Instructions to use lightx2v/Wan2.2-Distill-Loras with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Wan2.2-Distill-Loras 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("Wan-AI/Wan2.2-I2V-A14B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("lightx2v/Wan2.2-Distill-Loras") prompt = "A man with short gray hair plays a red electric guitar." input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") image = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Diffusion Single File
How to use lightx2v/Wan2.2-Distill-Loras 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
- Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
what is the successor of "250928-dyno" t2v model?
#13
by dipper9876 - opened
From my limited testing, I still find the 250928-dyno high noise model superior, thank you for releasing that.
Are the 1217 loras distilled using a different method?
Is a successor to 250928-dyno planned?
I ran a few tests, and I think the old dyno is better for the high, so it would be great if we got the same treatment for this new version too, ie a full model rather than the lora. Or however it was done with 2050928-dyno