Image-to-Video
Diffusers
Safetensors
MOVA
image-text-to-video
image-to-audio-video
image-text-to-audio-video
MOVA
OpenMOSS
SII
MOSI
sglang-diffusion
Instructions to use OpenMOSS-Team/MOVA-360p with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OpenMOSS-Team/MOVA-360p 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("OpenMOSS-Team/MOVA-360p", 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
Add pipeline tag and link to paper
#4
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the
pipeline_tag: any-to-anyto the metadata for better discoverability of multimodal models. - Updating the paper source link to point to the official Hugging Face paper page.
- Adding a link to the project page.
- Adding a sample usage section with an inference command sourced from the GitHub README.
- Removing
library_name: diffusersfrom the metadata as the official repository currently lists Diffusers integration as a "TODO" item.