Why “Consistency” Is the Hardest Hurdle in AI Creation

Most people who have used AI to generate content have experienced the same kind of frustration: after finally getting an image you are happy with, you change the angle or the shot, and the result becomes completely different. You try to reproduce the effect you just had, only to find that you can never get back to it. This is not a matter of luck, but a structural limitation. Understanding it is more important than learning more prompt-writing tricks.

Why AI Naturally Lacks Consistency

In traditional filming, consistency is “physically guaranteed”: the same actor stands in the same room, and when the camera changes angle, the person captured is still that person and the room is still that room. Because they are the same physical entities, consistency does not require extra maintenance. The logic of AI generation is completely different. Every generation is an independent resampling—the model constructs an image from scratch based on the description you provide. It has no concept of “what kind of person was generated last time,” nor does it have memory across shots. This leads to three consequences:

1. Details drift: facial features, hairstyles, and clothing details fluctuate within a reasonable range each time. Each image looks fine on its own, but when placed together, they are no longer the same person.
2. References fail: even if you use the previous image as a reference, the model will only “refer to” it, not “copy” it.
3. It cannot be reproduced: the same description generated at different times may produce noticeably different results. So consistency is not a problem that can be solved simply by “writing better prompts”; it requires mechanisms outside the generation process.

The Three Levels of Consistency

In real projects, the word “consistency” includes three different layers, each with different difficulty levels and solutions.

• Level one: character consistency requires the same character to remain stable across multiple shots. This is the most basic layer and also the easiest to notice—viewers are extremely good at recognizing faces, and even a slight deviation can break immersion. The difficulty is that a character contains a large number of detail dimensions: facial structure, body proportions, hairstyle, clothing, and accessories. These dimensions are hard to fully cover in prompts, and even harder to stabilize.
• Level two: scene consistency requires the same space to maintain unchanged spatial relationships across multiple shots. This is easier to overlook than character consistency, but it is just as much a source of serious flaws—if the background changes in shot/reverse shot, or the positions of doors and windows in the same room shift, viewers will immediately notice that “this is not the same place.” The difficulty is that spatial relationships are structural information, and structure is the easiest thing to be broken up during generation.
• Level three: style consistency requires a group of images to share the same visual texture. This layer is the most abstract, but it has the greatest impact on whether something “looks like a film.” Style consistency includes several aspects such as tonal foundation, the optical character of the lens, and lighting logic. When it collapses, it is not as glaring as character drift; instead, it appears as a pervasive sense of “mismatch”—some images are cooler, some warmer, some sharp, some soft-focused, making the whole thing look like footage pieced together from different sources.

Three Layers of Consistency, Three Solutions

They also correspond to different solution approaches.

• Character consistency relies on assetization. Turn the character from “a piece of description” into “an asset”: first extract the subject at the script level and define the character’s base state as well as script-related sub-states; then generate character images and fix the appearance as a visual reference; afterward, when generating multiple shots, reference this fixed appearance instead of describing it again. NadouPro’s character asset library supports searches by name, tag, gender, age range, skin color, hair color, body type, and other conditions. In essence, it turns “the same person” into a reusable entry. The three-view character images and 3×3 expression grids in multi-panel layouts also serve to lock multiple angles in a single generation.
• Scene consistency relies on multiple views and panoramas. A single image cannot guarantee spatial stability, but a set of angle references generated under the same baseline can. Panorama generation can produce panoramic images that can be used for multi-view scene consistency, while multi-view generation outputs images from different angles based on a subject image—supporting custom horizontal angles from 0–315 degrees and pitch from -30 to 90 degrees, as well as preset viewpoints such as oblique side view, tilted view, low-angle front view, top view, back view, and fisheye. Establish a spatial reference set first, then generate shot by shot, and continuity errors will be greatly reduced.
• Style consistency relies on fixed parameters. The solution for this layer is different from the other two: it does not depend on reference materials, but on whether the settings are fixed. If variables such as tonal foundation and the optical character of the lens are left to the randomness of each generation, the style will inevitably drift.
• NadouPro’s approach in this regard is to turn them into cinematography parameters: color science is used to choose the color foundation of film or digital cinema cameras, producing images with specified base color and tonality; lens groups are used to select the characteristics of cinema lens series in terms of sharpness, soft focus, bokeh, flare, and distortion tendencies; combined with focal length control and depth of field control, these four items form a set of optical settings that can be reused as a whole. The key is that the entire set of materials shares the same settings. As long as the settings do not change, the style will not speak in different voices, and the images will be able to fit together.

An Easily Overlooked Question of Order

There is actually a proper sequence for solving the three layers of consistency. The reasonable order is: define the style first, then the scene, and finally the character. The reason is very practical: style is a parameterized setting, and the earlier it is fixed, the less trouble there is later; the scene is the spatial baseline, and only after it is set do the character’s movements have a reference; the character is the most detailed asset, so doing it last can prevent character references from becoming invalid after scene adjustments. Many people are used to burying themselves in character work first, only to discover after making dozens of images that the overall tone is inconsistent, at which point the amount of rework becomes very large.

Finally

Consistency is difficult because it conflicts with the natural characteristics of generative models—the model is good at “giving a new reasonable result each time,” while creation requires “giving the same result each time.” So the ways to solve it are essentially all about establishing constraints outside generation: constraining appearance, constraining space, and constraining style. The clearer the constraints, the less freedom there is in generation, but the higher the completeness of the work. This is also a kind of trade-off: AI gives you infinite possibilities, but the value of a work comes precisely from how many of those possibilities you cut away.

Contains AI-generated information that may be incorrect. Please verify independently.