tts-voice-creator-clone-and-design-2/architecture/character-visual-identity.md

# SOP — Character visual identity and appearance forms

Status: implemented. Covers how a character's avatar and concept (reference) sheet stay the same
person, and how one character holds several appearances ("forms") without splitting into several
characters or voices.

## Single source of truth

* Appearance facts come from the sheet's `physical`, `clothing`, `age_estimate`, `race_species`,
  `gender`, `title`, `profession`, `archetype` (and the user overrides `visual_appearance`,
  `visual_species`, `visual_costume`). Both prompts are built by
  `_character_visual_facts` in `routes/conversation.py`; the active form is overlaid first
  (`_character_form_sheet`, which never lets a form change its own `id`/`name`).
* `image_prompt` (portrait) and `concept_art_prompt` (reference sheet) are **deterministic** —
  built server-side from those facts with fixed camera/lighting/period text. Sheet extraction no
  longer asks the LLM to write `image_prompt` (it drifted from the concept sheet); the prompt is
  generated right after extraction by `csAutoGenerateExternalPrompts`, and again before any
  avatar-only or per-form generation. A hand-edited prompt (`*_user_edited`) is never overwritten.
* Only the two prompts describe an appearance. `voice_design_prompt` and the SillyTavern prompt
  belong to the character as a whole, so they always live on the base sheet.

## Stale detection

`_character_identity_fingerprint` (server) and `_libIdentityFingerprint` (browser) hash the same
field list; a test runs the browser code in Node and compares, including list values and non-BMP
characters. The three `visual_*` overrides are appended only when set, so pairs made before they
existed keep a valid fingerprint. A pair needs review when its prompt version is not current, its
fingerprint is missing or differs, or `identity_consistent` is false. Regenerating **only** the
avatar or **only** the reference image sets `identity_consistent = false` on an existing pair,
because a lone regeneration produces a new face.

## Where the data lives

| | base appearance | a form |
|---|---|---|
| profile image | record `image` | `form.image` |
| reference image, version, fingerprint, digests, `identity_consistent` | `sheet.*` | `form.*` |
| Physical / Clothing text | `sheet.physical/clothing` | `form.physical/clothing` |

All visual results are written through one function (`_libSaveCharacterVisual`), which re-reads the
stored record, writes to the active form or the base, and saves. (It used to write the base
reference image to the record root, and the consistent-pair generator kept it in memory only, so a
base pair lost its reference image and metadata on reload.)

`GET /api/characters/visual-pair-review` checks the base pair and every form that has images, each
against its own metadata and its own fingerprint, and reports form pairs under the form name.

## Forms (max 8 per character, enforced in `core/validation.py`)

Add (starts as a copy of the base Physical/Clothing; only non-empty keys are stored because an
empty key would blank the base value when overlaid), rename (unique, ≤160 chars), delete (confirm;
the character, its voice and the base look stay), show, and generate matched images for one form or
for all forms. Generation rebuilds that form's prompts first (unless hand-edited), saves the pair on
that form, then restores whichever appearance was active. Editing Physical/Clothing while a form is
active changes only that form.


## Image prompt hygiene and the turnaround (1.34.x)

* **Setting.** Prompts describe the books as *The Dark Eye* high-fantasy medieval dress (about 900-1300 AD: wool and linen tunics, belted robes, leather, mail where the sheet names it). The earlier "early Renaissance 1420-1500" wording pulled uniforms, coats, hats and boots that read as musketeer/cowboy/wartime.
* **Wording.** Extracted costume text is rewritten at prompt time (`_period_wording`: Uniform, Weste, Hut, Sporen, Jacke, Schal … become surcoat, jerkin, hood, boots, tunic …). Stored sheets are not changed.
* **Injuries and dirt.** `_strip_injury_clauses` removes clauses about blood, wounds, bandages, burns, limping, torn clothes, soot and dirt from appearance, costume and the story moment; the rest of the sentence stays. The portrait baseline asks for clean skin and garments.
* **Variety.** Characters with armour or weapons get an individual garment cut and colour scheme (`_character_design_variation`, stable per name; colours the sheet names win) instead of one shared list.
* **The turnaround stays one T2I triptych.** Drawing front/side/rear one by one with the edit model was tried (2026-09-20) and reverted the next day: it returned three near-identical front views, mixed rendering styles between panels, cropped bodies, and copied the wireframe pose guide into the costume — exactly the failure the triptych was introduced to fix. The portrait/sheet face mismatch is still open and needs a different approach.
* **Props** get an object-only period sentence, so a tile no longer turns into a portrait of the character.
* Older saved prompts are cleaned the same way at render time. Existing image pairs are unchanged until regenerated.


## Automatic image check (`POST /api/character-image-check`)

A vision model reports **only what it sees** — figure count, the view of each figure left to right,
whether every head and foot is in frame, whether the figures are the same person, apparent gender,
ear shape (pointed/round/hidden), wounds, and background (plain/scenery). The expected values come from
the character sheet and the comparison happens in code (`core/character_image_check.py`). Asking a model
"is this a woman, as required?" invites agreement; asking "what gender is this?" does not, and the
expected gender is never revealed in the question.

* Kinds: `portrait`, `turnaround`, `scene`, `same_person` (profile vs. a sheet figure).
* **Fail-safe:** an unreachable checker, an unreadable answer, or a missing observation is a failure,
  never a pass. An unreadable answer is returned verbatim (truncated) so it can be diagnosed.
* Thinking is switched off: a reasoning model spent the whole budget deliberating over a confusing image
  and returned nothing.
* **Model:** the active LLM by default (`llm_model`), overridable with the `image_check_model` setting.
  The model already loaded reads images, so a check never makes llama-swap unload it for a separate
  vision model on a GPU with no room for both.

**Calibration (2026-09-21)** on seven panels whose defects were known (three front views, elf ears on a
human, a wireframe rendered as a figure, cut-off feet, a man where the sheet says woman, a forest behind a
studio portrait, and one correct same-person pair), run twice:

| model | correct | wrong | unreadable | per check |
|---|---|---|---|---|
| local Qwen3.8-27B (`default`) | 14/14 | 0 | 0 | ~4 s |
| Ling 3.0 Flash VL (`vision`, free, OpenRouter) | 8/14 | 0 | 6 | ~17 s |

Ling never judged wrongly but on every turnaround ignored the JSON-only instruction and answered in prose.
It also has free-tier rate limits and sends the images to a third party.