Character Consistency Across AI Image Models: Who Actually Delivers

Character consistency is one of the hardest problems in AI image generation. Creating one strong image of a character is relatively straightforward. Creating ten images where the same character looks recognizably identical across different poses, outfits, and environments is a different challenge entirely. We tested four leading image models with the same character prompts to see which one actually delivers reliable consistency.

The test was simple: define a character with specific visual attributes, generate a reference portrait, then use that reference to produce the same character in five different scenarios. We evaluated facial consistency, body proportions, clothing preservation, and overall identity retention across each model's output.

The test setup#

We created a character with distinctive features: a young woman with short red hair, green eyes, a scar on her left cheek, wearing a black leather jacket over a white t-shirt. We generated a reference portrait using each model, then used that reference to create the character in five different scenes: standing in a rain-soaked Tokyo street, sitting in a coffee shop, walking through a forest, standing against a graffiti wall, and in a close-up portrait with different lighting.

Each model was tested with its standard character consistency features: reference image support, character description anchoring, or IP adapter capabilities. We used each model's recommended approach for maintaining character identity.

GPT Image 2 results#

GPT Image 2 showed strong facial consistency across most scenes. The scar, eye color, and hair style remained recognizable in the majority of generations. Where GPT Image 2 struggled was with clothing details: the leather jacket occasionally changed style, and the white t-shirt sometimes appeared as a different neckline or fit.

The model handled lighting changes well, maintaining character identity even in dramatically different lighting conditions. The Tokyo rain scene and the forest scene both preserved the character's facial features accurately. GPT Image 2's strength is that it combines character consistency with strong instruction following, so scene descriptions do not pull the character off-model as easily.

Nano Banana Pro results#

Nano Banana Pro maintained overall character identity but with more variation in specific features. The hair color stayed consistent, but the exact style varied slightly between generations. The scar was present in most images but sometimes appeared on the wrong side or with different proportions.

Where Nano Banana Pro excelled was in maintaining the overall mood and aesthetic of the character. Even when specific features drifted slightly, the character felt like the same person. For projects where visual consistency matters more than pixel-perfect accuracy, Nano Banana Pro's results were usable with minor touch-ups.

Seedream results#

Seedream produced some of the most visually striking character images, with strong attention to atmosphere and mood. However, character consistency was more variable. The facial features changed noticeably between scenes, and the scar was often absent or misplaced in non-portrait scenes.

Seedream's strength is in the quality of individual images. Each generation looks polished and professional, but the character identity does not persist reliably across a series. For single-image projects or scenes where character identity is less critical, Seedream produces excellent results. For character series, it requires more manual intervention.

Ideogram results#

Ideogram showed the most consistent character identity across the test scenes. The facial features, scar placement, and overall character design remained recognizable in nearly all generations. Ideogram's reference handling appears specifically tuned for character preservation.

The trade-off was that Ideogram's overall image quality and artistic range was sometimes narrower than the other models. The character consistency came at the cost of some creative flexibility in the scene composition and visual style. For projects where character consistency is the primary requirement, Ideogram's reliability is a significant advantage.

What this means for your workflow#

No single model dominates across all aspects of character consistency. The best model depends on which aspect matters most for your project: facial accuracy, clothing preservation, overall aesthetic feel, or scene adaptability.

For most character work, we recommend starting with the model that best matches your quality requirements, then testing a small batch before committing to a large series. Document the prompts, settings, and reference images that produce the best results for your specific character.

Practical recommendations#

• For character series with strict identity requirements: test Ideogram or GPT Image 2 first.
• For creative character work where mood matters more than exact features: Nano Banana Pro or Seedream may produce more visually interesting results.
• For all models: generate a character sheet before committing to a full production run.
• Keep the same reference image, prompt structure, and generation settings throughout a project.
• Accept that some manual correction may be needed for perfect consistency.