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1. add model zoo readme for clarification;
2. update README.md; Signed-off-by: lawrence-cj <cjs1020440147@icloud.com>
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## 🔥 1. We provide all the links of Sana pth and diffusers safetensor below | ||
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| 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 | | ||
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## ❗ 2. Make sure to use correct precision(fp16/bf16/fp32) for training and inference. | ||
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### We provide two samples to use fp16 and bf16 weights, respectively. | ||
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❗️Make sure to set `variant` and `torch_dtype` in diffusers pipelines to the desired precision. | ||
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#### 1). For fp16 models | ||
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```python | ||
import torch | ||
from diffusers import SanaPipeline | ||
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pipe = SanaPipeline.from_pretrained( | ||
"Efficient-Large-Model/Sana_1600M_1024px_diffusers", | ||
variant="fp16", | ||
torch_dtype=torch.float16, | ||
) | ||
pipe.to("cuda") | ||
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pipe.vae.to(torch.bfloat16) | ||
pipe.text_encoder.to(torch.bfloat16) | ||
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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] | ||
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image[0].save("sana.png") | ||
``` | ||
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#### 2). For bf16 models | ||
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```python | ||
# run `pip install -U diffusers` before use Sana in diffusers | ||
import torch | ||
from diffusers import SanaPAGPipeline | ||
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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") | ||
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pipe.text_encoder.to(torch.bfloat16) | ||
pipe.vae.to(torch.bfloat16) | ||
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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') | ||
``` |