Instructions to use lightx2v/Wan2.2-Distill-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Wan2.2-Distill-Models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lightx2v/Wan2.2-Distill-Models", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use lightx2v/Wan2.2-Distill-Models with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +14 -0
config.json
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{
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"_class_name": "WanModel",
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"_diffusers_version": "0.33.0",
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"dim": 5120,
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"eps": 1e-06,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"in_dim": 36,
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"model_type": "i2v",
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"num_heads": 40,
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"num_layers": 40,
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"out_dim": 16,
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"text_len": 512
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}
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