Instructions to use Efficient-Large-Model/SoL-Refiner-LTX-2.3-Multi-Step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Efficient-Large-Model/SoL-Refiner-LTX-2.3-Multi-Step with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Efficient-Large-Model/SoL-Refiner-LTX-2.3-Multi-Step", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download tokenizer/tokenizer.json from Efficient-Large-Model/SoL-Refiner-LTX-2.3-Multi-Step: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/Efficient-Large-Model/SoL-Refiner-LTX-2.3-Multi-Step/resolve/main/tokenizer/tokenizer.json
- Command line
-
hf download hf://Efficient-Large-Model/SoL-Refiner-LTX-2.3-Multi-Step/tokenizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Efficient-Large-Model/SoL-Refiner-LTX-2.3-Multi-Step/resolve/main/tokenizer/tokenizer.json
33.4 MB
- Xet hash:
- e48a4d6d4b1b14a42abfa38b36429f876529af08797a8b9e6536fc4ffc313905
- Size of remote file:
- 33.4 MB
- SHA256:
- b5f448d83e9bb4010eead49589d18b9e35c2f16427b6c200ad08342c571f4f10
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