refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-이미지-생성-및-제어-파이프라인
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title: 이미지 생성 및 제어 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: [Image Generation Pipeline, Controlled Diffusion Pipeline, ControlNet Pipeline]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.92
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verification_status: applied
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tags: [diffusion, image-gen, controlnet, flux, comfyui]
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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/diffusers/ComfyUI
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---
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# 이미지 생성 및 제어 파이프라인
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## 매 한 줄
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> **"매 control 은 conditioning 의 stack"**. 2026 image gen pipeline 은 base model (FLUX.1 / SDXL / SD3.5) → control adapter (ControlNet / IP-Adapter / T2I-Adapter) → LoRA → refiner 의 layered conditioning. ComfyUI 는 매 node graph 로 이를 explicit, diffusers 는 매 pipeline class 로 abstraction.
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## 매 핵심
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### 매 pipeline 단계
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- **Prompt encoding**: T5 + CLIP encoder, dual conditioning
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- **Latent init**: noise 또는 img2img latent
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- **Conditioning injection**: ControlNet (structure), IP-Adapter (style ref), LoRA (concept)
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- **Sampling**: Euler / DPM-Solver++ / Flow matching, 20-50 steps
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- **Decoding**: VAE → pixel space, optional refiner
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### 매 control modality
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- **Structure**: canny, depth, pose, segmentation — 매 spatial constraint
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- **Identity**: IP-Adapter Face, InstantID, PuLID — 매 face preservation
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- **Style**: IP-Adapter Style, style-LoRA — 매 reference style
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- **Concept**: textual inversion, custom LoRA — 매 specific subject
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### 매 응용
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1. Product photography 의 매 batch generation (sku × pose × bg).
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2. Game asset pipeline — 매 concept → portrait → animation pose 일관성.
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3. UI/UX prototyping — 매 wireframe-to-mockup conversion.
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## 💻 패턴
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### diffusers FLUX + ControlNet
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```python
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import torch
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from diffusers import FluxControlNetPipeline, FluxControlNetModel
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controlnet = FluxControlNetModel.from_pretrained(
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"InstantX/FLUX.1-dev-Controlnet-Canny",
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torch_dtype=torch.bfloat16,
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)
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pipe = FluxControlNetPipeline.from_pretrained(
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"black-forest-labs/FLUX.1-dev",
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controlnet=controlnet,
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torch_dtype=torch.bfloat16,
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).to("cuda")
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image = pipe(
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prompt="cyberpunk samurai, neon rain",
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control_image=canny_image,
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controlnet_conditioning_scale=0.7,
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num_inference_steps=28,
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guidance_scale=3.5,
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).images[0]
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```
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### Multi-ControlNet stacking
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```python
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from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
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cn_pose = ControlNetModel.from_pretrained("xinsir/controlnet-openpose-sdxl-1.0")
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cn_depth = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0")
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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_pose, cn_depth],
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torch_dtype=torch.float16,
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).to("cuda")
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result = pipe(
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prompt="warrior pose, mountain backdrop",
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image=[pose_img, depth_img],
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controlnet_conditioning_scale=[0.8, 0.5],
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num_inference_steps=30,
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).images[0]
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```
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### IP-Adapter style transfer
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```python
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pipe.load_ip_adapter(
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"h94/IP-Adapter",
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subfolder="sdxl_models",
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weight_name="ip-adapter-plus_sdxl_vit-h.safetensors",
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)
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pipe.set_ip_adapter_scale(0.6)
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out = pipe(
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prompt="portrait of a knight",
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ip_adapter_image=style_reference,
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num_inference_steps=30,
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).images[0]
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```
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### LoRA composition
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```python
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pipe.load_lora_weights("lora_pack/", weight_name="anime_style.safetensors", adapter_name="anime")
