ControlNet-v1-1 is an updated version of the ControlNet architecture designed to enhance control over image generation by conditioning diffusion models with additional inputs such as edges, depth, poses, or other structural cues. Built to work alongside models like Stable Diffusion, it allows users to guide the generation process more precisely while maintaining high visual fidelity. This version improves upon the original with refinements to stability, performance, and compatibility. ControlNet-v1-1 enables use cases like pose transfer, depth-aware rendering, and detailed sketch-to-image workflows. It is especially useful for tasks that require consistency between input structure and output style or content. While official documentation is pending, it is actively integrated into community spaces and tools across Hugging Face. The model is released under the OpenRAIL license and is suitable for creative and research applications with responsible use constraints.
Features
- Conditions image generation with auxiliary inputs (e.g., edge, depth, pose)
- Compatible with Stable Diffusion and similar diffusion models
- Allows fine-grained user control over image content and layout
- Enhanced generation quality with structural fidelity
- Supports creative workflows like sketch-to-image or human pose transfer
- OpenRAIL license permits responsible creative and research use
- Widely adopted in community tools and Hugging Face Spaces
- Future documentation and improvements pending official merge into ControlNet
Project Samples
