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| # Kandinsky Inpainting Project | |
| This project uses the Kandinsky inpainting pipeline and a mask generation script to perform inpainting on an image. The mask is generated using the YOLOv8s seg model from the Ultralytics library for mask generation. The mask is then inverted and used in the Kandinsky inpainting pipeline | |
| ## Installation | |
| To install the necessary requirements, you can use pip: | |
| ```bash | |
| pip install -r requirements.txt | |
| wandb login | |
| huggingface-cli login | |
| cd scripts | |
| ``` | |
| This will install all necessary libraries for this project, including PIL, numpy, Ultralytics wandb, diffuser etc. | |
| ### cd in to scripts | |
| specify/create the folders manually before this | |
| ```bash | |
| python run.py --image_path /path/to/image.jpg --prompt 'prompt' --negative_prompt 'negative prompt' --output_dir /path/to/output --mask_dir /path/to/mask --uid unique_id | |
| ``` | |
| ### Some Experiments | |
| Here are some of my experiments with the following models | |
| - https://huggingface.co/runwayml/stable-diffusion-inpainting | |
| - https://huggingface.co/lllyasviel/sd-controlnet-seg | |
| - kandinsky-community/kandinsky-2-2-decoder-inpaint | |
| - https://wandb.ai/vikramxd/product_placement_api/reports/Generated-Image-Pipeline-Call-1-24-03-22-21-45-35---Vmlldzo3MjYxMzcy | |
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