只需0.64小时微调，就能实现高效高质的图像生成。
Simple and Fast Distillation of Diffusion Models
- 通过简化蒸馏流程，显著缩短微调时间
- 在2次采样内达到4.53的FID得分
- 单模型支持可变采样步数，实用性强
基于扩散的生成模型在各类任务中表现出强大性能，但采样速度较慢。为实现高效且高质量的生成，已有多种基于蒸馏的加速方法被提出，但通常需要耗时的精细调优和复杂设计，难以实际应用。为此，我们提出简单快速的扩散模型蒸馏（SFD），简化了现有方法的范式，将微调时间缩短至原来的1/1000。从基础蒸馏方法出发，通过识别并解决若干影响生成效率与质量的关键小因素，显著提升性能。该方法可使用单一蒸馏模型实现可变函数评估次数（NFE）的采样。大量实验表明，SFD在少步图像生成任务中实现了样本质量与微调成本的良好平衡。例如，在CIFAR-10上仅用0.64小时微调（单块NVIDIA A100 GPU），即可在NFE=2时达到4.53的FID得分。代码已开源。
原文摘要 · Abstract (English)
Diffusion-based generative models have demonstrated their powerful performance across various tasks, but this comes at a cost of the slow sampling speed. To achieve both efficient and high-quality synthesis, various distillation-based accelerated sampling methods have been developed recently. However, they generally require time-consuming fine tuning with elaborate designs to achieve satisfactory performance in a specific number of function evaluation (NFE), making them difficult to employ in practice. To address this issue, we propose Simple and Fast Distillation (SFD) of diffusion models, which simplifies the paradigm used in existing methods and largely shortens their fine-tuning time up to 1000$\times$. We begin with a vanilla distillation-based sampling method and boost its performance to state of the art by identifying and addressing several small yet vital factors affecting the synthesis efficiency and quality. Our method can also achieve sampling with variable NFEs using a single distilled model. Extensive experiments demonstrate that SFD strikes a good balance between the sample quality and fine-tuning costs in few-step image generation task. For example, SFD achieves 4.53 FID (NFE=2) on CIFAR-10 with only 0.64 hours of fine-tuning on a single NVIDIA A100 GPU. Our code is available at https://github.com/zju-pi/diff-sampler.
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