LoRA自動評価・品質管理システム
完全ガイド 2026年版

作成日: 2026-06-08 | 対象: キャラLoRA 100体量産 | 目標: 合格率 29% → 70%

モデル: grok-4.3 | 推定コスト: ¥330 | ソース: 18本

1結論：合格率29%→70%への最短ルート

合格率改善ロードマップ（12週間）

最短ルートの核心は 「採点コストを下げながら採点精度を上げる」 ことです。現状はAPIに全依存していますが、SSIM/ArcFace等のローカル指標で70%の前捌きをすることでコストを1/4に削減しつつ、PDCAでパラメータを最適化して合格率を倍増させます。^[1]

2市場規模：LoRA品質評価ツール市場

自動化の経済効果試算

合格率29%→70%を達成した場合、再生成回数が平均2.4回から1.4回に減少し、計算コストと時間コストを合わせて月間約30万円相当の効率化が見込めます。^[4]

3競合TOP10：既存LoRA評価手法の比較

出典: ComfyUI_FaceAnalysis ドキュメント^[5] / pyiqa ライブラリ^[6] / InsightFace ArcFace 論文^[7]

推奨スタック（コスト×精度最適化）

4技術スタック：採点自動化アーキテクチャ

コードA: lora_evaluator.py（Grok/Gemini Vision API 5軸自動採点）

# lora_evaluator.py
# LoRA 5軸自動採点 - Grok/Gemini Vision API デュアル対応
# usage: python lora_evaluator.py --lora-dir D:/loras --provider grok
import asyncio, base64, json, os, time, datetime
from pathlib import Path
from typing import Dict, List, Optional
import httpx

# --- 設定 ---
GROK_API_KEY   = os.getenv("XAI_API_KEY", "")
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "")
COST_LOG  = Path("D:/projects/fanza3_mass/grok_router_costs.jsonl")
SCORES_OUT = Path("scores.jsonl")

EVAL_SYSTEM = (
    "You are a strict anime character LoRA quality evaluator. "
    "Score the three provided images (face close-up / bikini / lingerie) on 5 axes. "
    "Return ONLY valid JSON. No explanation."
)
EVAL_USER = (
    "Score these 3 images on 5 axes (0-100 each).\n"
    "consistency: character features (hair/eyes/face) are consistent across all 3 images\n"
    "cute: overall cuteness and appeal of the character\n"
    "ero: erotic appeal and sensuality\n"
    "quality: image quality (resolution, no artifacts, no deformation)\n"
    "total: overall score\n\n"
    'Return ONLY this JSON format: {"consistency":75,"cute":80,"ero":70,"quality":85,"total":78}'
)

class LoRAEvaluator:
    def __init__(self, provider: str = "grok"):
        self.provider = provider
        self.sem = asyncio.Semaphore(4)

    def _encode(self, path: Path) -> tuple[str, str]:
        ext = path.suffix.lower().lstrip(".")
        mime = "image/jpeg" if ext in ("jpg", "jpeg") else "image/png"
        with open(path, "rb") as f:
            return base64.b64encode(f.read()).decode("utf-8"), mime

    def _log_cost(self, provider: str, cost: float, usage: dict):
        rec = {
            "timestamp": datetime.datetime.now().isoformat(),
            "kind": "lora_eval_vision",
            "model": f"{provider}_vision",
            "cost_usd": cost,
            "prompt_tokens": usage.get("prompt_tokens", 0),
            "completion_tokens": usage.get("completion_tokens", 0),
        }
        COST_LOG.parent.mkdir(parents=True, exist_ok=True)
        with open(COST_LOG, "a", encoding="utf-8") as f:
            f.write(json.dumps(rec, ensure_ascii=False) + "\n")

    def _parse_scores(self, text: str) -> Optional[Dict]:
        text = text.strip()
        # JSON抽出 (前後のテキストを除去)
        start = text.find("{")
        end   = text.rfind("}") + 1
        if start >= 0 and end > start:
            try:
                return json.loads(text[start:end])
            except json.JSONDecodeError:
                pass
        return None

    async def _score_grok(self, images: List[Path]) -> tuple[Dict, float]:
        content = [{"type": "text", "text": EVAL_USER}]
        for img in images:
            b64, mime = self._encode(img)
            content.append({
                "type": "image_url",
                "image_url": {"url": f"data:{mime};base64,{b64}"}
            })
        body = {
            "model": "grok-4.3",
            "messages": [
                {"role": "system", "content": EVAL_SYSTEM},
                {"role": "user",   "content": content}
            ],
            "max_tokens": 120,
            "temperature": 0.05,
        }
        for attempt in range(3):
            try:
                async with httpx.AsyncClient(timeout=90) as client:
                    resp = await client.post(
                        "https://api.x.ai/v1/chat/completions",
                        headers={"Authorization": f"Bearer {GROK_API_KEY}",
                                 "Content-Type": "application/json"},
                        json=body
                    )
                    resp.raise_for_status()
                    d = resp.json()
                    usage = d.get("usage", {})
                    cost  = usage.get("cost_in_usd_ticks", 0) / 1e8
                    text  = d["choices"][0]["message"]["content"]
                    scores = self._parse_scores(text)
                    if scores:
                        self._log_cost("grok", cost, usage)
                        return scores, cost
                    raise ValueError(f"JSON parse failed: {text[:80]}")
            except Exception as e:
                if attempt == 2:
                    raise RuntimeError(f"Grok API failed: {e}") from e
                await asyncio.sleep(2 ** attempt)

