How to Train a Custom AI Model on Leonardo AI for Consistent Brand Visuals

Maintaining visual consistency across hundreds of images is one of the biggest challenges in AI image generation. Every new prompt produces slightly different aesthetics, making it impossible to build a coherent brand library or game asset set. Leonardo AI‘s Phoenix custom model training solves this by letting you teach the AI your exact visual style. Here’s how.

Step 1: Prepare Your Reference Dataset

Gather 10-50 high-quality images that exemplify your target visual style. For a brand, this might include existing marketing photography, product shots, and design mockups. For a game, collect concept art, character designs, and environment paintings. Quality matters more than quantity — every reference image should perfectly represent the style you want to replicate. Remove any outliers that deviate from your core aesthetic, as they’ll confuse the model.

Step 2: Create a New Phoenix Dataset

In Leonardo AI, navigate to the “Training” section and click “Create New Dataset.” Upload your reference images and assign descriptive tags to each one — not generic tags like “image” but specific ones like “dark fantasy castle, moody lighting, stone textures, atmospheric fog.” These tags teach the model what elements matter in your style. Tag quality directly impacts model quality, so be thorough and precise.

Step 3: Configure and Launch Training

Select your dataset and configure training parameters. Set the training strength (higher = closer to your reference style, lower = more creative freedom). Choose whether to enable style-only training (preserves composition flexibility) or full training (replicates both style and composition patterns). Click “Start Training” — the process typically takes 15-30 minutes. Leonardo will display progress updates and a quality score upon completion.

Step 4: Test Your Custom Model

Once training completes, your custom model appears in the model selector dropdown. Switch to it and generate several test images using prompts that describe scenes different from your reference set but in your trained style. For example, if your references were fantasy castles, prompt for a fantasy marketplace or a fantasy ship — different content, same style. Evaluate consistency across generations: do they all share the same color palette, lighting approach, texture detail, and overall mood? If results are inconsistent, add more reference images and retrain.

Step 5: Build Your Asset Library

With a validated custom model, start generating your full asset library. Create a prompt template system: “In [your trained model style], [scene description], [lighting], [composition].” Use this template for every generation to maintain maximum consistency. Save all outputs to your Leonardo project gallery, organized by category (characters, environments, items, UI elements). Export at your required resolution and format — Leonardo supports up to 4K output with transparent backgrounds for game sprites.