OneDiff
Accelerated Image Generation for Diffusion Models
Supercharge Your Image Generation
OneDiff is a high-performance acceleration library for diffusion models, delivering up to 3x faster image generation while maintaining quality and compatibility with popular frameworks.
Optimized Kernels
Custom CUDA kernels designed specifically for diffusion model operations.
Drop-in Integration
Seamless compatibility with Diffusers, ComfyUI, and Stable Diffusion WebUI.
Memory Optimization
Reduced VRAM usage allowing for larger batch sizes and higher resolutions.
Full Feature Support
Compatible with ControlNet, LoRA, textual inversion, and other advanced techniques.
Technical Capabilities
Breakthrough Performance
OneDiff delivers exceptional performance improvements across various scenarios:
- Up to 3x faster image generation for standard Stable Diffusion models
- Up to 2.5x faster for SDXL and other large models
- Up to 40% reduction in VRAM usage
- Batch processing with near-linear scaling
- Optimized for NVIDIA RTX 30/40 series and A100/H100 GPUs
These improvements are achieved through a combination of kernel fusion, memory layout optimization, and algorithm refinements specifically designed for diffusion model workloads.
Wide Framework Compatibility
OneDiff integrates seamlessly with popular diffusion frameworks and tools:
- Hugging Face Diffusers - Direct API compatibility
- ComfyUI - Custom nodes for easy integration
- Stable Diffusion WebUI - Extension available
- PyTorch - Native PyTorch operator support
- ONNX Runtime - Export capabilities for deployment
Our Python API makes it easy to incorporate OneDiff into your existing image generation pipeline with minimal code changes.
Advanced Feature Support
OneDiff supports the full range of modern diffusion model techniques:
- ControlNet - All condition types supported with optimized performance
- LoRA & LyCORIS - Full compatibility with model fine-tuning techniques
- Textual Inversion - Support for custom embeddings and concepts
- CLIP Skip - Configurable CLIP text encoder layers
- Sampling Methods - All major samplers including DPM++, Euler a, DDIM, and more
- Upscalers - Compatible with various upscaling techniques
- Img2Img - Optimized image-to-image generation
- Inpainting - Full support for masked generation
Use Cases
Content Creation
Generate images faster for creative workflows, enabling real-time feedback and iteration for artists and designers.
Batch Processing
Process large volumes of images efficiently for dataset generation, product visualization, and content libraries.
Interactive Applications
Build responsive AI image generation applications with lower latency and higher throughput for better user experience.
Multi-User Services
Serve more users with the same hardware resources, reducing costs and improving scalability for image generation services.
Ready to accelerate your image generation?
Get started with OneDiff today and experience the difference in speed and efficiency.