Antigravity Agent
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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>
208 lines
8.6 KiB
Markdown
208 lines
8.6 KiB
Markdown
---
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id: wiki-2026-0508-스테이블-디퓨전을-이용한-오픈소스-기반-정밀-이미지-합성-
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title: 스테이블 디퓨전 기반 정밀 이미지 합성 및 해부학적 오류 수정 파이프라인
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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: [SD Anatomy Fix Pipeline, ControlNet Anatomy, AfterDetailer, ADetailer]
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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: [stable-diffusion, controlnet, adetailer, comfyui, anatomy, inpaint]
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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/diffusers/SDXL/FLUX
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---
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# 스테이블 디퓨전 기반 정밀 합성 + 해부학적 오류 수정
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## 매 한 줄
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> **"매 anatomy fix 의 본질 은 detection + inpaint 의 loop"**. 매 SDXL/FLUX base 의 raw output 에서 hand/face/text 의 결함 은 inevitable — 매 ADetailer 의 auto-detect, 매 ControlNet 의 pose/depth lock, 매 second-pass inpaint 가 매 결합되어 production-quality 의 reliable pipeline 의 형성. 매 2026 의 open-source workflow 는 ComfyUI 의 graph + node API 가 standard.
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## 매 핵심
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### 매 problem space
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- **Hand 의 6-finger / fused digits**: SD/SDXL 의 chronic. FLUX.1 dev 에서 의 상당 개선, but 의 not perfect.
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- **Face degradation at low pixel**: 매 small face crop 에서 의 detail loss → ADetailer 가 face crop → high-res inpaint.
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- **Text 의 illegible**: SDXL 의 weak. FLUX 의 strong (그러나 아직 결함 있음).
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- **Eye 의 asymmetry**: gaze direction, pupil size 의 mismatch.
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### 매 도구 (open source)
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- **ComfyUI**: 매 node-based workflow. 매 reproducible JSON.
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- **A1111 / Forge / reForge**: 매 web UI. 매 ADetailer extension 의 default.
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- **ControlNet**: pose/depth/canny/openpose/MediaPipeFace 의 conditioning.
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- **ADetailer**: face/hand 의 auto-detect → inpaint.
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- **MeshGraphormer / DWPose**: 매 hand pose 의 estimation → ControlNet hand_refiner.
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- **IP-Adapter FaceID Plus v2**: 매 face consistency.
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### 매 pipeline 구성
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1. **Base generation** (SDXL/FLUX) — high-level composition.
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2. **ADetailer face pass** — face crop → upscale → inpaint with same prompt.
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3. **ADetailer hand pass** — DWPose 검출 → ControlNet hand_refiner → inpaint.
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4. **Manual touch-up** — 잔여 결함 의 mask + inpaint.
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5. **Upscale** — Real-ESRGAN / SUPIR / FLUX-Upscale.
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### 매 응용
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1. Character art (anime, realistic portrait).
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2. Fashion editorial (pose-precise).
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3. Comic / manga panel.
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4. Game asset (consistent character sheets).
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## 💻 패턴
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### Pattern 1 — ADetailer pipeline (diffusers)
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```python
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from diffusers import StableDiffusionXLPipeline, StableDiffusionXLInpaintPipeline
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from ultralytics import YOLO
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import torch
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base = StableDiffusionXLPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
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).to("cuda")
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inpaint = StableDiffusionXLInpaintPipeline.from_pretrained(
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"diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16
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).to("cuda")
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face_det = YOLO("face_yolov8n.pt")
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def adetailer_face(img, prompt):
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boxes = face_det(img)[0].boxes.xyxy.cpu().numpy()
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for x1,y1,x2,y2 in boxes:
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crop = img.crop((x1,y1,x2,y2)).resize((1024,1024))
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mask = make_face_mask(crop)
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fixed = inpaint(prompt=prompt, image=crop, mask_image=mask,
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num_inference_steps=25, strength=0.5).images[0]
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img.paste(fixed.resize((int(x2-x1),int(y2-y1))), (int(x1),int(y1)))
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return img
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```
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### Pattern 2 — Hand fix with DWPose + ControlNet
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```python
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from controlnet_aux import DWposeDetector
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from diffusers import StableDiffusionXLControlNetInpaintPipeline, ControlNetModel
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dwpose = DWposeDetector.from_pretrained("yzd-v/DWPose")
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hand_cn = ControlNetModel.from_pretrained("hr16/ControlNet-HandRefiner-pruned",
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torch_dtype=torch.float16)
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def fix_hands(img, prompt):
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pose = dwpose(img, hand_only=True) # depth-style hand map
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mask = hand_mask_from_dwpose(pose)
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pipe = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0",
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controlnet=hand_cn, torch_dtype=torch.float16
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).to("cuda")
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return pipe(prompt=prompt, image=img, mask_image=mask,
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control_image=pose, num_inference_steps=30,
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controlnet_conditioning_scale=0.9).images[0]
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```
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### Pattern 3 — ControlNet OpenPose lock
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```python
