Instructions to use Muapi/dispatch-style-lora-ltx2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/dispatch-style-lora-ltx2.3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/dispatch-style-lora-ltx2.3") prompt = "A man with short gray hair plays a red electric guitar." output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - LTX.io
How to use Muapi/dispatch-style-lora-ltx2.3 with LTX.io:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download Muapi/dispatch-style-lora-ltx2.3 --local-dir models/dispatch-style-lora-ltx2.3 hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Text/image-to-video with the LoRA on the HQ two-stage base pipeline uv run python -m ltx_pipelines.ti2vid_two_stages_hq \ --checkpoint-path path/to/checkpoint.safetensors \ --distilled-lora path/to/distilled_lora.safetensors 0.8 \ --spatial-upsampler-path path/to/spatial_upsampler.safetensors \ --gemma-root models/gemma-3-12b \ --lora models/dispatch-style-lora-ltx2.3/<weights>.safetensors 1.0 \ --prompt "your prompt here" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8 - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things

- Xet hash:
- 231f0b161cfef73a48459dc1211160501c7ce76506bb45159cc7a15a9c3a13cf
- Size of remote file:
- 1.47 MB
- SHA256:
- 0617933fd2db25d6f56e747105a55d46eede78647c18eee4bd60c1f027f219b4
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