    async def _score_gemini(self, images: List[Path]) -> tuple[Dict, float]:
        parts = [{"text": EVAL_USER}]
        for img in images:
            b64, mime = self._encode(img)
            parts.append({"inline_data": {"mime_type": mime, "data": b64}})
        body = {
            "system_instruction": {"parts": [{"text": EVAL_SYSTEM}]},
            "contents": [{"parts": parts}],
            "generationConfig": {
                "temperature": 0.05,
                "maxOutputTokens": 120,
                "responseMimeType": "application/json"
            }
        }
        for attempt in range(3):
            try:
                url = (
                    "https://generativelanguage.googleapis.com/v1beta/"
                    f"models/gemini-2.0-flash:generateContent?key={GEMINI_API_KEY}"
                )
                async with httpx.AsyncClient(timeout=90) as client:
                    resp = await client.post(url, json=body)
                    resp.raise_for_status()
                    d = resp.json()
                    text = d["candidates"][0]["content"]["parts"][0]["text"]
                    scores = self._parse_scores(text)
                    if scores:
                        usage = d.get("usageMetadata", {})
                        # Gemini Flash は無料枠内ではコスト0
                        tok_in  = usage.get("promptTokenCount", 0)
                        tok_out = usage.get("candidatesTokenCount", 0)
                        cost = (tok_in * 0.075 + tok_out * 0.30) / 1e6
                        self._log_cost("gemini", cost, {
                            "prompt_tokens": tok_in, "completion_tokens": tok_out})
                        return scores, cost
                    raise ValueError(f"JSON parse failed: {text[:80]}")
            except Exception as e:
                if attempt == 2:
                    raise RuntimeError(f"Gemini API failed: {e}") from e
                await asyncio.sleep(2 ** attempt)

    def is_pass(self, scores: Dict) -> bool:
        axes = ["consistency", "cute", "ero", "quality", "total"]
        avg = sum(scores.get(a, 0) for a in axes) / len(axes)
        return avg >= 70 and scores.get("consistency", 0) >= 65

    async def evaluate_lora(self, lora_name: str, images_dir: Path) -> Dict:
        async with self.sem:
            images = [
                images_dir / "face.png",
                images_dir / "bikini.png",
                images_dir / "lingerie.png"
            ]
            missing = [str(p) for p in images if not p.exists()]
            if missing:
                print(f"[SKIP] {lora_name}: Missing {missing}")
                return {"lora_name": lora_name, "error": f"Missing: {missing}", "passed": False}

            t0 = time.time()
            try:
                if self.provider == "grok":
                    scores, cost = await self._score_grok(images)
                else:
                    scores, cost = await self._score_gemini(images)
            except RuntimeError as e:
                return {"lora_name": lora_name, "error": str(e), "passed": False}

            elapsed = time.time() - t0
            passed  = self.is_pass(scores)
            axes    = ["consistency", "cute", "ero", "quality", "total"]
            avg     = sum(scores.get(a, 0) for a in axes) / len(axes)

            result = {
                "lora_name":   lora_name,
                "timestamp":   datetime.datetime.now().isoformat(),
                "provider":    self.provider,
                "passed":      passed,
                "avg":         round(avg, 1),
                "elapsed_sec": round(elapsed, 2),
                "cost_usd":    round(cost, 6),
                **scores
            }
            SCORES_OUT.parent.mkdir(parents=True, exist_ok=True)
            with open(SCORES_OUT, "a", encoding="utf-8") as f:
                f.write(json.dumps(result, ensure_ascii=False) + "\n")

            status = "PASS" if passed else "FAIL"
            print(f"[{status}] {lora_name}: avg={avg:.1f} "
                  f"consistency={scores.get('consistency',0)} ${cost:.4f}")
            return result

    async def evaluate_batch(self, lora_list: List[Dict]) -> List[Dict]:
        tasks = [
            self.evaluate_lora(item["name"], Path(item["dir"]))
            for item in lora_list
        ]
        return await asyncio.gather(*tasks, return_exceptions=False)

async def main():
    import argparse
    parser = argparse.ArgumentParser(description="LoRA 5-axis auto evaluator")
    parser.add_argument("--lora-dir", required=True)
    parser.add_argument("--provider", default="grok", choices=["grok", "gemini"])
    args = parser.parse_args()

    evaluator  = LoRAEvaluator(provider=args.provider)
    lora_root  = Path(args.lora_dir)
    lora_list  = [
        {"name": d.name, "dir": str(d)}
        for d in sorted(lora_root.iterdir())
        if d.is_dir() and (d / "face.png").exists()
    ]
    if not lora_list:
        print(f"No LoRA directories found in {lora_root}")
        return

    print(f"Evaluating {len(lora_list)} LoRAs with {args.provider}...")
    results = await evaluator.evaluate_batch(lora_list)
    passed  = sum(1 for r in results if isinstance(r, dict) and r.get("passed"))
    total   = len(results)
    print(f"\n=== Final Results ===")
    print(f"Total: {total} | Passed: {passed} | Rate: {passed/total*100:.1f}%")

if __name__ == "__main__":
    asyncio.run(main())

コードB: local_metrics.py（ローカル画像品質メトリクス）

# local_metrics.py
# LoRA品質ローカル計測 - SSIM / LPIPS / ArcFace / CLIP-IQA
# pip install scikit-image lpips insightface pyiqa onnxruntime-gpu torch torchvision
import warnings
warnings.filterwarnings("ignore")
import json, os
import numpy as np
from pathlib import Path
from typing import Dict, List, Optional, Union
from PIL import Image

# --- ライブラリの存在確認 ---
try:
    from skimage.metrics import structural_similarity
    SSIM_OK = True
except ImportError:
    SSIM_OK = False
    print("[warn] skimage not found. SSIM disabled.")

try:
    import torch
    import lpips as lpips_lib
    LPIPS_OK = True
except ImportError:
    LPIPS_OK = False
    print("[warn] lpips not found. LPIPS disabled.")

try:
    import insightface
    from insightface.app import FaceAnalysis
    ARCFACE_OK = True
except ImportError:
    ARCFACE_OK = False
    print("[warn] insightface not found. ArcFace disabled.")

try:
    import pyiqa
    CLIPIQA_OK = True
except ImportError:
    CLIPIQA_OK = False
    print("[warn] pyiqa not found. CLIP-IQA disabled.")

if not LPIPS_OK:
    import torch  # fallback import


class LocalMetrics:
    """ローカル完結の画像品質・一貫性メトリクス計測クラス"""

