Can you imagine creating perfect photos from scratch?
Image: Luke J. Allen, Public domain, via Wikimedia Commons
Can you imagine creating perfect photos from scratch?
Imagine you're a photographer stuck in a rainy day, unable to capture the perfect shot.
Think of a diffusion model as a magical assistant that gradually turns a blurry photo into a clear one, step by step, by learning from many good photos.
Example
Start with a blurry image (like a rainy photo), and the assistant refines it through 100 steps until it's a clear photo (like a sunny day photo).
Remember this
A denoising diffusion probabilistic model helps turn blurry images into clear ones by learning from many good photos.
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
Stable Diffusion
Stable Diffusion generates images from text descriptions
denoising score matching does: learns to denoise, which equals learning the score
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Langevin dynamics does: adds noise to gradient descent to sample from a distribution
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the reverse process learns: p_θ(x_{t-1}|x_t)
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Markov chain Monte Carlo
MCMC samples from complex posterior distributions
Metropolis–Hastings algorithm
Metropolis-Hastings algorithm samples from difficult distributions
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