Instructions to use lightx2v/Minimax-h3-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Minimax-h3-Turbo 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("lightx2v/Minimax-h3-Turbo", 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
| license: apache-2.0 | |
| language: | |
| - en | |
| - zh | |
| base_model: | |
| - MiniMaxAI/MiniMax-H3 | |
| pipeline_tag: image-to-video | |
| library_name: diffusers | |
| tags: | |
| - t2v | |
| - i2v | |
| - r2v | |
| Please check [our repo](https://github.com/ModelTC/Minimax-H3-Turbo) or [LightX2V](https://github.com/ModelTC/LightX2V/tree/main/examples/minimax_h3) to reproduce the results. | |
| Please check [model specifications](https://github.com/ModelTC/Minimax-H3-Turbo#model-specs) for more details |