---
id: wiki-2026-0508-소셜-미디어-그래픽-및-마케팅-캠페인-제작
title: 소셜 미디어 그래픽 및 마케팅 캠페인 제작
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Social Media Graphic, Marketing Campaign Creation, AI Marketing Asset Pipeline]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [ai-image-generation, marketing, social-media, brand-identity, workflow, batch-pipeline]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: comfyui-and-flux
---

# 소셜 미디어 그래픽 및 마케팅 캠페인 제작

## 매 한 줄
> **"매 brand-locked aesthetic + multi-aspect-ratio + 매 hundreds of variants in hours"**. 매 2026 의 marketing team 의 매 standard pipeline: FLUX 1.2 + brand LoRA + automated aspect ratio generation + Recraft text rendering — 매 manual designer 작업 의 80% 자동화, 매 final taste judgment 만 human.

## 매 핵심

### 매 Campaign asset 의 종류
- **Hero image** (1:1, 16:9, 9:16): Instagram, Facebook, Story/Reel.
- **Thumbnail variants**: A/B test 용 다양한 angle/composition.
- **Banner ad** (Google Display, Meta): typography-heavy.
- **Carousel** (Instagram, LinkedIn): 매 narrative sequence.
- **Animated**: Reel, TikTok — Sora 2 / Runway Gen-4.

### 매 Brand consistency 의 lever
- **Brand LoRA**: 매 logo, color palette, mascot 의 LoRA 학습 (50-200 images).
- **Style reference (--sref / IP-Adapter)**: 매 fixed aesthetic vector.
- **Color palette enforcement**: post-processing color quantization.
- **Typography**: Recraft v4 / Imagen 4 의 native text rendering, 매 final composite 은 Figma/After Effects.

### 매 응용
1. **Campaign brief → 100 variants in 2 hours** (concept exploration).
2. **Personalization at scale** — segment 별 visual variant.
3. **Localization** — 매 region 별 typography + cultural cue swap.

## 💻 패턴

### Campaign generator pipeline
```python
from dataclasses import dataclass
from pathlib import Path

@dataclass
class CampaignBrief:
    product: str
    audience: str
    mood: list[str]
    aspect_ratios: list[str]
    variants_per_ratio: int
    brand_lora: str
    style_ref: str | None

def generate_campaign(brief: CampaignBrief, out: Path):
    pipe = load_flux_with_lora(brief.brand_lora)
    if brief.style_ref:
        pipe = attach_ip_adapter(pipe, brief.style_ref)
    for ar in brief.aspect_ratios:
        w, h = aspect_to_dims(ar)
        for i in range(brief.variants_per_ratio):
            prompt = build_prompt(brief, variant_seed=i)
            img = pipe(prompt, width=w, height=h,
                       generator=torch.Generator("cuda").manual_seed(1000 + i)).images[0]
            img.save(out / f"{ar}/{i:03d}.png")
```

### Aspect ratio dispatch
```python
ASPECTS = {
    "1:1":  (1024, 1024),  # IG feed
    "4:5":  (1024, 1280),  # IG portrait
    "9:16": (768, 1344),   # Story / Reel
    "16:9": (1344, 768),   # YT thumb / banner
    "21:9": (1536, 640),   # LinkedIn banner
}
```

### Brand LoRA training (kohya-ss / sd-scripts)
```bash
accelerate launch flux_train_network.py \
  --pretrained_model_name_or_path=black-forest-labs/FLUX.1.2-dev \
  --train_data_dir=./brand-imgs \
  --output_dir=./loras/brand-v1 \
  --network_module=networks.lora_flux \
  --network_dim=32 \
  --learning_rate=1e-4 \
  --max_train_steps=2000 \
  --resolution=1024 \
  --train_batch_size=1
```

### Prompt template + variant seed
```python
def build_prompt(b: CampaignBrief, variant_seed: int) -> str:
    angles = ["overhead flat lay", "three-quarter hero", "macro close-up"]
    moods = b.mood
    angle = angles[variant_seed % len(angles)]
    mood = moods[variant_seed % len(moods)]
    return (
        f"{b.product}, {angle}, {mood}, "
        f"clean studio lighting, brand_v1 aesthetic, "
        f"target audience: {b.audience}, professional commercial photography"
    )
```

