Image-to-Video
Diffusers
Safetensors
English
LTX2Pipeline
fp8
unsloth
quantized
text-to-video
video-generation
Instructions to use unsloth/LTX-2-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use unsloth/LTX-2-FP8 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("unsloth/LTX-2-FP8", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "cute sloth typing on a computer (INT8)" 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
- Local Apps Settings
- Unsloth Desktop
Commit History
3 days ago
-
Add Lightricks/LTX-2 files (revision dfcc210838) for Unsloth Studio
4a4829e