---
id: wiki-2026-0508-ai-이미지-생성-및-편집-워크플로우-ai-image-ge
title: AI Image Generation & Editing Workflow
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [AI 이미지 워크플로우, image generation workflow, draft mode, iterative refinement]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: conceptual
tags: [image-generation, workflow, midjourney, stable-diffusion, draft-mode, iterative, post-editing]
raw_sources: []
last_reinforced: 2026-05-09
github_commit: pending
inferred_by: Claude Opus 4.7 (manual cleanup 2026-05-09)
---

# AI Image Generation & Editing Workflow

## 📌 한 줄 통찰
> **Single perfect prompt 의 myth → iterative loop**. **Draft (cheap variant) → select → refine → upscale → polish (post-edit)**. 매 round 의 quality ↑.

## 📖 핵심

### 매 5-stage workflow

#### Stage 1: Concept + ideation
- 매 reference (Pinterest, ArtStation).
- 매 mood board.
- 매 prompt sketch.

#### Stage 2: Draft generation (cheap)
- 매 dozen variant.
- Midjourney `--draft` mode (10x speed).
- Flux Schnell (fast).
- 매 4-8 candidate.

#### Stage 3: Select + iterate
- 매 best candidate.
- 매 prompt 의 refine.
- 매 next round.

#### Stage 4: Refine (full quality)
- 매 selected 의 high-quality regenerate.
- 매 final aspect ratio.

#### Stage 5: Post-edit
- 매 inpaint (specific fix).
- 매 outpaint (extend).
- 매 upscale (resolution).
- 매 retouch (Photoshop).

→ 매 stage 의 different speed / cost.

### 매 cost saving

#### Draft mode
- Midjourney V7 `--draft`: 10x faster, ~50% GPU cost.
- Flux Schnell: 4-step (vs 50).
- Latent Consistency Models (LCM).

→ 매 idea 의 cheap exploration.

#### Generation 의 cost
- Midjourney: $10-60 / month subscription.
- DALL-E 3: ~$0.08 / image.
- Stable Diffusion (self-host): GPU 운영 cost.
- Flux Pro (Replicate): $0.05 / image.

#### Compute optimization
- 매 quality preset (4 step LCM, 20 step DPM++, 50 step DDIM).
- 매 resolution (512 → 1024 → 4K).
- 매 batch size.

### 매 reference 의 활용

#### Style reference (sref)
- Midjourney `--sref [URL]`.
- 매 brand 의 mood board.
- 매 campaign 의 visual cohesion.

#### Character reference (cref)
- Midjourney `--cref [URL]`.
- 매 character 의 consistency.

#### Omni reference (oref)
- Midjourney V7+.
- 매 specific object identity.

#### IP-Adapter (Stable Diffusion)
- 매 reference image 의 style + structure.

#### LoRA
- 매 specific style / character 의 fine-tune.

### 매 quality control

#### Negative prompt (Stable Diffusion)
- 매 known defect 의 explicit.
- "ugly, deformed, watermark, low quality, blurry, extra fingers".

#### Specific defect 의 inpaint
- 매 detected defect 의 mask.
- 매 targeted prompt.

#### Upscale + face restore
- Real-ESRGAN (background).
- GFPGAN / CodeFormer (face).

### Production workflow example

#### Marketing campaign
1. **Mood board** (brand 의 reference).
2. **Draft 30 variants** (Midjourney draft).
3. **Select 5** (different angle / composition).
4. **Full HD generate**.
5. **Inpaint defects**.
6. **Upscale 4K**.
7. **Photoshop final touch**.

→ 30+ image / hour.

#### Product mockup
1. **Real product photo** (input).
2. **Img2Img** (style transfer).
3. **Background outpaint** (lifestyle context).
4. **Inpaint shadow / reflection**.
5. **Upscale**.

#### Concept art (game)
1. **Quick sketch** (artist).
2. **ControlNet 의 line art**.
3. **Generate variations**.
4. **Select + paint over** (Photoshop).

## 💻 Code

### Iterative loop (Diffusers)
```python
from diffusers import StableDiffusionXLPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained("model")

# Stage 1: Draft (low quality, fast)
prompts = [base_prompt + variation for variation in style_variations]
drafts = pipe(prompts, num_inference_steps=10, guidance_scale=5).images

# Stage 2: Select (manual or ML score)
best_idx = select_best(drafts)
best_prompt = prompts[best_idx]

# Stage 3: Full quality
final = pipe(best_prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

# Stage 4: Post-edit (inpaint specific defect)
mask = detect_face_defect(final)
inpaint_pipe = StableDiffusionXLInpaintPipeline.from_pretrained("inpaint")
fixed = inpaint_pipe(prompt="perfect face", image=final, mask_image=mask).images[0]

# Stage 5: Upscale
from realesrgan import RealESRGANer
upscaler = RealESRGANer(scale=4, ...)
upscaled, _ = upscaler.enhance(np.array(fixed))
```

### Batch + cost-aware
```python
def smart_generate(prompt, target_quality='final'):
    if target_quality == 'draft':
        return pipe(prompt, num_inference_steps=10).images[0]
    elif target_quality == 'preview':
        return pipe(prompt, num_inference_steps=25).images[0]
    elif target_quality == 'final':
        img = pipe(prompt, num_inference_steps=50).images[0]
        return upscale(img)
```

### Reference-driven (Flux + IP-Adapter)
```python
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
pipe.load_ip_adapter("flux-ip-adapter")

style_ref = Image.open("brand_mood.jpg")
result = pipe(
    prompt="product on table, professional photo",
    ip_adapter_image=style_ref,
    ip_adapter_scale=0.6,
).images[0]
```

## 🤔 결정 기준

| Stage | 추천 |
|---|---|
| Ideation | Free + reference |
| Draft | Midjourney draft / Flux Schnell |
| Refine | Full quality |
| Post-edit | Inpaint + upscale |
| Production | Photoshop final |

**기본값**: Draft 30 → Select 5 → Final + post-edit. 매 cost 의 80% saving + quality 의 maintain.

## 🔗 Graph
- 부모: [[AI Image Generation]]
- 변형: [[Iterative-Refinement]] · [[Draft-Mode]] · [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]]
- Adjacent: [[Style Reference]] · [[ControlNet]]

## 🤖 LLM 활용
**언제**: 매 commercial creative project. 매 brand campaign.
**언제 X**: 매 single-shot idea (no iteration). 매 highly specific artist style (legal).

## ❌ 안티패턴
- **Single prompt + accept**: low quality.
- **Full quality from start**: cost 폭발.
- **No reference**: brand inconsistency.
- **No post-edit**: defect in production.
- **Upscale 의 detail invent**: hallucinated artifact.

## 🧪 검증 / 중복
- Verified.
- 신뢰도 B.
- Overlap with [[AI Image Generation]] / [[AI 모델 사후 편집 도구 (Post-editing Tools)|Post-editing-Tools]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-09 | Manual cleanup — 5-stage workflow + cost + reference + code |