refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-image-prompt-작성-방법
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title: Image Prompt 작성 방법
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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: [Image Prompt Engineering, T2I Prompting, Diffusion Prompting]
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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, prompting, diffusion, midjourney, flux]
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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: text
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framework: FLUX/Midjourney/SDXL
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---
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# Image Prompt 작성 방법
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## 매 한 줄
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> **"매 image prompt 의 핵심 = 'Subject + Style + Composition + Quality modifier' 의 명시적 ordering."**. 2022 SD 1.5 era 의 keyword soup 에서 출발 → 2026 FLUX.1.1 Pro / Midjourney v7 / Imagen 4 era 는 natural-language 의 long-form description 의 superior 을 increasingly 한다. 매 prompt structure 의 deterministic 한 control 의 사용자 의 intent 의 model 의 latent space 의 mapping 을 direct.
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## 매 핵심
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### 매 Prompt anatomy (4-part structure)
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- **Subject**: "a red fox", "an elderly Japanese ceramicist" — concrete noun + descriptors.
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- **Style**: "oil painting", "cinematic photography, 35mm film", "Studio Ghibli" — medium / artist / movement.
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- **Composition**: "low-angle shot", "rule of thirds", "shallow depth of field f/1.4" — framing 의 explicit specification.
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- **Quality / technical**: "8k, sharp focus, golden hour lighting" — modifiers 의 final boost.
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### 매 Token weight (Midjourney/SDXL)
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- `(keyword:1.3)` — 매 emphasis 의 multiplier.
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- `[keyword]` — 매 de-emphasis (SD WebUI).
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- Midjourney `--stylize 250` (default 100) — 매 artistic license 의 control.
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- Midjourney `::` — 매 multi-prompt weight: `red fox::2 forest::0.5`.
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### 매 Negative prompt
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1. SDXL/SD3: `negative_prompt="blurry, low quality, deformed hands, watermark"`.
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2. Midjourney: `--no text logo` — exclusion list.
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3. FLUX: 매 native negative prompt support 의 X — instead 매 positive 의 rephrase.
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## 💻 패턴
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### Pattern 1: FLUX.1.1 Pro 의 long-form natural prompt
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```text
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A photorealistic portrait of an elderly Japanese ceramicist, weathered hands
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shaping a clay tea bowl on a wooden wheel. Soft window light from the left,
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shallow depth of field. Earthy tones, traditional workshop with shelves of
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glazed pottery in the background. Shot on Hasselblad H6D, 80mm lens, f/2.8.
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```
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### Pattern 2: Midjourney v7 structured prompt
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```text
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/imagine prompt: cyberpunk samurai standing in neon-lit Tokyo alleyway,
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holographic katana, rain-soaked street reflections, cinematic composition,
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volumetric lighting, by Syd Mead and Moebius --ar 16:9 --stylize 350 --v 7
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```
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### Pattern 3: SDXL 의 weighted prompt + negative
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```python
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from diffusers import StableDiffusionXLPipeline
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import torch
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pipe = StableDiffusionXLPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0",
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torch_dtype=torch.float16,
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).to("cuda")
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prompt = "(masterpiece:1.2), red fox in autumn forest, golden hour, " \
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"(detailed fur:1.3), cinematic photography, 8k"
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negative = "blurry, low quality, deformed, watermark, text"
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image = pipe(
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prompt=prompt,
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negative_prompt=negative,
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num_inference_steps=30,
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guidance_scale=7.5,
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).images[0]
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```
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### Pattern 4: ControlNet 의 pose + prompt
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```python
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from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
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from controlnet_aux import OpenposeDetector
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openpose = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
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pose_image = openpose(input_image)
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controlnet = ControlNetModel.from_pretrained("xinsir/controlnet-openpose-sdxl-1.0")
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pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0",
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controlnet=controlnet,
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)
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result = pipe(
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prompt="a knight in shining armor, dramatic lighting",
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image=pose_image,
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controlnet_conditioning_scale=0.8,
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).images[0]
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```
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### Pattern 5: LLM 의 prompt expansion
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```python
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import anthropic
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client = anthropic.Anthropic()
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def expand_prompt(short_idea: str) -> str:
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msg = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=300,
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messages=[{
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"role": "user",
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"content": f"Expand this image idea into a detailed FLUX prompt "
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f"(subject, style, composition, lighting, camera): {short_idea}"
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}],
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)
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return msg.content[0].text
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prompt = expand_prompt("lonely lighthouse at dusk")
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```
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### Pattern 6: Imagen 4 의 text rendering
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```text
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A vintage diner sign at night, neon letters reading "MOON CAFÉ — OPEN 24 HOURS",
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warm pink and cyan glow, rain reflections on asphalt, 1950s Americana aesthetic,
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cinematic wide shot
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```
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(Imagen 4 / FLUX 의 accurate text rendering 의 strong — 매 quote-wrapped text 의 use.)
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### Pattern 7: Aspect ratio + seed control
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```text
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/imagine prompt: misty mountain temple at sunrise --ar 21:9 --seed 42 --v 7
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```
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- `--ar 21:9` — cinematic widescreen.
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- `--seed 42` — 매 reproducibility.
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Photorealistic portrait | FLUX.1.1 Pro + long natural prompt |
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| Stylized art / illustration | Midjourney v7 + artist references |
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| Precise pose / composition | SDXL + ControlNet (openpose/canny/depth) |
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| Text in image | Imagen 4 / FLUX (quote-wrapped) |
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| Local / private | SDXL or FLUX.1-dev on RTX 4090 |
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| Batch / API workflow | Replicate / fal.ai / Together AI |
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**기본값**: FLUX.1.1 Pro (photoreal) or Midjourney v7 (stylized) + 4-part structured prompt.
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## 🔗 Graph
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- 부모: [[Generative-AI]] · [[Diffusion-Models]]
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- 변형: [[Vary Region (인페인팅)]] · [[ControlNet]] · [[LoRA-Fine-Tuning]]
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- Adjacent: [[Prompt_Engineering|Prompt-Engineering]]
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## 🤖 LLM 활용
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**언제**: prompt expansion / variation generation / style transfer description / negative prompt brainstorming.
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**언제 X**: 매 final image quality 의 judgment 의 X — 매 human-in-the-loop 의 visual evaluation 의 always 필요.
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## ❌ 안티패턴
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- **Keyword soup**: "8k, masterpiece, best quality, ultra detailed, ..." — 매 modern model 의 dilution. Natural language 의 prefer.
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- **Contradictory modifiers**: "photorealistic anime" — 매 latent space 의 confusion.
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- **Negative prompt overload**: 매 30+ negative tokens 의 generation 의 degrade.
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- **No seed control**: 매 reproducibility 의 X — 매 iteration / A/B 의 impossible.
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- **Ignoring aspect ratio**: 매 default square 의 composition 의 mismatch (cinematic shot 의 16:9 의 필요).
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## 🧪 검증 / 중복
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- Verified: Midjourney v7 docs (2026), Black Forest Labs FLUX docs, Stability AI SDXL paper.
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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 — full image prompt engineering guide |
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