    IMG_SIZE = 512

    def __init__(self, device: Optional[str] = None):
        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
        print(f"[LocalMetrics] device={self.device} | "
              f"SSIM={SSIM_OK} LPIPS={LPIPS_OK} ArcFace={ARCFACE_OK} CLIPIQA={CLIPIQA_OK}")

        self._lpips_fn   = None
        self._face_app   = None
        self._clipiqa_fn = None

        if LPIPS_OK:
            self._lpips_fn = lpips_lib.LPIPS(net='vgg').to(self.device)

        if ARCFACE_OK:
            self._face_app = FaceAnalysis(
                name='buffalo_l',
                providers=['CUDAExecutionProvider', 'CPUExecutionProvider']
            )
            ctx = 0 if self.device == "cuda" else -1
            self._face_app.prepare(ctx_id=ctx, det_size=(640, 640))

        if CLIPIQA_OK:
            self._clipiqa_fn = pyiqa.create_metric('clipiqa', device=self.device)

    # ------------------------------------------------------------------ utils
    def _load(self, path: Union[str, Path]) -> Image.Image:
        return Image.open(path).convert("RGB").resize(
            (self.IMG_SIZE, self.IMG_SIZE), Image.LANCZOS
        )

    def _to_tensor(self, img: Image.Image) -> "torch.Tensor":
        arr = np.array(img).astype(np.float32) / 127.5 - 1.0
        return torch.from_numpy(arr).permute(2, 0, 1).unsqueeze(0).to(self.device)

    def _to_bgr_array(self, img: Image.Image) -> np.ndarray:
        return np.array(img)[:, :, ::-1].copy()  # RGB→BGR

    # ------------------------------------------------------------------ SSIM
    def ssim(self, img1: Image.Image, img2: Image.Image) -> float:
        if not SSIM_OK:
            return 0.0
        a = np.array(img1.convert("L"))
        b = np.array(img2.convert("L"))
        score = structural_similarity(a, b, data_range=255)
        return max(0.0, float(score) * 100)  # 0-100

    # ------------------------------------------------------------------ LPIPS
    def lpips(self, img1: Image.Image, img2: Image.Image) -> float:
        if not LPIPS_OK or self._lpips_fn is None:
            return 0.0
        t1, t2 = self._to_tensor(img1), self._to_tensor(img2)
        with torch.no_grad():
            dist = self._lpips_fn(t1, t2).item()
        return max(0.0, (1.0 - dist) * 100)  # 類似度 0-100

    # ------------------------------------------------------------------ ArcFace
    def arcface(self, img1: Image.Image, img2: Image.Image) -> float:
        if not ARCFACE_OK or self._face_app is None:
            return 0.0
        f1 = self._face_app.get(self._to_bgr_array(img1))
        f2 = self._face_app.get(self._to_bgr_array(img2))
        if not f1 or not f2:
            return 0.0
        e1 = f1[0].embedding; e1 /= (np.linalg.norm(e1) + 1e-8)
        e2 = f2[0].embedding; e2 /= (np.linalg.norm(e2) + 1e-8)
        cosine = float(np.dot(e1, e2))
        return max(0.0, min(100.0, (cosine + 1.0) * 50.0))  # -1~1 → 0-100

    # ------------------------------------------------------------------ CLIP-IQA
    def clip_iqa(self, img: Image.Image) -> float:
        if not CLIPIQA_OK or self._clipiqa_fn is None:
            return 0.0
        import torchvision.transforms as T
        tensor = T.Compose([T.Resize((224, 224)), T.ToTensor()])(img).unsqueeze(0).to(self.device)
        with torch.no_grad():
            score = self._clipiqa_fn(tensor).item()
        return max(0.0, min(100.0, float(score) * 100))

    # ------------------------------------------------------------------ all-in-one
    def compute_all(
        self,
        generated_images: List[Union[str, Path]],
        reference_images: List[Union[str, Path]]
    ) -> Dict:
        """
        生成画像群と参照画像群から4指標を計算して0-100スコアを返す。
        Returns:
          {ssim, lpips_sim, arcface_sim, clip_iqa,
           local_consistency, local_quality, overall_local}
        """
        gen_imgs = [self._load(p) for p in generated_images]
        ref_imgs = [self._load(p) for p in reference_images]

        ssim_vals, lpips_vals, arc_vals, iqa_vals = [], [], [], []

        for gen in gen_imgs:
            per_ssim, per_lpips, per_arc = [], [], []
            for ref in ref_imgs:
                per_ssim.append(self.ssim(gen, ref))
                per_lpips.append(self.lpips(gen, ref))
                per_arc.append(self.arcface(gen, ref))
            ssim_vals.append(float(np.mean(per_ssim)))
            lpips_vals.append(float(np.mean(per_lpips)))
            arc_vals.append(float(np.mean(per_arc)))
            iqa_vals.append(self.clip_iqa(gen))

        ssim_avg   = round(float(np.mean(ssim_vals)),   2)
        lpips_avg  = round(float(np.mean(lpips_vals)),  2)
        arc_avg    = round(float(np.mean(arc_vals)),    2)
        iqa_avg    = round(float(np.mean(iqa_vals)),    2)

