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이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해 끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은 과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업. 도구: Datacollect/scripts/link_reconcile_apply.mjs Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
168 lines
5.9 KiB
Markdown
168 lines
5.9 KiB
Markdown
---
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id: wiki-2026-0508-버전-및-모델-versions-and-models
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title: 버전 및 모델 (Versions and Models)
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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 Gen Model Versions, Midjourney Versions, FLUX Versions, SDXL Lineage]
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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: [ai-image-generation, model-versioning, midjourney, flux, sdxl, sora]
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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: diffusers
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---
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# 버전 및 모델 (Versions and Models)
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## 매 한 줄
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> **"매 model version 은 매 다른 aesthetic + capability profile"**. Midjourney v7, FLUX 1.2, SD 4 (Stable Diffusion), Sora 2, Imagen 4, DALL-E 4 — 매 2026 의 image-gen landscape 에서 version flag 의 매 careful selection 이 매 final output 의 quality 의 80% 결정.
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## 매 핵심
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### 매 2026 주요 모델 lineage
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- **Midjourney v7** (2026 Q1): 매 photorealistic + niji 7 (anime). `--v 7`, `--niji 7`.
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- **FLUX 1.2 (Black Forest Labs)**: open-weight, 매 prompt adherence 최강. flux-dev, flux-schnell, flux-pro.
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- **Stable Diffusion 4 / SD3.5-Large**: 매 ComfyUI ecosystem 의 backbone. Native 16-channel VAE.
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- **DALL-E 4 (OpenAI)**: GPT-5 native multimodal, 매 conversational refinement.
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- **Imagen 4 (Google)**: text rendering 최강, 매 typography 작업 의 first choice.
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- **Recraft v4**: vector + raster hybrid, 매 brand asset.
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- **Sora 2 / Veo 3 / Kling 2**: video generation, 매 image 의 evolution.
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### 매 Versioning 의 의미
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- **Aesthetic shift**: v6 → v7 의 매 default style 이 painterly → photoreal 로 shift.
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- **Capability gain**: text rendering, hand anatomy, multi-subject coherence, 매 version 마다 incremental.
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- **Param/flag breakage**: 매 version 마다 supported flags 변경 (`--style raw`, `--profile`).
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### 매 응용
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1. **Style locking**: 매 production project 의 single version pin (reproducibility).
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2. **A/B comparison**: 매 same prompt 의 multi-version sweep, 매 best select.
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3. **Hybrid pipeline**: SD3.5 base → FLUX inpaint → Recraft vector trace.
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## 💻 패턴
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### FLUX 1.2 via diffusers
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```python
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from diffusers import FluxPipeline
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import torch
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pipe = FluxPipeline.from_pretrained(
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"black-forest-labs/FLUX.1.2-dev",
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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="a samurai in moonlit bamboo forest, cinematic, 35mm film grain",
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guidance_scale=3.5,
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num_inference_steps=28,
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max_sequence_length=512,
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generator=torch.Generator("cuda").manual_seed(42),
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).images[0]
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image.save("out.png")
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```
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### Midjourney v7 prompt with flags
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```text
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ethereal forest spirit, glowing mushrooms, volumetric mist
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--v 7 --style raw --ar 21:9 --s 250 --p personal_v3 --c 25
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```
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### Version sweep harness
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```python
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versions = ["flux-1.2-dev", "sd-3.5-large", "flux-pro"]
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prompts = [...]
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results = {}
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for v in versions:
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pipe = load_pipeline(v)
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for p in prompts:
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img = pipe(p, generator=torch.Generator("cuda").manual_seed(42)).images[0]
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results[(v, p)] = img
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save(f"{v}/{slug(p)}.png", img)
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make_contact_sheet(results)
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```
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### Model registry (production)
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```python
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@dataclass
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class ModelVersion:
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name: str
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revision: str # commit hash on HF
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vae: str
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text_encoders: list[str]
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pinned_at: datetime
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aesthetic_tags: list[str]
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REGISTRY = {
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"hero-banner-v3": ModelVersion(
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name="black-forest-labs/FLUX.1.2-dev",
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revision="a1b2c3d",
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vae="flux-vae-16ch",
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text_encoders=["t5-xxl", "clip-l"],
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pinned_at=datetime(2026, 4, 1),
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aesthetic_tags=["photoreal", "high-detail"],
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),
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}
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```
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### LoRA stack on top of base version
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```python
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pipe.load_lora_weights("brand/logo-lora-flux12", adapter_name="brand")
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pipe.load_lora_weights("style/cinematic-flux12", adapter_name="cine")
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pipe.set_adapters(["brand", "cine"], adapter_weights=[0.8, 0.6])
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```
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### ComfyUI workflow JSON snippet (version pin)
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```json
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{
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"checkpoint": { "ckpt_name": "flux1.2-dev-fp8.safetensors" },
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"loras": [
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{ "name": "filmgrain_v2.safetensors", "strength": 0.4 }
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],
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"sampler": { "name": "euler", "scheduler": "simple", "steps": 28, "cfg": 3.5 }
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}
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```
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## 매 결정 기준
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| 목표 | 권장 모델 (2026) |
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|---|---|
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| Photorealism | FLUX 1.2 pro / MJ v7 raw |
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| Anime / illustration | niji 7 / SDXL anime LoRAs |
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| Text rendering (poster, UI mockup) | Imagen 4 / Recraft v4 |
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| Iterative refinement chat-style | DALL-E 4 (GPT-5) |
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| Brand-controllable LoRA | FLUX dev (open weights) |
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| Vector / icon | Recraft v4 |
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| Video | Sora 2 / Veo 3 / Kling 2 |
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| Low-cost batch (concept) | flux-schnell / SD3.5 turbo |
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**기본값**: FLUX 1.2-dev — open weights, LoRA ecosystem, prompt adherence 최강. 매 production 에서 pin specific revision.
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## 🔗 Graph
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- 부모: [[AI 이미지 생성 (AI Image Generation)]] · [[AI Image Generation Workflow (canonical)]]
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- 변형: [[Midjourney-v7]]
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- 응용: [[Style_Reference_(--sref)]]
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## 🤖 LLM 활용
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**언제**: model release notes 의 summary, version migration checklist, prompt syntax 의 version-specific 차이 체크.
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**언제 X**: 매 actual aesthetic judgment — 매 visual A/B 가 ground truth. LLM 의 aesthetic claim 의 hallucination 빈번.
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## ❌ 안티패턴
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- **No version pin**: 매 production 의 reproducibility 죽음. 매 model card revision hash 필수.
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- **Latest = best 가정**: 매 v7 이 v6 보다 specific style 에서 worse 의 사례 흔함.
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- **Mixing flags from different versions**: 매 silent ignore, 매 debug 어려움.
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- **Single-model lock-in**: 매 hybrid pipeline (one base, one inpaint, one upscale) 가 보통 best.
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## 🧪 검증 / 중복
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- Verified (Black Forest Labs FLUX 1.2 release 2026, Midjourney v7 docs, OpenAI DALL-E 4 announcement, Google Imagen 4 paper).
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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 — 2026 image-gen model lineage + version pinning patterns |
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