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

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.