In many image generation workflows, character consistency quietly breaks over time.

A single image may look correct. A single result may resemble the reference. But across multiple generations, poses, or conditions, the character slowly drifts.

This is not primarily a rendering issue. It is not a creativity issue.

It is a delivery consistency problem.

What CCR Is

CCR (Character Consistency Runtime) is a narrow, application-level runtime standard designed to address this exact issue.

CCR does not:

generate images

train or fine-tune models

perform identity recognition

control system behavior

CCR does one thing only:

At runtime and before delivery, decide whether generated results still qualify as the same character.

Why This Is a Runtime Problem

Most pipelines implicitly trust single outputs:

generate → preview → deliver

This works for one-off images, but fails when a character must remain stable across:

multiple generations

different poses or outfits

repeated scenes

long-running workflows

Without explicit adjudication, small deviations accumulate until the character is no longer the same.

CCR exists to stop that drift before delivery.

How CCR Works (Conceptually)

CCR is positioned after generation and before delivery.

At a high level:

Character consistency anchors are defined and frozen

Multiple candidate results are generated

Each candidate is evaluated for measurable deviation

Only qualified results are allowed to pass

Failed results are rejected or rerun

CCR does not rely on subjective terms like “very similar” or “almost the same”. All decisions are based on explicit consistency rules and deviation thresholds.

Where CCR Can Be Used

CCR is scenario-agnostic.

It does not understand business logic, safety rules, or system intent. It only evaluates character consistency.

As long as a system requires repeatable, auditable character continuity, CCR can be applied.

Typical examples include:

public safety imaging workflows

assisted driving perception pipelines

digital avatars and recurring characters

content production with fixed roles

In all cases, CCR remains a consistency adjudication layer, not a governance or control system.

CCR as a Standard

CCR is best understood as:

A runtime consistency standard that can be adopted wherever character continuity matters.

It may borrow capabilities from existing controllable AI frameworks, but it does not define, replace, or represent those systems.

One-Sentence Summary

CCR is a runtime standard that decides whether AI-generated results are still the same character — and blocks delivery when they are not.

Author

yuer Proposer of Controllable AI Standards Author of EDCA OS GitHub: https://github.com/yuer-dsl

Contact: lipxtk@gmail.com