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@@ -23,7 +23,7 @@ This repository provides a Inpainting ControlNet checkpoint for [FLUX.1-dev](htt
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  * The model was trained on 12M laion2B and internal source images at resolution 768x768. The inference performs best at this size, with other sizes yielding suboptimal results.
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- * The recommended controlnet_conditioning_scale is 0.95.
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  * **Please note: This is only the alpha version during the training process. We will release an updated version when we feel ready.**
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@@ -56,11 +56,11 @@ From left to right: Input image | Masked image | SDXL inpainting | Ours
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  ``` Shell
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  # install diffusers
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- pip install -U diffusers
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  # clone this repo
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- git clone https://github.com/JPlin/FLUX-Controlnet-Inpainting.git
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- # run
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- python main.py -i <input_image> -m <mask_image> -p <prompt>
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  ```
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  ## LICENSE
 
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  * The model was trained on 12M laion2B and internal source images at resolution 768x768. The inference performs best at this size, with other sizes yielding suboptimal results.
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+ * The recommended controlnet_conditioning_scale is 0.9 - 0.95.
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  * **Please note: This is only the alpha version during the training process. We will release an updated version when we feel ready.**
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  ``` Shell
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  # install diffusers
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+ pip install diffusers==0.30.2
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  # clone this repo
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+ git clone https://github.com/alimama-creative/FLUX-Controlnet-Inpainting.git
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+ # modify the image_path, mask_path, and prompt in main.py. Run:
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+ python main.py
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  ```
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  ## LICENSE