### Post-process — palette enforcement
```python
from PIL import Image
import numpy as np
from sklearn.cluster import KMeans

BRAND_PALETTE = np.array([[27, 38, 59], [65, 90, 119], [224, 225, 221], [119, 141, 169]])

def snap_to_palette(img: Image.Image, palette=BRAND_PALETTE) -> Image.Image:
    arr = np.array(img).reshape(-1, 3)
    dists = np.linalg.norm(arr[:, None] - palette[None], axis=2)
    snapped = palette[dists.argmin(axis=1)]
    return Image.fromarray(snapped.reshape(img.size[1], img.size[0], 3).astype(np.uint8))
```

### Text overlay (Pillow → Figma export)
```python
from PIL import ImageDraw, ImageFont

def add_headline(img: Image.Image, text: str, font_path: str) -> Image.Image:
    draw = ImageDraw.Draw(img)
    font = ImageFont.truetype(font_path, size=72)
    box = draw.textbbox((0, 0), text, font=font)
    x = (img.width - (box[2] - box[0])) // 2
    y = img.height - (box[3] - box[1]) - 80
    draw.rectangle([x - 20, y - 20, x + box[2] + 20, y + box[3] + 20], fill=(0, 0, 0, 180))
    draw.text((x, y), text, font=font, fill="white")
    return img
```

### A/B variant export with metadata
```python
import json
def export_variant(img, variant_id, brief, out):
    img.save(out / f"{variant_id}.png")
    with open(out / f"{variant_id}.json", "w") as f:
        json.dump({
            "id": variant_id,
            "model": "flux-1.2-dev",
            "lora": brief.brand_lora,
            "prompt": ...,
            "seed": ...,
            "created": datetime.utcnow().isoformat(),
        }, f, indent=2)
```

### Animated extension (Sora 2 / Kling)
```python
# image-to-video
video = sora_client.create_video(
    image=hero_image_url,
    prompt="subtle camera push-in, gentle product rotation",
    duration=4,
    resolution="1080p",
)
```

## 매 결정 기준
| 자산 종류 | 권장 도구 (2026) |
|---|---|
| Hero photoreal | FLUX 1.2 + brand LoRA |
| Typography-heavy banner | Imagen 4 / Recraft v4 |
| Vector / icon | Recraft v4 |
| Animated reel | Sora 2 / Kling 2 / Runway Gen-4 |
| Localization variants | LLM prompt translate + Imagen 4 (text) |
| Final composite | Figma / Photoshop generative fill |

**기본값**: FLUX 1.2 + brand LoRA + IP-Adapter style ref + Pillow text overlay (or Figma final composite). 매 batch 100+ 변형 의 cost ~$5-20.

## 🔗 Graph
- 부모: [[AI 이미지 생성 및 편집 워크플로우 (AI Image Generation & Editing Workflow)]]
- 변형: [[Style_Reference_(--sref)]]
- Adjacent: [[버전 및 모델 (Versions and Models)]]

## 🤖 LLM 활용
**언제**: campaign brief → prompt template, headline/CTA copy 의 variant generation, brief → asset checklist.
**언제 X**: 매 final aesthetic taste — 매 brand director 의 review 필수. LLM 의 visual judgment hallucination.

## ❌ 안티패턴
- **No brand LoRA, free-floating prompts**: 매 inconsistent style — 매 brand erosion.
- **Single seed batch**: 매 variant 의 의미 없음. Seed sweep 필수.
- **Text rendering in FLUX direct**: 매 still error-prone (2026). Imagen 4 / Recraft / final overlay 권장.
- **No metadata trail**: 매 후속 reproduction / audit 불가.
- **Skipping color palette enforcement**: brand color 의 drift.

## 🧪 검증 / 중복
- Verified (Black Forest Labs FLUX 1.2 docs, Recraft v4 docs, kohya-ss flux training, Meta/Google ad spec sheets 2026).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — campaign pipeline, brand LoRA, multi-aspect, Sora 2 extension |