        # 複合スコア
        local_consistency = round(
            float(np.average([arc_avg, ssim_avg, lpips_avg], weights=[0.5, 0.3, 0.2])), 2
        )
        local_quality = round(
            float(np.average([iqa_avg, lpips_avg], weights=[0.6, 0.4])), 2
        )
        overall_local = round((local_consistency + local_quality) / 2, 2)

        return {
            "ssim":              ssim_avg,
            "lpips_sim":         lpips_avg,
            "arcface_sim":       arc_avg,
            "clip_iqa":          iqa_avg,
            "local_consistency": local_consistency,
            "local_quality":     local_quality,
            "overall_local":     overall_local,
        }

    def is_api_needed(self, local_scores: Dict) -> bool:
        """ローカルスコアだけでAPIが不要かを判定（コスト最適化）"""
        # 明らかに高品質 → API不要でPASS扱い
        if local_scores["local_consistency"] >= 82 and local_scores["local_quality"] >= 80:
            return False
        # 明らかに低品質 → API不要でFAIL扱い
        if local_scores["local_consistency"] < 40 or local_scores["local_quality"] < 35:
            return False
        # グレーゾーンのみAPIへ
        return True


if __name__ == "__main__":
    import sys
    m = LocalMetrics()
    gen_dir = Path(sys.argv[1]) if len(sys.argv) > 1 else Path(".")
    ref_dir = Path(sys.argv[2]) if len(sys.argv) > 2 else gen_dir
    gen_imgs = sorted(gen_dir.glob("*.png"))[:3]
    ref_imgs = sorted(ref_dir.glob("*.png"))[:3]
    if gen_imgs:
        result = m.compute_all(gen_imgs, ref_imgs)
        print(json.dumps(result, indent=2, ensure_ascii=False))
        print(f"API needed: {m.is_api_needed(result)}")
    else:
        print("No PNG images found.")

コードC: pdca_controller.py（採点→再学習自動トリガー）

# pdca_controller.py - LoRA PDCA自動改善コントローラー
import json, os
from datetime import datetime
from typing import Dict, List, Any
import numpy as np

class PDCAController:
    def __init__(
        self,
        scores_path:      str = "scores.jsonl",
        history_path:     str = "pdca_history.jsonl",
        kohya_config_path: str = "kohya_config.json"
    ):
        self.scores_path      = scores_path
        self.history_path     = history_path
        self.kohya_config_path = kohya_config_path
        self.axis_names = ["consistency", "cute", "ero", "quality", "total"]

    def load_scores(self) -> List[Dict]:
        scores = []
        if not os.path.exists(self.scores_path):
            return scores
        with open(self.scores_path, "r", encoding="utf-8") as f:
            for line in f:
                line = line.strip()
                if line:
                    scores.append(json.loads(line))
        return scores

    def calculate_metrics(self, scores: List[Dict]) -> Dict[str, Any]:
        if not scores:
            return {"pass_rate": 0.0,
                    "axis_avg": {k: 0.0 for k in self.axis_names},
                    "total": 0}
        total  = len(scores)
        passed = sum(1 for s in scores if s.get("passed", False))
        sums   = {k: 0.0 for k in self.axis_names}
        for s in scores:
            for ax in self.axis_names:
                sums[ax] += float(s.get(ax, 0.0))
        axis_avg = {k: round(v / total, 2) for k, v in sums.items()}
        return {
            "pass_rate":   round(passed / total * 100, 2),
            "axis_avg":    axis_avg,
            "overall_avg": round(np.mean(list(axis_avg.values())), 2),
            "total":       total,
        }

    def detect_issues(self, metrics: Dict) -> List[str]:
        issues = []
        if metrics["pass_rate"] < 40:
            issues.append("low_pass_rate")
        avg = metrics["axis_avg"]
        if avg.get("consistency", 0) < 65:
            issues.append("low_consistency")
        if avg.get("cute", 0) < 62:
            issues.append("low_cute")
        if avg.get("quality", 0) < 60:
            issues.append("low_quality")
        return issues

    def adjust_parameters(
        self, current_config: Dict, issues: List[str], metrics: Dict
    ) -> Dict:
        new = current_config.copy()
        dim   = float(current_config.get("dim",   8))
        alpha = float(current_config.get("alpha", 4))
        lr    = float(current_config.get("lr",    5e-5))
        steps = float(current_config.get("steps", 2000))

        if "low_pass_rate" in issues:
            dim   = min(16, dim + 4)        # rank増加
            steps = min(3000, steps + 400)  # 学習量増加

        if "low_consistency" in issues:
            lr    = min(1.2e-4, lr * 1.25) # lr上げ = キャラ特徴強化
            alpha = min(8, alpha + 2)

        if "low_cute" in issues:
            new["caption_quality_boost"] = True
            new["caption_dropout_rate"]  = 0.05  # キャプションを大切に

        if "low_quality" in issues:
            steps = min(3500, steps + 600)
            dim   = min(16, dim + 4)

        # 安全クリップ
        new["dim"]   = int(min(32, max(4,   dim)))
        new["alpha"] = int(min(16, max(1,   alpha)))
        new["lr"]    = round(min(2e-4, max(1e-5, lr)), 8)
        new["steps"] = int(min(4000, max(1200, steps)))
        new["updated_at"] = datetime.now().isoformat()
        return new

    def update_kohya_config(self, new_config: Dict):
        with open(self.kohya_config_path, "w", encoding="utf-8") as f:
            json.dump(new_config, f, indent=2, ensure_ascii=False)
        print(f"[PDCA] kohya config updated → {self.kohya_config_path}")
        print(f"[PDCA] dim={new_config['dim']} alpha={new_config['alpha']} "
              f"lr={new_config['lr']:.2e} steps={new_config['steps']}")

    def record_history(
        self, metrics: Dict, issues: List[str], new_config: Dict
    ):
        rec = {
            "timestamp":  datetime.now().isoformat(),
            "metrics":    metrics,
            "issues":     issues,
            "new_config": new_config,
        }
        with open(self.history_path, "a", encoding="utf-8") as f:
            f.write(json.dumps(rec, ensure_ascii=False) + "\n")

    def run_pdca_cycle(self) -> tuple[Dict, Dict]:
        scores  = self.load_scores()
        metrics = self.calculate_metrics(scores)
        issues  = self.detect_issues(metrics)

        print(f"[PDCA] Pass Rate={metrics['pass_rate']}% "
              f"Overall={metrics['overall_avg']} Issues={issues}")

        if os.path.exists(self.kohya_config_path):
            with open(self.kohya_config_path, "r", encoding="utf-8") as f:
                current_config = json.load(f)
        else:
            current_config = {"dim": 8, "alpha": 4, "lr": 5e-5, "steps": 2000}

        new_config = self.adjust_parameters(current_config, issues, metrics)

        if issues:
            self.update_kohya_config(new_config)
        else:
            print("[PDCA] No issues detected. Config unchanged.")

