CVPR 2023 - On Distillation of Guided Diffusion Models

In this episode we discuss On Distillation of Guided Diffusion Models by Authors: - Chenlin Meng - Robin Rombach - Ruiqi Gao - Diederik Kingma - Stefano Ermon - Jonathan Ho - Tim Salimans Affiliations: - Chenlin Meng: Stanford University - Robin Rombach: Stability AI & LMU Munich - Ruiqi Gao, Diederik Kingma, Jonathan Ho, Tim Salimans: Google Research, Brain Team - Stefano Ermon: Stanford University. The paper proposes an approach to distill classifier-free guided diffusion models, a type of high-resolution image generation model, into faster models that require fewer sampling steps. This is achieved by first learning a single model to match the output of the combined conditional and unconditional models and then progressively distilling that model to a diffusion model. The approach is shown to generate visually comparable images to the original model while being up to 256 times faster to sample from on ImageNet 64x64 and CIFAR-10 datasets. It also achieves high-fidelity images using as few as 1 to 4 denoising steps on ImageNet 256x256 and LAION datasets for diffusion models trained on the latent-space, and is effective in text-guided image editing and inpainting.