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10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
145 lines
5.0 KiB
Markdown
145 lines
5.0 KiB
Markdown
---
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id: wiki-2026-0508-style-transfer
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title: Style Transfer
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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: [Neural Style Transfer, NST, Style Transfer in AI]
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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: [style-transfer, neural-network, image-generation, diffusion]
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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: pytorch
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---
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# Style Transfer
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## 매 한 줄
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> **"매 content + style separation의 art"**. Gatys et al. (2015) 가 VGG feature space 의 Gram matrix 로 style 추출 → 매 content image 에 transfer 의 seminal work. 2026 의 modern state 는 diffusion-based (IP-Adapter, ControlNet style) + Midjourney --sref 의 mainstream.
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## 매 핵심
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### 매 origin (Gatys 2015)
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- VGG-19 의 conv layer activation 의 feature representation.
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- **Content loss**: 매 high-level layer (conv4_2) 의 feature MSE.
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- **Style loss**: 매 multiple layer 의 Gram matrix (feature correlation) MSE.
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- Optimization-based — 매 image pixel 자체 의 gradient descent (slow, ~minutes per image).
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### 매 evolution
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- **Fast NST** (Johnson 2016): feedforward network 의 single forward pass.
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- **AdaIN** (Huang 2017): Adaptive Instance Normalization — 매 arbitrary style 의 real-time.
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- **Diffusion-based** (2023+): IP-Adapter, ControlNet — 매 prompt + reference 의 zero-shot.
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### 매 응용
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1. Artistic image generation (Prisma, DeepArt — 매 historical).
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2. Midjourney --sref / --cref — 매 mainstream creative tool.
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3. Video stylization (Runway, Kaiber).
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4. Domain adaptation (synthetic → real).
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## 💻 패턴
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### Gram matrix (style representation)
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```python
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import torch
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import torch.nn as nn
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def gram_matrix(features):
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b, c, h, w = features.shape
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feat = features.view(b, c, h * w)
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gram = torch.bmm(feat, feat.transpose(1, 2))
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return gram / (c * h * w)
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```
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### AdaIN
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```python
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def adain(content_feat, style_feat, eps=1e-5):
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c_mean = content_feat.mean(dim=[2, 3], keepdim=True)
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c_std = content_feat.std(dim=[2, 3], keepdim=True) + eps
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s_mean = style_feat.mean(dim=[2, 3], keepdim=True)
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s_std = style_feat.std(dim=[2, 3], keepdim=True) + eps
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normalized = (content_feat - c_mean) / c_std
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return normalized * s_std + s_mean
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```
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### Gatys optimization (full)
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```python
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import torch.optim as optim
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from torchvision.models import vgg19
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vgg = vgg19(pretrained=True).features.eval().cuda()
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target = content_img.clone().requires_grad_(True)
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optimizer = optim.LBFGS([target])
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def closure():
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optimizer.zero_grad()
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feats = extract_features(target, vgg)
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c_loss = F.mse_loss(feats['content'], content_feats['content'])
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s_loss = sum(F.mse_loss(gram_matrix(feats[l]), gram_matrix(style_feats[l]))
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for l in style_layers)
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loss = c_loss + 1e6 * s_loss
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loss.backward()
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return loss
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for _ in range(300):
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optimizer.step(closure)
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```
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### IP-Adapter (diffusion-based, 2024+)
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```python
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from diffusers import StableDiffusionPipeline
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from ip_adapter import IPAdapter
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pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to("cuda")
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ip_model = IPAdapter(pipe, "h94/IP-Adapter", "models/ip-adapter_sd15.bin", "cuda")
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style_ref = Image.open("vangogh.jpg")
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images = ip_model.generate(pil_image=style_ref, prompt="a cat", num_samples=4, scale=0.7)
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```
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### Midjourney --sref (2026 mainstream)
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```
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/imagine prompt: a serene lake at sunset --sref https://example.com/style.jpg --sw 100
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| 매 일회성 art experiment | Gatys (구현 simple, slow ok) |
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| 매 real-time / video | AdaIN, fast NST |
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| 매 production creative | IP-Adapter + SDXL / FLUX |
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| 매 non-coder creative | Midjourney --sref |
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| 매 controllable structure + style | ControlNet + IP-Adapter combo |
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**기본값**: 매 2026 의 IP-Adapter (open) 또는 Midjourney --sref (closed).
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## 🔗 Graph
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- 부모: [[Generative-AI]] · [[Computer Vision|Computer-Vision]]
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- 변형: [[Style_Reference_(--sref)]] · [[ControlNet]] · [[IP-Adapter]]
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- 응용: [[AI 이미지 생성 (AI Image Generation)|Image-Generation]]
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- Adjacent: [[Diffusion-Models]]
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## 🤖 LLM 활용
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**언제**: 매 creative pipeline 의 style consistency, brand asset variant 생성, mood board 의 visual exploration.
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**언제 X**: 매 photo retouching (use Lightroom), 매 strict color grading (use LUTs), 매 face identity preservation 의 unstable.
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## ❌ 안티패턴
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- **Style weight 무한 증가**: 매 content 가 사라짐. balance 필수 (1e6 typical).
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- **Single VGG layer**: 매 multi-scale style 의 lost. 매 multiple layer aggregate.
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- **Diffusion 의 prompt 무시**: IP-Adapter scale 너무 높으면 prompt 의 무시. scale 0.5-0.8 sweet spot.
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## 🧪 검증 / 중복
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- Verified (Gatys et al. 2015 "A Neural Algorithm of Artistic Style"; Huang & Belongie 2017 AdaIN; Ye et al. 2023 IP-Adapter).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Gatys → AdaIN → diffusion (IP-Adapter, --sref) coverage |
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