        self.record_history(metrics, issues, new_config)
        return metrics, new_config

if __name__ == "__main__":
    controller = PDCAController()
    controller.run_pdca_cycle()

コードD: bias_corrector.py（採点バイアス補正・アンサンブル）

# bias_corrector.py - Grok/Gemini採点バイアス補正・Z-score正規化・アンサンブル
import json, numpy as np
from datetime import datetime
from typing import List, Dict, Optional

class BiasCorrector:
    """
    Grokは全体的に-8点程度厳し目・Geminiはやや甘め。
    Z-score正規化で揃えてからGrok×0.6+Gemini×0.4でアンサンブル。
    """
    GROK_OFFSET   = +8.0   # Grokへの加算補正
    GROK_WEIGHT   = 0.6
    GEMINI_WEIGHT = 0.4
    AXES = ["consistency", "cute", "ero", "quality", "total"]

    def __init__(self, kill_std_threshold: float = 0.5):
        self.kill_std_threshold = kill_std_threshold

    # ---- キャリブレーション ----
    def calibrate_grok(self, score: float) -> float:
        return min(100.0, score + self.GROK_OFFSET)

    # ---- Z-score正規化 (スコアリスト全体) ----
    @staticmethod
    def z_normalize(values: List[float], target_mean: float = 70.0,
                    target_std: float = 12.0) -> List[float]:
        if len(values) < 2:
            return values
        arr  = np.array(values, dtype=float)
        mean = np.mean(arr)
        std  = np.std(arr)
        if std < 1e-6:
            return [target_mean] * len(values)
        normalized = (arr - mean) / std * target_std + target_mean
        return np.clip(normalized, 0, 100).tolist()

    # ---- Killスイッチ ----
    def detect_anomaly(self, scores: Dict) -> bool:
        vals = [scores.get(a, 0) for a in self.AXES]
        if all(v >= 99 for v in vals):
            print("[BiasCorrector] KILL: all scores >= 99 (API hallucination)")
            return True
        if all(v <= 1 for v in vals):
            print("[BiasCorrector] KILL: all scores <= 1 (API failure)")
            return True
        if np.std(vals) < self.kill_std_threshold:
            print(f"[BiasCorrector] KILL: std={np.std(vals):.2f} too small")
            return True
        return False

    # ---- アンサンブル (1件) ----
    def ensemble_one(
        self,
        grok_scores:   Dict,
        gemini_scores: Dict
    ) -> Optional[Dict]:
        if self.detect_anomaly(grok_scores) or self.detect_anomaly(gemini_scores):
            return None

        result = {}
        for ax in self.AXES:
            g = self.calibrate_grok(float(grok_scores.get(ax, 0)))
            m = float(gemini_scores.get(ax, 0))
            result[ax] = round(
                np.clip(g * self.GROK_WEIGHT + m * self.GEMINI_WEIGHT, 0, 100), 2
            )
        return result

    # ---- バッチ処理 ----
    def process_batch(
        self,
        grok_file:   str,
        gemini_file: str,
        output_file: str = "calibrated_scores.jsonl"
    ) -> List[Dict]:
        def load(path):
            data = {}
            with open(path, encoding="utf-8") as f:
                for line in f:
                    line = line.strip()
                    if line:
                        rec = json.loads(line)
                        data[rec.get("lora_name", rec.get("id", ""))] = rec
            return data

        grok_data   = load(grok_file)
        gemini_data = load(gemini_file)
        keys        = set(grok_data) & set(gemini_data)

        # Z-score正規化のためにバッチ全体を収集してから正規化
        for ax in self.AXES:
            grok_vals   = [grok_data[k].get(ax,   0) for k in keys]
            gemini_vals = [gemini_data[k].get(ax, 0) for k in keys]
            normed_g = self.z_normalize(grok_vals)
            normed_m = self.z_normalize(gemini_vals)
            for i, k in enumerate(keys):
                grok_data[k][f"_znorm_{ax}"]   = normed_g[i]
                gemini_data[k][f"_znorm_{ax}"] = normed_m[i]

        results = []
        for k in keys:
            g_scores = {ax: grok_data[k][f"_znorm_{ax}"] for ax in self.AXES}
            m_scores = {ax: gemini_data[k][f"_znorm_{ax}"] for ax in self.AXES}
            g_scores["_calibrated_grok"] = True
            g_scores.pop("_calibrated_grok")
            ensembled = self.ensemble_one(g_scores, m_scores)
            if ensembled is None:
                continue
            rec = {
                "lora_name":    k,
                "calibrated":   ensembled,
                "passed":       ensembled.get("consistency", 0) >= 65
                                and sum(ensembled.values()) / len(self.AXES) >= 70,
                "processed_at": datetime.now().isoformat()
            }
            results.append(rec)

        with open(output_file, "w", encoding="utf-8") as f:
            for r in results:
                f.write(json.dumps(r, ensure_ascii=False) + "\n")
        print(f"[BiasCorrector] {len(results)} records → {output_file}")
        return results

if __name__ == "__main__":
    bc = BiasCorrector()
    bc.process_batch("grok_scores.jsonl", "gemini_scores.jsonl")

