Visual Chain-of-Thought Diffusion Models
This addresses the problem of lagging unconditional image generation quality for AI researchers and practitioners, representing an incremental but measurable improvement.
The paper tackles the performance gap between conditional and unconditional image diffusion models by proposing a two-stage sampling procedure that first generates a semantic embedding and then conditions image generation on it, achieving 25-50% FID improvement over standard unconditional generation.
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.