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# 4bit SanaPipeline
### 1. Environment setup
Follow the official [SVDQuant-Nunchaku](https://github.com/mit-han-lab/nunchaku) repository to set up the environment. The guidance can be found [here](https://github.com/mit-han-lab/nunchaku?tab=readme-ov-file#installation).
### 2. Code snap for inference
Here we show the code snippet for SanaPipeline. For SanaPAGPipeline, please refer to the [SanaPAGPipeline](https://github.com/mit-han-lab/nunchaku/blob/main/examples/sana_1600m_pag.py) section.
```python
import torch
from diffusers import SanaPipeline
from nunchaku.models.transformer_sana import NunchakuSanaTransformer2DModel
transformer = NunchakuSanaTransformer2DModel.from_pretrained("mit-han-lab/svdq-int4-sana-1600m")
pipe = SanaPipeline.from_pretrained(
"Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers",
transformer=transformer,
variant="bf16",
torch_dtype=torch.bfloat16,
).to("cuda")
pipe.text_encoder.to(torch.bfloat16)
pipe.vae.to(torch.bfloat16)
image = pipe(
prompt="A cute 🐼 eating 🎋, ink drawing style",
height=1024,
width=1024,
guidance_scale=4.5,
num_inference_steps=20,
generator=torch.Generator().manual_seed(42),
).images[0]
image.save("sana_1600m.png")
```
### 3. Online demo
1). Launch the 4bit Sana.
```bash
python app/app_sana_4bit.py
```
2). Compare with BF16 version
Refer to the original [Nunchaku-Sana.](https://github.com/mit-han-lab/nunchaku/tree/main/app/sana/t2i) guidance for SanaPAGPipeline
```bash
python app/app_sana_4bit_compare_bf16.py
```
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