Visual Chain-of-Thought Diffusion Models
Keywords: diffusion models, generative models, representation learning
TL;DR: We show that two-stage generative models which sample a semantic description of an image before generating the image itself can beat standard diffusion models on image quality.
Abstract: Recent progress with conditional image diffusion models has been stunning, and this holds true whether we are speaking about models conditioned on a text description, a scene layout, or a sketch. Unconditional image diffusion models are also improving but lag behind, as do diffusion models which are conditioned on lower-dimensional features like class labels. We propose to close the gap between conditional and unconditional models using a two-stage sampling procedure. In the first stage we sample an embedding describing the semantic content of the image. In the second stage we sample the image conditioned on this embedding and then discard the embedding. Doing so lets us leverage the power of conditional diffusion models on the unconditional generation task, which we show improves FID by 25 - 50% compared to standard unconditional generation.
Submission Number: 104
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