# Download the weights from this repo, plus the Gemma text encoder
hf download akshan-main/tiny-ltx-modular-pipe --local-dir models/tiny-ltx-modular-pipe
hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b# Fast pipeline (distilled model, no distilled LoRA needed)
uv run python -m ltx_pipelines.distilled \
--distilled-checkpoint-path models/tiny-ltx-modular-pipe/<distilled-checkpoint>.safetensors \
--spatial-upsampler-path models/tiny-ltx-modular-pipe/<spatial-upsampler>.safetensors \
--gemma-root models/gemma-3-12b \
--prompt "A beautiful sunset over the ocean" \
--output-path output.mp4
# For image-to-video, add: --image path/to/image.jpg 0 0.8# HQ pipeline (two-stage, higher quality)
uv run python -m ltx_pipelines.ti2vid_two_stages_hq \
--checkpoint-path models/tiny-ltx-modular-pipe/<checkpoint>.safetensors \
--distilled-lora models/tiny-ltx-modular-pipe/<distilled-lora>.safetensors 0.8 \
--spatial-upsampler-path models/tiny-ltx-modular-pipe/<spatial-upsampler>.safetensors \
--gemma-root models/gemma-3-12b \
--prompt "A beautiful sunset over the ocean" \
--output-path output.mp4
# For image-to-video, add: --image path/to/image.jpg 0 0.8Tiny LTX Modular Pipeline (Testing)
Tiny model for CI testing of the LTX modular pipeline. Not for inference.
Components: T5EncoderModel (32 dim), LTXVideoTransformer3DModel (32 dim, 1 layer), AutoencoderKLLTXVideo (32 channels, 1 block), FlowMatchEulerDiscreteScheduler.
Used by: tests/modular_pipelines/ltx/test_modular_pipeline_ltx.py
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# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen