Instructions to use SceneWorks/Sana_1600M_1024px_mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use SceneWorks/Sana_1600M_1024px_mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download SceneWorks/Sana_1600M_1024px_mlx --local-dir Sana_1600M_1024px_mlx
- Sana
How to use SceneWorks/Sana_1600M_1024px_mlx with Sana:
# Load the model and infer image from text import torch from app.sana_pipeline import SanaPipeline from torchvision.utils import save_image sana = SanaPipeline("configs/sana_config/1024ms/Sana_1600M_img1024.yaml") sana.from_pretrained("hf://SceneWorks/Sana_1600M_1024px_mlx") image = sana( prompt='a cyberpunk cat with a neon sign that says "Sana"', height=1024, width=1024, guidance_scale=5.0, pag_guidance_scale=2.0, num_inference_steps=18, ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Add SANA MLX mirror (verbatim repackage of NVIDIA diffusers checkpoint into SanaPipeline::from_snapshot layout)
eef346e verified Download text_encoder/tokenizer.json from SceneWorks/Sana_1600M_1024px_mlx: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/SceneWorks/Sana_1600M_1024px_mlx/resolve/main/text_encoder/tokenizer.json
- Command line
-
hf download hf://SceneWorks/Sana_1600M_1024px_mlx/text_encoder/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SceneWorks/Sana_1600M_1024px_mlx/resolve/main/text_encoder/tokenizer.json
17.5 MB
- Xet hash:
- b8faa3bc3643dc13c5e4ad543f160c0127b406e8aee124e3e872fcaecea8acd3
- Size of remote file:
- 17.5 MB
- SHA256:
- 3f289bc05132635a8bc7aca7aa21255efd5e18f3710f43e3cdb96bcd41be4922
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