Instructions to use Efficient-Large-Model/SANA-WM_bidirectional-diffusers 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 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", 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 model_index.json from Efficient-Large-Model/SANA-WM_bidirectional-diffusers: direct link, hf CLI and curl.
- Browser
- Download file 419 Bytes
-
https://huggingface.co/Efficient-Large-Model/SANA-WM_bidirectional-diffusers/resolve/main/model_index.json
- Command line
-
hf download hf://Efficient-Large-Model/SANA-WM_bidirectional-diffusers/model_index.json
-
curl -L -o model_index.json https://huggingface.co/Efficient-Large-Model/SANA-WM_bidirectional-diffusers/resolve/main/model_index.json
419 Bytes
| { | |
| "_class_name": "SanaWMPipeline", | |
| "_diffusers_version": "0.38.0", | |
| "tokenizer": [ | |
| "transformers", | |
| "GemmaTokenizerFast" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "Gemma2ForCausalLM" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKLLTX2Video" | |
| ], | |
| "transformer": [ | |
| "diffusers", | |
| "SanaWMTransformer3DModel" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ] | |
| } | |