Antigravity Agent
210 lines
7.7 KiB
Markdown
210 lines
7.7 KiB
Markdown
---
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id: wiki-2026-0508-사후-편집-post-editing
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title: 사후 편집 (Post-editing)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Post-editing, AI Image Post-editing, Image Refinement]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [image-generation, post-editing, midjourney, flux, photoshop, comfyui]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: ComfyUI/Photoshop-Generative-Fill
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---
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# 사후 편집 (Post-editing)
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## 매 한 줄
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> **"매 generation 은 draft, 매 final 은 post-edit 의 결과"**. 매 2026 production pipeline 에서 raw text-to-image output 의 직접 ship 의 X — 매 inpaint, upscale, color-grade, retouching 의 multi-stage refinement 가 standard. Midjourney/FLUX/Imagen 4 의 base generation + ComfyUI region-edit + Photoshop Generative Fill 의 hybrid workflow 가 매 commercial baseline.
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## 매 핵심
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### 매 post-editing 의 정의
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- 매 generated image 의 결함 의 fix + intent 의 align.
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- 매 stage: ① local fix (face/hand/text), ② global polish (color, contrast), ③ composition (crop, insert), ④ upscale (output res).
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- 매 zero-edit ship 의 X — 매 99% 의 commercial output 이 적어도 1 단계 의 post-edit 의 거침.
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### 매 도구 stack (2026)
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- **Midjourney V8 Editor**: 매 inpaint + extend (uncrop) + retexture 의 in-platform.
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- **Photoshop 2026 Generative Fill (FLUX-2 backed)**: 매 industry default — layer 호환.
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- **ComfyUI + FLUX.1 Fill / SDXL Inpaint**: 매 open-source pipeline. 매 reproducible.
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- **Magnific / Krea Upscale**: 매 1024 → 4K 의 detail-add upscaling.
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- **Topaz Photo AI**: 매 noise/blur 의 cleanup.
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- **Adobe Firefly 4**: 매 commercial-safe (training-data 의 license 명확).
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### 매 typical 결함 카테고리
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1. **해부학적 오류**: hand (6 fingers), feet, eye 의 asymmetry.
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2. **Text 의 garbled**: logo, sign, caption 의 letters 의 corruption.
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3. **Composition 의 mismatch**: edge 의 cut, perspective 의 break.
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4. **Style drift**: face 의 character 의 inconsistency (multi-shot).
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5. **Lighting 의 implausible**: shadow direction 의 conflict.
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### 매 응용
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1. Marketing / e-commerce visual production.
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2. Concept art / pre-viz.
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3. Editorial illustration.
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4. Game asset (texture, character).
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5. Architectural rendering의 humanization.
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## 💻 패턴
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### Pattern 1 — Inpaint (ComfyUI + FLUX.1 Fill)
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```python
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# ComfyUI API workflow snippet
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import json, requests, base64
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from PIL import Image
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def inpaint_region(image_path, mask_path, prompt):
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workflow = json.load(open("flux_fill.json"))
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workflow["3"]["inputs"]["image"] = base64.b64encode(open(image_path, "rb").read()).decode()
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workflow["4"]["inputs"]["mask"] = base64.b64encode(open(mask_path, "rb").read()).decode()
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workflow["6"]["inputs"]["text"] = prompt
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workflow["7"]["inputs"]["steps"] = 28
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workflow["7"]["inputs"]["cfg"] = 3.5
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r = requests.post("http://127.0.0.1:8188/prompt", json={"prompt": workflow})
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return r.json()["prompt_id"]
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# Fix 6-finger hand
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inpaint_region("draft.png", "hand_mask.png",
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"anatomically correct human hand, 5 fingers, natural pose")
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```
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### Pattern 2 — Hand-fix automated detection
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```python
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import cv2, mediapipe as mp
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mp_hands = mp.solutions.hands.Hands(static_image_mode=True, max_num_hands=4)
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def detect_bad_hands(img_path):
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img = cv2.imread(img_path)
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res = mp_hands.process(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
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bad = []
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if res.multi_hand_landmarks:
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for lm in res.multi_hand_landmarks:
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# Heuristic: finger length ratio, joint angles
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if not is_anatomically_valid(lm):
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bad.append(bbox_from_landmarks(lm, img.shape))
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return bad # → mask + inpaint
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```
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### Pattern 3 — Face consistency (IP-Adapter FaceID)
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```python
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from diffusers import FluxFillPipeline
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import torch
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pipe = FluxFillPipeline.from_pretrained(
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"black-forest-labs/FLUX.1-Fill-dev",
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torch_dtype=torch.bfloat16
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).to("cuda")
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# Reference face → swap into draft
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from ip_adapter import IPAdapterFaceID
