  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 FPS (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.
