Instructions to use vidfom/wan-t2v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vidfom/wan-t2v 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", 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
File size: 680 Bytes
f13a0f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | Metadata-Version: 2.1
Name: diffsynth
Version: 1.1.2
Summary: Enjoy the magic of Diffusion models!
Author: Artiprocher
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
License-File: LICENSE
Requires-Dist: torch>=2.0.0
Requires-Dist: torchvision
Requires-Dist: cupy-cuda12x
Requires-Dist: transformers==4.46.2
Requires-Dist: controlnet-aux==0.0.7
Requires-Dist: imageio
Requires-Dist: imageio[ffmpeg]
Requires-Dist: safetensors
Requires-Dist: einops
Requires-Dist: sentencepiece
Requires-Dist: protobuf
Requires-Dist: modelscope
Requires-Dist: ftfy
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