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Efficient-Large-Model / Sana_Sprint_0.6B_1024px_teacher
Model's Last Updated: July 31 2026
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Introduction of Sana_Sprint_0.6B_1024px_teacher
Model Details of Sana_Sprint_0.6B_1024px_teacher
🐱 Sana Model Card
This model serves as the Teacher in the figure below. It's not a few-step generative model but a fine-tuned diffusion model with (1) Dense Timestep Embedding and (2) QK Normalization discussed in the SANA-Sprint paper . Few-step generative models can be found in HF repo . Source code is available at https://github.com/NVlabs/Sana .
Training Pipeline
Model Description
- Developed by: NVIDIA, Sana
- Model type: Teacher model for 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
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.
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