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
Update README.md
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by vantagewithai - opened
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https://huggingface.co/TenStrip/LTX2.3-10Eros_Workflows
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https://huggingface.co/vantagewithai/LTX2.3-10Eros-GGUF/tree/main
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https://github.com/TenStrip/10S-Comfy-nodes
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https://huggingface.co/TenStrip/LTX2.3-10Eros_Workflows
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- **v1:** https://huggingface.co/vantagewithai/LTX2.3-10Eros-GGUF/tree/main
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- **v1.2:** https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.2-GGUF/tree/main
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Nodes:
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https://github.com/TenStrip/10S-Comfy-nodes
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