Instructions to use lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
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README.md
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Our inference framework utilizes [lightx2v](https://github.com/ModelTC/lightx2v), a highly efficient inference engine that supports multiple models. This framework significantly accelerates the video generation process while maintaining high quality output.
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```bash
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bash scripts/
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or using the lora version:
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```bash
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bash scripts/
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```
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We recommend using the **LCM scheduler** with the following settings:
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Our inference framework utilizes [lightx2v](https://github.com/ModelTC/lightx2v), a highly efficient inference engine that supports multiple models. This framework significantly accelerates the video generation process while maintaining high quality output.
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```bash
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bash scripts/wan/run_wan_t2v_distill_4step_cfg.sh
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```
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or using the lora version:
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```bash
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bash scripts/wan/run_wan_t2v_distill_4step_cfg_lora.sh
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```
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We recommend using the **LCM scheduler** with the following settings:
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