In this paper, we propose an efficient, fast, and versatile distillation
method to accelerate the generation of pre-trained diffusion models: Flash
Diffusion. The method reaches state-of-the-art performances in terms of FID and
CLIP-Score for few steps image generation on the COCO2014 and COCO2017
datasets, while requiring only several GPU hours of training and fewer
trainable parameters than existing methods. In addition to its efficiency, the
versatility of the method is also exposed across several tasks such as
text-to-image, inpainting, face-swapping, super-resolution and using different
backbones such as UNet-based denoisers (SD1.5, SDXL) or DiT (Pixart-$\alpha$),
as well as adapters. In all cases, the method allowed to reduce drastically the
number of sampling steps while maintaining very high-quality image generation.
The official implementation is available at
this https URL

本论文提出了一种高效、快速和多功能的蒸馏方法，Flash Diffusion，用于加速预训练扩散模型的生成，在 COCO2014 和 COCO2017 数据集上，在少量步骤的图像生成方面表现出最先进的 FID 和 CLIP-Score 性能，只需几个 GPU 小时的训练和比现有方法少得多的可训练参数。