## 🔥 1. We provide all the links of Sana pth and diffusers safetensor below

| Model                | Reso   | pth link                                                                                                                    | diffusers                                                                                                                                         | Precision     | Description    |
|----------------------|--------|-----------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------|---------------|----------------|
| Sana-0.6B            | 512px  | [Sana_600M_512px](https://huggingface.co/Efficient-Large-Model/Sana_600M_512px)                                             | [Efficient-Large-Model/Sana_600M_512px_diffusers](https://huggingface.co/Efficient-Large-Model/Sana_600M_512px_diffusers)                         | fp16/fp32     | Multi-Language |
| Sana-0.6B            | 1024px | [Sana_600M_1024px](https://huggingface.co/Efficient-Large-Model/Sana_600M_1024px)                                           | [Efficient-Large-Model/Sana_600M_1024px_diffusers](https://huggingface.co/Efficient-Large-Model/Sana_600M_1024px_diffusers)                       | fp16/fp32     | Multi-Language |
| Sana-1.6B            | 512px  | [Sana_1600M_512px](https://huggingface.co/Efficient-Large-Model/Sana_1600M_512px)                                           | [Efficient-Large-Model/Sana_1600M_512px_diffusers](https://huggingface.co/Efficient-Large-Model/Sana_1600M_512px_diffusers)                       | fp16/fp32     | -              |
| Sana-1.6B            | 512px  | [Sana_1600M_512px_MultiLing](https://huggingface.co/Efficient-Large-Model/Sana_1600M_512px_MultiLing)                       | [Efficient-Large-Model/Sana_1600M_512px_MultiLing_diffusers](https://huggingface.co/Efficient-Large-Model/Sana_1600M_512px_MultiLing_diffusers)   | fp16/fp32     | Multi-Language |
| Sana-1.6B            | 1024px | [Sana_1600M_1024px](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px)                                         | [Efficient-Large-Model/Sana_1600M_1024px_diffusers](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_diffusers)                     | fp16/fp32     | -              |
| Sana-1.6B            | 1024px | [Sana_1600M_1024px_MultiLing](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_MultiLing)                     | [Efficient-Large-Model/Sana_1600M_1024px_MultiLing_diffusers](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_MultiLing_diffusers) | fp16/fp32     | Multi-Language |
| Sana-1.6B            | 1024px | [Sana_1600M_1024px_BF16](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_BF16)                               | [Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers)           | **bf16**/fp32 | Multi-Language |
| Sana-1.6B            | 1024px | -                                                                                                                           | [mit-han-lab/svdq-int4-sana-1600m](https://huggingface.co/mit-han-lab/svdq-int4-sana-1600m)                                                       | **int4**      | Multi-Language |
| Sana-1.6B            | 2Kpx   | [Sana_1600M_2Kpx_BF16](https://huggingface.co/Efficient-Large-Model/Sana_1600M_2Kpx_BF16)                                   | [Efficient-Large-Model/Sana_1600M_2Kpx_BF16_diffusers](https://huggingface.co/Efficient-Large-Model/Sana_1600M_2Kpx_BF16_diffusers)               | **bf16**/fp32 | Multi-Language |
| Sana-1.6B            | 4Kpx   | [Sana_1600M_4Kpx_BF16](https://huggingface.co/Efficient-Large-Model/Sana_1600M_4Kpx_BF16)                                   | [Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers](https://huggingface.co/Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers)               | **bf16**/fp32 | Multi-Language |
| Sana-1.6B            | 4Kpx   | [Sana_1600M_4Kpx_BF16](https://huggingface.co/Efficient-Large-Model/Sana_1600M_4Kpx_BF16)                                   | [Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers](https://huggingface.co/Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers)               | **bf16**/fp32 | Multi-Language |
| ControlNet           |        |                                                                                                                             |                                                                                                                                                   |               |                |
| Sana-1.6B-ControlNet | 1Kpx   | [Sana_1600M_1024px_BF16_ControlNet_HED](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_BF16_ControlNet_HED) | Coming soon                                                                                                                                       | **bf16**/fp32 | Multi-Language |
| Sana-0.6B-ControlNet | 1Kpx   | [Sana_600M_1024px_ControlNet_HED](https://huggingface.co/Efficient-Large-Model/Sana_600M_1024px_ControlNet_HED)             | Coming soon                                                                                                                                       | fp16/fp32     | -              |

## ❗ 2. Make sure to use correct precision(fp16/bf16/fp32) for training and inference.

### We provide two samples to use fp16 and bf16 weights, respectively.

❗️Make sure to set `variant` and `torch_dtype` in diffusers pipelines to the desired precision.

#### 1). For fp16 models

```python
# run `pip install git+https://github.com/huggingface/diffusers` before use Sana in diffusers
import torch
from diffusers import SanaPipeline

pipe = SanaPipeline.from_pretrained(
    "Efficient-Large-Model/Sana_1600M_1024px_diffusers",
    variant="fp16",
    torch_dtype=torch.float16,
)
pipe.to("cuda")

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

prompt = 'a cyberpunk cat with a neon sign that says "Sana"'
image = pipe(
    prompt=prompt,
    height=1024,
    width=1024,
    guidance_scale=5.0,
    num_inference_steps=20,
    generator=torch.Generator(device="cuda").manual_seed(42),
)[0]

image[0].save("sana.png")
```

#### 2). For bf16 models

```python
# run `pip install git+https://github.com/huggingface/diffusers` before use Sana in diffusers
import torch
from diffusers import SanaPAGPipeline

pipe = SanaPAGPipeline.from_pretrained(
  "Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers",
  variant="bf16",
  torch_dtype=torch.bfloat16,
  pag_applied_layers="transformer_blocks.8",
)
pipe.to("cuda")

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

prompt = 'a cyberpunk cat with a neon sign that says "Sana"'
image = pipe(
    prompt=prompt,
    guidance_scale=5.0,
    pag_scale=2.0,
    num_inference_steps=20,
    generator=torch.Generator(device="cuda").manual_seed(42),
)[0]
image[0].save('sana.png')
```

## ❗ 3. 4K models

4K models need VAE tiling to avoid OOM issue.(16 GPU is recommended)

```python
# run `pip install git+https://github.com/huggingface/diffusers` before use Sana in diffusers
import torch
from diffusers import SanaPipeline

pipe = SanaPipeline.from_pretrained(
    "Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers",
    variant="bf16",
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

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

# for 4096x4096 image generation OOM issue, feel free adjust the tile size
if pipe.transformer.config.sample_size == 128:
    pipe.vae.enable_tiling(
        tile_sample_min_height=1024,
        tile_sample_min_width=1024,
        tile_sample_stride_height=896,
        tile_sample_stride_width=896,
    )
prompt = 'a cyberpunk cat with a neon sign that says "Sana"'
image = pipe(
    prompt=prompt,
    height=4096,
    width=4096,
    guidance_scale=5.0,
    num_inference_steps=20,
    generator=torch.Generator(device="cuda").manual_seed(42),
)[0]

image[0].save("sana_4K.png")
```

## ❗ 4. int4 inference

This int4 model is quantized with [SVDQuant-Nunchaku](https://github.com/mit-han-lab/nunchaku). You need first follow the [guidance of installation](https://github.com/mit-han-lab/nunchaku?tab=readme-ov-file#installation) of nunchaku engine, then you can use the following code snippet to perform inference with int4 Sana model.

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")
```