Instructions to use vidfom/Wan-T2V_14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vidfom/Wan-T2V_14B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vidfom/Wan-T2V_14B", 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 DiffSynth-Studio/diffsynth/models/__pycache__/svd_unet.cpython-311.pyc from vidfom/Wan-T2V_14B: direct link, hf CLI and curl.
- Browser
- Download file 232 kB
-
https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/diffsynth/models/__pycache__/svd_unet.cpython-311.pyc
- Command line
-
hf download hf://vidfom/Wan-T2V_14B/DiffSynth-Studio/diffsynth/models/__pycache__/svd_unet.cpython-311.pyc
-
curl -L -o svd_unet.cpython-311.pyc https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/diffsynth/models/__pycache__/svd_unet.cpython-311.pyc
232 kB
- Xet hash:
- 24959d596d381d27637964179e5d0700e35a064c416da4826a07dd5ba4d0be48
- Size of remote file:
- 232 kB
- SHA256:
- 9d161915545c3e84643e180774ef5e28bfab4e44f579016d326b52f1dd55887a
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