5収益試算：自動化の経済効果

コスト削減の内訳

6リスク：採点バイアス・過学習・障害対策

730日プラン：具体的実装ロードマップ

8撤退ライン：自動化が機能しないKPI閾値

9落とし穴TOP10：自動評価でよくある失敗

1. 採点プロンプトがあいまい → スコア散乱: "高品質"の定義を数値で与えないと採点結果がバラバラ。解決: 採点基準に具体的な例示（"consistency=90: 3枚全てで髪色・目色・顔形が完全一致"）を追加。^[13]
2. ArcFaceがアニメ顔を検出できない: 顔が小さい・横向き・chibi体型はdet_sizeを(320,320)に下げると検出率改善。ただし誤検出も増える。
3. LPIPSのネット選択ミス: net='alex'はfast・net='vgg'はより知覚的。LoRA一貫性にはvgg推奨。^[14]
4. API採点の一貫性が低い（同一画像で±15点ブレ）: temperature=0.05以下にする・response_formatでJSON強制することで解消。
5. ローカルメトリクスの参照画像が少なすぎる: 参照が1枚だとSSIM/LPIPSが角度・服装依存で不安定。最低3枚（正面・斜め・全身）の参照セットを用意。
6. バイアス補正のキャリブレーション不足: GROK_OFFSET=+8はデフォルト値。実際は採点時期・プロンプト変更で変わる。月1回の再キャリブレーション必須。
7. PDCAが収束せずパラメータが発散: lr × 1.25を毎サイクル繰り返すと上限に達する。pdca_controllerの安全クリップ（lr上限2e-4）は必ず設定。
8. scores.jsonlが未ソートで重複あり: 同一LoRAを複数回評価した場合の重複を除去するロジックが必要。lora_nameでdeduplicationを実装。
9. A/Bテストの交絡変数（学習データの質・量が一定でない）: パラメータ比較は同一キャラ・同一学習データでのみ有効。異なるキャラで比較するとパラメータ効果が見えない。
10. ダッシュボードを誰も見ない: 合格率が3日連続で目標-10pt以下のときにメール/Discord通知を送るアラート機能を追加。quality_dashboard.pyにst.experimental_rerunでループ監視を追加推奨。

10既存資産活用：grok_router.py統合

# fanza3_mass既存パイプラインへの統合例
# _prod_plain_golden_2026-05-22.py 末尾に追加

import sys
sys.path.insert(0, 'D:/projects/fanza3_mass/scripts')
from lora_evaluator import LoRAEvaluator
from local_metrics  import LocalMetrics
from pdca_controller import PDCAController
import asyncio

async def run_quality_gate(lora_name: str, smoke_dir: str) -> bool:
    """量産前品質ゲート - preflight() から呼び出す"""
    # 1. ローカル前捌き
    local = LocalMetrics()
    local_scores = local.compute_all(
        generated_images=[f"{smoke_dir}/face.png",
                          f"{smoke_dir}/bikini.png",
                          f"{smoke_dir}/lingerie.png"],
        reference_images=[f"{smoke_dir}/ref_face.png"]
    )
    print(f"[Gate] local: {local_scores}")

    if not local.is_api_needed(local_scores):
        passed = local_scores["local_consistency"] >= 75
        print(f"[Gate] API skipped → {'PASS' if passed else 'FAIL'}")
        return passed

    # 2. Grok API採点（必要な場合のみ）
    evaluator = LoRAEvaluator(provider="grok")
    result = await evaluator.evaluate_lora(lora_name, Path(smoke_dir))
    if not result.get("passed"):
        # 3. PDCA自動実行
        pdca = PDCAController()
        pdca.run_pdca_cycle()
        return False
    return True

def preflight(lora_name: str, smoke_dir: str) -> None:
    import sys
    if not asyncio.run(run_quality_gate(lora_name, smoke_dir)):
        print(f"[PREFLIGHT] FAIL: {lora_name} → 量産中止")
        sys.exit(2)

コードE: quality_dashboard.py（Streamlit可視化）

# quality_dashboard.py - LoRA合格率PDCA可視化ダッシュボード
# usage: streamlit run quality_dashboard.py -- --scores scores.jsonl
import streamlit as st, json, pandas as pd, numpy as np, os
import plotly.express as px, plotly.graph_objects as go

st.set_page_config(page_title="LoRA Quality Dashboard", layout="wide",
                   page_icon="🎯")
st.title("LoRA 品質管理 PDCA ダッシュボード")

AXES = ["consistency", "cute", "ero", "quality", "total"]

@st.cache_data(ttl=30)
def load_scores(path="scores.jsonl"):
    if not os.path.exists(path):
        return pd.DataFrame()
    rows = []
    with open(path, encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if line:
                rows.append(json.loads(line))
    return pd.DataFrame(rows)

@st.cache_data(ttl=30)
def load_history(path="pdca_history.jsonl"):
    if not os.path.exists(path):
        return pd.DataFrame()
    rows = []
    with open(path, encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if line:
                rows.append(json.loads(line))
    return pd.DataFrame(rows)

df      = load_scores()
hist_df = load_history()

# --- KPI Cards ---
col1, col2, col3, col4 = st.columns(4)
if not df.empty:
    total  = len(df)
    passed = df["passed"].sum() if "passed" in df.columns else 0
    rate   = passed / total * 100
    avg_c  = df["consistency"].mean() if "consistency" in df.columns else 0
    col1.metric("総LoRA数", total)
    col2.metric("合格数",  int(passed))
    col3.metric("合格率",  f"{rate:.1f}%", delta=f"{rate-29:.1f}pt vs 初期")
    col4.metric("avg consistency", f"{avg_c:.1f}", delta=f"{avg_c-58:.1f}pt vs 初期")

# --- 合格率トレンド ---
st.header("合格率トレンド")
if not hist_df.empty:
    try:
        hist_df["timestamp"] = pd.to_datetime(hist_df["timestamp"])
        metrics_expanded = pd.json_normalize(hist_df["metrics"])
        metrics_expanded["timestamp"] = hist_df["timestamp"].values
        fig = px.line(metrics_expanded, x="timestamp", y="pass_rate",
                      title="合格率推移 (目標: 70%)", markers=True,
                      labels={"pass_rate": "合格率 (%)", "timestamp": "日時"})
        fig.add_hline(y=70, line_dash="dash", line_color="red",
                      annotation_text="目標70%")
        fig.add_hline(y=29, line_dash="dot",  line_color="gray",
                      annotation_text="初期29%")
        st.plotly_chart(fig, use_container_width=True)
    except Exception as e:
        st.warning(f"トレンドデータなし: {e}")
else:
    st.info("pdca_history.jsonl が見つかりません。PDCAを実行してください。")

