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| title: Flux Inpaint Controlnet | |
| emoji: 🌍 | |
| colorFrom: indigo | |
| colorTo: gray | |
| sdk: gradio | |
| sdk_version: 4.44.0 | |
| app_file: app.py | |
| hf_oauth: true | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| ======= | |
| # Flux Inpaint AI Model | |
| This Space demonstrates the Flux inpaint AI model, which uses ControlNet for image inpainting tasks. | |
| ## How to use | |
| 1. Upload an input image | |
| 2. Upload a mask image (white areas will be inpainted) | |
| 3. Enter a prompt describing the desired output | |
| 4. Adjust the sliders for fine-tuning (optional) | |
| 5. Click "Submit" to generate the inpainted image | |
| ## Model Details | |
| This Space uses the following models: | |
| - Base model: black-forest-labs/FLUX.1-dev | |
| - ControlNet model: YishaoAI/flux-dev-controlnet-canny-kid-clothes | |
| The inpainting process uses a Canny edge detector for additional control. | |
| ## Parameters | |
| - Strength: Controls the strength of the inpainting effect (0-1) | |
| - Number of Inference Steps: More steps can lead to better quality but slower generation | |
| - Guidance Scale: Controls how closely the image follows the prompt | |
| - ControlNet Conditioning Scale: Adjusts the influence of the ControlNet model | |
| Enjoy experimenting with the Flux Inpaint AI Model! | |