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adapter = IPAdapterFaceID(pipe, "ip-adapter-faceid-flux.bin")
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result = adapter.generate(
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image=draft, mask=face_mask,
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face_image=reference_face,
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prompt="same person, professional headshot",
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num_inference_steps=30, guidance_scale=4.0,
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)
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```
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### Pattern 4 — Upscale + detail (Magnific-style)
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```python
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# SUPIR / FLUX-Upscale style
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from diffusers import StableDiffusionUpscalePipeline
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upscaler = StableDiffusionUpscalePipeline.from_pretrained(
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"stabilityai/stable-diffusion-x4-upscaler", torch_dtype=torch.float16
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).to("cuda")
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hi_res = upscaler(
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prompt="ultra-detailed photograph, sharp focus, 8k",
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image=Image.open("draft_1024.png"),
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num_inference_steps=20, guidance_scale=7,
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).images[0]
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hi_res.save("final_4k.png")
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```
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### Pattern 5 — Color grading (LUT in Pillow)
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```python
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from PIL import Image
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import numpy as np
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def apply_lut(img: Image.Image, lut_path: str) -> Image.Image:
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lut = np.load(lut_path) # shape (33,33,33,3)
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arr = np.asarray(img).astype(np.float32) / 255.0
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idx = (arr * 32).astype(np.int32)
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out = lut[idx[..., 0], idx[..., 1], idx[..., 2]]
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return Image.fromarray((out * 255).astype(np.uint8))
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# Apply teal-orange cinematic LUT
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final = apply_lut(Image.open("graded_input.png"), "teal_orange.npy")
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```
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### Pattern 6 — Photoshop scripting (Generative Fill)
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```javascript
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// Photoshop 2026 .jsx — Generative Fill via ExtendScript
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var doc = app.activeDocument;
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doc.selection.select([[120,80],[420,80],[420,380],[120,380]]);
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var generativeFill = stringIDToTypeID("generativeFill");
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var desc = new ActionDescriptor();
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desc.putString(stringIDToTypeID("prompt"), "remove power lines, clean sky");
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executeAction(generativeFill, desc, DialogModes.NO);
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doc.saveAs(new File("/out/cleaned.psd"));
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```
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### Pattern 7 — Batch QA loop
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```python
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def post_edit_pipeline(draft_path):
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img = load(draft_path)
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if has_bad_hands(img):
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img = inpaint_hands(img)
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if has_garbled_text(img):
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img = inpaint_text(img, target_text="ACME Corp")
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img = color_grade(img, lut="film_emulation.npy")
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img = upscale_4x(img)
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return img
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```
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## 매 결정 기준
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| 상황 | Approach |
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| 빠른 fix, 1장 | Photoshop Generative Fill |
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| Reproducible, batch | ComfyUI workflow JSON |
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| Face/character lock | IP-Adapter FaceID + inpaint |
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| Detail-add upscale | Magnific / SUPIR |
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| Commercial license worry | Adobe Firefly 4 |
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**기본값**: 매 ComfyUI + FLUX.1 Fill 의 reproducible base, 매 final touch 만 Photoshop.
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## 🔗 Graph
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- 부모: [[AI 이미지 생성 (AI Image Generation)]] · [[AI 이미지 생성 및 편집 워크플로우 (AI Image Generation & Editing Workflow)]]
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- 변형: [[Inpainting]] · [[Outpainting]] · [[Upscaling]]
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- 응용: [[상업용 브랜드 이미지 및 디자인 시스템 구축]] · [[Concept Art Workflow]]
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- Adjacent: [[ControlNet]] · [[IP-Adapter]] · [[Magnific Upscale]]
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## 🤖 LLM 활용
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**언제**: 매 prompt 의 generation, 매 mask 의 description, 매 QA 의 결함 카테고리화.
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**언제 X**: 매 final pixel-level decision (designer 의 eye 가 필요).
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## ❌ 안티패턴
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- **Re-roll forever**: 매 100 generations 의 spam 보다 매 1 inpaint 가 빠름.
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- **Single-pass ship**: 매 raw text-to-image 의 commercial use 의 X.
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- **Mask 의 too-tight**: 매 boundary artifact. 매 feather 8-16px 의 default.
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- **Upscale before fix**: 매 결함 의 amplification. 매 fix → upscale 의 순서.
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## 🧪 검증 / 중복
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- Verified (FLUX.1 Fill release notes 2025-11; Adobe Firefly 4 docs; ComfyUI manager wiki).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — multi-stage post-edit pipeline + 7 patterns |
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