음성 재생과 운영 배포 정리
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Yun Chan
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1020 changed files with 46863 additions and 2175 deletions
479
docs/avatar-art/personas/run_part_imagegen.py
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479
docs/avatar-art/personas/run_part_imagegen.py
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#!/usr/bin/env python3
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"""Generate persona-specific raster parts with the project imagegen wrapper.
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The renderer consumes 900x1125 transparent PNGs. gpt-image-2 generation sizes
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need 16px multiples, so this runner generates a 1024x1280 chroma-key draft and
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normalizes the result back to the app canvas.
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import platform
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import shlex
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import subprocess
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import sys
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from pathlib import Path
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import numpy as np
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from PIL import Image, ImageDraw, ImageFilter
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ROOT = Path(__file__).resolve().parents[3]
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SOURCE_PARTS = ROOT / "apps/web/public/avatar/seoyeon-live2d-psd-v2/parts"
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SOURCE_CROPS = ROOT / "docs/avatar-art/personas/source-part-crops"
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TARGET_CANVAS = (900, 1125)
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GEN_SIZE = "1024x1280"
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KEY = (0, 255, 0)
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WRAPPER = Path.home() / ".codex/imagegen-headless/codex_imagegen.sh"
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WSL_WRAPPER = "/mnt/c/Users/encep/.codex/imagegen-headless/codex_imagegen.sh"
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WSL_PYTHON = "/mnt/c/Users/encep/AppData/Local/Python/pythoncore-3.14-64/python.exe"
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GIT_BASH = Path("C:/Program Files/Git/bin/bash.exe")
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PERSONAS = {
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"P4": {
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"name": "하늘",
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"summary": "고2 여학생, 학업/시험 불안, 완벽주의, 긴장된 눈매와 깔끔한 교복 느낌",
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"palette": "soft black hair with cool brown highlights, pale warm skin, muted navy school uniform",
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},
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"P5": {
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"name": "도윤",
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"summary": "중3 남학생, 또래관계 갈등과 소외감, 경계심 있고 말수가 적은 표정",
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"palette": "dark brown short hair, neutral warm skin, subdued gray-blue school jacket",
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},
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"P6": {
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"name": "하린",
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"summary": "고3 여학생, 진로갈등과 부모 기대 압박, 차분하지만 흔들리는 표정",
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"palette": "deep ash brown medium hair, soft warm skin, neat cream and charcoal school styling",
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},
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"P7": {
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"name": "도현",
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"summary": "고3 남학생, 입시 번아웃과 무기력, 지친 눈매와 낮은 에너지",
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"palette": "black slightly messy short hair, low-saturation warm skin, dark school cardigan",
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},
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}
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CORE_PARTS = [
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"face-base",
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"hair-front",
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"hair-side-left-1",
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"hair-side-right-1",
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"brow-left",
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"brow-right",
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"brow-sad-left",
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"brow-sad-right",
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"eye-white-left",
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"eye-white-right",
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"iris-left",
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"iris-right",
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"pupil-left",
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"pupil-right",
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"lash-left",
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"lash-right",
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"mouth-neutral",
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"mouth-sad",
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"tear-left",
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"tear-right",
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]
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BODY_PARTS = [
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"hair-back-left",
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"hair-back-right",
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"hair-back-base",
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"body",
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"neck",
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"clavicle",
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"outfit",
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"outfit-inner",
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"outfit-outer",
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"ribbon",
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"face",
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"ear-left",
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"ear-right",
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"blush-left",
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"blush-right",
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"nose",
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"eye-left",
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"eye-right",
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"highlight-left",
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"highlight-right",
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"eyelid-upper-left",
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"eyelid-upper-right",
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"eyelid-lower-left",
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"eyelid-lower-right",
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"hair-bangs",
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"hair-side-left-2",
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"hair-side-right-2",
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"mouth-open",
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"mouth-open-small",
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"mouth-warm",
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"mouth-tired",
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"mouth-anxious",
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"mouth-startled",
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]
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def read_manifest() -> dict:
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path = ROOT / "docs/avatar-art/seoyeon/live2d-psd-v2/detailed-parts/detailed-parts-manifest.json"
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return json.loads(path.read_text(encoding="utf-8"))
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def resolved_alpha_box(part: dict) -> list[int] | None:
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alpha = part.get("alphaBox")
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if alpha:
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return [int(v) for v in alpha]
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ref = SOURCE_PARTS / f"{part['id']}.png"
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if not ref.exists():
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return None
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im = Image.open(ref).convert("RGBA")
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box = alpha_bbox(im)
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return [int(v) for v in box] if box else None
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def prompt_for(persona_code: str, part: dict) -> str:
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persona = PERSONAS[persona_code]
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alpha = resolved_alpha_box(part)
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origin = part.get("originPercent")
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return f"""Use case: stylized-concept
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Asset type: Live2D-style raster avatar part for Vignette persona {persona_code} {persona["name"]}
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Primary request: Generate exactly one isolated avatar part, matching the reference part's type and placement.
