Antigravity Agent
210 lines
7.6 KiB
Markdown
210 lines
7.6 KiB
Markdown
---
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id: wiki-2026-0508-소셜-미디어-그래픽-및-마케팅-캠페인-제작
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title: 소셜 미디어 그래픽 및 마케팅 캠페인 제작
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Social Media Graphic, Marketing Campaign Creation, AI Marketing Asset Pipeline]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [ai-image-generation, marketing, social-media, brand-identity, workflow, batch-pipeline]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: comfyui-and-flux
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---
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# 소셜 미디어 그래픽 및 마케팅 캠페인 제작
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 Campaign asset 의 종류
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- **Hero image** (1:1, 16:9, 9:16): Instagram, Facebook, Story/Reel.
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- **Thumbnail variants**: A/B test 용 다양한 angle/composition.
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- **Banner ad** (Google Display, Meta): typography-heavy.
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- **Carousel** (Instagram, LinkedIn): 매 narrative sequence.
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- **Animated**: Reel, TikTok — Sora 2 / Runway Gen-4.
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### 매 Brand consistency 의 lever
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- **Brand LoRA**: 매 logo, color palette, mascot 의 LoRA 학습 (50-200 images).
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- **Style reference (--sref / IP-Adapter)**: 매 fixed aesthetic vector.
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- **Color palette enforcement**: post-processing color quantization.
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- **Typography**: Recraft v4 / Imagen 4 의 native text rendering, 매 final composite 은 Figma/After Effects.
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### 매 응용
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1. **Campaign brief → 100 variants in 2 hours** (concept exploration).
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2. **Personalization at scale** — segment 별 visual variant.
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3. **Localization** — 매 region 별 typography + cultural cue swap.
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## 💻 패턴
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### Campaign generator pipeline
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```python
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from dataclasses import dataclass
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from pathlib import Path
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@dataclass
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class CampaignBrief:
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product: str
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audience: str
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mood: list[str]
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aspect_ratios: list[str]
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variants_per_ratio: int
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brand_lora: str
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style_ref: str | None
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def generate_campaign(brief: CampaignBrief, out: Path):
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pipe = load_flux_with_lora(brief.brand_lora)
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if brief.style_ref:
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pipe = attach_ip_adapter(pipe, brief.style_ref)
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for ar in brief.aspect_ratios:
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w, h = aspect_to_dims(ar)
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for i in range(brief.variants_per_ratio):
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prompt = build_prompt(brief, variant_seed=i)
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img = pipe(prompt, width=w, height=h,
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generator=torch.Generator("cuda").manual_seed(1000 + i)).images[0]
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img.save(out / f"{ar}/{i:03d}.png")
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```
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### Aspect ratio dispatch
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```python
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ASPECTS = {
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"1:1": (1024, 1024), # IG feed
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"4:5": (1024, 1280), # IG portrait
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"9:16": (768, 1344), # Story / Reel
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"16:9": (1344, 768), # YT thumb / banner
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"21:9": (1536, 640), # LinkedIn banner
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}
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```
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### Brand LoRA training (kohya-ss / sd-scripts)
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```bash
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accelerate launch flux_train_network.py \
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--pretrained_model_name_or_path=black-forest-labs/FLUX.1.2-dev \
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--train_data_dir=./brand-imgs \
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--output_dir=./loras/brand-v1 \
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--network_module=networks.lora_flux \
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--network_dim=32 \
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--learning_rate=1e-4 \
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--max_train_steps=2000 \
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--resolution=1024 \
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--train_batch_size=1
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```
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### Prompt template + variant seed
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```python
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def build_prompt(b: CampaignBrief, variant_seed: int) -> str:
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angles = ["overhead flat lay", "three-quarter hero", "macro close-up"]
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moods = b.mood
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angle = angles[variant_seed % len(angles)]
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mood = moods[variant_seed % len(moods)]
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return (
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f"{b.product}, {angle}, {mood}, "
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f"clean studio lighting, brand_v1 aesthetic, "
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f"target audience: {b.audience}, professional commercial photography"
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)
