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Efficient-Large-Model / Sana_Sprint_0.6B_1024px
Model's Last Updated: July 31 2026
text-to-image
Introduction of Sana_Sprint_0.6B_1024px
Model Details of Sana_Sprint_0.6B_1024px
🐱 Sana Model Card
Demos
Training Pipeline
Model Efficiency
SANA-Sprint is an ultra-efficient diffusion model for text-to-image (T2I) generation, reducing inference steps from 20 to 1-4 while achieving state-of-the-art performance. Key innovations include: (1) A training-free approach for continuous-time consistency distillation (sCM), eliminating costly retraining; (2) A unified step-adaptive model for high-quality generation in 1-4 steps; and (3) ControlNet integration for real-time interactive image generation. SANA-Sprint achieves 7.59 FID and 0.74 GenEval in just 1 step — outperforming FLUX-schnell (7.94 FID / 0.71 GenEval) while being 10× faster (0.1s vs 1.1s on H100). With latencies of 0.1s (T2I) and 0.25s (ControlNet) for 1024×1024 images on H100, and 0.31s (T2I) on an RTX 4090, SANA-Sprint is ideal for AI-powered consumer applications (AIPC).
Source code is available at https://github.com/NVlabs/Sana .
Model Description
- Developed by: NVIDIA, Sana
- Model type: One-Step Diffusion with Continuous-Time Consistency Distillation
- Model size: 0.6B parameters
- Model precision: torch.bfloat16 (BF16)
- Model resolution: This model is developed to generate 1024px based images with multi-scale heigh and width.
- License: NSCL v2-custom . Governing Terms: NVIDIA License. Additional Information: Gemma Terms of Use | Google AI for Developers for Gemma-2-2B-IT, Gemma Prohibited Use Policy | Google AI for Developers .
- Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Linear Diffusion Transformer that uses one fixed, pretrained text encoders ( Gemma2-2B-IT ) and one 32x spatial-compressed latent feature encoder ( DC-AE ).
- Resources for more information: Check out our GitHub Repository and the SANA-Sprint report on arXiv .
Model Sources
For research purposes, we recommend our
generative-models
Github repository (
https://github.com/NVlabs/Sana
), which is more suitable for both training and inference
MIT Han-Lab
provides free SANA-Sprint inference.
- Repository: https://github.com/NVlabs/Sana
- Demo: https://nv-sana.mit.edu/sprint
- Guidance: https://github.com/NVlabs/Sana/asset/docs/sana_sprint.md
Uses
Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
-
Generation of artworks and use in design and other artistic processes.
-
Applications in educational or creative tools.
-
Research on generative models.
-
Safe deployment of models which have the potential to generate harmful content.
-
Probing and understanding the limitations and biases of generative models.
Excluded uses are described below.
Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
Limitations and Bias
Limitations
- The model does not achieve perfect photorealism
- The model cannot render complex legible text
- fingers, .etc in general may not be generated properly.
- The autoencoding part of the model is lossy.
Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Runs of Efficient-Large-Model Sana_Sprint_0.6B_1024px on huggingface.co
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