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 the fp8mixed-learned model also "transformer-only" or the full model in fp8?
#6
by Winnougan - opened
Hi TenStrip, thanks for all of your hard work. I noticed you have the fp8-transformer version and the 10Eros_v1-fp8mixed_learned.safetensors. Is the fp8mixed also transformer only? Thanks.
"Fp8_mixed_learned is the better FP8 version and is a full checkpoint as well, quant by S1LV3RC01N."