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```
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### Post-process — palette enforcement
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```python
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from PIL import Image
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import numpy as np
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from sklearn.cluster import KMeans
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BRAND_PALETTE = np.array([[27, 38, 59], [65, 90, 119], [224, 225, 221], [119, 141, 169]])
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def snap_to_palette(img: Image.Image, palette=BRAND_PALETTE) -> Image.Image:
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arr = np.array(img).reshape(-1, 3)
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dists = np.linalg.norm(arr[:, None] - palette[None], axis=2)
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snapped = palette[dists.argmin(axis=1)]
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return Image.fromarray(snapped.reshape(img.size[1], img.size[0], 3).astype(np.uint8))
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```
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### Text overlay (Pillow → Figma export)
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```python
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from PIL import ImageDraw, ImageFont
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def add_headline(img: Image.Image, text: str, font_path: str) -> Image.Image:
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draw = ImageDraw.Draw(img)
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font = ImageFont.truetype(font_path, size=72)
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box = draw.textbbox((0, 0), text, font=font)
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x = (img.width - (box[2] - box[0])) // 2
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y = img.height - (box[3] - box[1]) - 80
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draw.rectangle([x - 20, y - 20, x + box[2] + 20, y + box[3] + 20], fill=(0, 0, 0, 180))
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draw.text((x, y), text, font=font, fill="white")
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return img
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```
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### A/B variant export with metadata
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```python
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import json
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def export_variant(img, variant_id, brief, out):
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img.save(out / f"{variant_id}.png")
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with open(out / f"{variant_id}.json", "w") as f:
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json.dump({
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"id": variant_id,
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"model": "flux-1.2-dev",
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"lora": brief.brand_lora,
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"prompt": ...,
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"seed": ...,
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"created": datetime.utcnow().isoformat(),
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}, f, indent=2)
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```
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### Animated extension (Sora 2 / Kling)
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```python
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# image-to-video
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video = sora_client.create_video(
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image=hero_image_url,
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prompt="subtle camera push-in, gentle product rotation",
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duration=4,
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resolution="1080p",
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)
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```
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## 매 결정 기준
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| 자산 종류 | 권장 도구 (2026) |
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|---|---|
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| Hero photoreal | FLUX 1.2 + brand LoRA |
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| Typography-heavy banner | Imagen 4 / Recraft v4 |
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| Vector / icon | Recraft v4 |
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| Animated reel | Sora 2 / Kling 2 / Runway Gen-4 |
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| Localization variants | LLM prompt translate + Imagen 4 (text) |
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| Final composite | Figma / Photoshop generative fill |
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**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.
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## 🔗 Graph
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- 부모: [[AI 이미지 생성 및 편집 워크플로우 (AI Image Generation & Editing Workflow)]] · [[Marketing-Asset-Pipeline]]
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- 변형: [[Brand-LoRA-Training]] · [[Style_Reference_(--sref)]]
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- 응용: [[Campaign-A-B-Testing]] · [[Localization-Pipeline]] · [[Personalization-at-Scale]]
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- Adjacent: [[버전 및 모델 (Versions and Models)]] · [[Recraft-v4]] · [[Sora-2]] · [[Figma-Generative-Fill]]
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## 🤖 LLM 활용
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**언제**: campaign brief → prompt template, headline/CTA copy 의 variant generation, brief → asset checklist.
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**언제 X**: 매 final aesthetic taste — 매 brand director 의 review 필수. LLM 의 visual judgment hallucination.
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## ❌ 안티패턴
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- **No brand LoRA, free-floating prompts**: 매 inconsistent style — 매 brand erosion.
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- **Single seed batch**: 매 variant 의 의미 없음. Seed sweep 필수.
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- **Text rendering in FLUX direct**: 매 still error-prone (2026). Imagen 4 / Recraft / final overlay 권장.
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- **No metadata trail**: 매 후속 reproduction / audit 불가.
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- **Skipping color palette enforcement**: brand color 의 drift.
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## 🧪 검증 / 중복
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- Verified (Black Forest Labs FLUX 1.2 docs, Recraft v4 docs, kohya-ss flux training, Meta/Google ad spec sheets 2026).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — campaign pipeline, brand LoRA, multi-aspect, Sora 2 extension |
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