diff --git a/assets-library/models-performance.json b/assets-library/models-performance.json index 8c6e256..0abb85c 100644 --- a/assets-library/models-performance.json +++ b/assets-library/models-performance.json @@ -27,5 +27,78 @@ "illustrious", "lightning-lora-qwen" ], - "models": {} + "models": { + "pony-v6": { + "rounds_used": 1, + "dimensions": { + "byeolyi": [], + "hanja": [], + "hanok": [], + "illustration": [], + "korean_traditional": [], + "auto_pastel": [ + 42.5 + ], + "auto_black": [ + 0.61 + ] + }, + "avg_scores": {}, + "best_combinations": [], + "verdict": "" + }, + "flux-dev": { + "rounds_used": 1, + "dimensions": { + "byeolyi": [], + "hanja": [], + "hanok": [], + "illustration": [], + "korean_traditional": [], + "auto_pastel": [ + 20.1, + 29.7 + ], + "auto_black": [ + 3.11, + 0.0 + ] + }, + "avg_scores": {}, + "best_combinations": [], + "verdict": "" + }, + "pulid-flux": { + "rounds_used": 1, + "dimensions": { + "byeolyi": [], + "hanja": [], + "hanok": [], + "illustration": [], + "korean_traditional": [], + "auto_pastel": [ + 30.2 + ], + "auto_black": [ + 0.73 + ] + }, + "avg_scores": {}, + "best_combinations": [], + "verdict": "" + }, + "qwen-image-2512": { + "rounds_used": 1, + "dimensions": { + "byeolyi": [], + "hanja": [], + "hanok": [], + "illustration": [], + "korean_traditional": [] + }, + "avg_scores": {}, + "best_combinations": [], + "verdict": "워크플로우 결함 — 5KB 검정 latent (v1~v4 4차 시도 모두 실패)" + } + } } \ No newline at end of file diff --git a/assets-library/prompts/failed-patterns.json b/assets-library/prompts/failed-patterns.json index 9e26dfe..2af2f1f 100644 --- a/assets-library/prompts/failed-patterns.json +++ b/assets-library/prompts/failed-patterns.json @@ -1 +1,19 @@ -{} \ No newline at end of file +{ + "qwen-image-2512-GGUF-Q4-SamplerCustomAdvanced-v4": { + "attempts": 4, + "patterns": [ + "EmptyLatentImage", + "EmptyQwenImageLayeredLatentImage", + "ModelSamplingAuraFlow+EmptySD3LatentImage", + "SamplerCustomAdvanced+ModelSamplingAuraFlow" + ], + "all_results": "5KB 검정 latent", + "hypothesis": "Qwen-Image GGUF Q4_K_S + CUDA 13.0 + PyTorch 2.12 환경 호환성 또는 다른 quant 필요 (Q8/fp16)", + "next_attempts": [ + "non-GGUF Qwen-Image safetensors", + "Lightning LoRA 적용", + "다른 sampler (dpmpp_2m/ddim)", + "다른 shift 값 (1.73/5.0)" + ] + } +} \ No newline at end of file diff --git a/eval/meeting-103-grid.html b/eval/meeting-103-grid.html new file mode 100644 index 0000000..d35b28a --- /dev/null +++ b/eval/meeting-103-grid.html @@ -0,0 +1,159 @@ + + +meeting-103 v2.1 — ComfyUI 자율 R&D + 자산 라이브러리 + + + +

meeting-103 v2.1 — ComfyUI 자율 R&D

+
+ 3-stage 파이프라인 · 14 모델 풀 (9 설치 / 4 미설치) · 자산 라이브러리 7종 누적 · + 합격 기준: 별이 ≥8 · 한자 ≥4.5 · 한옥 ≥4.5 +
+ +
+

📊 R1 진행 상태

+ +
+ +
+

🎯 자산 라이브러리 R1 누적

+ +
+ +

라운드 1 — A 별이 (3 모델 비교)

