title: SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency
    Distillation
summary: 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.
paper_authors._id: 67d3b008e18f86384bd33fa1
paper_authors.name: Junsong Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-20T10:48:02.847Z
paper_authors._id: 645b5b09bc7518912e1f9733
paper_authors.avatarUrl: /avatars/4d35f728b41f93881a9b67c337f4d1df.svg
paper_authors.fullname: Chen
paper_authors.type: user
paper_authors.user: Lawrence-cj
paper_authors._id: 67d3b008e18f86384bd33fa2
paper_authors.name: Shuchen Xue
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-16T21:17:00.042Z
paper_authors._id: 64cf5e81a2e7f9ff61eb3a0c
paper_authors.avatarUrl: /avatars/cbaa11daec9d4113bf7de93fe9b9ee86.svg
paper_authors.fullname: scxue
paper_authors.type: user
paper_authors.user: Cauthyyy
paper_authors._id: 67d3b008e18f86384bd33fa3
paper_authors.name: Yuyang Zhao
paper_authors._id: 67d3b008e18f86384bd33fa4
paper_authors.name: Jincheng Yu
paper_authors._id: 67d3b008e18f86384bd33fa5
paper_authors.name: Sayak Paul
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:56:06.327Z
paper_authors._id: 5f7fbd813e94f16a85448745
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1649681653581-5f7fbd813e94f16a85448745.jpeg
paper_authors.fullname: Sayak Paul
paper_authors.type: user
paper_authors.user: sayakpaul
paper_authors._id: 67d3b008e18f86384bd33fa6
paper_authors.name: Junyu Chen
paper_authors._id: 67d3b008e18f86384bd33fa7
paper_authors.name: Han Cai
paper_authors._id: 67d3b008e18f86384bd33fa8
paper_authors.name: Enze Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T11:03:11.565Z
paper_authors._id: 616961bdd3f656f79ad18ec1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/616961bdd3f656f79ad18ec1/NYpxas378lDevRuUDepJJ.jpeg
paper_authors.fullname: xieenze
paper_authors.type: user
paper_authors.user: xieenze
paper_authors._id: 67d3b008e18f86384bd33fa9
paper_authors.name: Song Han
paper_title: SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency
    Distillation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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)