Instructions to use TenStrip/LTX2.3-10Eros with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TenStrip/LTX2.3-10Eros 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("TenStrip/LTX2.3-10Eros", 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
Is there an LTX2.3 1.1 (11Eros) in the works?
#50
by iamscr3wballl - opened
Is there plans to update with a "11Eros" version with the 1.1 fixes, or planning to just keep running with the LTX2.3 v1.0 base?
The new version is continued from 1.3, 1.3 is basically a reset to base model and now continuing on top of that that erased the 1.2 issues. My new merge methods are way more complex and won't lead it into the state like 1.2 where the data puts too much tendency over prompt and image leading to the random added anatomy issue.