---
id: wiki-2026-0508-positive-prompt
title: Positive Prompt
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Positive Prompts, Prompt, Prompt Description]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [prompt-engineering, image-generation, stable-diffusion, midjourney, flux]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: diffusers, comfyui
---

# Positive Prompt

## 매 한 줄
> **"매 image generation에서 desired content 를 describe — subject, style, composition, quality."**. Stable Diffusion / FLUX / Midjourney 핵심 input. Negative prompt와 짝을 이루며, 2024-2025 modern model (FLUX.1, SD3, MJ v7)에서 매 natural language description이 weighted token보다 우세.

## 매 핵심

### 매 구성 요소
- **Subject**: "a woman, a robot, a cathedral".
- **Action / pose**: "running through forest", "sitting at desk".
- **Style**: "oil painting", "cyberpunk", "studio Ghibli".
- **Composition**: "wide angle", "close-up", "rule of thirds".
- **Lighting**: "golden hour", "rim light", "volumetric".
- **Quality modifier**: "highly detailed", "8k" (older models — modern은 less needed).
- **Artist / reference**: "in the style of Greg Rutkowski" (controversial).

### 매 model별 syntax
- **SD 1.5 / SDXL**: `(token:1.3)` weighted, BREAK 분리, comma list.
- **FLUX.1 / SD3**: 매 natural language paragraph가 best — token weighting less effective.
- **Midjourney v7**: `--ar 16:9 --stylize 200 --chaos 20` flag, natural prompt.
- **DALL-E 3 / GPT-Image**: 매 conversational, descriptive paragraph.

### 매 modern best practice (2025)
- Natural language sentence > comma keyword stuffing.
- 매 subject specific, then style, then technical.
- Reference image (img2img, IPAdapter, FLUX Redux) 매 단어보다 강력.
- LoRA / fine-tune이 style token 대체.

### 매 응용
1. Concept art, illustration.
2. Marketing asset gen.
3. Product mockup, fashion.
4. Storyboard, film pre-vis.
5. Game asset (texture, character sheet).

## 💻 패턴

### Diffusers SDXL (weighted)
```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), portrait of a samurai warrior, "
          "intricate armor, cherry blossoms, golden hour, "
          "cinematic lighting, depth of field")
neg = "low quality, blurry, deformed hands, extra fingers"

img = pipe(prompt, negative_prompt=neg, num_inference_steps=30,
           guidance_scale=7.0).images[0]
```

### FLUX.1 (natural language)
```python
from diffusers import FluxPipeline
import torch
pipe = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-dev',
                                     torch_dtype=torch.bfloat16).to('cuda')

prompt = ("A wide cinematic shot of a samurai standing under cherry "
          "blossoms at golden hour. He wears intricate red and black "
          "armor. Soft volumetric light filters through petals. "
          "Shallow depth of field with the warrior in sharp focus.")

img = pipe(prompt, guidance_scale=3.5, num_inference_steps=28,
           max_sequence_length=512).images[0]
```

### Compel (advanced weighting, SD)
```python
from compel import Compel
compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
embeds = compel("a cat++ playing piano in a (jazz bar)1.3")
img = pipe(prompt_embeds=embeds).images[0]
```

### Midjourney v7 prompt format
```text
/imagine prompt: a samurai under cherry blossoms, golden hour,
volumetric light, cinematic --ar 21:9 --stylize 300 --v 7
```

### Modular template (programmatic)
```python
def build_prompt(subject, style, light, mood):
    return (f"{subject}, {style} style, {light} lighting, "
            f"{mood} mood, highly detailed composition")

p = build_prompt("a lone astronaut on Mars",
                 "concept art", "soft sunset", "melancholic")
```

### LoRA-augmented (style token)
```python
pipe.load_lora_weights('artist_style.safetensors')
prompt = "<lora:artist_style:0.8> portrait of woman, watercolor"
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| FLUX / SD3 / DALL-E 3 | Natural paragraph, descriptive |
| SDXL / SD 1.5 | Comma-separated, weighted tokens |
| Midjourney | Natural + flags (--ar, --stylize) |
| Specific style reproduction | LoRA + 짧은 prompt |
| Reference matching | img2img / IPAdapter > prompt |
| Batch programmatic | Template + parameter slot |

**기본값**: modern model은 natural sentence, legacy SD는 weighted comma list.

## 🔗 Graph
- 부모: [[Prompt_Engineering]] · [[Image_Generation]] · [[Diffusion_Models]]
- 변형: [[Negative_Prompt]] · [[Weighted_Prompts]] · [[Prompt_Templates]]
- 응용: [[Stable_Diffusion]] · [[FLUX]] · [[Midjourney]] · [[DALL-E]]
- Adjacent: [[LoRA]] · [[IPAdapter]] · [[ControlNet]] · [[ComfyUI]]

## 🤖 LLM 활용
**언제**: image gen API wrapper, batch asset generation, prompt template system, A/B test variation.
**언제 X**: 매 reference image가 있으면 img2img / IPAdapter — 매 prompt만으론 매 정확 못 reproduce.

## ❌ 안티패턴
- **Keyword spam**: "8k, hyperdetailed, ultra hd, masterpiece, best quality, ..." — 매 modern model에 무의미.
- **Contradictory style mix**: "anime, photorealistic, oil painting" — 매 confused output.
- **Overweight `(token:2.0)`**: 매 artifact, oversaturation.
- **Artist names without consent**: 매 ethical issue + many platforms ban.
- **Same prompt for all models**: 매 model별 syntax 다름 — port 필요.

## 🧪 검증 / 중복
- Verified (FLUX.1 model card, SDXL paper, Midjourney v7 docs, diffusers library docs).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — positive prompt structure + model-specific syntax (FLUX, SDXL, MJ v7) |
