Image-to-Video
Diffusers
Safetensors
English
text-to-video
lora
video-generation
wan
wan2.1
diffusion-pipe
musubi-tuner
identity
Instructions to use fwwrsd/ohwx-wan-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fwwrsd/ohwx-wan-lora 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/Wan2.1-I2V-14B-720P", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("fwwrsd/ohwx-wan-lora") prompt = "ohwx" 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") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- Xet hash:
- f4e6b585dcadaff210f6b44d0b39eeba9421776bffef1dba7002fddbefc33f69
- Size of remote file:
- 231 MB
- SHA256:
- 7100ff17ddf1e3ee78d0cc52ea267708c69c46cb17c363743ca468b5219717a2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.