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 LTX2.3‑10Eros full‑parameter training or LoRA training?
#28
by chuminglin - opened
Hi, I have a question about the training mode of the model LTX2.3‑10Eros. Could you please clarify whether this model is trained via full‑parameter full train or only LoRA fine‑tune (lora train)? Thanks a lot for your answer.
It's a merge with sulphur lora. Sulphur right now is a rank 768 lora training which is plenty to equal a finetune-level adjustment.