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Ultra-Fast One-Step Text-to-Image — 1024px Images in 1 to 4 Steps

Ultra-fast one-step text-to-image generation (SANA-Sprint 1.6B). Generates 1024px images in just 1 – 4 diffusion steps, versus 20+ steps for a typical diffusion model. On an H100, produces a 1024×1024 image in ~0.1 seconds — roughly 10× faster than FLUX-schnell while scoring better on image-quality benchmarks. Runs at ~0.31 s on an RTX 4090.

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What it does

SANA-Sprint is a small, extremely fast text-to-image model from NVIDIA and MIT Han Lab. Give it a text prompt — a scene description, a subject, a mood — and it generates a 1024×1024 image in a fraction of a second. The key trick behind its speed: instead of the usual 20+ "denoising steps" that a normal diffusion model needs, SANA-Sprint has been trained to produce a good image in as few as 1 step (and never more than 4). On benchmark tests it beats FLUX-schnell — a well-known "fast" image model — on both image quality (FID 7.59 vs 7.94) and prompt-following (GenEval 0.74 vs 0.71), while running roughly ten times faster. It supports both English and Chinese prompts and produces images at 1024×1024 by default (multi-scale height and width also supported). Important: the underlying model is released under NVIDIA's Open Model License and is intended for research use — see the License section below before deploying it commercially..

Problem it solves

• Interactive, real-time generation – At ~0.1s per 1024×1024 image on an H100 (and ~0.31s on an RTX 4090), the model is fast enough for interactive prompt iteration, live demos, and AIPC-style consumer apps
• Ultra-low compute cost – 1-4 diffusion steps instead of 20+ means dramatically less GPU time per image — good for batch generation, low-cost inference, or hardware-constrained deployment
• Beats the fast-tier competition – Outperforms FLUX-schnell on both image quality (FID) and prompt following (GenEval) while running ~10× faster
• Modern quality at small size – 1.6B parameters (BF16) is small enough to run comfortably on consumer GPUs while still producing high-quality 1024px output
• Bilingual prompts – Native English + Chinese prompt support out of the box
• Real-time ControlNet – The SANA-Sprint family also includes a ControlNet variant for structure-guided generation at real-time speeds (0.25s on H100 for 1024px)

Input/Output

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• Input: Text prompt (English or Chinese)

  

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• Options:
  • Diffusion steps (1 – 4, default 2)
    • Fewer steps = faster generation; higher steps trade a little speed for better quality
    • The model was specifically distilled to work well anywhere in this 1-4 step range
  • Random seed (-1 = random, default 1)
    • Set a fixed integer to reproduce the exact same image
  • Output width (px) — default 1024
  • Output height (px) — default 1024
  • Advanced Options:
    • Classifier-free guidance (CFG) scale — default 4.5
      • Higher values push the model to follow the prompt more strictly; lower values allow more creative freedom </aside>

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• Output: Generated image

  • 1024 × 1024 native resolution (multi-scale H × W supported)

  • Photorealistic or stylized depending on the prompt

    

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Accuracy & Speed

Technical Details

Compliance & Provenance