| --- |
| 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 |
| --- |
| |
| <div align="center"> |
| <img src="zhare-logo.png" alt="Zhare-AI Logo" width="200" height="auto" style="margin-bottom: 20px;"> |
| </div> |
|
|
| # Stable Diffusion 1.5 WebGPU by Zhare-AI |
|
|
| <div align="center"> |
| |
|  |
|  |
|  |
|  |
|
|
| **Privacy-preserving text-to-image generation in your browser with WebGPU acceleration** |
|
|
| </div> |
|
|
| 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. |
|
|
| <div align="center"> |
| <img src="zhare-logo.png" alt="Zhare-AI - Democratizing AI" width="150" height="auto"> |
| <p><em>Democratizing AI through distributed computing and privacy-preserving technology</em></p> |
| </div> |
|
|
| ## 🌟 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 |
| - 🔒 **Privacy-First**: All processing happens locally, protecting user prompts and generated content |
| - 📱 **Cross-Platform**: Compatible with desktop and mobile browsers |
| - 🛠️ **Production-Ready**: Optimized for real-world web applications |
|
|
| ## 🚀 Quick Start |
|
|
| ### Installation & Setup |
|
|
| ```bash |
| # Clone or download the model |
| git lfs install |
| git clone https://huggingface.co/Zhare-AI/sd-1-5-webgpu |
| ``` |
|
|
| ## 📊 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 512×512 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+** | ✅ Full Support | Excellent | Primary recommendation | |
| | **Microsoft Edge 113+** | ✅ Full Support | Excellent | Primary recommendation | |
| | **Firefox 141+** | ✅ Stable Support | Very Good | Recent WebGPU implementation | |
| | **Safari 17.4+** | 🔶 Experimental | Good | Behind feature flag | |
| | **Mobile Chrome 121+** | 🔶 Limited | Fair | Android only, limited memory | |
|
|
| *All browsers support WebAssembly fallback for universal compatibility* |
|
|
| ## 📝 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**: 512×512 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 |
|
|
| ## 🛡️ 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 |
|
|
| ✅ **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 512×512 (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 |
|
|
| ## 📜 License: CreativeML OpenRAIL-M |
|
|
| This model is released under the **CreativeML OpenRAIL-M** license, which allows for: |
|
|
| ✅ **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 |
|
|
| <div align="center"> |
| <img src="zhare-logo.png" alt="Zhare-AI" width="120" height="auto" style="margin: 20px 0;"> |
| </div> |
|
|
| **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 |
|
|
| ## 📚 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, Björn}, |
| 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 |
|
|
| --- |
|
|
| <div align="center"> |
| <img src="zhare-logo.png" alt="Zhare-AI" width="100" height="auto"> |
| |
| **🚀 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) | [📧 Contact Us](mailto:contact@zhare.ai) | [💬 Join Discussion](https://github.com/Zhare-AI) |
|
|
| </div> |
