Hey everyone! Newbie ComfyUI user here. I struggled to find a good inpainting workflow for automatically masking and changing clothes, so after a lot of trial and error, here’s what I came up with. It's not perfect, but it works surprisingly well for me, and hopefully, it’ll be useful to you too.This workflow focuses on making image editing a bit more streamlined. It uses automatic segmentation to identify and mask elements like clothing and fashion accessories. Then it uses ControlNet to maintain image structure and a custom inpainting technique (based on Fooocus inpaint) to seamlessly replace or modify parts of the image (in the SDXL version).Here’s a breakdown of the process:<ul><li>Automatic Masking: Uses semantic segmentation to automatically create masks for clothes and fashion elements.</li><li>Image Preparation: Crops and prepares the image for editing.</li><li>Structure Preservation: Employs ControlNet to maintain image structure (in the SDXL version, Flux didn't need that in my testing).</li><li>Fooocus-based Inpainting: Applies inpainting techniques adapted from Fooocus (SDXL).</li><li>Final Assembly: Stitches the edited image back together.</li></ul>I hope this helps anyone facing similar challenges. Feel free to modify and improve it!Workflows:This page contains three workflow variations:<ol><li>SDXL: The primary workflow. Uses ControlNet for structure and Fooocus-based inpainting (In my opinion, offers the best balance of speed and quality).</li><li>Flux Fill: A workflow that uses the new Flux Fill model. Does not require ControlNet to my testing.</li><li>Flux Fill GGUF: Similar to Flux Fill but utilizes the GGUF model format for potential performance benefits.</li></ol>Getting Started:You'll need to install the following custom nodes and models:1. Custom Nodes:The necessary nodes can be found through the ComfyUI Manager. However, some users have reported installation issues regarding the fashion masking nodes. Here's a guide:<ul><li>Nodes Repository: <a target="_blank" rel="ugc" href="https://github.com/StartHua/Comfyui_segformer_b2_clothes">https://github.com/StartHua/Comfyui_segformer_b2_clothes</a></li><li>Installation:<ol><li>Install the nodes via ComfyUI Manager.</li><li>Navigate to your ComfyUI custom nodes directory: \ComfyUI\custom_nodes\Comfyui_segformer_b2_clothes</li><li>Open a command prompt in that directory (you can type cmd in the folder path and press enter).</li><li>Run the following command: pip install -r requirements.txt</li></ol></li></ul>2. Segmentation Models:You'll need the model files from Hugging Face (links below). These links only contain the files needed tu run the nodes, not the nodes themselves. Download the model.safetensor, preprocessor_config.json, and config.json files and place them in the following directories:<ul><li>Segformer B2 Clothes:<ul><li>Hugging Face Link: <a target="_blank" rel="ugc" href="https://huggingface.co/mattmdjaga/segformer_b2_clothes">https://huggingface.co/mattmdjaga/segformer_b2_clothes</a></li><li>Place files in: \ComfyUI\models\segformer_b2_clothes</li></ul></li><li>Segformer B3 Fashion:<ul><li>Hugging Face Link: <a target="_blank" rel="ugc" href="https://huggingface.co/sayeed99/segformer-b3-fashion">https://huggingface.co/sayeed99/segformer-b3-fashion</a></li><li>Place files in: \ComfyUI\models\segformer_b3_fashion</li></ul>(The workflow includes a switch to select between these two segmentation models. They have different strengths and weaknesses, so try both if one doesn't work well. Remember to adjust the mask expansion as needed.)</li></ul>3. Fooocus Inpaint Models:<ul><li>Hugging Face Link: <a target="_blank" rel="ugc" href="https://huggingface.co/lllyasviel/fooocus_inpaint">https://huggingface.co/lllyasviel/fooocus_inpaint</a> (You need inpaint_v26.fooocus.patch, fooocus_lama.safetensors and fooocus_inpaint_head.pth from this repository, place them in ComfyUI\models\inpaint)</li></ul>Feel free to ask if you have any questions. Happy inpainting!

[FLUX | SDXL] Auto clothes inpainting

GGUF version of the flux fill workflow, for both Unet and T5. Should be useful for people with a less beefy GPU. An heavily quantized model requires better prompting.

Files with SHA256: 03f25ef569fc79942a39b56a4da2705e653973c9420da56cae1fd51e8485448c