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
1.0 is the best
#54
by ApexArtist - opened
I use turbo for upscale pass 1.0 seems to the cleanest
1.1 seems too dark
1.2 seems it changes the original too much
1.0 seems to be the sweet spot ..
Correct me
1.2有REF的Lora吗?
Do you mean the 4steps lora?
I use turbo for upscale pass 1.0 seems to the cleanest
1.1 seems too dark
1.2 seems it changes the original too much
1.0 seems to be the sweet spot ..Correct me
I'd do it if I knew which LoRa you are talking about!
FL2V