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banyapon authored Jul 12, 2024
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## Cloth Segmentation and Inpainting Labs

## Run this work ##
Jupyter notebook with the example pipeline: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/18RenTYhuPVip9SHdMLn-vnK0K57B--um#scrollTo=D0h2Y-oOCnXJ)
Jupyter notebook with the example pipeline:

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/18RenTYhuPVip9SHdMLn-vnK0K57B--um#scrollTo=D0h2Y-oOCnXJ)

# Key Points
![](https://github.com/banyapon/StableDiffusionInpaint-ClothSegments/blob/main/images/screen1.png?raw=true)
## 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.

```bash
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.

![](https://github.com/banyapon/StableDiffusionInpaint-ClothSegments/blob/main/images/complete.jpg?raw=true)

## 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.


## References
- https://github.com/mberkay0
- https://github.com/ternaus/cloths_segmentation

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