Instructions to use Efficient-Large-Model/LongLive-Plug-Wan2.1-T2V-14B-few-step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Efficient-Large-Model/LongLive-Plug-Wan2.1-T2V-14B-few-step with PEFT:
Task type is invalid.
- Inference
- Notebooks
- Google Colab
- Kaggle
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Download README.md from Efficient-Large-Model/LongLive-Plug-Wan2.1-T2V-14B-few-step: direct link, hf CLI and curl.
- Browser
- Download file 699 Bytes
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https://huggingface.co/Efficient-Large-Model/LongLive-Plug-Wan2.1-T2V-14B-few-step/resolve/main/README.md
- Command line
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hf download hf://Efficient-Large-Model/LongLive-Plug-Wan2.1-T2V-14B-few-step/README.md
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curl -L -o README.md https://huggingface.co/Efficient-Large-Model/LongLive-Plug-Wan2.1-T2V-14B-few-step/resolve/main/README.md
699 Bytes
| license: apache-2.0 | |
| base_model: Wan-AI/Wan2.1-T2V-14B | |
| base_model_relation: adapter | |
| library_name: peft | |
| pipeline_tag: text-to-video | |
| model_name: LongLive-Plug-Wan2.1-T2V-14B-few-step | |
| tags: | |
| - lora | |
| - distillation | |
| - few-step | |
| # LongLive-Plug-Wan2.1-T2V-14B-few-step | |
| A LoRA adapter for faster, few-step video generation using [Wan2.1-T2V-14B](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B). | |
| This is an adapter and requires the corresponding base model. | |
| ## Usage | |
| Use with the matching [CFG LoRA](https://huggingface.co/Efficient-Large-Model/LongLive-Plug-Wan2.1-T2V-14B-cfg). | |
| Recommended LoRA weights: **few-step : CFG = 1 : 0.5**. | |
| These are adapter weights, not the inference CFG scale. | |