+
+
1-1 +
+
1-1: Pony V6 XL A 별이
+
model=pony-v6, steps=28, cfg=7, sampler=dpmpp_2m karras, seed=42, 768×1344
+
+ 자동 평가:
+ - 크기: 1501KB ✓
+ - 검정: 0.61% ✓
+ - 파스텔: 42.5% (R1 1위)
+ - 별이 8/10 · 한복 정합성 · Cute Horror — 시각 평가 필요 +
+
+ auto-pass + vision-check +
+
+
1-2 +
+
1-2: Flux Dev A 별이 (text only)
+
model=flux-dev Q5 GGUF, steps=25, cfg=1, guidance=3.5, FluxGuidance, seed=42
+
+ 자동 평가:
+ - 크기: 1121KB ✓
+ - 검정: 3.11% ⚠️ (DESIGN.md §10 한계 근접)
+ - 파스텔: 20.1%
+ - 별이 정합성 — 시각 평가 +
+
+ black-3pct + vision-check +
+
+
1-3 +
+
1-3: Flux Dev + PuLID A 별이 ⭐
+
model=flux-dev + PuLID weight=0.85 fusion=mean, ref=byeolyi-default-768x1344, end_at=0.8
+
+ 자동 평가:
+ - 크기: 1065KB ✓
+ - 검정: 0.73% ✓
+ - 파스텔: 30.2%
+ - 별이 얼굴 ID matching 핵심 — 시각 평가 필수 +
+
+ auto-pass + vision-id-check +
+
+
+ +

라운드 1 — B 한옥

+
+
1-4 +
+
1-4: Flux Dev B 한옥 (text only)
+
model=flux-dev Q5 GGUF, NOT japanese / pagoda / wabi-sabi negative, seed=42
+
+ 자동 평가:
+ - 크기: 1724KB ✓
+ - 검정: 0.0% ✓ (완벽)
+ - 파스텔: 29.7%
+ - 한옥 vs 일본 구분 — 시각 평가 필수 +
+
+ auto-pass + korean-vs-japanese +
+
+
+ +

라운드 1 — C 60갑자 한자카드

+
+
+ 1-5: Qwen-2512 C 한자카드 ❌ +
5.5KB 검정 latent 실패
(v6 R1 3차 + 본 R1 v4 = 총 4차 연속) +

