Instructions to use Efficient-Large-Model/SANA-WM_bidirectional-diffusers-refiner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Efficient-Large-Model/SANA-WM_bidirectional-diffusers-refiner with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Efficient-Large-Model/SANA-WM_bidirectional-diffusers-refiner", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download text_encoder/processor_config.json from Efficient-Large-Model/SANA-WM_bidirectional-diffusers-refiner: direct link, hf CLI and curl.
- Browser
- Download file 70 Bytes
-
https://huggingface.co/Efficient-Large-Model/SANA-WM_bidirectional-diffusers-refiner/resolve/main/text_encoder/processor_config.json
- Command line
-
hf download hf://Efficient-Large-Model/SANA-WM_bidirectional-diffusers-refiner/text_encoder/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/Efficient-Large-Model/SANA-WM_bidirectional-diffusers-refiner/resolve/main/text_encoder/processor_config.json
70 Bytes
| { | |
| "image_seq_length": 256, | |
| "processor_class": "Gemma3Processor" | |
| } | |