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pipe.load_lora_weights("lora_pack/", weight_name="my_character.safetensors", adapter_name="char")
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pipe.set_adapters(["anime", "char"], adapter_weights=[0.7, 0.9])
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img = pipe(prompt="my character in anime style, school uniform").images[0]
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```
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### Img2img refinement
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```python
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from diffusers import AutoPipelineForImage2Image
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refiner = AutoPipelineForImage2Image.from_pretrained(
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"stabilityai/stable-diffusion-xl-refiner-1.0",
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torch_dtype=torch.float16,
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).to("cuda")
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refined = refiner(
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prompt=prompt,
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image=base_image,
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strength=0.3,
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num_inference_steps=20,
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).images[0]
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```
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### ComfyUI API workflow
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```python
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import json, urllib.request
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workflow = json.load(open("workflows/portrait_pipeline.json"))
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workflow["6"]["inputs"]["text"] = "cyberpunk samurai"
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workflow["12"]["inputs"]["seed"] = 12345
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req = urllib.request.Request(
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"http://127.0.0.1:8188/prompt",
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data=json.dumps({"prompt": workflow}).encode(),
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headers={"Content-Type": "application/json"},
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)
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resp = urllib.request.urlopen(req).read()
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print(resp)
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```
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### Batch pipeline with caching
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```python
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from functools import lru_cache
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@lru_cache(maxsize=8)
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def encode_prompt(prompt: str):
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return pipe.encode_prompt(prompt, device="cuda")
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def generate_batch(prompts: list[str], control_imgs: list, seeds: list[int]):
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results = []
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for p, c, s in zip(prompts, control_imgs, seeds):
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embeds = encode_prompt(p)
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gen = torch.Generator("cuda").manual_seed(s)
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img = pipe(
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prompt_embeds=embeds[0],
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pooled_prompt_embeds=embeds[1],
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control_image=c,
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generator=gen,
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).images[0]
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results.append(img)
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return results
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```
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## 매 결정 기준
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| 상황 | Pipeline |
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|---|---|
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| Highest fidelity, slow | FLUX.1-dev + ControlNet + refiner |
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| Real-time / interactive | SDXL Turbo / FLUX Schnell, 4-8 steps |
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| Face consistency | InstantID / PuLID + IP-Adapter Face |
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| Style consistency batch | Style-LoRA + fixed seed offset |
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| Local-only (Apple Silicon) | MLX + SDXL or DrawThings, FLUX.1 quantized |
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**기본값**: FLUX.1-dev + 1 ControlNet (canny/depth) + IP-Adapter, 28 steps, guidance 3.5.
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## 🔗 Graph
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- 부모: [[AI 이미지 생성 (AI Image Generation)]] · [[Diffusion_Models]]
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- 변형: [[초상화 및 애니메이션 스타일 제어]] · [[ComfyUI]]
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- 응용: [[AI 이미지 생성 및 편집 워크플로우 (AI Image Generation & Editing Workflow)]] · [[AI 이미지 품질 최적화 및 디버깅 (Image Quality Optimization & Debugging)]]
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- Adjacent: [[ControlNet]] · [[LoRA]] · [[FLUX]]
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## 🤖 LLM 활용
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**언제**: prompt rewriting, control image 의 caption 추출, workflow JSON 생성, error diagnosis.
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**언제 X**: VAE/UNet 의 inner forward — 매 결정론적, LLM 의 X.
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## ❌ 안티패턴
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- **Conditioning over-stack**: 매 5+ control 동시 — 매 conflict, blurry output.
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- **CFG too high (>7 on FLUX)**: oversaturated, plastic.
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- **LoRA stacking without weight tuning**: 매 incompatible concept blend.
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- **Missing seed control**: 매 batch 마다 random — 재현성 손실.
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- **VAE mismatch**: 매 model VAE 와 다른 VAE 사용 → color shift.
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## 🧪 검증 / 중복
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- Verified (diffusers 0.30+, ComfyUI 2026-04, FLUX.1 model card).
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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 — image gen pipeline + control modalities |
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