README.md

---
tags:
- SanaControlNetPipeline
base_model:
- Efficient-Large-Model/Sana_600M_1024px_diffusers
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>

# Model card

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.


### 🧨 Diffusers 

### 1. How to use `SanaControlNetPipeline` with `🧨diffusers`

```python
# run `pip install git+https://github.com/huggingface/diffusers` before use Sana in diffusers
import torch
from diffusers import SanaControlNetModel, SanaControlNetPipeline
from diffusers.utils import load_image

pipe = SanaControlNetPipeline.from_pretrained(
    "ishan24/Sana_600M_1024px_ControlNetPlus_diffusers",
    variant="fp16",
    torch_dtype=torch.float16,
    device_map="balanced"
)

pipe.vae.to(torch.bfloat16)
pipe.text_encoder.to(torch.bfloat16)

cond_image = load_image(
    "https://huggingface.co/ishan24/Sana_600M_1024px_ControlNet_diffusers/resolve/main/hed_example.png"
)
prompt='a cat with a neon sign that says "Sana"'
image = pipe(
    prompt,
    control_image=cond_image,
).images[0]
image.save("sana.png")
```