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
Subtitles and Captions
Love this model so far...only problem for me is that pretty much every video I create comes with captions or subtitles in the video. It would be one thing if the subtitles actually captured what was being said, but they are a mess of letters and symbols that usually don't even make sense. Has anyone had any luck with taming these?
Don't use quotation marks and just add the line after semicolon then do a paragraph return and continue prompt;
she says: what she says.
Then, stuff happens. he replies: what he replies.
Then, ...
Don't use quotation marks and just add the line after semicolon then do a paragraph return and continue prompt;
she says: what she says.
Then, stuff happens. he replies: what he replies.
Then, ...
that works! thanks man!