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name: ltx-2 description: "Routes LTX-2 audio-video generation, training, data preparation, core APIs, and performance-backend tasks across the ltx-core, ltx-pipelines, ltx-trainer, and ltx-kernels packages." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

LTX-2 repo skill

Use this repo skill when a task involves Lightricks LTX-2 / LTX-2.3 / LTX-2.5 audio-video generation, LTX training/fine-tuning, package APIs, model asset layout, CUDA/backend troubleshooting, or dataset preprocessing for LTX Trainer.

This skill is a self-contained operating guide for the LTX-2 package family. It distills the repository evidence into bundled references and helpers; do not rely on original repository docs, examples, or scripts during normal use.

Setup and checks

If you are working from a source checkout, install the packages with the repository's workspace sync and then run the environment checker:

uv sync
python path/to/ltx-2/scripts/check_ltx2_environment.py --json

If your task needs optional acceleration, add only the backend that the selected route requires: read performance-backends before installing natten, ltx-kernels, or other accelerator-specific extras.

Read references/package-map.md for the monorepo package roles and references/model-assets.md for LTX-2.5 split assets versus LTX-2.3 monolith assets. Use references/troubleshooting.md for cross-cutting install, checkpoint, Gemma, and CUDA problems.

Routes

User request	Read
Choose a generation pipeline, build a CLI/Python inference command, use LTX-2.5 split checkpoints, use image/video/audio conditioning, retake, HDR/EXR, Dub-It, DFR, or text-to-audio.	sub-skills/inference-pipelines/SKILL.md
Validate a dataset manifest, plan scene splitting/captioning/preprocessing, choose resolution buckets, inspect .precomputed/, prepare references or masks.	sub-skills/data-preparation/SKILL.md
Choose a training mode, patch trainer YAML, validate a config, plan launch/monitor/resume, troubleshoot LoRA/full fine-tuning.	sub-skills/training-workflows/SKILL.md
Write custom Python code using ltx_core model builders, schedulers, guiders, conditioning items, ModelPaths, LoRA/SDOps, quantization policies, or tensor shape contracts.	sub-skills/core-components/SKILL.md
Choose CUDA/FP8/NVFP4/offload/compile/DiffVAE/NATTEN/ltx-kernels/multi-GPU performance paths or diagnose backend readiness.	sub-skills/performance-backends/SKILL.md

Common operating rules

• LTX model paths are local files or directories. Do not pass Hugging Face URLs where the code expects a filesystem path.
• LTX-2.5 split layout uses one file per component: transformer, packed text encoder, video VAE, audio VAE, optional upsamplers, optional duration head, and optional LoRAs.
• LTX-2.3/legacy monolith layout uses one large .safetensors plus a matching Gemma directory; LTX-2.5 components are not interchangeable with LTX-2.3 components.
• Video frame counts generally follow the VAE temporal grid (F % T == 1; default T=8) and width/height generally must be divisible by the VAE spatial factor (default 32).
• Real generation, preprocessing, training, kernel builds, model downloads, and external uploads are expensive or stateful. Build and validate commands first, then ask for approval before running them.
• For accelerator or optional backend claims, distinguish import/parser checks from actual CUDA/kernel/runtime checks. A CPU import is not proof that generation/training will run.

Maintenance and freshness

Read references/repo-provenance.md before deciding whether this skill matches a current checkout. If the source commit, package metadata, public APIs, or docs changed materially, run refresh-repo-skill rather than editing this skill ad hoc.

Router import metadata is in references/repo-routing-metadata.json. This creation run was requested with not import, so the runtime tree is prepared for verification but not installed into the managed repo-skill library by this workflow.