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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-20 23:52:15 +09:00

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title

스테이블 디퓨전 기반 정밀 합성 + 해부학적 오류 수정

매 한 줄

"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.

매 핵심

매 problem space

Hand 의 6-finger / fused digits: SD/SDXL 의 chronic. FLUX.1 dev 에서 의 상당 개선, but 의 not perfect.
Face degradation at low pixel: 매 small face crop 에서 의 detail loss → ADetailer 가 face crop → high-res inpaint.
Text 의 illegible: SDXL 의 weak. FLUX 의 strong (그러나 아직 결함 있음).
Eye 의 asymmetry: gaze direction, pupil size 의 mismatch.

매 도구 (open source)

ComfyUI: 매 node-based workflow. 매 reproducible JSON.
A1111 / Forge / reForge: 매 web UI. 매 ADetailer extension 의 default.
ControlNet: pose/depth/canny/openpose/MediaPipeFace 의 conditioning.
ADetailer: face/hand 의 auto-detect → inpaint.
MeshGraphormer / DWPose: 매 hand pose 의 estimation → ControlNet hand_refiner.
IP-Adapter FaceID Plus v2: 매 face consistency.

매 pipeline 구성

Base generation (SDXL/FLUX) — high-level composition.
ADetailer face pass — face crop → upscale → inpaint with same prompt.
ADetailer hand pass — DWPose 검출 → ControlNet hand_refiner → inpaint.
Manual touch-up — 잔여 결함 의 mask + inpaint.
Upscale — Real-ESRGAN / SUPIR / FLUX-Upscale.

매 응용

Character art (anime, realistic portrait).
Fashion editorial (pose-precise).
Comic / manga panel.
Game asset (consistent character sheets).

💻 패턴

Pattern 1 — ADetailer pipeline (diffusers)

from diffusers import StableDiffusionXLPipeline, StableDiffusionXLInpaintPipeline
from ultralytics import YOLO
import torch

base = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
inpaint = StableDiffusionXLInpaintPipeline.from_pretrained(
    "diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16
).to("cuda")
face_det = YOLO("face_yolov8n.pt")

def adetailer_face(img, prompt):
    boxes = face_det(img)[0].boxes.xyxy.cpu().numpy()
    for x1,y1,x2,y2 in boxes:
        crop = img.crop((x1,y1,x2,y2)).resize((1024,1024))
        mask = make_face_mask(crop)
        fixed = inpaint(prompt=prompt, image=crop, mask_image=mask,
                        num_inference_steps=25, strength=0.5).images[0]
        img.paste(fixed.resize((int(x2-x1),int(y2-y1))), (int(x1),int(y1)))
    return img

Pattern 2 — Hand fix with DWPose + ControlNet

from controlnet_aux import DWposeDetector
from diffusers import StableDiffusionXLControlNetInpaintPipeline, ControlNetModel

dwpose = DWposeDetector.from_pretrained("yzd-v/DWPose")
hand_cn = ControlNetModel.from_pretrained("hr16/ControlNet-HandRefiner-pruned",
                                          torch_dtype=torch.float16)

def fix_hands(img, prompt):
    pose = dwpose(img, hand_only=True)            # depth-style hand map
    mask = hand_mask_from_dwpose(pose)
    pipe = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0",
        controlnet=hand_cn, torch_dtype=torch.float16
    ).to("cuda")
    return pipe(prompt=prompt, image=img, mask_image=mask,
                control_image=pose, num_inference_steps=30,
                controlnet_conditioning_scale=0.9).images[0]

Pattern 3 — ControlNet OpenPose lock

from controlnet_aux import OpenposeDetector
from diffusers import StableDiffusionXLControlNetPipeline

op = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
pose_map = op(reference_image, hand_and_face=True)

cn = ControlNetModel.from_pretrained("xinsir/controlnet-openpose-sdxl-1.0",
                                     torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=cn, torch_dtype=torch.float16
).to("cuda")
out = pipe(prompt="cinematic shot of a woman dancing", image=pose_map,
           num_inference_steps=30, guidance_scale=6.5).images[0]

