**: Hardware-accelerated inference with automatic WebAssembly fallback - πŸ”’ **Privacy-First**: All processing happens locally, protecting user prompts and generated content - πŸ“± **Cross-Platform**: Compatible with desktop and mobile browsers - πŸ› οΈ **Production-Ready**: Optimized for real-world web applications ## πŸš€ Quick Start ### Installation & Setup ```bash # Clone or download the model git lfs install git clone https://huggingface.co/Zhare-AI/sd-1-5-webgpu ``` ## πŸ“Š Performance Specifications ### Model Architecture | Component | Description | Approximate Size | |-----------|-------------|------------------| | **Text Encoder** | CLIP ViT-L/14 for text understanding | ~500MB | | **UNet** | Core diffusion model for image generation | ~3.4GB | | **VAE Decoder** | Converts latents to final images | ~160MB | | **VAE Encoder** | Encodes images to latent space | ~160MB | | **Safety Checker** | Content filtering (optional) | ~600MB | **Total Model Size**: ~4.8GB (without safety checker: ~4.2GB) ### Browser Performance Benchmarks *Generation time for 512Γ—512 images with 20 inference steps:* | Hardware Category | Example Device | Typical Performance | |------------------|----------------|-------------------| | **High-End Desktop** | RTX 4090, RTX 4080 | 3-8 seconds | | **Gaming Desktop** | RTX 3080, RTX 3070 | 8-15 seconds | | **Intel Arc GPUs** | Arc A750, Arc A770 | 8-15 seconds | | **AMD High-End** | RX 7900 XT/XTX | 6-12 seconds | | **Apple Silicon** | M2 Max, M1 Ultra | 10-20 seconds | | **Integrated GPUs** | Intel Iris Xe | 25-50 seconds | | **WebAssembly Fallback** | CPU-only devices | 2-10 minutes | ### System Requirements - **Minimum VRAM**: 4GB (recommended: 6GB+) - **System RAM**: 8GB minimum, 16GB recommended - **Storage**: 5GB free space for model files - **Browser**: Chrome 113+, Edge 113+ (WebGPU), or any modern browser (WebAssembly fallback) ## 🌐 Browser Compatibility | Browser | WebGPU Support | Performance Level | Notes | |---------|---------------|------------------|-------| | **Chrome 113+** | βœ… Full Support | Excellent | Primary recommendation | | **Microsoft Edge 113+** | βœ… Full Support | Excellent | Primary recommendation | | **Firefox 141+** | βœ… Stable Support | Very Good | Recent WebGPU implementation | | **Safari 17.4+** | πŸ”Ά Experimental | Good | Behind feature flag | | **Mobile Chrome 121+** | πŸ”Ά Limited | Fair | Android only, limited memory | *All browsers support WebAssembly fallback for universal compatibility* ## πŸ“ Model Details ### Training Information This model is based on Stable Diffusion v1.5 with the following training characteristics: - **Base Dataset**: LAION-5B filtered subset (~590M image-text pairs) - **Training Resolution**: 512Γ—512 pixels - **Architecture**: Latent Diffusion Model with CLIP ViT-L/14 text encoder - **Precision**: Originally trained in FP32, optimized to FP16 for browser deployment ### Optimization for Web Deployment - **ONNX Conversion**: Optimized computational graph for web inference - **WebGPU Kernels**: Custom compute shaders for GPU acceleration - **Memory Efficiency**: Attention slicing and dynamic memory management - **Cross-Platform**: WebAssembly fallback ensures universal browser support ## πŸ›‘οΈ Ethical Use and Safety ### Built-in Safety Features - **Content Filter**: Optional NSFW detection and filtering - **Prompt Sanitization**: Basic filtering of potentially harmful prompts - **Local Processing**: No data transmission ensures privacy protection ### Responsible Use Guidelines βœ… **Encouraged Uses:** - Creative art and design projects - Educational demonstrations of AI capabilities - Rapid prototyping for applications - Personal creative exploration - Research and development ❌ **Prohibited Uses:** - Creating harmful, offensive, or illegal content - Generating misleading information or deepfakes - Violating copyright or intellectual property rights - Any use that violates the CreativeML OpenRAIL-M license terms ### Privacy and Data Protection - **Zero Data Collection**: All processing occurs locally in your browser - **No Server Communication**: Model runs entirely offline after initial download - **User Control**: Complete control over generated