# --- 5軸レーダーチャート ---
st.header("5軸レーダーチャート")
if not df.empty and all(ax in df.columns for ax in AXES):
    lora_names = df["lora_name"].unique().tolist() if "lora_name" in df.columns else []
    if lora_names:
        sel = st.selectbox("LoRAを選択", lora_names)
        row = df[df["lora_name"] == sel].iloc[-1]
        vals = [row.get(ax, 0) for ax in AXES]
        fig2 = go.Figure()
        fig2.add_trace(go.Scatterpolar(
            r=vals + [vals[0]], theta=AXES + [AXES[0]],
            fill="toself", name=sel, line_color="#0066cc"
        ))
        fig2.update_layout(
            polar=dict(radialaxis=dict(visible=True, range=[0, 100])),
            showlegend=True, title=f"{sel} 5軸スコア"
        )
        st.plotly_chart(fig2, use_container_width=True)
    avg_vals = [df[ax].mean() for ax in AXES]
    fig3 = go.Figure()
    fig3.add_trace(go.Scatterpolar(
        r=avg_vals + [avg_vals[0]], theta=AXES + [AXES[0]],
        fill="toself", name="全体平均", line_color="#00c853"
    ))
    fig3.update_layout(
        polar=dict(radialaxis=dict(visible=True, range=[0, 100])),
        title="全LoRA平均スコア"
    )
    st.plotly_chart(fig3, use_container_width=True)

# --- 不合格LoRA一覧 ---
st.header("不合格LoRA一覧 / 再学習候補")
if not df.empty:
    failed_cols = ["lora_name", "consistency", "cute", "ero", "quality", "total", "passed"]
    avail_cols  = [c for c in failed_cols if c in df.columns]
    failed_df   = df[df["passed"] == False][avail_cols] if "passed" in df.columns else df[avail_cols]
    if not failed_df.empty:
        st.dataframe(failed_df.sort_values("consistency", ascending=True),
                     use_container_width=True)
        if st.button("PDCAコントローラーを実行"):
            import subprocess
            result = subprocess.run(["python", "pdca_controller.py"], capture_output=True)
            st.success(f"PDCA完了: {result.stdout.decode()[:200]}")
    else:
        st.success("全LoRAが合格基準を満たしています！")

# --- コスト累計 ---
st.header("APIコスト累計")
cost_log = "D:/projects/fanza3_mass/grok_router_costs.jsonl"
if os.path.exists(cost_log):
    costs = []
    with open(cost_log, encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if line:
                rec = json.loads(line)
                if "lora_eval" in rec.get("kind", ""):
                    costs.append(rec)
    total_usd = sum(c.get("cost_usd", 0) for c in costs)
    total_jpy = total_usd * 155
    st.metric("LoRA評価APIコスト合計", f"¥{total_jpy:,.0f} (${total_usd:.4f})")
    if costs:
        cost_df = pd.DataFrame(costs)
        cost_df["timestamp"] = pd.to_datetime(cost_df["timestamp"])
        cost_df["cost_jpy"]  = cost_df["cost_usd"] * 155
        fig4 = px.bar(cost_df, x="timestamp", y="cost_jpy",
                      color="model", title="採点APIコスト推移")
        st.plotly_chart(fig4, use_container_width=True)
else:
    st.info("コストログなし")

st.caption("streamlit run quality_dashboard.py | 30秒ごとに自動更新")

コードF: ab_test_manager.py（A/Bテスト管理）

# ab_test_manager.py - LoRA A/Bテスト管理 + Bayesian風最良パラメータ推薦
import json, os, itertools, random
from datetime import datetime
from typing import Dict, List, Optional
from pathlib import Path

RESULT_PATH = Path("ab_results.jsonl")

class ABTestManager:
    """
    dim×alpha×lr×stepsの組み合わせをA/Bテストで評価し
    Bayesian Optimization風の簡易実装で最良パラメータを推薦。
    """
    PARAM_GRID = {
        "dim":   [4, 8, 16],
        "alpha": [1, 4,  8],
        "lr":    [1e-4, 5e-5],
        "steps": [1500, 2000, 3000],
    }

    def __init__(self, result_path: Path = RESULT_PATH):
        self.result_path = result_path

    def generate_all_experiments(self) -> List[Dict]:
        keys = list(self.PARAM_GRID.keys())
        combos = list(itertools.product(*self.PARAM_GRID.values()))
        return [
            {**dict(zip(keys, c)),
             "experiment_id": f"exp_{i:04d}",
             "status": "pending"}
            for i, c in enumerate(combos)
        ]

    def pending_experiments(self) -> List[Dict]:
        done_ids = set()
        if self.result_path.exists():
            with open(self.result_path, encoding="utf-8") as f:
                for line in f:
                    if line.strip():
                        done_ids.add(json.loads(line).get("experiment_id", ""))
        return [e for e in self.generate_all_experiments()
                if e["experiment_id"] not in done_ids]

    def record(self, experiment_id: str, config: Dict, scores: Dict):
        axes    = ["consistency", "cute", "ero", "quality", "total"]
        avg     = sum(scores.get(a, 0) for a in axes) / len(axes)
        passed  = avg >= 70 and scores.get("consistency", 0) >= 65
        rec = {
            "experiment_id":   experiment_id,
            "timestamp":       datetime.now().isoformat(),
            "config":          config,
            "scores":          scores,
            "avg":             round(avg, 2),
            "passed":          passed,
        }
        with open(self.result_path, "a", encoding="utf-8") as f:
            f.write(json.dumps(rec, ensure_ascii=False) + "\n")
        print(f"[ABTest] {experiment_id}: avg={avg:.1f} "
              f"{'PASS' if passed else 'FAIL'}")
        return rec

    def load_results(self) -> List[Dict]:
        if not self.result_path.exists():
            return []
        results = []
        with open(self.result_path, encoding="utf-8") as f:
            for line in f:
                if line.strip():
                    results.append(json.loads(line))
        return results

