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
what's the training difference butween minimax_h3_fl2v_turbo_4step_v1.0_768p and minimax_h3_fl2v_turbo_4step_v1.1_768p
#47
by gaga-lee97 - opened
Thank you very much!
I would like to know what the training setting difference between v1.0 and v.1.1?
I try to train my own 4nfe fl2va lora under the released training code but find that:
- the motion, texture and layout all look good, but
- colors always become warmer than 20 nfe baseline which tend to be cooler and darker.
This issue happens to inference with the released minimax_h3_fl2v_turbo_4step_v1.0_768p lora ckpt as well.
But I find minimax_h3_fl2v_turbo_4step_v1.1_768p fixed the color bias! That's amazing!
So I want to know what the difference between v1.0 and v1.1 during training ? any hypeparameter change, different training data ?
Thank you very much!