---
id: wiki-2026-0508-버전-및-모델-versions-and-models
title: 버전 및 모델 (Versions and Models)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Image Gen Model Versions, Midjourney Versions, FLUX Versions, SDXL Lineage]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [ai-image-generation, model-versioning, midjourney, flux, sdxl, sora]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: diffusers
---

# 버전 및 모델 (Versions and Models)

## 매 한 줄
> **"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.

## 매 핵심

### 매 2026 주요 모델 lineage
- **Midjourney v7** (2026 Q1): 매 photorealistic + niji 7 (anime). `--v 7`, `--niji 7`.
- **FLUX 1.2 (Black Forest Labs)**: open-weight, 매 prompt adherence 최강. flux-dev, flux-schnell, flux-pro.
- **Stable Diffusion 4 / SD3.5-Large**: 매 ComfyUI ecosystem 의 backbone. Native 16-channel VAE.
- **DALL-E 4 (OpenAI)**: GPT-5 native multimodal, 매 conversational refinement.
- **Imagen 4 (Google)**: text rendering 최강, 매 typography 작업 의 first choice.
- **Recraft v4**: vector + raster hybrid, 매 brand asset.
- **Sora 2 / Veo 3 / Kling 2**: video generation, 매 image 의 evolution.

### 매 Versioning 의 의미
- **Aesthetic shift**: v6 → v7 의 매 default style 이 painterly → photoreal 로 shift.
- **Capability gain**: text rendering, hand anatomy, multi-subject coherence, 매 version 마다 incremental.
- **Param/flag breakage**: 매 version 마다 supported flags 변경 (`--style raw`, `--profile`).

### 매 응용
1. **Style locking**: 매 production project 의 single version pin (reproducibility).
2. **A/B comparison**: 매 same prompt 의 multi-version sweep, 매 best select.
3. **Hybrid pipeline**: SD3.5 base → FLUX inpaint → Recraft vector trace.

## 💻 패턴

### FLUX 1.2 via diffusers
```python
from diffusers import FluxPipeline
import torch

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1.2-dev",
    torch_dtype=torch.bfloat16,
).to("cuda")

image = pipe(
    prompt="a samurai in moonlit bamboo forest, cinematic, 35mm film grain",
    guidance_scale=3.5,
    num_inference_steps=28,
    max_sequence_length=512,
    generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("out.png")
```

### Midjourney v7 prompt with flags
```text
ethereal forest spirit, glowing mushrooms, volumetric mist
--v 7 --style raw --ar 21:9 --s 250 --p personal_v3 --c 25
```

### Version sweep harness
```python
versions = ["flux-1.2-dev", "sd-3.5-large", "flux-pro"]
prompts = [...]
results = {}
for v in versions:
    pipe = load_pipeline(v)
    for p in prompts:
        img = pipe(p, generator=torch.Generator("cuda").manual_seed(42)).images[0]
        results[(v, p)] = img
        save(f"{v}/{slug(p)}.png", img)
make_contact_sheet(results)
```

### Model registry (production)
```python
@dataclass
class ModelVersion:
    name: str
    revision: str  # commit hash on HF
    vae: str
    text_encoders: list[str]
    pinned_at: datetime
    aesthetic_tags: list[str]

REGISTRY = {
    "hero-banner-v3": ModelVersion(
        name="black-forest-labs/FLUX.1.2-dev",
        revision="a1b2c3d",
        vae="flux-vae-16ch",
        text_encoders=["t5-xxl", "clip-l"],
        pinned_at=datetime(2026, 4, 1),
        aesthetic_tags=["photoreal", "high-detail"],
    ),
}
```

### LoRA stack on top of base version
```python
pipe.load_lora_weights("brand/logo-lora-flux12", adapter_name="brand")
pipe.load_lora_weights("style/cinematic-flux12", adapter_name="cine")
pipe.set_adapters(["brand", "cine"], adapter_weights=[0.8, 0.6])
```

### ComfyUI workflow JSON snippet (version pin)
```json
{
  "checkpoint": { "ckpt_name": "flux1.2-dev-fp8.safetensors" },
  "loras": [
    { "name": "filmgrain_v2.safetensors", "strength": 0.4 }
  ],
  "sampler": { "name": "euler", "scheduler": "simple", "steps": 28, "cfg": 3.5 }
}
```

## 매 결정 기준
| 목표 | 권장 모델 (2026) |
|---|---|
| Photorealism | FLUX 1.2 pro / MJ v7 raw |
| Anime / illustration | niji 7 / SDXL anime LoRAs |
| Text rendering (poster, UI mockup) | Imagen 4 / Recraft v4 |
| Iterative refinement chat-style | DALL-E 4 (GPT-5) |
| Brand-controllable LoRA | FLUX dev (open weights) |
| Vector / icon | Recraft v4 |
| Video | Sora 2 / Veo 3 / Kling 2 |
| Low-cost batch (concept) | flux-schnell / SD3.5 turbo |

**기본값**: FLUX 1.2-dev — open weights, LoRA ecosystem, prompt adherence 최강. 매 production 에서 pin specific revision.

## 🔗 Graph
- 부모: [[AI 이미지 생성 (AI Image Generation)]] · [[AI Image Generation Workflow (canonical)]]
- 변형: [[Midjourney-v7]]
- 응용: [[Style_Reference_(--sref)]]

## 🤖 LLM 활용
**언제**: model release notes 의 summary, version migration checklist, prompt syntax 의 version-specific 차이 체크.
**언제 X**: 매 actual aesthetic judgment — 매 visual A/B 가 ground truth. LLM 의 aesthetic claim 의 hallucination 빈번.

## ❌ 안티패턴
- **No version pin**: 매 production 의 reproducibility 죽음. 매 model card revision hash 필수.
- **Latest = best 가정**: 매 v7 이 v6 보다 specific style 에서 worse 의 사례 흔함.
- **Mixing flags from different versions**: 매 silent ignore, 매 debug 어려움.
- **Single-model lock-in**: 매 hybrid pipeline (one base, one inpaint, one upscale) 가 보통 best.

## 🧪 검증 / 중복
- Verified (Black Forest Labs FLUX 1.2 release 2026, Midjourney v7 docs, OpenAI DALL-E 4 announcement, Google Imagen 4 paper).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — 2026 image-gen model lineage + version pinning patterns |
