Antigravity Agent
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211 lines
7.6 KiB
Markdown
211 lines
7.6 KiB
Markdown
---
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id: wiki-2026-0508-상업용-제품-사진-및-브랜드-로고-디자인
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title: 상업용 제품 사진 및 브랜드 로고 디자인
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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: [Commercial Product Photography, Brand Logo with AI, AI Branding]
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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, branding, commercial, product-photography, flux, midjourney]
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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: diffusers
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---
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# 상업용 제품 사진 및 브랜드 로고 디자인
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## 매 한 줄
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> **"매 commercial 의 deliverable 매 consistency × controllability × license 의 3축"**. AI 의 product hero shot, lifestyle shot, packshot, logo 의 generate 의 매 reference image (IP-Adapter), pose/composition (ControlNet), brand LoRA 의 의해 production-grade 도달. 매 2026 의 FLUX.1 + LoRA + Krea/Magnific upscaler 의 standard pipeline.
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## 매 핵심
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### 매 deliverable 종류
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- **Packshot**: clean white/seamless bg, hero product 단독.
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- **Lifestyle**: 매 context (hand, table, scene) 의 product.
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- **Hero**: cinematic, dramatic light, brand storytelling.
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- **Logo**: vector-clean, scalable, monogram/wordmark.
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### 매 control 축
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- **Reference image**: IP-Adapter 의 product 의 visual identity 보존.
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- **Composition**: ControlNet (depth/canny) 의 layout lock.
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- **Style**: brand LoRA 의 color palette / mood consistency.
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- **License**: commercial-cleared model + asset trail.
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### 매 응용
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1. E-commerce 의 multi-angle packshot batch.
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2. Lookbook 의 lifestyle shot 의 mood-board iteration.
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3. Logo 의 monogram/lockup variation 탐색.
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## 💻 패턴
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### Packshot — clean bg 의 product
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```python
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from diffusers import FluxPipeline
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import torch
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pipe = FluxPipeline.from_pretrained(
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"black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
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).to("cuda")
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img = pipe(
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prompt=(
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"product photography of a minimalist ceramic coffee mug, "
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"matte white seamless background, soft shadow, studio lighting, "
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"centered composition, sharp focus, 50mm lens, commercial catalog style"
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),
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height=1024, width=1024, guidance_scale=3.5, num_inference_steps=28,
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).images[0]
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```
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### IP-Adapter — product identity 보존
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```python
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from diffusers import StableDiffusionXLPipeline
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from transformers import CLIPVisionModelWithProjection
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import torch
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pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
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pipe.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter-plus_sdxl_vit-h.safetensors")
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pipe.set_ip_adapter_scale(0.7)
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product_ref = Image.open("client_product.png") # 매 actual product image
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img = pipe(
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prompt="product on marble counter, morning light, lifestyle scene",
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ip_adapter_image=product_ref,
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num_inference_steps=30, guidance_scale=6.0,
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).images[0]
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```
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### ControlNet — composition lock
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```python
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from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
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from controlnet_aux import CannyDetector
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canny = CannyDetector()
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ctrl_img = canny(Image.open("layout_sketch.png"), 100, 200)
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cn = ControlNetModel.from_pretrained("diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16)
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pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0", controlnet=cn, torch_dtype=torch.float16
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).to("cuda")
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img = pipe(
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prompt="luxury watch packshot, dark moody studio, rim light",
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image=ctrl_img, controlnet_conditioning_scale=0.7,
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).images[0]
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```
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### Brand LoRA — style consistency
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```python
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# 매 12-30 brand asset 의 train 한 LoRA 의 load
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pipe.load_lora_weights("./loras/acme-brand-v3.safetensors", adapter_name="brand")
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pipe.set_adapters(["brand"], adapter_weights=[0.85])
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img = pipe(prompt="<acme> hero shot, sneaker on rocky terrain, golden hour").images[0]