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Reference image role: the input image is a tight crop of the source Seoyeon PSD v2 part. Use it for the exact part category, silhouette, edge quality, and local proportions.
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Subject: {persona["summary"]}
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Part id: {part["id"]}
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Final canvas requirement: the delivered project asset must be a 900x1125 transparent PNG. This generation draft is a cropped part source; post-processing will resize the detected part to source alphaBox {alpha} and paste it at originPercent {origin}.
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Crop constraint: fill the draft with only this one part, with a small clean margin. Do not draw surrounding face, hair, body, sheet cells, labels, or other avatar parts.
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Style/medium: polished anime Live2D PSD part, clean painted edges, matching Vignette avatar renderer style.
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Color palette: {persona["palette"]}.
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Background: perfectly flat solid #00ff00 chroma-key background. Do not use #00ff00 in the part.
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Constraints: output one part only, not a character sheet, not multiple variants, no full body unless the part id requires it, no text, no watermark, no shadow, no floor, no frame. Preserve the reference part's transparent silhouette logic and visual scale.
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Avoid: collage, sprite sheet, labels, full character, extra facial features, extra parts outside this one part, gradients in the background.
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"""
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def ensure_dirs(persona_code: str) -> tuple[Path, Path, Path]:
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root = ROOT / f"docs/avatar-art/personas/{persona_code}"
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raw = root / "generated-raw"
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final = ROOT / f"apps/web/public/avatar/{persona_code.lower()}-live2d-generated/parts"
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prompts = root / "prompts"
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raw.mkdir(parents=True, exist_ok=True)
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final.mkdir(parents=True, exist_ok=True)
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prompts.mkdir(parents=True, exist_ok=True)
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return raw, final, prompts
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def make_reference_crop(part: dict) -> Path:
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part_id = part["id"]
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ref = SOURCE_PARTS / f"{part_id}.png"
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if not ref.exists():
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raise FileNotFoundError(ref)
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SOURCE_CROPS.mkdir(parents=True, exist_ok=True)
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out = SOURCE_CROPS / f"{part_id}.png"
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if out.exists():
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return out
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im = Image.open(ref).convert("RGBA")
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box = resolved_alpha_box(part) or alpha_bbox(im)
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if not box:
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im.save(out, optimize=True)
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return out
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x0, y0, x1, y1 = [int(v) for v in box]
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pad_x = max(8, int((x1 - x0) * 0.28))
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pad_y = max(8, int((y1 - y0) * 0.28))
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crop_box = (
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max(0, x0 - pad_x),
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max(0, y0 - pad_y),
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min(im.width, x1 + pad_x),
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min(im.height, y1 + pad_y),
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)
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crop = im.crop(crop_box)
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crop.save(out, optimize=True)
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return out
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def alpha_bbox(im: Image.Image, threshold: int = 8) -> tuple[int, int, int, int] | None:
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arr = np.array(im.convert("RGBA"))
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alpha = arr[:, :, 3]
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ys, xs = np.where(alpha > threshold)
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if len(xs) == 0:
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return None
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return int(xs.min()), int(ys.min()), int(xs.max() + 1), int(ys.max() + 1)
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def procedural_blush(target_box: list[int] | tuple[int, int, int, int]) -> Image.Image:
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x0, y0, x1, y1 = [int(v) for v in target_box]
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layer = Image.new("RGBA", TARGET_CANVAS, (0, 0, 0, 0))
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draw = ImageDraw.Draw(layer)
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w = x1 - x0
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h = y1 - y0
