2nd/10_Wiki/Topic_Programming/AI_and_ML/image prompt 작성 방법.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

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

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

/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

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

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

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

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

/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

🤖 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