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/__init__.py from vidfom/Wan-T2V_14B: direct link, hf CLI and curl.
- Browser
- Download file 145 Bytes
-
https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/diffsynth/__init__.py
- Command line
-
hf download hf://vidfom/Wan-T2V_14B/DiffSynth-Studio/diffsynth/__init__.py
-
curl -L -o __init__.py https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/diffsynth/__init__.py
145 Bytes
| from .data import * | |
| from .models import * | |
| from .prompters import * | |
| from .schedulers import * | |
| from .pipelines import * | |
| from .controlnets import * | |