Instructions to use zenlm/zen-video-i2v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zenlm/zen-video-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("zenlm/zen-video-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
zen-video-i2v
Repackaged from Wan-AI/Wan2.2-I2V-A14B (Apache-2.0, Alibaba Wan team); NOT trained from scratch.
zen-video-i2v is a faithful re-host of the Wan2.2-I2V-A14B image-to-video MoE diffusion model under the zenlm namespace. The high-noise and low-noise expert weights, the Wan VAE, the umT5-XXL text encoder, tokenizer, and configs are byte-for-byte the upstream release. No fine-tuning or modification has been performed.
License
Apache License 2.0, inherited unchanged from upstream Wan-AI/Wan2.2-I2V-A14B. Credit belongs to the Alibaba Wan team. See the bundled NOTICE.
Attribution
- Upstream model: Wan-AI/Wan2.2-I2V-A14B
- Upstream authors: Alibaba Wan team
- Upstream license: Apache-2.0
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Model tree for zenlm/zen-video-i2v
Base model
Wan-AI/Wan2.2-I2V-A14B