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inset_x = max(2, int(w * 0.12))
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inset_y = max(2, int(h * 0.18))
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draw.ellipse(
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(x0 + inset_x, y0 + inset_y, x1 - inset_x, y1 - inset_y),
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fill=(238, 142, 158, 74),
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)
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return layer.filter(ImageFilter.GaussianBlur(radius=max(7, int(min(w, h) * 0.15))))
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def remove_key_and_resize(
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src: Path,
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dst: Path,
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target_box: list[int] | tuple[int, int, int, int] | None,
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part_id: str = "",
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) -> None:
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if part_id.startswith("blush-") and target_box:
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dst.parent.mkdir(parents=True, exist_ok=True)
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procedural_blush(target_box).save(dst, optimize=True)
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return
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im = Image.open(src).convert("RGBA")
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if im.size != TARGET_CANVAS:
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im = im.resize(TARGET_CANVAS, Image.Resampling.LANCZOS)
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arr = np.array(im, dtype=np.uint8)
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rgb = arr[:, :, :3].astype(np.int16)
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alpha = arr[:, :, 3].astype(np.int16)
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r = rgb[:, :, 0]
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g = rgb[:, :, 1]
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b = rgb[:, :, 2]
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max_rb = np.maximum(r, b)
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green_delta = g - max_rb
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hard_key = ((g > 80) & (green_delta > 18)) | ((g > 105) & (g > r * 1.16) & (g > b * 1.16))
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soft_key = (g > 64) & (green_delta > 5) & ~hard_key
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arr[hard_key, 3] = 0
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if np.any(soft_key):
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arr[soft_key, 1] = np.clip(max_rb[soft_key] + 3, 0, 255).astype(np.uint8)
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arr[soft_key, 3] = np.clip(alpha[soft_key] * 0.72, 0, 255).astype(np.uint8)
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arr[(arr[:, :, 3] > 0) & (arr[:, :, 3] < 18), 3] = 0
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im = Image.fromarray(arr, "RGBA")
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if target_box:
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src_box = alpha_bbox(im, threshold=12)
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if src_box:
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x0, y0, x1, y1 = src_box
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tx0, ty0, tx1, ty1 = [int(v) for v in target_box]
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target_w = max(1, tx1 - tx0)
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target_h = max(1, ty1 - ty0)
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crop = im.crop((x0, y0, x1, y1)).resize((target_w, target_h), Image.Resampling.LANCZOS)
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placed = Image.new("RGBA", TARGET_CANVAS, (0, 0, 0, 0))
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placed.alpha_composite(crop, (tx0, ty0))
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im = placed
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dst.parent.mkdir(parents=True, exist_ok=True)
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im.save(dst, optimize=True)
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def validate_box(path: Path, expected_box: list[int] | tuple[int, int, int, int] | None) -> dict:
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im = Image.open(path).convert("RGBA")
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box = alpha_bbox(im, threshold=8)
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result: dict[str, object] = {"box": list(box) if box else None, "ok": False}
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if not expected_box or not box:
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return result
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expected = [int(v) for v in expected_box]
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actual = [int(v) for v in box]
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drift = [actual[i] - expected[i] for i in range(4)]
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result["expectedBox"] = expected
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result["drift"] = drift
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result["ok"] = max(abs(v) for v in drift) <= 2
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return result
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def to_wsl_path(path: Path) -> str:
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resolved = path.resolve()
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drive = resolved.drive.rstrip(":").lower()
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rest = resolved.as_posix()[2:]
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return f"/mnt/{drive}{rest}"
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def to_msys_path(path: Path) -> str:
