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
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Download DiffSynth-Studio/examples/diffsynth/README.md from vidfom/Wan-T2V_14B: direct link, hf CLI and curl.
- Browser
- Download file 705 Bytes
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https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/examples/diffsynth/README.md
- Command line
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hf download hf://vidfom/Wan-T2V_14B/DiffSynth-Studio/examples/diffsynth/README.md
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curl -L -o README.md https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/examples/diffsynth/README.md
705 Bytes
DiffSynth
DiffSynth is the initial version of our video synthesis framework. In this framework, you can apply video deflickering algorithms to the latent space of diffusion models. You can refer to the original repo for more details.
We provide an example for video stylization. In this pipeline, the rendered video is completely different from the original video, thus we need a powerful deflickering algorithm. We use FastBlend to implement the deflickering module. Please see sd_video_rerender.py.
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea