> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs-dev.ltx.io/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs-dev.ltx.io/_mcp/server.

# Overview

> Fine-tune LTX with the LTX Trainer, from LoRAs and IC-LoRAs to full multimodal fine-tuning.

The LTX Trainer exposes the same toolkit we use to train production LTX models. Use it to fine-tune LTX on your own data,
from a quick style LoRA to full multimodal fine-tuning. The `train-model` agent makes training approachable even without ML
expertise, while experts keep full manual control over every parameter. It's built around a single conditioning
framework that covers every training scenario.

> **Tip**
>
> **Start with the agent.** Describe what you want and `/train-model` trains it for you. You can run it in Claude Code, or
> follow the [Quick Start](/open-source-model/ltx-trainer/quick-start) to train by hand.

## What You Can Train

**One strategy, every mode.** A single **flexible** conditioning framework expresses 13+ training modes — switch
between them by editing a few lines of training config (set which modality is generated and compose conditions),
rather than picking a separate strategy or writing code. Text- and image-to-video, video and audio extension,
inpainting and outpainting, audio-to-video, video-to-audio (Foley), text-to-audio, and in-context (IC-LoRA)
transformations are all driven by the same config block.

**Joint audio + video.** Train both modalities together through the model's cross-modal attention, or freeze one to
condition the other (e.g. generate Foley from a fixed video, or video from fixed audio).

**In-context control (IC-LoRA).** Learn transformations from paired videos or audio — depth and pose control, style
transfer, deblurring, colorization, and more.

**LoRA or full fine-tuning.** Train lightweight, portable LoRA adapters, or update all model parameters with
distributed FSDP for larger adaptations.

**Multiple model versions, one API.** The trainer supports LTX-2, LTX-2.3, and LTX 2.5 through the same configuration
schema — the architecture is detected automatically from the checkpoint metadata, so there's no model-version flag to
set. Just point `model_path` and the matching `text_encoder_path` at the version you're training.

**Runs on accessible hardware.** 80GB VRAM is recommended, but a low-VRAM path (INT8 quantization, 8-bit optimizer,
reduced rank) brings LoRA training to 32GB consumer GPUs such as the RTX 5090.

> **Note**
>
> Across model versions, the large majority of LoRAs and IC-LoRAs trained on LTX-2.3 run on LTX-2.5 without changes; a small number of exceptions exist, so validate an adapter previously trained before production use.

## Start Here

#### [Quick Start](/open-source-model/ltx-trainer/quick-start)

Install, preprocess a dataset, and launch your first training run.

#### [Training Modes](/open-source-model/ltx-trainer/training-modes)

The flexible strategy and all 13+ modes, with config for each.