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
-
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
metadata
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.
This is an adapter and requires the corresponding base model.
Usage
Use with the matching CFG LoRA.
Recommended LoRA weights: few-step : CFG = 1 : 0.5.
These are adapter weights, not the inference CFG scale.