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On Distillation of Guided Diffusion Models
Chenlin Meng · Ruiqi Gao · Diederik Kingma · Stefano Ermon · Jonathan Ho · Tim Salimans

@
in NeurIPS 2022 Workshop on Score-Based Methods »
Event URL: https://openreview.net/forum?id=6QHpSQt6VR- »
Classifier-free guided diffusion models have recently been shown to be highly effective at high-resolution image generation, and they have been widely used in large-scale diffusion frameworks including DALL$\cdot$E 2, GLIDE and Imagen. However, a downside of classifier-free guided diffusion models is that they are computationally expensive at inference time since they require evaluating two diffusion models, a class-conditional model and an unconditional model, hundreds of times. To deal with this limitation, we propose an approach to distilling classifier-free guided diffusion models into models that are fast to sample from: Given a pre-trained classifier-free guided model, we first learn a single model to match the output of the combined conditional and unconditional models, and then we progressively distill that model to a diffusion model that requires much fewer sampling steps. On ImageNet 64x64 and CIFAR-10, our approach is able to generate images visually comparable to that of the original model using as few as 4 sampling steps, achieving FID/IS scores comparable to that of the original model while being up to 256 times faster to sample from.

Author Information

Chenlin Meng (Stanford University)
Ruiqi Gao (Google Brain)
Diederik Kingma (Google)
Stefano Ermon (Stanford)
Jonathan Ho (Google)
Tim Salimans (Google Brain Amsterdam)

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