Multi-Student Diffusion Distillation for Better One-Step Generators
Keywords: Diffusion distillation, One-step generative models, Mixture of experts
TL;DR: We distill a diffusion model into multiple (potentially smaller) students for a better quality-latency tradeoff.
Abstract: Diffusion models achieve high-quality sample generation at the cost of a lengthy multistep inference procedure. To overcome this, diffusion distillation techniques produce student generators capable of matching or surpassing the teacher in a single step. However, the student model’s inference speed is limited by the size of the teacher architecture, preventing real-time generation for computationally heavy applications. In this work, we introduce Multi-Student Distillation (MSD), a framework to distill a conditional teacher diffusion model into multiple single-step generators. Each student generator is responsible for a subset of possible conditioning data, thereby obtaining higher generation quality for the same capacity. MSD trains multiple distilled students allowing smaller sizes and, therefore, faster inference. Also, MSD offers a lightweight quality boost over single-student distillation with the same architecture. We demonstrate MSD is effective by training multiple same-sized or smaller students on single-step distillation using distribution matching and adversarial distillation techniques. With smaller students, MSD obtains competitive results with a faster inference time for single-step generation. Using same-sized students, MSD with 4 students sets new state-of-the-art results for one-step image generation: FID 1.20 on ImageNet-64×64 and 8.20 on zero-shot COCO2014.
Primary Area: generative models
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Submission Number: 8104
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