Pattern 4 — Multi-ControlNet (depth + canny + pose)

from diffusers import MultiControlNetModel
controlnets = MultiControlNetModel([
    ControlNetModel.from_pretrained("xinsir/controlnet-depth-sdxl-1.0",  torch_dtype=torch.float16),
    ControlNetModel.from_pretrained("xinsir/controlnet-canny-sdxl-1.0",  torch_dtype=torch.float16),
    ControlNetModel.from_pretrained("xinsir/controlnet-openpose-sdxl-1.0", torch_dtype=torch.float16),
])
result = pipe(prompt=p,
              image=[depth_map, canny_map, pose_map],
              controlnet_conditioning_scale=[0.6, 0.4, 0.9]).images[0]

Pattern 5 — Differential diffusion (soft mask)

# Soft strength per-pixel mask: 0.0 keep, 1.0 fully regen
import numpy as np
from PIL import Image

def soft_mask(face_box, img_size, edge_blur=24):
    m = np.zeros(img_size, dtype=np.float32)
    x1,y1,x2,y2 = face_box
    m[y1:y2, x1:x2] = 1.0
    from scipy.ndimage import gaussian_filter
    return Image.fromarray((gaussian_filter(m, edge_blur)*255).astype(np.uint8))

# Pass into pipe.diff_diffusion_strength=

Pattern 6 — Face restore (CodeFormer / GFPGAN)

from codeformer import CodeFormer
cf = CodeFormer(weight_path="codeformer.pth", device="cuda")
restored = cf.enhance(img_np, w=0.7)  # 0=keep id, 1=full repair

Pattern 7 — ComfyUI workflow JSON snippet

{
  "1": {"class_type": "KSampler",
        "inputs": {"steps": 30, "cfg": 6.5, "sampler_name": "dpmpp_2m_sde",
                   "scheduler": "karras", "model": ["4",0], "positive":["6",0],
                   "negative":["7",0], "latent_image":["5",0]}},
  "20":{"class_type":"FaceDetailer",
        "inputs":{"image":["1",0],"model":["4",0],
                  "bbox_detector":"face_yolov8m.pt",
                  "wildcard":"perfect symmetric eyes, sharp focus",
                  "guide_size":768,"max_size":1024,"steps":20}}
}

매 결정 기준

상황	Approach
Anime portrait, hand 결함	A1111 + ADetailer (face_yolov8n + hand_yolov8n)
Realistic full body	SDXL + Multi-ControlNet (pose+depth)
Reproducible production	ComfyUI workflow JSON + Git
Maximum quality, slow	FLUX.1 dev + Differential Diffusion
Real-time iteration	SDXL Lightning / Turbo

기본값: 매 SDXL base + ADetailer face/hand pass + 1 manual inpaint round.

🔗 Graph

부모: AI 이미지 생성 (AI Image Generation) · Stable Diffusion
변형: ControlNet · ADetailer · IP-Adapter
응용: 사후 편집 (Post-editing) · Brand Consistency Maintenance
Adjacent: FLUX · ComfyUI

🤖 LLM 활용

언제: 매 ComfyUI workflow JSON 의 authoring/debugging, 매 prompt + negative-prompt 의 systematic generation. 언제 X: 매 fine pixel-level inpaint 결정 — 매 visual judgment 가 필요.

❌ 안티패턴

Single-pass at full res: 매 face/hand 의 detail starvation. 매 second-pass crop+upscale 의 mandatory.
Wrong ControlNet 조합: 매 pose + canny 동시에 high weight → 매 over-constrain → composition 의 collapse.
Hand_refiner 없는 SDXL hand: 매 5% 미만 의 success rate.
ADetailer denoise 1.0: 매 identity 의 destroyed. 매 0.4–0.6 의 sweet spot.

🧪 검증 / 중복

Verified (Mikubill/sd-webui-controlnet, Bing-su/adetailer; HuggingFace diffusers docs).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — ADetailer + ControlNet hand_refiner pipeline

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