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
Understanding Joy-AI echo Lora and Eros model
#56
by YTGaming - opened
First, thank you so much for the model and your work on it. It's amazing. I was wondering on the JoyAI-echo lora that is mentioned in the changelog and in your workflow. Should this Lora be included and enabled when using the newer Eros models? Or is it already "baked-in", if that is the correct terminology? I'm still only a few months into this, but what you've done is pretty damn awesome. The Likeness nodes seem to really help.
It's only added to expand the model and add the reference tensors, the actual content isn't really baked in to the model's behavior. But if you load the lora at high strength you'll get it's effect.