Multi-student Diffusion Distillation for Better One-step Generators

• URL: http://arxiv.org/abs/2410.23274v1
• Date: Wed, 30 Oct 2024 17:54:56 GMT
• Title: Multi-student Diffusion Distillation for Better One-step Generators
• Authors: Yanke Song, Jonathan Lorraine, Weili Nie, Karsten Kreis, James Lucas,
• Abstract summary: Multi-Student Distillation (MSD) is a framework to distill a conditional teacher diffusion model into multiple single-step generators. MSD trains multiple distilled students, allowing smaller sizes and, therefore, faster inference. Using 4 same-sized students, MSD sets a new state-of-the-art for one-step image generation: FID 1.20 on ImageNet-64x64 and 8.20 on zero-shot COCO2014.
• Score: 29.751205880199855
• License:
• 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 the 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 gets competitive results with faster inference for single-step generation. Using 4 same-sized students, MSD sets a new state-of-the-art for one-step image generation: FID 1.20 on ImageNet-64x64 and 8.20 on zero-shot COCO2014.

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