Understanding Diffusion Models for Image Generation and Inpainting

Name: Understanding Diffusion Models for Image Generation and Inpainting
Uploaded: 2023-04-17 12:30:03
Description: Diffusion models can be used to generate coherent images from noise and improve inpainting by producing higher quality samples.

This article is a summary of a YouTube video "What are Diffusion Models?" by Ari Seff

TLDR Diffusion models can be used to generate coherent images from noise and improve inpainting by producing higher quality samples.

Key insights

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The basic idea behind diffusion models is to start from a noisy image, gradually remove the noise, and end up with a coherent image, which has had success in image generation and other conditional settings.
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Diffusion models can be interpreted as a kind of latent variable generative model, maximizing a lower bound to calculate p of x0.
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Classifier-free diffusion guidance sets the conditioning label to a null label with some probability during training, producing higher quality samples under human evaluation.
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Diffusion models can calculate a variational lower bound on log likelihood, competitive on density estimation benchmarks dominated by auto aggressive models.

Timestamped Summary

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00:00
Diffusion models generate coherent images from noise.
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01:31
Learning to undo the steps of a Markov chain with a Gaussian transition parameterized by a variance that increases with time.
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03:09
By taking small steps forward and training a reverse process, a reasonable sample can be produced.
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05:35
We can use a variational lower bound training objective to train a single network for the reverse process in diffusion models.
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07:29
Maximizing the expected density assigned to the data and the similarity of the approximate posterior to the prior can reduce variance and improve training efficiency.
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09:31
A reparameterization and simpler variational bound are proposed to reduce variance and improve sample quality in the training process.
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11:42
Conditioning on full context, classifier-free diffusion guidance can be used to improve inpainting by producing higher quality samples.
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13:45
Score networks can approximate the denoising diffusion objective for density estimation.