---
id: wiki-2026-0508-2026년-인공지능-시각-언어-생성-패러다임-전환-및-연속
title: 2026 AI Visual Language Generation Paradigm Shift
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [continuous creative workflow, visual AI 2026, draft mode paradigm, prompt engineering visual]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: conceptual
tags: [image-generation, midjourney-v7, draft-mode, prompt-engineering, continuous-workflow, visual-ai]
raw_sources: []
last_reinforced: 2026-05-09
github_commit: pending
---

# 2026 AI Visual Language Generation Paradigm Shift

## 📌 한 줄 통찰
> **Single shot → continuous workflow**. 매 draft mode 의 fast iteration + omni reference 의 consistency + post-edit 의 polish. 매 prompt 의 camera / lighting science 의 vocabulary.

## 📖 핵심 paradigm shift

### 매 evolution

#### 2022-2023 (Era 1): Single shot
- 매 prompt → image.
- 매 luck.
- 매 generic output.

#### 2023-2024 (Era 2): Iterative
- 매 multiple variation.
- 매 prompt iterate.
- 매 inpaint.

#### 2025-2026 (Era 3): Continuous workflow
- 매 draft mode (cheap explore).
- 매 reference (style, character, omni).
- 매 post-edit pipeline.
- 매 production-quality output.

### 매 5-layer prompt structure

#### 1. Subject
- 매 specific entity (person, object, scene).
- 매 physical detail.
- 매 emotional / narrative context.

#### 2. Medium
- "Oil painting, watercolor, digital art, photo".
- 매 era / school ("Renaissance, Bauhaus, Cyberpunk").

#### 3. Environment / Composition
- 매 location.
- 매 framing ("close-up, wide shot, low angle").
- 매 background.

#### 4. Lighting
- 매 type ("Golden hour, volumetric, chiaroscuro, rim light").
- 매 source ("softbox, natural, neon").

#### 5. Technical parameter
- 매 lens ("85mm, 24mm, macro").
- 매 depth ("shallow, deep").
- 매 ratio ("--ar 16:9").
- 매 quality ("--q 2, 8k").

### 매 photography vocabulary
- **Lens**: 매 85mm portrait, 24mm wide, 100mm macro.
- **Aperture**: f/1.4 (shallow DOF), f/8 (sharp).
- **Lighting type**: golden hour, blue hour, soft light, hard light.
- **Composition**: rule of thirds, leading lines, symmetry.
- **Color theory**: complementary, analogous, monochrome.

### Continuous workflow

#### Step 1: Mood board
- 매 reference (Pinterest, ArtStation).
- 매 style direction.

#### Step 2: Draft generation
- 매 30+ variant.
- Midjourney `--draft` (10x speed).
- Flux Schnell (4 step).

#### Step 3: Selection
- 매 promising 5-10.
- 매 visual review.

#### Step 4: Refinement
- 매 prompt iterate.
- 매 reference (sref / cref / oref).

#### Step 5: Full quality
- 매 selected 의 high-quality.

#### Step 6: Post-edit
- 매 inpaint defects.
- 매 outpaint extend.
- 매 face restoration.

#### Step 7: Upscale
- Real-ESRGAN.
- Magnific.
- Topaz.

#### Step 8: Final touch (optional)
- Photoshop.
- Lightroom (color grade).

### 매 reference 의 type

#### Style reference (sref)
- 매 brand 의 mood.
- 매 visual coherence.

#### Character reference (cref)
- 매 person consistency.
- 매 series / campaign.

#### Omni reference (oref) — Midjourney V7
- 매 specific object identity.
- 매 product mockup.

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

### 매 model 의 specific control

#### Midjourney V7
- `--draft`, `--sref`, `--cref`, `--oref`.
- `--s` (stylize), `--c` (chaos), `--w` (weird).
- 매 minimal natural language.

#### DALL-E 3
- 매 natural language.
- 매 GPT-4 의 expansion.
- 매 negation 약.

#### Stable Diffusion / Flux
- 매 weighted prompt: `(keyword:1.2)`.
- 매 negative prompt 강.
- 매 LoRA, ControlNet, IP-Adapter.

