Instructions to use Muapi/dispatch-style-lora-ltx2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

dispatch-style-lora-ltx2.3 / preview.jpg

Commit History

Add Dispatch Style Lora LTX2.3

6a0af66
verified

Muapi commited on May 23

Commit History

Add Dispatch Style Lora LTX2.3 6a0af66 verified

Add Dispatch Style Lora LTX2.3

6a0af66
verified