Installation & Usage

Installation

# From the repository root
uv sync --frozen

# Or install as a package
pip install -e packages/ltx-pipelines

Requirements

LTX-2 Model Checkpoint - Local .safetensors file
Gemma Text Encoder - Local Gemma model directory
Spatial Upscaler - Required by two-stage pipelines, for the upsampling stage
Distilled LoRA - Required by two-stage non-distilled pipelines, used for the stage-2 refinement

Running Pipelines

All pipelines can be run directly from the command line. Each pipeline module is executable:

# Run a pipeline (example: two-stage text-to-video)
python -m ltx_pipelines.ti2vid_two_stages \
    --checkpoint-path path/to/checkpoint.safetensors \
    --distilled-lora path/to/distilled_lora.safetensors 0.8 \
    --spatial-upsampler-path path/to/upsampler.safetensors \
    --gemma-root path/to/gemma \
    --prompt "A beautiful sunset over the ocean" \
    --output-path output.mp4

# View all available options for any pipeline
python -m ltx_pipelines.ti2vid_two_stages --help

Available pipeline modules

ltx_pipelines.ti2vid_two_stages - Two-stage text/image-to-video (recommended). (docs, source)
ltx_pipelines.ti2vid_two_stages_hq - Two-stage text/image-to-video (different sampler, better quality). (docs, source)
ltx_pipelines.ti2vid_one_stage - Single-stage text/image-to-video. (docs, source)
ltx_pipelines.t2a_one_stage - Single-stage text-to-audio (audio-only output). (docs, source)
ltx_pipelines.distilled - Fast text/image-to-video pipeline using only the distilled model. (docs, source)
ltx_pipelines.ic_lora - Video-to-video with IC-LoRA. (docs, source)
ltx_pipelines.keyframe_interpolation - Keyframe interpolation. (docs, source)
ltx_pipelines.a2vid_two_stage - Audio-to-video generation conditioned on an input audio. (docs, source)
ltx_pipelines.retake - Regenerate a time region of an existing video. (docs, source)
ltx_pipelines.hdr_ic_lora - Video-to-video with HDR output (linear float via LogC3 inverse decode). (docs, source)
ltx_pipelines.lipdub - Lip dubbing / re-voicing with IC-LoRA and audio reference conditioning. (docs, source)

Use --help with any pipeline module to see all available options and parameters.

Common CLI flags

These flags are shared across the pipeline CLIs (they come from a common base parser); run a module with --help for its full set.

--seed <int> - random seed for reproducible generation (default 10).
--offload {none,cpu,disk} - offload transformer weights to reduce peak GPU memory. cpu holds them in system RAM; disk streams them from disk when RAM is also limited (slower). Default none.
--quantization {fp8-cast,fp8-scaled-mm} - run the transformer in FP8 to cut memory. fp8-cast downcasts a bf16 checkpoint on the fly (any FP8-capable GPU); fp8-scaled-mm expects an fp8 checkpoint and native FP8 support (best on Hopper+).
--max-batch-size <int> - max batch per transformer forward pass (default 1). Higher values reduce layer-streaming transfers at the cost of peak memory.
--compile [key=value ...] - enable torch.compile, optionally overriding the compilation config.
--lora <path> [strength] - apply a LoRA (repeatable; default strength 1.0).
--enhance-prompt - rewrite the prompt with the built-in enhancer before generation.

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