Leonardo AI has firmly established itself as one of the leading tools in the world of generative image creation due to its ease of use and powerful rendering capabilities. However, many users recently began noticing a strange limitation: Leonardo AI was consistently refusing or failing to generate images featuring multiple distinct characters. This limitation surprised and frustrated users seeking dynamic group scenes or narrative compositions.
TLDR: Leonardo AI has difficulties generating scenes with multiple characters, resulting in blended or missing figures. Users discovered that separating prompts and using a structured technique significantly improved the accuracy and clarity of multi-character rendering. The solution involves prompt separation, spatial descriptions, and narrative role assignment. While imperfect, this technique vastly reduces character overcrowding and confusion in AI-generated artworks.
The Problem: Leonardo AI’s Trouble with Multiple Characters
Within the expanding community of AI art creators, one common request involves generating scenes with more than one person. Whether it’s a romantic couple, a group of adventurers, or a bustling social gathering, users expect to depict interactive or dramatic moments with clarity.
However, artists using Leonardo AI frequently reported outcomes where:
- Characters merged into a single entity
- One character appeared sharply while others were deformed or omitted
- The entire scene lacked spatial coherence
This phenomenon wasn’t limited to casual users; even advanced prompt engineers experienced various degrees of failure when attempting multi-character generation. Many theorized resource limitations or internal constraints within Leonardo’s image synthesis mechanisms. Unlike single-subject compositions, multiple elements seemed to overload the system’s ability to comprehend and render clear visual separation.
Analyzing Why Overcrowding Occurs
Leonardo AI typically operates based on transformer architecture and latent diffusion. While these technologies are excellent at understanding abstract visual instructions, the problem appears when the prompt becomes semantically dense. Instead of parsing “a man and a woman sitting on a park bench with a dog,” the system struggles to establish distinct, separated entities in physical space.
Two core issues arise:
- Token Compression: When text prompts are too complex, important tokens may lose influence, making some characters less likely to be visualized.
- Scene Merging: The AI may interpret joint activities as a blend, resulting in characters morphing into incomprehensible shapes or overlapping visuals.
These outcomes significantly hinder the creation of narrative art, storyboards, and group illustrations — essential tools for writers, illustrators, and game designers.
The Prompt Separation Technique: A Surprising Fix
Frustrated by these limitations, a group of prompt engineers and digital artists within online communities began experimenting. Through thousands of trial-and-error iterations, they developed what became known as the Prompt Separation Technique. This approach aimed to separate descriptions of individual characters while still preserving their spatial relationships.
This technique consists of three essential principles:
- Use Punctuated Separation: Instead of writing “two men and a woman,” users write “One man, wearing a sailor outfit. Another man, in a black robe. A woman with red hair, looking left.”
- Define Spatial Arrangement: Including phrases like “stands on the left,” “sits in the center,” or “leans on the wall to the right” helps orient the rendering engine without confusion.
- Add Role-Based Context: Clarify character roles such as “a warrior leading the group” or “a child watching from the background” to enforce narrative significance.
This revised prompt structure provides clearer instructions, reducing the likelihood of overcrowding or character blending. By treating each character as a modular entity, the AI is less likely to confuse composite visual signals.
Examples Before and After Applying the Technique
Consider the following two prompts:
Prompt A (Unstructured): “A knight, a mage, and a dragon flying above them in a stormy sky.”
Result: The knight and mage often overlap, and the dragon’s position is ambiguous or entirely missing.
Prompt B (Structured with Technique): “A knight in shining armor stands on the left, gripping a sword. A mage in a blue robe stands next to him on the right, holding a glowing staff. A dragon flies above them in a stormy sky, wings outstretched.”
Result: Each character appears clearly separated and maintains its position in a balanced composition.
The Prompt Separation Technique does require longer, more deliberate phrasing, but the increase in visual fidelity more than compensates for the additional effort.
Limitations Still Remain
Despite its improvement, the Prompt Separation Technique is not a silver bullet. Situations involving six or more characters, chaotic battle scenes, or subtle emotional exchanges still pose challenges. Leonardo’s image generation model occasionally falls back into ambiguity or misinterprets overlapping narrative elements.
Tips to improve performance include:
- Using image guides or sketches as input, when available.
- Breaking larger scenes into panels, generating them independently, and combining manually afterward.
- Experimenting with multiple prompt variations of the same scene.
Nonetheless, for common compositions featuring two to four characters, users report up to a 70% improvement in coherence and character distinction.
Community Response and Evolving Best Practices
As more creators adopt this new strategy, forums and educational platforms have started incorporating dedicated modules about prompt structuring. Leonardo AI’s developers have also taken notice, hinting at future UI enhancements that may allow layered character construction or visual previews prior to rendering.
Additionally, several plugins and prompt generators now come equipped with Prompt Separation Toolkits — formatted templates that guide users through role placement and spacing suggestions.
It appears the creative community’s imaginative workaround has reshaped how multi-subject prompts are written and approached, strengthening Leonardo AI’s utility and pushing the boundaries of generative design.
Conclusion
Leonardo AI’s troubles with multi-character image generation disappointed many creators, but the solution emerged not from the software developers, but from the user base itself. By dissecting how prompts interact with generative models and creating structured techniques, artists restored the ability to represent complex human interactions and scenes.
Though not without its limits, the Prompt Separation Technique represents a practical, user-driven innovation — another example of how collaboration can transform frustrating tools into intuitive creative companions.
FAQ
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Q: Why can’t Leonardo AI handle multiple characters well?
A: Its rendering model often merges multiple entities due to token overlap and unclear spatial positioning, leading to distorted or missing figures. -
Q: What is Prompt Separation Technique?
A: A structured way of writing prompts that isolates each character’s description, spatial position, and narrative role for better visual accuracy. -
Q: How many characters can be rendered successfully with this technique?
A: Typically, two to four characters can be rendered with much better clarity. Results degrade with increased complexity or vague descriptions. -
Q: Does using punctuation and spacing really make a difference?
A: Yes. It helps the AI parse distinct ideas instead of blending them into single entities. -
Q: Will Leonardo AI improve this in future updates?
A: Developers are aware of the issue and are exploring ways to improve multi-character composition, but no official feature has been introduced yet.
