This is a simplified guide to an AI model called Flux-Controlnet-Inpaint maintained by Fermatresearch. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
The flux-controlnet-inpaint model, created by fermatresearch, builds on the FLUX.1-dev architecture to provide controlled image inpainting capabilities. This implementation allows integration with Canny ControlNet and supports LoRA model weights for enhanced control over the generation process. It joins a family of similar FLUX-based models like flux-dev-inpainting and flux-schnell-inpainting.
Model Inputs and Outputs
The model takes multiple inputs to provide fine-grained control over the inpainting process, from basic image manipulation to advanced neural network parameters. The output consists of modified images in your choice of format.
Inputs
- Prompt - Text description guiding the image generation
- Conditioning Scale - Controls ControlNet strength (0.2 for depth, 0.4 for canny)
- Images - Source image, control image, and mask for inpainting
- Strength - Controls the intensity of img2img modification
- HyperFlux Settings - Toggle for 8-step generation pipeline
- LoRA Parameters - Custom weight paths and scaling factors
- Technical Parameters - Inference steps, guidance scale, seed, and output format options
Outputs
- Image Array - Generated images in specified format (jpg, png, or webp)
Capabilities
The model excels at controlled image in...
Click here to read the full guide to Flux-Controlnet-Inpaint
Top comments (0)