Image-to-Video
Diffusers
Safetensors
English
text-to-video
anime
video-generation
diffusion-transformer
flow-matching
wan
commercial-use
Instructions to use aidealab/AnimeGen-I2V with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aidealab/AnimeGen-I2V 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("aidealab/AnimeGen-I2V", 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
- Xet hash:
- 28d643efb62a92b01eacb14729cf717ccee50dc40a594e1ee289a1e772f2996c
- Size of remote file:
- 28.6 GB
- SHA256:
- 6696eef948b9577c7d44e94d46cd49e1d32abd1f1cb8e17bbd71014b2aa32ada
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