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resolved = path.resolve()
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drive = resolved.drive.rstrip(":").lower()
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rest = resolved.as_posix()[2:]
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return f"/{drive}{rest}"
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def run_wrapper(raw: Path, ref: Path, prompt: str, quality: str) -> subprocess.CompletedProcess[str]:
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if platform.system().lower().startswith("windows"):
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bash_exe = str(GIT_BASH) if GIT_BASH.exists() else "bash"
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wrapper = to_msys_path(WRAPPER) if GIT_BASH.exists() else WSL_WRAPPER
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raw_path = to_msys_path(raw) if GIT_BASH.exists() else to_wsl_path(raw)
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ref_path = to_msys_path(ref) if GIT_BASH.exists() else to_wsl_path(ref)
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py_prefix = "" if GIT_BASH.exists() else f"PYTHON={shlex.quote(WSL_PYTHON)} "
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command = " ".join(
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[
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py_prefix + shlex.quote(wrapper),
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"--out",
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shlex.quote(raw_path),
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"--size",
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shlex.quote(GEN_SIZE),
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"--quality",
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shlex.quote(quality),
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"-i",
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shlex.quote(ref_path),
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"--prompt",
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shlex.quote(prompt),
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]
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)
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return subprocess.run([bash_exe, "-lc", command], cwd=ROOT, text=True, capture_output=True, check=False)
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args = [
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"bash",
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str(WRAPPER),
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"--out",
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str(raw),
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"--size",
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GEN_SIZE,
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"--quality",
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quality,
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"-i",
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str(ref),
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"--prompt",
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prompt,
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]
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return subprocess.run(args, cwd=ROOT, text=True, capture_output=True, check=False)
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def run_one(persona_code: str, part: dict, quality: str, overwrite: bool, reprocess_existing: bool) -> dict:
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raw_dir, final_dir, prompts_dir = ensure_dirs(persona_code)
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part_id = part["id"]
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prompt = prompt_for(persona_code, part)
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prompt_path = prompts_dir / f"{part_id}.txt"
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prompt_path.write_text(prompt, encoding="utf-8")
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ref = make_reference_crop(part)
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raw = raw_dir / f"{part_id}.png"
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final = final_dir / f"{part_id}.png"
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if not ref.exists():
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raise FileNotFoundError(ref)
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target_box = resolved_alpha_box(part)
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if target_box is None:
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final.parent.mkdir(parents=True, exist_ok=True)
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Image.new("RGBA", TARGET_CANVAS, (0, 0, 0, 0)).save(final, optimize=True)
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return {
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"persona": persona_code,
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"part": part_id,
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"status": "empty-source",
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"final": str(final),
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"boxValidation": {"box": None, "ok": True, "emptySource": True},
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}
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if final.exists() and reprocess_existing and raw.exists():
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remove_key_and_resize(raw, final, target_box, part_id)
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return {
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"persona": persona_code,
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"part": part_id,
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"status": "reprocessed",
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"raw": str(raw),
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"final": str(final),
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"boxValidation": validate_box(final, target_box),
