Create README.md

README.md

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+---
+library_name: sana
+tags:
+- text-to-image
+- Sana
+- 1024px_based_image_size
+language:
+- en
+- zh
+base_model:
+- Efficient-Large-Model/Sana_600M_1024px
+pipeline_tag: text-to-image
+---
+
+<p align="center" style="border-radius: 10px">
+  <img src="https://raw.githubusercontent.com/NVlabs/Sana/refs/heads/main/asset/logo.png" width="35%" alt="logo"/>
+</p>
+
+<div style="display:flex;justify-content: center">
+  <a href="https://huggingface.co/collections/Efficient-Large-Model/sana-673efba2a57ed99843f11f9e"><img src="https://img.shields.io/static/v1?label=Demo&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/"><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/abs/2410.10629"><img src="https://img.shields.io/static/v1?label=Arxiv&message=Sana&color=red&logo=arxiv"></a> &ensp;
+  <a href="https://nv-sana.mit.edu/"><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
+<p align="center" border-raduis="10px">
+  <img src="https://raw.githubusercontent.com/NVlabs/Sana/refs/heads/main/asset/Sana.jpg" width="80%" alt="teaser_page1"/>
+</p>
+
+## Model
+<p align="center" border-raduis="10px">
+  <img src="https://raw.githubusercontent.com/NVlabs/Sana/refs/heads/main/asset/model-incremental.jpg" width="80%" alt="teaser_page1"/>
+</p>
+
+We introduce **Sana**, a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution.
+Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU.
+
+Source code is available at https://github.com/NVlabs/Sana.
+
+### Model Description
+
+- **Developed by:** NVIDIA, Sana
+- **Model type:** Linear-Diffusion-Transformer-based text-to-image generative model
+- **Model size:** 1648M parameters
+- **Model resolution:** This model is developed to generate 1024px based images with multi-scale heigh and width.
+- **License:** [CC BY-NC-SA 4.0 License](./LICENSE.txt)
+- **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 report on arXiv](https://arxiv.org/abs/2410.10629).
+
+### 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 and for which most advanced diffusion sampler like Flow-DPM-Solver is integrated.
+[MIT Han-Lab](https://nv-sana.mit.edu/) provides free Sana inference.
+- **Repository:** ttps://github.com/NVlabs/Sana
+- **Demo:** https://nv-sana.mit.edu/
+
+### 🧨 Diffusers 
+
+PR developing: [Sana](https://github.com/huggingface/diffusers/pull/9982) and [DC-AE](https://github.com/huggingface/diffusers/pull/9708)
+
+
+## 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.