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from controlnet_aux import OpenposeDetector
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from diffusers import StableDiffusionXLControlNetPipeline
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op = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
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pose_map = op(reference_image, hand_and_face=True)
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cn = ControlNetModel.from_pretrained("xinsir/controlnet-openpose-sdxl-1.0",
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torch_dtype=torch.float16)
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pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0",
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controlnet=cn, torch_dtype=torch.float16
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).to("cuda")
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out = pipe(prompt="cinematic shot of a woman dancing", image=pose_map,
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num_inference_steps=30, guidance_scale=6.5).images[0]
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```
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### Pattern 4 — Multi-ControlNet (depth + canny + pose)
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```python
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from diffusers import MultiControlNetModel
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controlnets = MultiControlNetModel([
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ControlNetModel.from_pretrained("xinsir/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16),
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ControlNetModel.from_pretrained("xinsir/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16),
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ControlNetModel.from_pretrained("xinsir/controlnet-openpose-sdxl-1.0", torch_dtype=torch.float16),
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])
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result = pipe(prompt=p,
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image=[depth_map, canny_map, pose_map],
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controlnet_conditioning_scale=[0.6, 0.4, 0.9]).images[0]
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```
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### Pattern 5 — Differential diffusion (soft mask)
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```python
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# Soft strength per-pixel mask: 0.0 keep, 1.0 fully regen
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import numpy as np
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from PIL import Image
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def soft_mask(face_box, img_size, edge_blur=24):
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m = np.zeros(img_size, dtype=np.float32)
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x1,y1,x2,y2 = face_box
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m[y1:y2, x1:x2] = 1.0
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from scipy.ndimage import gaussian_filter
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return Image.fromarray((gaussian_filter(m, edge_blur)*255).astype(np.uint8))
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# Pass into pipe.diff_diffusion_strength=
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```
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### Pattern 6 — Face restore (CodeFormer / GFPGAN)
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```python
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from codeformer import CodeFormer
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cf = CodeFormer(weight_path="codeformer.pth", device="cuda")
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restored = cf.enhance(img_np, w=0.7) # 0=keep id, 1=full repair
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```
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### Pattern 7 — ComfyUI workflow JSON snippet
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```json
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{
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"1": {"class_type": "KSampler",
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"inputs": {"steps": 30, "cfg": 6.5, "sampler_name": "dpmpp_2m_sde",
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"scheduler": "karras", "model": ["4",0], "positive":["6",0],
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"negative":["7",0], "latent_image":["5",0]}},
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"20":{"class_type":"FaceDetailer",
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"inputs":{"image":["1",0],"model":["4",0],
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"bbox_detector":"face_yolov8m.pt",
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"wildcard":"perfect symmetric eyes, sharp focus",
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"guide_size":768,"max_size":1024,"steps":20}}
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}
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Anime portrait, hand 결함 | A1111 + ADetailer (face_yolov8n + hand_yolov8n) |
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| Realistic full body | SDXL + Multi-ControlNet (pose+depth) |
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| Reproducible production | ComfyUI workflow JSON + Git |
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| Maximum quality, slow | FLUX.1 dev + Differential Diffusion |
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| Real-time iteration | SDXL Lightning / Turbo |
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**기본값**: 매 SDXL base + ADetailer face/hand pass + 1 manual inpaint round.
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## 🔗 Graph
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- 부모: [[AI 이미지 생성 (AI Image Generation)]] · [[Stable Diffusion]]
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- 변형: [[ControlNet]] · [[ADetailer]] · [[IP-Adapter]]
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- 응용: [[사후 편집 (Post-editing)]] · [[Brand Consistency Maintenance|Character Consistency]]
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- Adjacent: [[FLUX]] · [[ComfyUI]]
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## 🤖 LLM 활용
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**언제**: 매 ComfyUI workflow JSON 의 authoring/debugging, 매 prompt + negative-prompt 의 systematic generation.
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**언제 X**: 매 fine pixel-level inpaint 결정 — 매 visual judgment 가 필요.
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## ❌ 안티패턴
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- **Single-pass at full res**: 매 face/hand 의 detail starvation. 매 second-pass crop+upscale 의 mandatory.
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- **Wrong ControlNet 조합**: 매 pose + canny 동시에 high weight → 매 over-constrain → composition 의 collapse.
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- **Hand_refiner 없는 SDXL hand**: 매 5% 미만 의 success rate.
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- **ADetailer denoise 1.0**: 매 identity 의 destroyed. 매 0.4–0.6 의 sweet spot.
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## 🧪 검증 / 중복
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- Verified (Mikubill/sd-webui-controlnet, Bing-su/adetailer; HuggingFace diffusers docs).
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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 — ADetailer + ControlNet hand_refiner pipeline |
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