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__/sd3_text_encoder.cpython-311.pyc from vidfom/Wan-T2V_14B: direct link, hf CLI and curl.
- Browser
- Download file 116 kB
-
https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/diffsynth/models/__pycache__/sd3_text_encoder.cpython-311.pyc
- Command line
-
hf download hf://vidfom/Wan-T2V_14B/DiffSynth-Studio/diffsynth/models/__pycache__/sd3_text_encoder.cpython-311.pyc
-
curl -L -o sd3_text_encoder.cpython-311.pyc https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/diffsynth/models/__pycache__/sd3_text_encoder.cpython-311.pyc
116 kB
- Xet hash:
- 3d640d717c99476c93c000778457ab0a4b14d3f67076a8a927a76940ac338edd
- Size of remote file:
- 116 kB
- SHA256:
- f95b38fd383ecb067e897c23537b90f4799d47c29d17fa0515343d0daeabd9c0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.