워크플로우 패턴:
SamplerCustomAdvanced + ModelSamplingAuraFlow + EmptySD3LatentImage +

다음 시도 (R2):
non-GGUF safetensors / Lightning LoRA / 다른 sampler/shift +
+
+ +
+

🔗 검수 채널

+ +
+ + + + diff --git a/eval/meeting-103-meta.json b/eval/meeting-103-meta.json new file mode 100644 index 0000000..bb73efd --- /dev/null +++ b/eval/meeting-103-meta.json @@ -0,0 +1,84 @@ +{ + "session": "meeting-103-v2.1", + "started_at": "2026-05-19T21:47:00+09:00", + "current_round": 1, + "rounds": [ + { + "round": 1, + "name": "초기 매트릭스 (5 조합)", + "status": "completed", + "combinations": [ + { + "id": "1-1", + "model": "pony-v6", + "prompt_set": "A", + "status": "OK", + "auto_eval": { + "size_kb": 1501, + "black_pct": 0.61, + "pastel_pct": 42.5 + }, + "needs_vision": true + }, + { + "id": "1-2", + "model": "flux-dev", + "prompt_set": "A", + "status": "OK", + "auto_eval": { + "size_kb": 1121, + "black_pct": 3.11, + "pastel_pct": 20.1 + }, + "flags": [ + "black-3pct" + ] + }, + { + "id": "1-3", + "model": "flux-dev+pulid", + "prompt_set": "A", + "status": "OK", + "auto_eval": { + "size_kb": 1065, + "black_pct": 0.73, + "pastel_pct": 30.2 + }, + "needs_vision_id": true + }, + { + "id": "1-4", + "model": "flux-dev", + "prompt_set": "B", + "status": "OK", + "auto_eval": { + "size_kb": 1724, + "black_pct": 0.0, + "pastel_pct": 29.7 + }, + "needs_vision": true + }, + { + "id": "1-5", + "model": "qwen-image-2512", + "prompt_set": "C", + "status": "FAILED", + "size_bytes": 5540, + "issue": "5KB 검정 latent (4차 연속)" + } + ], + "best_pastel": "1-1", + "learning": { + "qwen_workflow_3rd_attempt_failed": true, + "next_round_hint": "R2 — Qwen 비-GGUF + Stage 3 Flux Refiner (Pony 1-1 → refiner) + 다른 PuLID fusion" + } + } + ], + "thresholds": { + "byeolyi": 8, + "hanja": 4.5, + "hanok": 4.5 + }, + "raw_html_url": "https://kakao-kakao2-server.tail31bd37.ts.net/choijaewook/8460s-image-rd/raw/branch/main/eval/meeting-103-grid.html", + "vault_path": "D:\\Vault\\8460s-image-rd\\eval\\meeting-103-grid.html" +} \ No newline at end of file diff --git a/results/meeting-103/round1/1-1.png b/results/meeting-103/round1/1-1.png new file mode 100644 index 0000000..590fe54 Binary files /dev/null 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b/results/meeting-103/round1/meta.json new file mode 100644 index 0000000..987acab --- /dev/null +++ b/results/meeting-103/round1/meta.json @@ -0,0 +1,99 @@ +{ + "round": 1, + "captured_at": "2026-05-19T21:57:10.975065", + "combos": [ + { + "id": "1-1", + "prompt_id": "3b6d1ef1-163c-484b-93bd-1965a5dd59b3", + "models_used": [ + "pony-v6" + ], + "prompt_set": "A", + "stage": "1-only", + "status": "OK", + "size_bytes": 1537272, + "width": 768, + "height": 1344, + "aspect": 0.5714, + "black_pct": 0.61, + "pastel_pct": 42.5, + "thumb_size_kb": 540 + }, + { + "id": "1-2", + "prompt_id": "472d7fc4-0234-40f9-be7d-3e25e3d04b32", + "models_used": [ + "flux-dev" + ], + "prompt_set": "A", + "stage": "1-only", + "status": "OK", + "size_bytes": 1148193, + "width": 768, + "height": 1344, + "aspect": 0.5714, + "black_pct": 3.11, + "pastel_pct": 20.1, + "thumb_size_kb": 374 + }, + { + "id": "1-3", + "prompt_id": "117742cb-1615-44ec-9f7b-5c558af278f8", + "models_used": [ + "flux-dev", + "pulid-flux" + ], + "prompt_set": "A", + "stage": "1+pulid", + "status": "OK", + "size_bytes": 1090948, + "width": 768, + "height": 1344, + "aspect": 0.5714, + "black_pct": 0.73, + "pastel_pct": 30.2, + "thumb_size_kb": 377 + }, + { + "id": "1-4", + "prompt_id": "b4ec98ac-2f7f-4dd5-9e47-5bfdcc78cf3c", + "models_used": [ + "flux-dev" + ], + "prompt_set": "B", + "stage": "1-only", + "status": "OK", + "size_bytes": 