The Hardest Problem in AI Visual Content
You’ve generated the perfect character: a grizzled detective in a tan trench coat, mid-40s, sharp jawline, slightly graying temples. The image looks exactly right. Now you need 50 more scenes with the same person. By scene three, the coat is brown. By scene ten, the detective looks 25 years old. By scene twenty, he’s a completely different person.
Character consistency across multiple AI generations is the single most requested and least solved problem in the space. Every model handles it differently, no approach is foolproof, and the techniques that work require deliberate planning rather than casual prompting.
Why Models Struggle With This
Most generative models don’t have a persistent memory of characters between generations. Each prompt creates a new sampling from the model’s latent space. Even identical text prompts produce different faces, body proportions, and clothing details because the random seed changes each time.
The model isn’t “forgetting” your character. It never “knew” them in the first place. Your text description is a loose specification that maps to a region of possibilities, not a fixed point. “A woman with short black hair and green eyes” could describe thousands of different faces, and the model will explore that space freely unless you constrain it.
Reference Images Are Non-Negotiable
The most reliable consistency technique is reference-based generation. You create or select one definitive image of your character and feed it to the model as a visual anchor for every subsequent generation. This works across most current platforms, though the specific implementation varies.
Kling’s character reference system lets you upload a face image that the model will maintain across generations. Midjourney’s character reference feature works similarly. Runway allows you to use a source frame as the starting point for video generation, carrying the character’s appearance forward.
The quality of your reference image matters enormously. A well-lit, front-facing portrait with clear features gives the model the most information to work with. Side profiles, heavily shadowed faces, or images with occlusions (sunglasses, masks) give the model less to anchor on, which means more drift between generations.
Building a Character Sheet
Professional creators working on multi-scene projects start with a character sheet: a set of reference images showing the character from multiple angles, in different lighting conditions, and with consistent clothing. This is borrowed directly from animation production, and it works for the same reasons.
A strong character sheet includes a front-facing headshot, a three-quarter view, a full-body shot, and close-ups of distinctive features (a specific tattoo, a unique piece of jewelry, a scar). When you have these references available, you can select the most appropriate angle for each new scene you generate.
The consistent character guide on PixelDojo walks through the full process of building effective character sheets and using them across different models. The techniques differ significantly between image and video generation, and the guide covers both.
Model-Specific Approaches
Different models require different consistency strategies. Kling 3 has built-in character ID features that work well for facial consistency but sometimes struggle with clothing and accessories. The Kling 3 prompting guide covers specific prompt structures that help maintain full-body consistency alongside facial matching.
FLUX-based workflows often rely on IP-Adapter or similar conditioning methods that inject visual features from a reference image into the generation pipeline. These require more technical setup but offer fine-grained control over how strongly the reference influences the output.
For video models, image-to-video workflows provide the strongest consistency. You generate a single high-quality frame of your character using an image model, then use that frame as the starting point for video generation. This locks in appearance for the duration of the clip, though you still need to manage consistency between separate clips.
Practical Workflow for Multi-Scene Projects
The workflow that currently produces the best results combines several techniques. Start by generating your definitive character image using the most controllable model available. Build a reference sheet with multiple angles. Use reference-based generation for each new scene, selecting the angle most relevant to the new shot. Review output against your reference sheet and regenerate anything that drifts too far. Finally, use light post-processing to correct minor inconsistencies that slip through.
It’s not fully automatic, and it requires planning. But it produces results that are coherent enough for professional content, which is what matters.
