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
FL2V Turbo 4-step v1.2 (768p) released — improved audio quality
#52
by lightx2v - opened
We’re excited to release the MiniMax-H3 FL2V Turbo 4-step v1.2 768p LoRA!
Compared with v1.1, this release focuses on improving the audio generation quality. It produces cleaner and more stable audio with fewer artifacts while retaining the same fast 4-step generation setup.
Model Weights
Recommended Inference Settings
- Steps: 4
- Video shift: 6
- Audio shift: 3
- Sampler: Euler
- Resolution: Up to 768p
Comparison with v1.1
The following comparisons use the same input, prompt, seed, and inference settings. Please enable audio when playing the videos.
v1.1
v1.2
nice job!