Create README.md

README.md

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+---
+library_name: sana, sana-sprint
+tags:
+- text-to-image
+- SANA-Sprint
+- 1024px_based_image_size
+- BF16
+- One-step diffusion
+language:
+- en
+- zh
+base_model:
+- Efficient-Large-Model/Sana_Sprint_1.6B_1024px
+pipeline_tag: text-to-image
+---
+<p align="center" style="border-radius: 10px">
+  <img src="https://nvlabs.github.io/Sana/Sprint/asset/SANA-Sprint.png" width="50%" alt="logo"/>
+</p>
+
+<div style="display:flex;justify-content: center">
+  <a href="https://huggingface.co/collections/Efficient-Large-Model/sana-sprint-67d6810d65235085b3b17c76"><img src="https://img.shields.io/static/v1?label=Weights&message=Huggingface&color=yellow"></a> &ensp;
+  <a href="https://github.com/NVlabs/Sana"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=blue&logo=github"></a> &ensp;
+  <a href="https://nvlabs.github.io/Sana/Sprint/"><img src="https://img.shields.io/static/v1?label=Project&message=Github&color=blue&logo=github-pages"></a> &ensp;
+  <!-- <a href="https://hanlab.mit.edu/projects/sana/"><img src="https://img.shields.io/static/v1?label=Page&message=MIT&color=darkred&logo=github-pages"></a> &ensp; -->
+  <a href="https://arxiv.org/pdf/2503.09641"><img src="https://img.shields.io/static/v1?label=Arxiv&message=SANA-Sprint&color=red&logo=arxiv"></a> &ensp;
+  <a href="https://nv-sana.mit.edu/sprint"><img src="https://img.shields.io/static/v1?label=Demo&message=MIT&color=yellow"></a> &ensp;
+  <a href="https://discord.gg/rde6eaE5Ta"><img src="https://img.shields.io/static/v1?label=Discuss&message=Discord&color=purple&logo=discord"></a> &ensp;
+</div>
+
+# 🐱 Sana Model Card
+
+## Demos
+
+<div align="center">
+  <a href="https://www.youtube.com/watch?v=nI_Ohgf8eOU" target="_blank">
+    <img src="https://img.youtube.com/vi/nI_Ohgf8eOU/0.jpg" alt="Demo Video of SANA-Sprint" style="width: 48%; display: block; margin: 0 auto; display: inline-block;">
+  </a>
+  <a href="https://www.youtube.com/watch?v=OOZzkirgsAc" target="_blank">
+    <img src="https://img.youtube.com/vi/OOZzkirgsAc/0.jpg" alt="Demo Video of SANA-Sprint" style="width: 48%; display: block; margin: 0 auto; display: inline-block;">
+  </a>
+</div>
+
+
+## Training Pipeline
+
+<p align="center" border-raduis="10px">
+  <img src="https://nvlabs.github.io/Sana/Sprint/asset/content/paradigm.png" width="90%" alt="teaser_page1"/>
+</p>
+
+## Model Efficiency
+
+<p align="center" border-raduis="10px">
+  <img src="https://nvlabs.github.io/Sana/Sprint/asset/content/teaser.png" width="95%" alt="teaser_page1"/>
+</p>
+
+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:** [NSCL v2-custom](./LICENSE.txt). Governing Terms:  NVIDIA License.  Additional Information:  [Gemma Terms of Use  |  Google AI for Developers](https://ai.google.dev/gemma/terms) for Gemma-2-2B-IT, [Gemma Prohibited Use Policy  |  Google AI for Developers](https://ai.google.dev/gemma/prohibited_use_policy).
+- **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](https://huggingface.co/google/gemma-2-2b-it))
+and one 32x spatial-compressed latent feature encoder ([DC-AE](https://hanlab.mit.edu/projects/dc-ae)).
+- **Resources for more information:** Check out our [GitHub Repository](https://github.com/NVlabs/Sana) and the [SANA-Sprint report on arXiv](https://arxiv.org/pdf/2503.09641).
+
+
+### 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](https://nv-sana.mit.edu/sprint) 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](https://github.com/huggingface/diffusers/pull/11074)
+
+```python
+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.