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}
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if final.exists() and not overwrite:
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return {
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"persona": persona_code,
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"part": part_id,
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"status": "exists",
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"final": str(final),
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"boxValidation": validate_box(final, target_box),
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}
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os.environ["PYTHONUTF8"] = "1"
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result = run_wrapper(raw, ref, prompt, quality)
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if result.returncode != 0:
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return {
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"persona": persona_code,
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"part": part_id,
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"status": "failed",
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"stdout": result.stdout[-2000:],
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"stderr": result.stderr[-4000:],
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}
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remove_key_and_resize(raw, final, target_box, part_id)
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return {
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"persona": persona_code,
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"part": part_id,
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"status": "generated",
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"raw": str(raw),
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"final": str(final),
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"boxValidation": validate_box(final, target_box),
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}
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def main() -> int:
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parser = argparse.ArgumentParser()
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parser.add_argument("--personas", nargs="+", default=["P4", "P5", "P6", "P7"])
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parser.add_argument("--parts", nargs="*", default=[])
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parser.add_argument("--tier", choices=["core", "body", "all"], default="core")
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parser.add_argument("--quality", choices=["low", "medium", "high", "auto"], default="low")
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parser.add_argument("--concurrency", type=int, default=4)
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||||
parser.add_argument("--overwrite", action="store_true")
|
||||
parser.add_argument("--reprocess-existing", action="store_true")
|
||||
parser.add_argument("--dry-run", action="store_true")
|
||||
args = parser.parse_args()
|
||||
|
||||
manifest = read_manifest()
|
||||
all_parts = {part["id"]: part for part in manifest["parts"]}
|
||||
part_ids = args.parts
|
||||
if not part_ids:
|
||||
if args.tier == "core":
|
||||
part_ids = CORE_PARTS
|
||||
elif args.tier == "body":
|
||||
part_ids = BODY_PARTS
|
||||
else:
|
||||
part_ids = list(all_parts.keys())
|
||||
|
||||
jobs = []
|
||||
for persona_code in args.personas:
|
||||
if persona_code not in PERSONAS:
|
||||
raise ValueError(f"unknown persona {persona_code}")
|
||||
_, _, prompts_dir = ensure_dirs(persona_code)
|
||||
plan_parts = []
|
||||
for pid in part_ids:
|
||||
if pid not in all_parts:
|
||||
continue
|
||||
part = dict(all_parts[pid])
|
||||
part["sourceCrop"] = str(make_reference_crop(all_parts[pid]).relative_to(ROOT))
|
||||
plan_parts.append(part)
|
||||
plan = {
|
||||
"persona": persona_code,
|
||||
"name": PERSONAS[persona_code]["name"],
|
||||
"sourceArtSet": "seoyeon-live2d-psd-v2",
|
||||
"targetArtSet": f"{persona_code.lower()}-live2d-generated",
|
||||
"canvas": TARGET_CANVAS,
|
||||
"generationSize": GEN_SIZE,
|
||||
"parts": plan_parts,
|
||||
}
|
||||
plan_path = prompts_dir.parent / ("generation-plan-last-run.json" if args.parts else "generation-plan.json")
|
||||
plan_path.write_text(json.dumps(plan, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
for pid in part_ids:
|
||||
if pid not in all_parts:
|
||||
raise KeyError(pid)
|
||||
prompt = prompt_for(persona_code, all_parts[pid])
|
||||
(prompts_dir / f"{pid}.txt").write_text(prompt, encoding="utf-8")
|
||||
jobs.append((persona_code, all_parts[pid]))
|
||||
|
||||
queue_path = ROOT / "docs/avatar-art/personas/generation-queue.jsonl"
|
||||
queue_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
queue_path.write_text(
|
||||
"".join(json.dumps({"persona": p, "part": part["id"]}, ensure_ascii=False) + "\n" for p, part in jobs),
|
||||
encoding="utf-8",
|
||||
)
|
||||
|
||||
if args.dry_run:
|
||||
print(f"dry-run jobs={len(jobs)} queue={queue_path}")
|
||||
return 0
|
||||
|
||||
results = []
|
||||
with ThreadPoolExecutor(max_workers=max(1, args.concurrency)) as pool:
|
||||
futures = [
|
||||
pool.submit(run_one, persona, part, args.quality, args.overwrite, args.reprocess_existing)
|
||||
for persona, part in jobs
|
||||
]
|
||||
for future in as_completed(futures):
|
||||
result = future.result()
|
||||
results.append(result)
|
||||
print(json.dumps(result, ensure_ascii=False), flush=True)
|
||||
|
||||
summary_path = ROOT / "docs/avatar-art/personas/generation-results.json"
|
||||
summary_path.write_text(json.dumps(results, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
failed = [item for item in results if item["status"] == "failed"]
|
||||
return 1 if failed else 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
Loading…
Add table
Add a link
Reference in a new issue