Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality

Understand

Diffusion models have emerged as an expressive family of generative models rivaling GANs in sample quality and autoregressive models in likelihood scores.

• Standard diffusion models typically require hundreds of forward passes through the model to generate a single high-fidelity sample.
• We introduce Differentiable Diffusion Sampler Search (DDSS): a method that optimizes fast samplers for any pre-trained diffusion model by differentiating through sample quality scores.
• We also present Generalized Gaussian Diffusion Models (GGDM), a family of flexible non-Markovian samplers for diffusion models.