    def recommend_best(self) -> Dict:
        results = self.load_results()
        if not results:
            return {"message": "No results yet. Run experiments first."}
        # 合格品から最良を選ぶ
        passed = [r for r in results if r.get("passed")]
        pool   = passed if passed else results
        best   = max(pool, key=lambda r: (
            r.get("scores", {}).get("consistency", 0) * 0.4 +
            r.get("avg", 0) * 0.6
        ))
        return {
            "best_config":     best["config"],
            "best_avg":        best["avg"],
            "best_consistency": best.get("scores", {}).get("consistency", 0),
            "experiment_id":   best["experiment_id"],
            "total_tested":    len(results),
            "pass_rate":       round(len(passed) / len(results) * 100, 1),
        }

    def run_next_n(self, n: int = 5,
                   evaluator_fn=None) -> List[Dict]:
        """
        次にテストすべきn件を選んで実行。
        evaluator_fn: (config) -> scores_dict のコールバック。
        Noneの場合はモックスコアで動作確認。
        """
        pending = self.pending_experiments()
        if not pending:
            print("[ABTest] All experiments done!")
            return []

        # Bayesian風: まだ試していない組み合わせを優先
        # 既存結果から高評価パラメータ範囲を絞り込む
        results = self.load_results()
        if results:
            # 合格率の高いdim・steps値を優先
            passed = [r for r in results if r.get("passed")]
            if passed:
                best_dims  = {r["config"]["dim"]  for r in passed}
                best_steps = {r["config"]["steps"] for r in passed}
                # 優先順位付きソート
                def priority(e):
                    score = 0
                    if e["dim"] in best_dims:  score += 2
                    if e["steps"] in best_steps: score += 1
                    return -score
                pending.sort(key=priority)

        selected = pending[:n]
        records  = []
        for exp in selected:
            config = {k: exp[k] for k in self.PARAM_GRID.keys()}
            if evaluator_fn:
                scores = evaluator_fn(config)
            else:
                # モックスコア（テスト用）
                base = 50 + config["dim"] * 2 + config["steps"] / 100
                scores = {
                    "consistency": min(100, int(base + random.gauss(0, 8))),
                    "cute":        min(100, int(base + random.gauss(5, 6))),
                    "ero":         min(100, int(base + random.gauss(0, 10))),
                    "quality":     min(100, int(base + random.gauss(3, 7))),
                    "total":       min(100, int(base + random.gauss(2, 6))),
                }
            rec = self.record(exp["experiment_id"], config, scores)
            records.append(rec)

        best = self.recommend_best()
        print(f"\n[ABTest] Best so far: {best}")
        return records

if __name__ == "__main__":
    mgr = ABTestManager()
    print(f"Total experiments: {len(mgr.generate_all_experiments())}")
    print(f"Pending: {len(mgr.pending_experiments())}")
    mgr.run_next_n(n=6)
    print("\nRecommendation:", mgr.recommend_best())

11採点バイアス補正：AIモデル別傾向と補正設計

12量産100体での品質ゲート実装例

13自己採点

14関連DR一覧

• DR LoRA学習パラメータ最適化 2026-06-08 — dim/alpha/lr/stepsの最適値探索
• DR LoRA少枚数高精度学習法 2026-06-04 — 15〜50枚での高精度学習
• DR LoRA失敗パターン・再現性チェックリスト 2026-06-02 — 失敗22パターン
• DR LoRA量産高速化×一貫性 2026-06-01 — 一貫性維持の量産技術
• DR 採点AI 65固定回避・評価設計 2026-06-04 — AIスコア65固定問題の回避
• DR 品質ゲート・評価ループ設計 2026-05-30 — preflight()設計原則

15脚注・参考文献

1. Justin21523 anime-lora-pipeline: CLIP/LPIPS evaluation scripts — https://github.com/Justin21523/anime-lora-pipeline
2. Civitai Model Statistics 2026 — https://civitai.com/models
3. MLOps Market Report 2026 (MLOps in 2026 From MLflow to LLMOps) — https://medium.com/codex/mlops-in-2026-from-mlflow...
4. ComfyUI Batch Processing 2026 — https://apatero.com/blog/comfyui-batch-processing...
5. ComfyUI_FaceAnalysis FaceEmbedDistance Node — https://www.runcomfy.com/comfyui-nodes/ComfyUI_FaceAnalysis/FaceEmbedDistance
6. chaofengc/IQA-PyTorch (pyiqa) — https://deepwiki.com/chaofengc/IQA-PyTorch/3-iqa-metrics
7. InsightFace ArcFace Paper (Additive Angular Margin Loss) — https://www.insightface.ai/research/arcface
8. BRISQUE, NIQE, CLIP-IQA No-Reference IQA Comparison — https://dataworlds.substack.com/p/the-no-reference-avenger-battling
9. xAI Grok API Pricing June 2026 — https://the-rogue-marketing.github.io/grok-xai-api-pricing-may-2026/
10. Kohya_ss LoRA Training Settings 2026 — https://www.propelrc.com/kohya-lora-training-settings-explained/
11. xAI Batch API Documentation — https://docs.x.ai/developers/advanced-api-usage/batch-api
12. ArcFace Architecture and Face Similarity — https://medium.com/@ichigo.v.gen12/arcface-architecture...
13. ComfyUI Production Quality Scoring — https://www.runflow.io/blog/comfyui-workflows-production-ready
14. LPIPS: Learned Perceptual Image Patch Similarity — https://github.com/richzhang/PerceptualSimilarity
15. Gemini API Pricing 2026 — https://ai.google.dev/gemini-api/docs/pricing
16. Language-Guided Bayesian Optimization for LoRA Hyperparameter Search (2026) — https://arxiv.org/abs/2602.11171
17. SDXL LoRA Consistency Score 92.9 vs Base 90.1 — https://www.apatero.com/blog/anime-character-consistency-complete-guide-2025
18. Streamlit 2026 Dashboard Guide — https://tech-insider.org/streamlit-tutorial-python-dashboard-2026/

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