Image-to-Video
Diffusers
Safetensors
English
4d-generation
image-to-4d
diffusion
novel-view-synthesis
point-trajectory
Instructions to use Yanran21/MoGe4D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Yanran21/MoGe4D 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("Yanran21/MoGe4D", torch_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:
- 07b68053f306c8e63752421c2e71e40181eec7bfcafe4d2cf0a80d58938fd69b
- Size of remote file:
- 34.3 GB
- SHA256:
- 6734aee63fcd5136d0ddb2a2f51a5fbdebdb13f6342c4a9ffa080e683c7e3a79
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