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
Wan-T2V_14B / DiffSynth-Studio /diffsynth /extensions /ImageQualityMetric /trainer /models /base_model.py
Download DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py from vidfom/Wan-T2V_14B: direct link, hf CLI and curl.
- Browser
- Download file 80 Bytes
-
https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py
- Command line
-
hf download hf://vidfom/Wan-T2V_14B/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py
-
curl -L -o base_model.py https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py
80 Bytes
| from dataclasses import dataclass | |
| class BaseModelConfig: | |
| pass | |