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
4-step 768p V1.0 test results
We are continuously improving the visual quality of generated videos, with a particular focus on fine-detail rendering, resolution, and video duration. Below are examples generated with the 4-step v1.0 768p model. This version supports resolutions up to 768 Γ 1344. You can access and use the model through the LightX2V or MiniMax-H3-Turbo repository.
hi, wanted to ask what the ETA is to the ref2va ? :)thank you so much for these loras!
wonderful! im waiting for ref2va
Can't wait for ref2va to come out too!!
Thank you for the great work. Itβs working very well. When I use Ref2VA, I use it with FL2VA + Ref LoRA by Kijai, and this LoRA seems to be working without any issues.
Thank you for the great work. Itβs working very well. When I use Ref2VA, I use it with FL2VA + Ref LoRA by Kijai, and this LoRA seems to be working without any issues.
pruned or unpruned?
From all my personal testing 0.1 is better quality than 1.0
If you compare both loras results, you will see that 768p videos are in higher rez. So... is this counter-intuitive and are 768p made for higher rez and quality and in just 4 steps?