FLUX.1-dev-Controlnet-Inpainting-Beta
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FLUX.1 Dev Controlnet Inpainting Beta
Overview :
FLUX.1-dev-Controlnet-Inpainting-Beta is an image restoration model developed by Alibaba's creative team, featuring significant improvements in image restoration. It supports direct processing and generation at 1024x1024 resolution without the need for additional upscaling, providing higher quality and more detailed outputs. The model has been fine-tuned to capture and reproduce more details in the restoration areas, with enhanced prompt explanations that allow for more precise control over the generated content.
Target Users :
The target audience includes professionals in image processing, designers, artists, and developers interested in image restoration. This model offers high-quality image restoration, making it ideal for users who need to repair old photos, remove unwanted elements from images, or engage in artistic creations.
Total Visits: 29.7M
Top Region: US(17.94%)
Website Views : 61.8K
Use Cases
Repair an old photo with scratches using the model.
Remove power poles or other unwanted objects from a scenic photo.
Restore a damaged painting due to age in artistic creations.
Features
Supports direct processing and generation at 1024x1024 resolution without additional upscaling.
Fine-tuned to capture and reproduce more details in restoration areas.
Enhanced prompt explanations provide more precise control over generated content.
Demonstrates the model's restoration effects through images generated using the ComfyUI workflow.
Allows for varying restoration effects through parameter adjustments.
Provides detailed usage guidelines and parameter adjustment recommendations for optimal restoration results.
Integrated with the Diffusers library for easy use by developers.
How to Use
1. Install the required version of Diffusers: pip install diffusers==0.30.2
2. Clone the model's repository: git clone https://github.com/alimama-creative/FLUX-Controlnet-Inpainting.git
3. Configure image_path, mask_path, and prompt in main.py, then execute: python main.py
4. Adjust control-strength, controlend-percent, and true-cfg parameters as needed for different restoration effects.
5. Refer to the provided ComfyUI workflow example to fine-tune parameters for optimal restoration results.
6. Use the Inference API provided by Hugging Face for model inference.
7. Check the model card and documentation for more details and use cases.
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