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```
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### Logo — vector-style 의 raster
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```python
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# Step 1: SDXL 의 raster logo
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img = pipe(
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prompt=(
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"minimalist logo design, geometric monogram letter A, "
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"single color, flat vector style, high contrast, white background, "
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"centered, no gradient, no shadow"
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),
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negative_prompt="3d, photorealistic, gradient, texture, shadow, busy",
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guidance_scale=8.0,
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).images[0]
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# Step 2: vectorize (vtracer / SVG)
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import subprocess
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img.save("logo.png")
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subprocess.run(["vtracer", "--input", "logo.png", "--output", "logo.svg",
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"--mode", "polygon", "--colormode", "binary"])
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```
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### Magnific / Krea-style upscale (commercial finish)
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```python
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from diffusers import StableDiffusionXLImg2ImgPipeline
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upsc = StableDiffusionXLImg2ImgPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16
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).to("cuda")
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# 매 1024 → 2048 의 detail enhance pass
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hi = img.resize((2048, 2048), Image.LANCZOS)
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final = upsc(prompt="ultra detailed product photo, sharp",
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image=hi, strength=0.25, num_inference_steps=20).images[0]
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```
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### Multi-angle batch (e-comm)
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```python
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angles = [
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"front view, eye-level",
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"3/4 view, slight high angle",
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"top-down flat lay",
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"profile silhouette, side light",
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"macro detail, texture close-up",
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]
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for i, angle in enumerate(angles):
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img = pipe(
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prompt=f"product photography of [PRODUCT], {angle}, white seamless bg, studio light",
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ip_adapter_image=product_ref,
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generator=torch.Generator("cuda").manual_seed(42 + i),
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).images[0]
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img.save(f"packshot_{i:02d}.png")
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```
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### Background swap (Bria RMBG / Replicate)
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```python
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from transformers import AutoModelForImageSegmentation
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rmbg = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-2.0", trust_remote_code=True)
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# 매 mask 의 추출 → composite 의 new bg
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mask = rmbg(img)
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composite = Image.composite(img, new_bg, mask)
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```
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## 매 결정 기준
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| 상황 | Approach |
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| 실제 product 의 identity 보존 | IP-Adapter scale 0.6-0.8 + brand LoRA |
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| Layout 정확 lock | ControlNet (canny/depth) scale 0.7 |
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| Logo design exploration | high CFG (8+) + negative "3d, gradient" |
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| Lifestyle scene | FLUX + lifestyle prompt + soft IP-Adapter (0.4) |
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| 4K 의 commercial finish | SDXL refiner img2img strength=0.25 |
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| 절대 license clean | FLUX.1 [pro] commercial / Adobe Firefly |
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**기본값**: FLUX.1 base + IP-Adapter + brand LoRA + refiner upscale.
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## 🔗 Graph
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- 부모: [[상업용 브랜드 이미지 및 디자인 시스템 구축]] · [[AI 이미지 생성 및 편집 워크플로우 (AI Image Generation & Editing Workflow)]]
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- 변형: [[미드저니 및 스테이블 디퓨전의 부분 편집 기법]]
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- Adjacent: [[IP-Adapter]] · [[ControlNet]]
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## 🤖 LLM 활용
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**언제**: e-comm catalog batch, brand exploration, logo concept ideation, lookbook mood board.
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**언제 X**: 매 client 의 strict legal requirement (talent likeness, trademarked product) — license-cleared pipeline 의 의무.
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## ❌ 안티패턴
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- **No reference image**: product identity drift — 매 useless to client.
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- **Brand LoRA 의 over-weight (>1.0)**: 매 over-fit, novelty 사라짐.
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- **Logo 의 photoreal prompt**: vector-incompatible — flat/single-color 의 specify.
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- **License 의 무시 (raw SDXL output 의 commercial use)**: 매 legal exposure.
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- **No upscale pass**: low-resolution 의 catalog quality 미달.
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## 🧪 검증 / 중복
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- Verified (FLUX.1 model card, IP-Adapter paper, ControlNet paper, Diffusers docs 2024-2026).
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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 — packshot/lifestyle/logo + IP-Adapter/ControlNet/LoRA |
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