The rise of AI-generated art has brought a new era of efficiency and experimentation to visual storytelling, enabling artists and developers to create stunning, unique illustrations in a matter of minutes. Among the evolving tools in this space, Leonardo AI has emerged as a powerful platform for generating detailed and stylized character illustrations. But like many evolving systems, it’s not without its challenges. A recent concern from the user community revolves around Leonardo AI’s inconsistent rendering of clothing details, a vital element for creators depending on visual fidelity. Fortunately, artists and prompt engineers discovered a smart yet simple fix—known as the Layer Instruction Technique.
TL;DR
Leonardo AI, although powerful in illustration generation, has recently shown issues in accurately rendering clothing details, particularly in layered or textured garments. Artists discovered that prompting the AI with a structured, order-based approach—called the Layer Instruction Technique—significantly improved output fidelity. This technique involves explicitly instructing the AI to render clothing in tangible, separated layers. It’s a straightforward, effective solution that’s rapidly becoming standard practice among professional users of the platform.
The Problem: Missing and Simplified Clothing Details
As AI-generated illustrations gained popularity, users began to notice a troubling pattern with Leonardo AI: characters were inconsistently dressed, with critical elements of costume design either blurred, ignored, or merged into body features. This issue seemed most prevalent when generating:
- Fantasy characters with ornate armor or robes
- Period-specific costumes like Victorian dresses or samurai gear
- Layered modern outfits, such as jackets over shirts
For artists depending on AI to envision detailed character drafts, this led to repeated revisions or post-editing, undermining the time-saving benefits of the tool. In particular, designers in game development and graphic novels were among the most impacted, as their work demands precision to ensure consistency across frames or scenes.
Instead of rendering clothing as layers of physical material, the model often blurred seams, omitted accessories like belts or buttons, or fused bodily anatomy with garments. A shirt might appear as a shaded pattern on the character’s chest rather than a distinct article of clothing.
Where AI Struggles: Understanding Contextual Layering
This issue boils down to how AI understands and interprets visual prompts. While text-to-image models are trained on vast datasets of labeled images, they often infer relationships probabilistically. This creates inconsistencies when rendering complex textures or varied materials layered together. Clothing, especially historical or conceptual costumes, involves:
- Multiple fabrics interacting (e.g., silk under leather armor)
- Functional design elements (buttons, lacing, pockets)
- Visual symmetry between left/right sides of the body
Unless prompted correctly, AI can’t always determine where one layer ends and another begins. As a result, overlapping components are rendered inaccurately or blended together illogically. The AI doesn’t “know” that a trench coat should hang over a turtleneck any more than it knows that a belt goes over a tunic—it just guesses based on visual resemblance.
Enter the Fix: The Layer Instruction Technique
The community-developed solution to these rendering issues is simple yet ingenious: give the AI explicit hierarchical visual instructions. The Layer Instruction Technique (LIT) involves structuring prompts in a way that defines not just clothing items, but the order in which they are worn and rendered. Essentially, it tells the model, “This shirt goes first, then the vest goes on top, and finally the jacket wraps over both.”
This type of structured approach proved so effective that many artists began incorporating it as a default configuration into their prompts. Here’s an example comparison:
Before Using LIT:
“A female knight in silver armor with a deep green velvet cloak.”
This often resulted in armor that meshed with the skin or a cloak blurred into the body, sometimes even omitting the cloak entirely.
After Using LIT:
“A female knight in silver under-armor, then covered by silver chestplate and gauntlets, layered with a deep green velvet cloak pinned at the shoulders, flowing over the armor.”
Suddenly, the rendered image accurately depicted the platinum sheen of armor protruding underneath the rich folds of the green cloak.
How To Use LIT in Your Prompts
If you’re using Leonardo AI and want to implement this method, follow these steps to improve your clothing accuracy:
- Break clothing into layers. Think about what’s worn closest to the skin and proceed outward.
- Define the material and color of each item. Mention textures and tones to aid visual accuracy.
- Use transition words. Terms like “under,” “covered by,” and “layered with” guide the AI.
- Group logical items. For example, combine “white cotton shirt and tan linen trousers” before describing “a navy-blue waistcoat.”
For example, a well-structured prompt might look like:
“A 16th-century merchant, wearing a white linen undershirt tucked into brown trousers, layer over a fitted grey doublet with silver buttons, topped with a heavy fur-lined cloak made of dark wool.”
This allows Leonardo AI to ‘stack’ the items visually, restoring fidelity to each garment’s appearance and interaction with others.
Going a Step Further: Prompt Engineering Tricks
Beyond simple ordering, users can enhance results by incorporating spatial references and composition cues. Here are a few expert tips:
- Use directional cues like “flowing behind the body,” “pinched at the waist,” or “falling over her left shoulder.”
- Reference historical styles such as “modeled after Victorian corsetry” or “styled like 80s club fashion.”
- Specify accessories separately: say “leather boots up to mid-calf” instead of lumping them under ‘outfit.’
These improvements go beyond helping the AI ‘see’ garments—they help designers influence the AI’s artistic biases and stylization choices.
Why This Matters: Creative Professionals vs Casual Users
While casual users of Leonardo AI may not notice or care if a sleeve is fused into an arm or a cape disappears altogether, professional creatives cannot afford those inaccuracies. Game designers, comic book artists, and even fashion concept illustrators rely on precisely previewed outfits for:
- Team presentations and client pitches
- Consistency across narratives and character arcs
- Commercial reuse, merchandise production, or animation pipelines
With clothing being an essential storytelling device—especially across genres such as fantasy, sci-fi, or period drama—misrendered garments mean more than a visual slip; they undermine context, character personality, and narrative continuity.
Community Response and Leonardo AI Updates
Since widespread discussion around LIT began on forums and Discord channels, Leonardo AI developers have taken notice. In recent updates, there are rumors that future models may integrate an awareness of layering mechanics directly into the rendering engine. Meanwhile, community-developed prompt templates are increasingly available on resource hubs and marketplaces, helping new users apply LIT without starting from scratch.
Several creators are even publishing visual ‘prompt maps’—diagrams linking descriptive phrases with layered garment illustrations—to guide learners from casual experimentation to professional workflows.
Conclusion
AI art tools like Leonardo offer incredible potential, but their real utility lies in how well users can harness and shape their output. The Layer Instruction Technique marks a turning point in that effort, proving that with the right approach, even complex design elements like layered clothing can be rendered with consistency and remarkable accuracy. As the technology continues to evolve, techniques like LIT remind us that collaboration—between human creativity and machine interpretation—is the heart of successful AI artistry.
