LTX Trainer Tutorial: How to Colorize Black and White Footage

This tutorial is based on a project by VFX creator Doug Hogan, who trained a custom IC-LoRA using a newly released open-source trainer, with an unusual personal twist: Doug is colorblind, which made evaluating his own colorization results an interesting test in itself. Watch his walkthrough above, then follow the steps below to train your own.

What you're building

This tutorial walks through training an IC-LoRA (in-context LoRA) that takes black-and-white video as input and outputs a colorized version. The distinction between an IC-LoRA and a standard LoRA matters here: a standard LoRA teaches a model to generate something new, while an IC-LoRA teaches it to transform an input into a different output, more like a learned effect or filter than a new generation. Once you understand this pattern, the same approach extends to other transformation tasks: relighting a plate, deblurring, restoration, or style transfer.

What you'll need

• The LTX Trainer, LTX's open-source training tool
• A set of color video clips to build your training pairs from (existing LTX-generated clips work well, since they don't need separate sourcing or licensing)
• Enough local VRAM to run training, or access to a GPU machine
• Optional but recommended: an AI coding assistant like Claude Code to handle environment setup and dataset validation

Step 1: Build your training pairs

Instead of sourcing black-and-white and color footage separately, generate your own paired dataset from clips you already have:

1. Start with a set of color video clips.
2. Create a black-and-white copy of each clip.
3. Pair them up: the black-and-white version becomes the training input, and the original color version becomes the target output.

This gives you a complete paired dataset without any additional labeling or sourcing work, since every clip already contains its own "answer."

Step 2: Set up your environment

Install the LTX Trainer and its dependencies. If you're using an AI coding assistant to handle this step, have it:

• Scan your machine for existing model weights already installed (for example, in a ComfyUI folder) so it can skip re-downloading anything already present.
• Retrieve any gated models using your Hugging Face credentials.

This step alone can save significant time; a base LTX model install is roughly 46GB, and skipping a redundant download is a meaningful shortcut on a slow connection.

Step 3: Validate your dataset before training

Before you start a training run, confirm that your dataset's input and target columns are labeled correctly. A single mislabeled field name can cause the trainer to silently ignore your black-and-white reference footage and run an entire training job that produces nothing usable, without any error to tell you why. If you're using a coding agent, have it review the dataset file specifically for this kind of mismatch before proceeding. This check takes minutes; catching the problem after a multi-hour training run does not.

Step 4: Configure the trainer

LTX Trainer configuration lives in a single readable file covering the base model, training duration, and output location. Stick close to the recommended defaults for your first run. Don't spend time tuning settings before you've confirmed your dataset is valid; a bad dataset won't be fixed by any learning rate.

Step 5: Run a smoke test

Before committing to a full run, do a short "smoke test": a brief training pass meant only to confirm the pipeline runs end to end, fits inside your available VRAM, and produces output. You're not evaluating colorization quality yet, just confirming nothing breaks. Once the smoke test completes cleanly, commit to the full multi-hour run.

Step 6: Monitor training with validation clips

While the full run is in progress, check validation clips periodically for directional signals rather than a perfect match: are skies trending blue, is vegetation trending green, do skin tones look plausible. Once those signals are moving in the right direction, let the run continue to completion.

Step 7: Test on unseen footage

Once training finishes, run your IC-LoRA on clips it never saw during training. Expect some color drift at this stage, which additional training time can reduce, but you should see clearly correct directional predictions: greens where vegetation should be, blues where sky should be, and so on. The model isn't recovering hidden color data, since none exists in a black-and-white source; it's generating plausible color from patterns it learned during training.

Why this is a good first project

Colorization is a strong starting point if you're trying LoRA training with the LTX Trainer for the first time. The data is simple to generate, results are easy to evaluate visually, and pairing the process with an AI coding assistant for setup and validation removes much of the friction that normally makes training tutorials intimidating. Once you've got this working, the same paired-data approach applies directly to relighting, deblurring, footage restoration, and other transformation tasks.