This project explores the application of cloth segmentation to inpainting, allowing for modification or removal of objects from clothing images using Stable Diffusion Inpainting and a RunwayML model.

Jupyter notebook with the example pipeline:

This code leverages a pre-trained model, which means you don't need to train the model from scratch. You can immediately use it for inference (making predictions).

The primary goal is to identify and segment different clothing items within an image.

The functions from iglovikov_helper_functions seem to be part of a custom or external library designed to simplify common tasks in deep learning projects.

pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")

This line of code is the heart of setting up the Stable Diffusion model for image inpainting tasks within your Python environment.

It fetches the Stable Diffusion Inpainting model (weights, architecture, configuration) that has been trained by RunwayML specifically for the task of filling in missing or masked areas of images.

It creates an instance of the StableDiffusionInpaintPipeline class and initializes it with the downloaded model.

It assigns this pipeline to the pipe variable, allowing you to easily call functions on pipe to perform inpainting on your images.

• https://github.com/mberkay0
• https://github.com/ternaus/cloths_segmentation