1765589, + "width": 768, + "height": 1344, + "aspect": 0.5714, + "black_pct": 0.0, + "pastel_pct": 29.7, + "thumb_size_kb": 512 + }, + { + "id": "1-5", + "prompt_id": "54ee2273-7585-4618-8b51-b74ded23cf05", + "models_used": [ + "qwen-image-2512" + ], + "prompt_set": "C", + "stage": "1-only", + "workflow_variant": "SamplerCustomAdvanced v4", + "status": "FAILED_SMALL", + "size_bytes": 5540, + "width": 768, + "height": 1344, + "aspect": 0.5714, + "black_pct": 100.0, + "pastel_pct": 0.0, + "thumb_size_kb": 1 + } + ], + "learning": { + "passed_count": 4, + "failed_count": 1, + "best_pastel": "1-1", + "next_round_hint": "auto-determine based on round learning" + } +} \ No newline at end of file diff --git a/scripts/__pycache__/round_pipeline.cpython-313.pyc b/scripts/__pycache__/round_pipeline.cpython-313.pyc new file mode 100644 index 0000000..5c45337 Binary files /dev/null and b/scripts/__pycache__/round_pipeline.cpython-313.pyc differ diff --git a/scripts/round_pipeline.py b/scripts/round_pipeline.py new file mode 100644 index 0000000..857186e --- /dev/null +++ b/scripts/round_pipeline.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python3 +""" +meeting-103 v2.1 라운드 파이프라인 +- 결과 회수 (HTTP /view) +- 썸네일 생성 (1MB 미만) +- 자동 평가 (black/pastel/aspect) +- 자산 라이브러리 7종 누적 +""" +import json +import os +import sys +import urllib.request +from pathlib import Path +from PIL import Image + +REPO_ROOT = Path("/mnt/ssd1/dev/projects/8460s-image-rd") +COMFY = "http://100.123.6.62:8188" + +THRESHOLDS = {"byeolyi": 8, "hanja": 4.5, "hanok": 4.5} +PROTECTED = {"qwen-image-2.0", "qwen-image-2512", "qwen-image-layered", "qwen-edit-2511", + "flux-dev", "flux-schnell", "flux-kontext", "flux-fill", "flux-redux", + "pulid-flux", "pony-v6", "illustrious", "lightning-lora-qwen"} + + +def http_view(filename): + """ComfyUI HTTP /view 으로 PNG 다운로드 (bytes 반환)""" + url = f"{COMFY}/view?filename={filename}&type=output" + with urllib.request.urlopen(url, timeout=20) as r: + return r.read() + + +def history_outputs(prompt_id): + """prompt_id → 출력 파일명 목록""" + url = f"{COMFY}/history/{prompt_id}" + with urllib.request.urlopen(url, timeout=10) as r: + data = json.load(r) + if not data: + return [] + info = list(data.values())[0] + outs = info.get("outputs", {}) + return [f["filename"] for n, i in outs.items() for f in i.get("images", [])] + + +def fetch_round_results(round_n, combos): + """combos = [{"id": "1-1", "prompt_id": "...", "expected_filename": "..."}]""" + round_dir = REPO_ROOT / "results" / "meeting-103" / f"round{round_n}" + round_dir.mkdir(parents=True, exist_ok=True) + results = [] + for c in combos: + fns = history_outputs(c["prompt_id"]) if c.get("prompt_id") else [] + fn = fns[0] if fns else c.get("expected_filename") + if not fn: + results.append({**c, "status": "no_filename"}) + continue + try: + data = http_view(fn) + out_png = round_dir / f"{c['id']}.png" + out_png.write_bytes(data) + size = len(data) + # 썸네일 (1MB 미만 의무) + img = Image.open(out_png).convert("RGB") + thumb = img.copy() + thumb.thumbnail((512, 768)) + thumb_path = round_dir / f"{c['id']}_thumb.png" + thumb.save(thumb_path, "PNG", optimize=True) + assert thumb_path.stat().st_size < 1_000_000, "thumbnail >1MB" + # 자동 평가 + w, h = img.size + pixels = list(img.getdata()) + total = len(pixels) + near_black = sum(1 for r, g, b in pixels if r < 30 and g < 30 and b < 30) + pastel = sum(1 for r, g, b in pixels if (r > 150 and b > 150) or (r > 200 and g > 150 and b > 150)) + results.append({ + **c, "status": "OK" if size > 50000 else "FAILED_SMALL", + "size_bytes": size, "width": w, "height": h, + "aspect": round(w / h, 4), + "black_pct": round(near_black / total * 100, 2), + "pastel_pct": round(pastel / total * 100, 1), + "thumb_size_kb": thumb_path.stat().st_size // 1024, + }) + except Exception as e: + results.append({**c, "status": f"ERROR:{type(e).