Recent advancements in diffusion models have positioned them at the forefront
of image generation. Despite their superior performance, diffusion models are
not without drawbacks; they are characterized by complex architectures and
substantial computational demands, resulting in significant latency due to
their iterative sampling process. To mitigate these limitations, we introduce a
dual approach involving model miniaturization and a reduction in sampling
steps, aimed at significantly decreasing model latency. Our methodology
leverages knowledge distillation to streamline the U-Net and image decoder
architectures, and introduces an innovative one-step DM training technique that
utilizes feature matching and score distillation. We present two models,
SDXS-512 and SDXS-1024, achieving inference speeds of approximately 100 FPS
(30x faster than SD v1.5) and 30 FP (60x faster than SDXL) on a single GPU,
respectively. Moreover, our training approach offers promising applications in
image-conditioned control, facilitating efficient image-to-image translation.

通过模型微型化、减少采样步骤和知识蒸馏的双重方法，减小模型延迟，本研究提出了一种在图像生成领域优化扩散模型性能的方法。通过简化 U-Net 和图像解码器的结构并引入一步式 DM 训练技术，利用特征匹配和得分蒸馏，我们实现了两个模型 SDXS-512 和 SDXS-1024，在单个 GPU 上实现了约 100 帧每秒（比 SD v1.5 快 30 倍）和 30 帧每秒（比 SDXL 快 60 倍）的推理速度。此外，我们的训练方法还在图像条件控制方面具有潜在的应用，可以实现高效的图像转换。