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
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