__name__}", "error": str(e)[:120]}) + return results + + +def update_models_performance(round_n, combo_results): + """자산 라이브러리 1 — 모델 차원별 점수 누적""" + path = REPO_ROOT / "assets-library" / "models-performance.json" + data = json.loads(path.read_text()) + for c in combo_results: + if c.get("status") != "OK": + continue + for model in c.get("models_used", []): + mdata = data["models"].setdefault(model, { + "rounds_used": 0, + "dimensions": {dim: [] for dim in data["_dimensions"]}, + "avg_scores": {}, "best_combinations": [], "verdict": "" + }) + mdata["rounds_used"] = max(mdata["rounds_used"], round_n) + scores = c.get("scores", {}) + for dim, score in scores.items(): + if score is not None and dim in mdata["dimensions"]: + mdata["dimensions"][dim].append(score) + for dim, arr in mdata["dimensions"].items(): + if arr: + mdata["avg_scores"][dim] = round(sum(arr) / len(arr), 2) + path.write_text(json.dumps(data, indent=2, ensure_ascii=False)) + return path + + +def save_workflow_to_library(combo, workflow_json, round_n): + """자산 라이브러리 2 — winner/partial 워크플로 보존""" + scores = combo.get("scores", {}) + if not scores: + return None + if all(scores.get(d, 0) >= t for d, t in THRESHOLDS.items()): + category = "winner" + elif max(scores.values()) >= 5: + category = "partial" + else: + return None + best_dim = max(scores, key=scores.get) + model = (combo.get("models_used") or ["unknown"])[0] + name = f"{category}-R{round_n}-{combo['id']}-{model}.json" + path = REPO_ROOT / "assets-library" / "workflows" / name + path.write_text(json.dumps(workflow_json, indent=2, ensure_ascii=False)) + # 인덱스 갱신 + idx_path = REPO_ROOT / "assets-library" / "workflows" / "_index.json" + idx = json.loads(idx_path.read_text()) + entry = {"name": name, "category": category, "best_dimension": best_dim, + "scores": scores, "round": round_n, "model": model} + idx.setdefault(category, []).append(entry) + idx["count"] = len(idx.get("winner", [])) + len(idx.get("partial", [])) + idx_path.write_text(json.dumps(idx, indent=2, ensure_ascii=False)) + return path + + +def save_results_meta(round_n, results, learning=None): + """라운드 메타 JSON 저장 (그리드 보조)""" + path = REPO_ROOT / "results" / "meeting-103" / f"round{round_n}" / "meta.json" + payload = { + "round": round_n, + "captured_at": __import__("datetime").datetime.now().isoformat(), + "combos": results, + "learning": learning or {} + } + path.write_text(json.dumps(payload, indent=2, ensure_ascii=False)) + return path + + +def extract_learning(results): + """라운드 학습 추출 (RAG-style)""" + passed = [] + failed = [] + for r in results: + if r.get("status") != "OK": + failed.append({"id": r["id"], "reason": r.get("status")}) + continue + if r.get("black_pct", 0) > 5: + failed.append({"id": r["id"], "reason": "검정 5%+", "black_pct": r["black_pct"]}) + else: + passed.append({"id": r["id"], "pastel_pct": r.get("pastel_pct"), "size": r.get("size_bytes")}) + best = sorted([r for r in results if r.get("status") == "OK"], + key=lambda x: x.get("pastel_pct", 0), reverse=True) + return { + "passed_count": len(passed), + "failed_count": len(failed), + "best_pastel": best[0]["id"] if best else None, + "next_round_hint": "auto-determine based on round learning" + } + + +if __name__ == "__main__": + print("round_pipeline.py — 라이브러리, import 후 사용")