repaint

Maintainer: cjwbw

Total Score

3

Last updated 5/28/2024
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Github LinkView on Github
Paper LinkView on Arxiv

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Model overview

repaint is an AI model for inpainting, or filling in missing parts of an image, using denoising diffusion probabilistic models. It was developed by cjwbw, who has created several other notable AI models like stable-diffusion-v2-inpainting, analog-diffusion, and pastel-mix. The repaint model can fill in missing regions of an image while keeping the known parts harmonized, and can handle a variety of mask shapes and sizes, including extreme cases like every other line or large upscaling.

Model inputs and outputs

The repaint model takes in an input image, a mask indicating which regions are missing, and a model to use (e.g. CelebA-HQ, ImageNet, Places2). It then generates a new image with the missing regions filled in, while maintaining the integrity of the known parts. The user can also adjust the number of inference steps to control the speed vs. quality tradeoff.

Inputs

  • Image: The input image, which is expected to be aligned for facial images.
  • Mask: The type of mask to apply to the image, such as random strokes, half the image, or a sparse pattern.
  • Model: The pre-trained model to use for inpainting, based on the content of the input image.
  • Steps: The number of denoising steps to perform, which affects the speed and quality of the output.

Outputs

  • Mask: The mask used to generate the output image.
  • Masked Image: The input image with the mask applied.
  • Inpaint: The final output image with the missing regions filled in.

Capabilities

The repaint model can handle a wide variety of inpainting tasks, from filling in random strokes or half an image, to more extreme cases like upscaling an image or inpainting every other line. It is able to generate meaningful and harmonious fillings, incorporating details like expressions, features, and logos into the missing regions. The model outperforms state-of-the-art autoregressive and GAN-based inpainting methods in user studies across multiple datasets and mask types.

What can I use it for?

The repaint model could be useful for a variety of image editing and content creation tasks, such as:

  • Repairing damaged or corrupted images
  • Removing unwanted elements from photos (e.g. power lines, obstructions)
  • Generating new image content to expand or modify existing images
  • Upscaling low-resolution images while maintaining visual coherence

By leveraging the power of denoising diffusion models, repaint can produce high-quality, realistic inpaintings that seamlessly blend with the known parts of the image.

Things to try

One interesting aspect of the repaint model is its ability to handle extreme inpainting cases, such as filling in every other line of an image or upscaling with a large mask. These challenging scenarios can showcase the model's strengths in generating coherent and meaningful fillings, even when faced with a significant amount of missing information.

Another intriguing possibility is to experiment with the number of denoising steps, as this allows the user to balance the speed and quality of the inpainting. Reducing the number of steps can lead to faster inference, but may result in less harmonious fillings, while increasing the steps can improve the visual quality at the cost of longer processing times.

Overall, the repaint model represents a powerful tool for image inpainting and manipulation, with the potential to unlock new creative possibilities for artists, designers, and content creators.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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