SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency
  Distillation
Authors: name: Junsong Chen, name: Shuchen Xue, name: Yuyang Zhao, name: Jincheng Yu, name: Sayak Paul, name: Junyu Chen, name: Han Cai, name: Enze Xie, name: Song Han

This paper presents SANA-Sprint, an efficient diffusion model for ultra-fast
text-to-image (T2I) generation. SANA-Sprint is built on a pre-trained
foundation model and augmented with hybrid distillation, dramatically reducing
inference steps from 20 to 1-4. We introduce three key innovations: (1) We
propose a training-free approach that transforms a pre-trained flow-matching
model for continuous-time consistency distillation (sCM), eliminating costly
training from scratch and achieving high training efficiency. Our hybrid
distillation strategy combines sCM with latent adversarial distillation (LADD):
sCM ensures alignment with the teacher model, while LADD enhances single-step
generation fidelity. (2) SANA-Sprint is a unified step-adaptive model that
achieves high-quality generation in 1-4 steps, eliminating step-specific
training and improving efficiency. (3) We integrate ControlNet with SANA-Sprint
for real-time interactive image generation, enabling instant visual feedback
for user interaction. SANA-Sprint establishes a new Pareto frontier in
speed-quality tradeoffs, achieving state-of-the-art performance with 7.59 FID
and 0.74 GenEval in only 1 step - outperforming FLUX-schnell (7.94 FID / 0.71
GenEval) while being 10x faster (0.1s vs 1.1s on H100). It also achieves 0.1s
(T2I) and 0.25s (ControlNet) latency for 1024 x 1024 images on H100, and 0.31s
(T2I) on an RTX 4090, showcasing its exceptional efficiency and potential for
AI-powered consumer applications (AIPC). Code and pre-trained models will be
open-sourced.