SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency
  Distillation
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.
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Junsong Chen
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Chen
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Shuchen Xue
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Cauthyyy
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Yuyang Zhao
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Jincheng Yu
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Sayak Paul
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Sayak Paul
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Junyu Chen
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Han Cai
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Han Cai
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Enze Xie
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2025-03-14T11:03:11.565Z
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xieenze
user
xieenze
67d3b008e18f86384bd33fa9
Song Han
SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency
  Distillation
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.
SANA-Sprint is an efficient diffusion model for ultra-fast text-to-image generation, utilizing hybrid distillation and integrating ControlNet for interactive real-time image generation with high-quality outputs and minimal latency.
diffusion model text-to-image (T2I) generation pre-trained foundation model hybrid distillation flow-matching model continuous-time consistency distillation (sCM) latent adversarial distillation (LADD) unified step-adaptive model ControlNet interactive image generation FID GenEval AI-powered consumer applications (AIPC)