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
metadata
license: apache-2.0
language:
- en
- zh
base_model:
- MiniMaxAI/MiniMax-H3
pipeline_tag: image-to-video
library_name: diffusers
tags:
- t2v
- i2v
- r2v
MiniMax-H3 Turbo
Please check our repository or the LightX2V MiniMax-H3 examples to reproduce the results.
Please check the model specifications for more details.
Online App
Try the MiniMax-H3 Turbo LoRA directly in LightX2V Studio:
- LightX2V Studio: x2v.light-ai.top
The Studio currently uses the FL2V 8-step v1.0 768p LoRA, which provides improved video and audio generation quality with 8-step inference.
The model version deployed in the Studio may be updated over time.
Studio Preview
Online API
Integrate MiniMax-H3 Turbo into your application through the LightX2V API:
- API Documentation: x2v.light-ai.top/api-docs
