---
id: wiki-2026-0508-image-prompt-작성-방법
title: Image Prompt 작성 방법
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Image Prompt Engineering, T2I Prompting, Diffusion Prompting]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [image-generation, prompting, diffusion, midjourney, flux]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: text
  framework: FLUX/Midjourney/SDXL
---

# Image Prompt 작성 방법

## 매 한 줄
> **"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.

## 매 핵심

### 매 Prompt anatomy (4-part structure)
- **Subject**: "a red fox", "an elderly Japanese ceramicist" — concrete noun + descriptors.
- **Style**: "oil painting", "cinematic photography, 35mm film", "Studio Ghibli" — medium / artist / movement.
- **Composition**: "low-angle shot", "rule of thirds", "shallow depth of field f/1.4" — framing 의 explicit specification.
- **Quality / technical**: "8k, sharp focus, golden hour lighting" — modifiers 의 final boost.

### 매 Token weight (Midjourney/SDXL)
- `(keyword:1.3)` — 매 emphasis 의 multiplier.
- `[keyword]` — 매 de-emphasis (SD WebUI).
- Midjourney `--stylize 250` (default 100) — 매 artistic license 의 control.
- Midjourney `::` — 매 multi-prompt weight: `red fox::2 forest::0.5`.

### 매 Negative prompt
1. SDXL/SD3: `negative_prompt="blurry, low quality, deformed hands, watermark"`.
2. Midjourney: `--no text logo` — exclusion list.
3. FLUX: 매 native negative prompt support 의 X — instead 매 positive 의 rephrase.

## 💻 패턴

### Pattern 1: FLUX.1.1 Pro 의 long-form natural prompt
```text
A photorealistic portrait of an elderly Japanese ceramicist, weathered hands
shaping a clay tea bowl on a wooden wheel. Soft window light from the left,
shallow depth of field. Earthy tones, traditional workshop with shelves of
glazed pottery in the background. Shot on Hasselblad H6D, 80mm lens, f/2.8.
```

### Pattern 2: Midjourney v7 structured prompt
```text
/imagine prompt: cyberpunk samurai standing in neon-lit Tokyo alleyway,
holographic katana, rain-soaked street reflections, cinematic composition,
volumetric lighting, by Syd Mead and Moebius --ar 16:9 --stylize 350 --v 7
```

### Pattern 3: SDXL 의 weighted prompt + negative
```python
from diffusers import StableDiffusionXLPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
).to("cuda")

prompt = "(masterpiece:1.2), red fox in autumn forest, golden hour, " \
         "(detailed fur:1.3), cinematic photography, 8k"
negative = "blurry, low quality, deformed, watermark, text"

image = pipe(
    prompt=prompt,
    negative_prompt=negative,
    num_inference_steps=30,
    guidance_scale=7.5,
).images[0]
```

### Pattern 4: ControlNet 의 pose + prompt
```python
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
from controlnet_aux import OpenposeDetector

openpose = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
pose_image = openpose(input_image)

controlnet = ControlNetModel.from_pretrained("xinsir/controlnet-openpose-sdxl-1.0")
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=controlnet,
)

result = pipe(
    prompt="a knight in shining armor, dramatic lighting",
    image=pose_image,
    controlnet_conditioning_scale=0.8,
).images[0]
```

### Pattern 5: LLM 의 prompt expansion
```python
import anthropic

client = anthropic.Anthropic()

def expand_prompt(short_idea: str) -> str:
    msg = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=300,
        messages=[{
            "role": "user",
            "content": f"Expand this image idea into a detailed FLUX prompt "
                       f"(subject, style, composition, lighting, camera): {short_idea}"
        }],
    )
    return msg.content[0].text

prompt = expand_prompt("lonely lighthouse at dusk")
```

### Pattern 6: Imagen 4 의 text rendering
```text
A vintage diner sign at night, neon letters reading "MOON CAFÉ — OPEN 24 HOURS",
warm pink and cyan glow, rain reflections on asphalt, 1950s Americana aesthetic,
cinematic wide shot
```
(Imagen 4 / FLUX 의 accurate text rendering 의 strong — 매 quote-wrapped text 의 use.)

### Pattern 7: Aspect ratio + seed control
```text
/imagine prompt: misty mountain temple at sunrise --ar 21:9 --seed 42 --v 7
```
- `--ar 21:9` — cinematic widescreen.
- `--seed 42` — 매 reproducibility.

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Photorealistic portrait | FLUX.1.1 Pro + long natural prompt |
| Stylized art / illustration | Midjourney v7 + artist references |
| Precise pose / composition | SDXL + ControlNet (openpose/canny/depth) |
| Text in image | Imagen 4 / FLUX (quote-wrapped) |
| Local / private | SDXL or FLUX.1-dev on RTX 4090 |
| Batch / API workflow | Replicate / fal.ai / Together AI |

**기본값**: FLUX.1.1 Pro (photoreal) or Midjourney v7 (stylized) + 4-part structured prompt.

## 🔗 Graph
- 부모: [[Generative-AI]] · [[Diffusion-Models]]
- 변형: [[Vary-Region-인페인팅]] · [[ControlNet]] · [[LoRA-Fine-Tuning]]
- Adjacent: [[Prompt_Engineering|Prompt-Engineering]]

## 🤖 LLM 활용
**언제**: prompt expansion / variation generation / style transfer description / negative prompt brainstorming.
**언제 X**: 매 final image quality 의 judgment 의 X — 매 human-in-the-loop 의 visual evaluation 의 always 필요.

## ❌ 안티패턴
- **Keyword soup**: "8k, masterpiece, best quality, ultra detailed, ..." — 매 modern model 의 dilution. Natural language 의 prefer.
- **Contradictory modifiers**: "photorealistic anime" — 매 latent space 의 confusion.
- **Negative prompt overload**: 매 30+ negative tokens 의 generation 의 degrade.
- **No seed control**: 매 reproducibility 의 X — 매 iteration / A/B 의 impossible.
- **Ignoring aspect ratio**: 매 default square 의 composition 의 mismatch (cinematic shot 의 16:9 의 필요).

## 🧪 검증 / 중복
- Verified: Midjourney v7 docs (2026), Black Forest Labs FLUX docs, Stability AI SDXL paper.
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — full image prompt engineering guide |
