🐱 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: 1.6B parameters
• Model precision: torch.bfloat16 (BF16)
• Model resolution: This model is developed to generate 1024px based images with multi-scale heigh and width.
• License: Apache License 2.0. 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

🧨 Diffusers

Under construction PR

from diffusers import SanaSprintPipeline
import torch

pipeline = SanaSprintPipeline.from_pretrained(
    "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers",
    torch_dtype=torch.bfloat16
)
pipeline.to("cuda:0")

prompt = "a tiny astronaut hatching from an egg on the moon"

image = pipeline(prompt=prompt, num_inference_steps=2).images[0]
image.save("sana_sprint.png")

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.