eliahuhorwitz/Conffusion
Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
This tool helps researchers and practitioners using image-to-image generative AI models, like those for super-resolution or inpainting, to get reliable results. It takes a corrupted or incomplete image and, instead of just generating a new version, provides a range of possible pixel values for the reconstructed image. This means you get a visual output along with a statistical guarantee that the true pixel value falls within the provided upper and lower bounds.
144 stars. No commits in the last 6 months.
Use this if you need to use diffusion models for image reconstruction tasks and require statistical certainty about the generated pixel values, especially in high-stakes applications.
Not ideal if your primary goal is just to quickly generate high-quality images without needing quantified uncertainty or if you are not working with diffusion models for image-to-image tasks.
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144
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4
Language
Python
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Last pushed
Nov 27, 2022
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