character-consistency

name

character-consistency

description

Maintain character identity and style accuracy when generating or editing images of a recurring character with multimodal image models (e.g., Nano Banana, GPT Image, Flux Kontext, Seedream, or any reference-conditioned model). Use this skill whenever the user asks to generate, edit, or iterate on images of a named or established character — especially when they mention keeping the character "on-model", consistent, accurate, or in-style, when generations keep drifting from the character's design, or when they want to set up, improve, or debug a character-generation workflow, dataset, reference sheet, or pipeline. Trigger even if the user just says a character "doesn't look right" across generations.

Character Consistency

Keep a recurring character on-model across generations and edits: proportions, colors, distinctive marks, finger counts, facial structure — every trait that makes the character that character.

Two principles govern everything in this skill:

1. Simplest fix first. There is a ladder of approaches from a one-line guideline to a fine-tuned model. Always start at the lowest rung that plausibly solves the user's problem, and add a heavier layer only when the previous one demonstrably falls short and the new layer solves a clear, measurable problem.
2. Aim for the smallest possible edit. Image models preserve identity best when the reference material is already close to the target and the prompt only describes the delta. Every approach on the ladder is a way of shrinking what the model has to invent.

The ladder of approaches

#	Approach	Effort	What it is	Reference file
1	Corrective guidelines	Minutes	Short, targeted rules for what the model gets wrong; delta-only prompting	references/approach-1-guidelines.md
2	Reference images & atlas	An hour	Attach a master reference / character atlas; with a few references, pick the closest one per request	references/approach-2-references.md
3	Full dataset system	Hours–days	Curated 10–20 image dataset + tags + closest-reference selection per generation	references/approach-3-dataset-system.md
4	Pipelines & model routing	Hours	Two-stage identity/composition split, systematic model comparison, narrow routing rules	references/approach-4-pipelines-routing.md
5	Post-processing & training	Days+	Refinement pass with an editing model; character LoRA or fine-tune	references/approach-5-postprocess-training.md

Approaches stack: a dataset system still uses corrective guidelines; a fine-tuned model still benefits from an atlas and routing rules.

Triage: choose the right approach with the user

Don't assume — ask. Before recommending or executing anything, establish the user's situation (skip questions the conversation already answers):

1. What do they have? Nothing but a description • one good image • a handful of references • a curated dataset • a trained model
2. What's breaking? One recurring detail (fingers, a mark, a color) • overall style drift • pose/angle failures • identity collapses in scenes/edits • nothing yet, they're setting up
3. What's the volume? A few one-off images • ongoing production use with many prompts
4. What can they run? Chat model only • API/MCP image tools • a platform with datasets and routing • training infrastructure

Then map to a starting rung:

• One-off images, or a single recurring mistake → Approach 1. A one-line corrective guideline often fixes what a whole dataset can't.
• Has 1+ good images, identity drifts broadly → Approach 2. A master reference/atlas is the biggest consistency win per unit of effort.
• Production use, many varied prompts (poses, angles, expressions) → Approach 3. Range requires a dataset.
• Identity fine alone but collapses in scenes/backgrounds, or different models win different jobs → Approach 4.
• Approaches 1–4 applied and specific errors still persist → Approach 5. Never start here.

State the recommendation and the reasoning briefly, confirm with the user, then read the matching reference file and execute. If the user's ask is a generation request and assets already exist, run the per-generation loop below at whatever rung they're on.

The per-generation loop (applies at every rung)

Whenever actually generating or editing a character image:

1. Classify the request — text-to-image or image-to-image; character alone or in a scene; which pose/angle/expression/action.
2. Gather the best conditioning available at the current rung — guidelines (rung 1), atlas or closest reference (rungs 2–3), pipeline stage plan (rung 4).
3. Prompt the delta only. Describe what should change from the reference; never re-describe the art style — reference-conditioned models learn style from images, and long style descriptions fight the reference and cause drift.
4. Invoke whatever image capability is available — an MCP image tool, a configured API (fal, Replicate, OpenAI, Google, etc.), or the platform's built-in generation — adapting the package to its interface. If several models are available, prefer the strongest at reference-following and say which was used.
5. Verify the output against the character's defining traits (build a checklist from the references if none exists), checking historically-wrong traits first. State what's on-model and what drifted.
6. On drift, fix at the cheapest level: sharpen a guideline → swap the reference → adjust the pipeline → try another model → fix the assets → only then consider the next rung of the ladder. Tell the user which fix you're applying and why.

Debugging drift: inspect the whole flow

When outputs keep going off-model, review every stage before blaming the model or escalating: the original request → the prompt actually sent → the guidelines/tags included → the reference(s) selected → intermediate generations (in multi-stage pipelines) → the final result. Most "model problems" are a bloated prompt, a wrong reference, or a dirty asset. Detailed debugging and the model-comparison rubric live in references/approach-4-pipelines-routing.md.

Recommended asset layout (a convention, not a requirement)

<character-name>/
├── guidelines.md     # rung 1: short corrective rules
├── atlas.png         # rung 2: master reference sheet
├── dataset/          # rung 3: 10–20 varied but consistent images
└── identity.md       # optional: checklist of defining traits for verification

Adapt to whatever the user actually has — loose folders, a single image, or images pasted in chat all work.