LTX Trainer

MCP server for the official LTX-2.3 video model trainer — LoRA, IC-LoRA, and full fine-tuning from any AI agent.

Wraps the official LTX-2 trainer by Lightricks. All operations shell out to uv run python packages/ltx-trainer/scripts/... inside the LTX-2 monorepo — this server never imports trainer internals.

Companion servers

This is one of three MCP servers designed to work together for agent-driven video training workflows:

• klippbok-mcp — dataset curation (scene selection, captioning)
• musubi-mcp — Wan / FLUX / Z-Image training
• ltx-trainer-mcp (this repo) — LTX-2 / LTX-2.3 training

Together: curate a dataset in Klippbok, train Wan / FLUX / Z-Image through Musubi, train LTX-2.3 video through this server — all from the same agent conversation.

Platform support

Platform	Server itself	Dataset prep / training
Linux (CUDA)	✅	✅
Windows (WSL2)	✅	✅
Windows (native)	✅	❌ (triton + bitsandbytes are Linux-only)
macOS	✅	❌

The MCP server installs and runs on any platform — ltx_check_installation will flag the Linux-only requirement so agents can warn the user up front. Config authoring (ltx_create_config, ltx_read_config, ltx_update_config) is fully functional everywhere.

Quick start

git clone https://github.com/hoodtronik/ltx-trainer-mcp
cd ltx-trainer-mcp

# Clone LTX-2 somewhere (sibling dir is conventional)
git clone https://github.com/Lightricks/LTX-2 ../LTX-2

# Install from the repo
uv venv .venv
uv pip install --python .venv/Scripts/python.exe -e .

# Smoke test
LTX_TRAINER_DIR=../LTX-2 .venv/Scripts/python.exe -m ltx_trainer_mcp.server
# (the process blocks waiting for stdio MCP protocol input — this is expected)

Client configuration

Claude Desktop

claude_desktop_config.json:

{
  "mcpServers": {
    "ltx-trainer": {
      "command": "uv",
      "args": ["run", "--directory", "/abs/path/to/ltx-trainer-mcp", "ltx-trainer-mcp"],
      "env": {
        "LTX_TRAINER_DIR": "/abs/path/to/LTX-2"
      }
    }
  }
}

The full three-server stack

{
  "mcpServers": {
    "klippbok": {
      "command": "uv",
      "args": ["run", "--directory", "/abs/path/to/klippbok-mcp", "klippbok-mcp"],
      "env": {
        "KLIPPBOK_PYTHON": "/abs/path/to/klippbok-venv/bin/python",
        "GEMINI_API_KEY": "..."
      }
    },
    "musubi": {
      "command": "uv",
      "args": ["run", "--directory", "/abs/path/to/musubi-mcp", "musubi-mcp"],
      "env": {
        "MUSUBI_TUNER_DIR": "/abs/path/to/musubi-tuner",
        "MUSUBI_PYTHON": "/abs/path/to/musubi-venv/bin/python"
      }
    },
    "ltx-trainer": {
      "command": "uv",
      "args": ["run", "--directory", "/abs/path/to/ltx-trainer-mcp", "ltx-trainer-mcp"],
      "env": { "LTX_TRAINER_DIR": "/abs/path/to/LTX-2" }
    }
  }
}

Tools

Dataset prep

Tool	Description
ltx_split_scenes	Split long videos into per-scene clips
ltx_caption_videos	Auto-caption video clips ([VISUAL]/[SPEECH]/[SOUNDS]/[TEXT])
ltx_process_dataset	Compute video latents + text embeddings (heavy preprocess step)

Config authoring — the primary customization interface

Tool	Description
ltx_list_configs	Enumerate built-in templates
ltx_create_config	Generate a training YAML from template + overrides
ltx_read_config	Load an existing YAML as structured data
ltx_update_config	Modify specific fields in-place and return a diff

Training

Tool	Description
ltx_train	Launch training (24h timeout); single-GPU or accelerate launch with DDP/FSDP
ltx_list_checkpoints	Enumerate .safetensors files under an output dir, newest first

Inference

Tool	Description
ltx_generate	Run one of 8 ltx-pipelines pipelines (ti2vid/distilled/ic_lora/a2vid/retake/…)

System

Tool	Description
ltx_check_installation	Report on OS / GPU / uv / template availability

Resources

URI	Content
ltx://docs/quick-start	Upstream quick-start guide
ltx://docs/dataset-preparation	Scene split, captioning, process_dataset usage
ltx://docs/training-modes	LoRA / audio-video / IC-LoRA / full fine-tuning
ltx://docs/configuration-reference	Every YAML config key the trainer accepts
ltx://docs/troubleshooting	Common failure modes and fixes
knowledge://index	Table of contents for the community LoRA training knowledge base
knowledge://ltx23_training	Curated LTX-2.3 hyperparameters, resolution rules, INT8 vs full config
knowledge://dataset_quality	Captioning / clip-count / resolution guidance
knowledge://hardware_profiles	VRAM requirements + block-swap tuning by GPU tier
knowledge://common_failures	Diagnostic lookup for loss misbehaviour mid-run
knowledge://<other>	Cross-arch training docs (wan22_training, flux2_klein_training, hunyuanvideo_training, …)

ltx://docs/* reads the corresponding markdown file from the LTX-2 monorepo at request time. knowledge://* serves curated community training notes — bundled with the package and mirrored across klippbok-mcp, musubi-mcp, and this server so agents get the same reference regardless of which server they call. Files flagged INSUFFICIENT DATA are exposed verbatim so the orchestration agent sees the gap and asks the user rather than guessing. Loading priority for knowledge://*: $KNOWLEDGE_BASE_DIR env var (for live-sync with a central knowledge base), else the bundled copy.

Prompts

Prompt	Description
plan_ltx_training	Draft an end-to-end plan from goal + hardware + source + mode
diagnose_ltx_issue	Classify a trainer failure from log output + optional config path

Environment variables

Variable	Required?	Default	Purpose
LTX_TRAINER_DIR	yes	—	Absolute path to the LTX-2 monorepo
LTX_UV_PATH	no	uv	Override if uv isn't on PATH
KNOWLEDGE_BASE_DIR	no	bundled	Override source folder for knowledge://* resources. Live-syncs with a central LoRA training knowledge base; falls back to the bundled copy when unset or invalid.

License

Apache 2.0. Wraps the Apache 2.0-licensed LTX-2 trainer.