Instructions to use addtime/MMGT_pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use addtime/MMGT_pretrained with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("addtime/MMGT_pretrained", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Add model card for MMGT
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face.
This PR improves the documentation for the MMGT model by:
- Adding the
image-to-videopipeline tag for better discoverability. - Adding
library_name: diffusersas the repository contains a standard Diffusers-compatible structure. - Linking the model card to the research paper and GitHub repository.
- Providing a summary of the two-stage framework (SMGA and MM-HAA).
- Including sample CLI usage instructions for audio-to-video and pose-to-video generation found in the official repository.
- Adding the BibTeX citation for researchers.
Feel free to merge this if it looks good to you!