Instructions to use lightx2v/Wan2.1-Distill-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Wan2.1-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("Wan-AI/Wan2.1-T2V-14B,Wan-AI/Wan2.1-I2V-14B-480P,Wan-AI/Wan2.1-I2V-14B-720P", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("lightx2v/Wan2.1-Distill-Models") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use lightx2v/Wan2.1-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
- Local Apps
- Draw Things
Update README.md
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README.md
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```bash
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pip install -r requirements.txt
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```
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Or refer to [Quick Start Documentation](https://
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4. Select and modify configuration file
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```
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#### Documentation
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- **Quick Start Guide**: [LightX2V Quick Start](https://
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- **Complete Usage Guide**: [LightX2V Model Structure Documentation](https://
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- **Configuration Guide**: [Configuration Files](https://github.com/ModelTC/LightX2V/tree/main/configs/distill)
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- **Quantization Usage**: [Quantization Documentation](https://
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- **Parameter Offload**: [Offload Documentation](https://
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#### Performance Advantages
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- VAE encoder/decoder
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- Tokenizers
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Refer to [LightX2V Documentation](https://github.com/ModelTC/LightX2V/blob/main/docs/EN/source/
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```bash
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pip install -r requirements.txt
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```
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Or refer to [Quick Start Documentation](https://github.com/ModelTC/LightX2V/blob/main/docs/EN/source/getting_started/quickstart.md) to use docker
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4. Select and modify configuration file
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```
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#### Documentation
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- **Quick Start Guide**: [LightX2V Quick Start](https://github.com/ModelTC/LightX2V/blob/main/docs/EN/source/getting_started/quickstart.md)
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- **Complete Usage Guide**: [LightX2V Model Structure Documentation](https://github.com/ModelTC/LightX2V/blob/main/docs/EN/source/getting_started/model_structure.md)
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- **Configuration Guide**: [Configuration Files](https://github.com/ModelTC/LightX2V/tree/main/configs/distill)
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- **Quantization Usage**: [Quantization Documentation](https://github.com/ModelTC/LightX2V/blob/main/docs/EN/source/method_tutorials/quantization.md)
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- **Parameter Offload**: [Offload Documentation](https://github.com/ModelTC/LightX2V/blob/main/docs/EN/source/method_tutorials/offload.md)
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#### Performance Advantages
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- VAE encoder/decoder
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- Tokenizers
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Refer to [LightX2V Documentation](https://github.com/ModelTC/LightX2V/blob/main/docs/EN/source/getting_started/model_structure.md) for how to organize the complete model directory.
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