StableDiffusionInpaint-ClothSegments

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.

Cloth Segmentation and Inpainting Labs

Run this work

Jupyter notebook with the example pipeline:

Key Points

Pre-trained Model:

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).

Image Segmentation:

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

Custom Library:

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.

Retrieves:

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.

Sets up:

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

Makes it usable:

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

Result:

References

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