content and prompts - **GDPR Compliant**: No personal data processing or storage ## ⚠️ Limitations and Considerations ### Technical Limitations - **Resolution**: Optimized for 512Γ—512 (other resolutions may reduce quality) - **Batch Size**: Single image generation only in browser environment - **Memory Constraints**: Limited by browser and device VRAM/RAM - **Generation Speed**: Slower than dedicated server hardware ### Content Limitations - **Language Bias**: Best performance with English prompts - **Cultural Representation**: Training data may reflect Western/English-speaking biases - **Artistic Style**: Tendency toward photorealistic and digital art styles - **Consistency**: Multiple generations from same prompt may vary significantly ### Browser-Specific Considerations - **WebGPU Availability**: Limited to supporting browsers and devices - **Memory Management**: Browser security limits may affect large model loading - **Performance Variance**: Significant variation across different devices and browsers ## πŸ“œ License: CreativeML OpenRAIL-M This model is released under the **CreativeML OpenRAIL-M** license, which allows for: βœ… **Permitted:** - Commercial and non-commercial use - Distribution and modification - Creation of derivative works - Integration into applications and services 🚫 **Restrictions:** - Must not be used to generate harmful content - Cannot be used for illegal activities - Must include license terms in any distribution - Derivative works must maintain the same license restrictions **Full License Text**: Available at [CreativeML OpenRAIL-M License](https://huggingface.co/spaces/CompVis/stable-diffusion-license) ### License Compliance When using this model: 1. **Include License**: Provide license terms to end users 2. **Respect Restrictions**: Ensure use cases comply with content restrictions 3. **Derivative Works**: Apply same license to modified versions 4. **Attribution**: Credit original Stable Diffusion creators and Zhare-AI adaptation ## 🏒 About Zhare-AI
Zhare-AI
**Zhare-AI** is focused on democratizing AI technology by making powerful models accessible directly in web browsers. Our mission is to enable privacy-preserving AI applications that put users in control of their data and creative processes. - **Website**: [zhare.ai](https://zhare.ai) - **Focus**: Distributed AI computing and browser-based AI applications - **Philosophy**: Privacy-first, user-controlled AI experiences - **Vision**: Making AI accessible, private, and distributed ### Our Mission We believe AI should be: - **Accessible** to everyone, regardless of infrastructure - **Private** with complete user data control - **Distributed** across devices rather than centralized servers - **Transparent** with open-source implementations ## πŸ“š Citation and References ### Cite This Work ```bibtex @misc{zhare-ai-sd15-webgpu-2025, title={Stable Diffusion 1.5 WebGPU: Browser-Optimized Text-to-Image Generation}, author={Zhare-AI}, year={2025}, howpublished={\url{https://huggingface.co/Zhare-AI/sd-1-5-webgpu}}, note={WebGPU-optimized implementation for privacy-preserving browser-based image generation} } ``` ### Original Stable Diffusion Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, BjΓΆrn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` ## 🀝 Community and Support ### Getting Help - **Issues**: Report technical problems via the repository issues - **Discussions**: Join the community discussion for tips and examples - **Documentation**: Comprehensive guides available in the repository ### Contributing We welcome contributions to improve browser compatibility, performance, and user experience: - Performance optimizations for different hardware - Browser compatibility improvements - Documentation enhancements - Example applications and tutorials ---
Zhare-AI **πŸš€ Ready to create amazing images directly in your browser?** *This model brings the power of Stable Diffusion to web applications while keeping your data completely private and secure.* **Developed with ❀️ by Zhare-AI for the open-source community** [🌐 Visit Zhare.ai](https://zhare.ai) | [πŸ“§ Contact Us](mailto:contact@zhare.ai) | [πŸ’¬ Join Discussion](https://github.com/Zhare-AI)