### 매 emerging (2026)

#### Video generation
- Sora (OpenAI).
- Veo 2 (Google).
- Runway Gen-3.
- Kling.
- 매 image → video.
- 매 1 minute clip.

#### 3D generation
- 매 image / text → 3D mesh.
- 매 game asset.
- TripoSR, InstantMesh.

#### Real-time generation
- LCM (Latent Consistency Model).
- SDXL Turbo.
- 매 < 1 sec / image.

## 💻 Code

### Iterative workflow (production)
```python
class CreativeWorkflow:
    def __init__(self, model="midjourney"):
        self.model = model
    
    def explore(self, base_prompt: str, n_drafts=30):
        """Stage 1: Draft."""
        variations = self.generate_variations(base_prompt)
        return self.batch_generate(variations, draft=True)
    
    def select(self, drafts, criteria="visual_quality"):
        """Stage 2: Select."""
        scored = [(d, self.score(d, criteria)) for d in drafts]
        return sorted(scored, key=lambda x: -x[1])[:5]
    
    def refine(self, selected_image, refinement_prompt):
        """Stage 3: Refine."""
        return self.generate(refinement_prompt, reference=selected_image)
    
    def post_edit(self, image):
        """Stage 4: Post-edit."""
        defects = self.detect_defects(image)
        for d in defects:
            image = self.inpaint(image, d.mask, prompt=d.fix_prompt)
        return image
    
    def upscale(self, image):
        """Stage 5: Upscale."""
        return self.upscaler.enhance(image, scale=4)
```

### Reference-driven generation
```python
def generate_with_references(prompt, style_ref=None, character_ref=None):
    parts = [prompt]
    if style_ref:
        parts.append(f"--sref {style_ref}")
    if character_ref:
        parts.append(f"--cref {character_ref}")
    
    full_prompt = " ".join(parts)
    return midjourney.generate(full_prompt)
```

### Prompt builder (5-layer)
```python
def build_prompt(subject, medium, env, lighting, params):
    return f"{subject}, {medium}, {env}, {lighting} {params}"

prompt = build_prompt(
    subject="elegant woman, age 30, blue eyes, smiling",
    medium="oil painting, Renaissance style",
    env="close-up portrait, marble background",
    lighting="chiaroscuro, dramatic light, volumetric",
    params="85mm lens, shallow depth of field --ar 3:2 --s 500"
)
```

### Batch + cost optimization
```python
def cost_aware_batch(prompts, target='exploration'):
    if target == 'exploration':
        return [generate(p, draft=True, steps=10) for p in prompts]
    elif target == 'production':
        return [generate(p, steps=50, upscale=True) for p in prompts]
```

## 🤔 결정 기준

| Goal | Workflow |
|---|---|
| Brand campaign | sref + multi-iteration + post-edit |
| Character consistency | cref / oref + LoRA |
| Quick concept | Draft mode |
| Final polish | Full quality + post-edit + upscale |
| Video | Sora / Veo / Runway |
| 3D asset | TripoSR / InstantMesh |

**기본값**: 5-layer prompt + draft mode + reference + post-edit + upscale 의 sequence.

## 🔗 Graph
- 부모: [[AI Image Generation]]
- 변형: [[Draft-Mode]] · [[Omni Reference]]
- Tools: [[Midjourney-V7]] · [[Flux]]

## 🤖 LLM 활용
**언제**: 매 commercial creative project. 매 visual brand.
**언제 X**: 매 throwaway. 매 highly specific artist (legal).

## ❌ 안티패턴
- **Single prompt 의 expectation**: cliche / generic.
- **No reference**: brand inconsistency.
- **Skip post-edit**: defect ship.
- **Generic vocab ("nice picture")**: 매 specific 의 더 좋음.
- **Full quality from start**: cost 폭발.

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

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-09 | Manual cleanup — paradigm shift + 5-layer + workflow + emerging tech |
