---
id: wiki-2026-0508-초상화-및-애니메이션-스타일-제어
title: 초상화 및 애니메이션 스타일 제어
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Portrait Style Control, Animation Style Control, Identity-Preserving Generation]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [diffusion, portrait, animation, identity, style-transfer]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: PyTorch/diffusers
---

# 초상화 및 애니메이션 스타일 제어

## 매 한 줄
> **"매 identity 보존 + 매 style 변환의 직교 분리"**. portrait/animation 도메인의 매 핵심 challenge — 같은 사람이 매 frame 마다 같아야 하고 (identity), 매 style/pose 는 자유롭게 (control). 2026 의 매 답: InstantID / PuLID (identity) + IP-Adapter (style) + ControlNet pose (motion) 의 stack.

## 매 핵심

### 매 3축 분리
- **Identity axis**: 매 face embedding (ArcFace) 으로 lock — InstantID, PuLID
- **Style axis**: 매 reference image embedding 으로 modulate — IP-Adapter
- **Motion axis**: 매 pose / depth 로 frame structure — OpenPose / DWPose

### 매 animation consistency 기법
- **Reference frame**: 매 첫 frame 을 anchor 로 IP-Adapter 적용
- **Temporal LoRA**: 매 AnimateDiff motion module 로 inter-frame coherence
- **Latent warp**: 매 prev frame latent 을 optical flow 로 warp 후 noise add
- **Cross-frame attention**: 매 frame 의 attention key/value 를 공유

### 매 응용
1. Avatar / VTuber pipeline — 매 same face × multi-emotion × multi-outfit.
2. Character sheet generation — 매 turnaround (front/side/back).
3. Short animation — 매 character 의 8-frame walk cycle.

## 💻 패턴

### InstantID portrait generation
```python
from diffusers import StableDiffusionXLInstantIDPipeline
from insightface.app import FaceAnalysis
import cv2, numpy as np

face_app = FaceAnalysis(name="antelopev2", providers=["CUDAExecutionProvider"])
face_app.prepare(ctx_id=0, det_size=(640, 640))

face_img = cv2.imread("ref.jpg")
face_info = face_app.get(face_img)[0]
face_emb = face_info["embedding"]

pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=instantid_controlnet,
    torch_dtype=torch.float16,
).to("cuda")
pipe.load_ip_adapter_instantid("instantid_ip-adapter.bin")

out = pipe(
    prompt="anime portrait, school uniform",
    image_embeds=face_emb,
    image=face_kps,            # face keypoints
    ip_adapter_scale=0.8,
    controlnet_conditioning_scale=0.8,
    num_inference_steps=30,
).images[0]
```

### PuLID identity preservation
```python
from pulid.pipeline_v1_1 import PuLIDPipeline

pulid = PuLIDPipeline()
id_emb = pulid.get_id_embedding(["ref1.jpg", "ref2.jpg"])

img = pulid.inference(
    prompt="cyberpunk character, neon city",
    id_embedding=id_emb,
    id_scale=0.9,
    cfg_scale=1.2,
    steps=4,                   # SDXL Lightning
)[0]
```

### IP-Adapter style + face combined
```python
pipe.load_ip_adapter(
    "h94/IP-Adapter",
    subfolder="sdxl_models",
    weight_name=["ip-adapter-plus-face_sdxl_vit-h.safetensors",
                 "ip-adapter-plus_sdxl_vit-h.safetensors"],
)
pipe.set_ip_adapter_scale([0.7, 0.4])  # face stronger than style

img = pipe(
    prompt="portrait, watercolor style",
    ip_adapter_image=[face_ref, style_ref],
).images[0]
```

### AnimateDiff motion generation
```python
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler

adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3")
pipe = AnimateDiffPipeline.from_pretrained(
    "SG161222/Realistic_Vision_V5.1_noVAE",
    motion_adapter=adapter,
    torch_dtype=torch.float16,
).to("cuda")

frames = pipe(
    prompt="character walking, side view",
    num_frames=16,
    num_inference_steps=25,
    guidance_scale=7.5,
).frames[0]
```

### Cross-frame attention sharing
```python
def cross_frame_attention(self, x, prev_kv=None):
    q = self.to_q(x)
    k, v = self.to_k(x), self.to_v(x)
    if prev_kv is not None:
        # 매 prev frame 의 key/value 를 concat
        k = torch.cat([prev_kv["k"], k], dim=1)
        v = torch.cat([prev_kv["v"], v], dim=1)
    out = scaled_dot_product_attention(q, k, v)
    return self.to_out(out), {"k": k, "v": v}
```

### Turnaround sheet (multi-pose)
```python
poses = ["front view", "3/4 view", "side view", "back view"]
turnaround = []
for pose in poses:
    img = pipe(
        prompt=f"character portrait, {pose}, neutral expression",
        image_embeds=face_emb,
        image=pose_skeleton[pose],
        controlnet_conditioning_scale=0.9,
        generator=torch.Generator("cuda").manual_seed(42),  # 매 fixed seed
    ).images[0]
    turnaround.append(img)
```

### Emotion variation with locked identity
```python
emotions = ["smiling", "angry", "surprised", "sad", "neutral"]
for emo in emotions:
    img = pipe(
        prompt=f"portrait, {emo} expression",
        image_embeds=face_emb,
        ip_adapter_scale=0.85,           # 매 identity strong
        guidance_scale=4.5,
        generator=torch.Generator("cuda").manual_seed(7),
    ).images[0]
    img.save(f"emo_{emo}.png")
```

## 매 결정 기준
| 목표 | 조합 |
|---|---|
| Highest face fidelity | PuLID + InstantID + IP-Adapter Face |
| Style transfer with face | IP-Adapter Face (0.8) + IP-Adapter Style (0.4) |
| Animation, single character | AnimateDiff + reference attention + IP-Adapter |
| Game character sheet | InstantID + ControlNet pose × 4 with shared seed |
| Real-time avatar | SDXL Lightning / FLUX Schnell + cached identity emb |

**기본값**: InstantID + IP-Adapter (style 0.4, face 0.7) + 매 fixed seed for batch.

## 🔗 Graph
- 부모: [[이미지 생성 및 제어 파이프라인]] · [[AI 이미지 생성 (AI Image Generation)]]
- 변형: [[ComfyUI]] · [[InstantID]]
- 응용: [[AI 모델 사후 편집 도구 (Post-editing Tools)]]
- Adjacent: [[IP-Adapter]] · [[ControlNet]]

## 🤖 LLM 활용
**언제**: prompt 의 emotion / pose 변형 generation, character sheet plan 작성, style description 추출.
**언제 X**: face embedding 의 inner space — geometric, LLM 의 X.

## ❌ 안티패턴
- **No fixed seed in batch**: 매 turnaround 마다 face drift.
- **IP-Adapter scale > 1.0**: 매 prompt 무시, reference 의 over-copy.
- **Identity + Style conflict**: 매 같은 weight → identity blur.
- **Missing pose normalization**: pose skeleton 의 scale 이 prompt 와 불일치.
- **AnimateDiff w/o reference**: 매 frame consistency 없는 flicker.

## 🧪 검증 / 중복
- Verified (InstantX InstantID paper 2024, PuLID v1.1 release notes 2025, AnimateDiff v3).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — portrait/animation identity+style control |
