Instructions to use Lightricks/LTX-Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-Video 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("Lightricks/LTX-Video", 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") - Inference
- Notebooks
- Google Colab
- Kaggle
fp8 version
#21
by MayensGuds - opened
quants would be amazing <3, if not fancy quants are available it'd be nice to have fp8 version pls <3
I already saw an Int8 version in this video, and it can run with as little as 6GB of VRAM: https://youtu.be/nur4_b4yzM0?t=421
I already saw an Int8 version in this video, and it can run with as little as 6GB of VRAM: https://youtu.be/nur4_b4yzM0?t=421
it can be loaded as fp8 but the weights wills till be bf16 or fp16 (i don't which one is the current weights)
Update! check my repo i quant the weights
Update! check my repo i quant the weights
How does the model perform with the weight quantised?