---
id: wiki-2026-0508-시리즈물-및-다중-샷-워크플로우-series-and-mul
title: 시리즈물 및 다중 샷 워크플로우 (Series and Multi-shot Workflow)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Multi-shot Workflow, Series Generation, Consistent Image Series]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [image-generation, midjourney, character-consistency, series, workflow]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: Midjourney/FLUX/ComfyUI
---

# 시리즈물 및 다중 샷 워크플로우

## 매 한 줄
> **"매 single-shot 의 generation 은 1세대, 매 multi-shot consistency 가 2세대 의 challenge"**. 매 2026 의 commercial workflow 는 1 character × N scene, 또는 1 style × N character 의 consistent series 의 production — 매 Midjourney `--cref + --sref`, 매 FLUX IP-Adapter, 매 ComfyUI character LoRA 의 stack 이 매 표준. 매 single best image 의 시대 는 끝남.

## 매 핵심

### 매 series workflow 의 dimension
1. **Character lock**: 매 same person 의 N pose/scene.
2. **Style lock**: 매 same painterly look 의 N subject.
3. **World lock**: 매 same environment lighting/mood.
4. **Outfit lock**: 매 same clothing 의 different pose.

### 매 도구 stack (2026)
- **Midjourney V8**: `--cref` (char) + `--sref` (style) + `--ow` (outfit weight).
- **FLUX.1 + IP-Adapter Plus + LoRA**: 매 reproducible, customizable.
- **ComfyUI + InstantID / PuLID**: 매 face-only ID lock.
- **Krea Realtime**: 매 fast iteration storyboard.
- **Runway Gen-4**: 매 video extension.

### 매 typical pipeline
1. **Character sheet 의 generation** (front/side/back, neutral lighting).
2. **Reference image 의 selection** (best of 4–8).
3. **Optional: LoRA 의 train** (Replicate, fal, RunPod).
4. **Multi-shot generation** (cref + sref, 또는 LoRA + sref).
5. **Post-edit** (face restore, hand fix).
6. **Color/style 의 batch grading**.

### 매 응용
1. Comic/manhwa panels.
2. Brand campaign (model 5–10 shot).
3. Game character sheet (idle/run/attack/death).
4. Children's book illustration series.
5. Storyboard for video.

## 💻 패턴

### Pattern 1 — Midjourney character + style lock
```text
# Step 1 — Character sheet
/imagine character sheet of a young female adventurer, leather jacket, \
  short red hair, freckles, neutral expression, T-pose, white background \
  --ar 1:1 --v 7

# Step 2 — Save best as cref URL
# Step 3 — Generate scenes
/imagine the same character climbing a snowy mountain \
  --cref https://cdn.example.com/char.png --cw 100 \
  --sref 2934852919 --sw 200 --ar 16:9 --v 7

/imagine the same character drinking tea in a tavern at night \
  --cref https://cdn.example.com/char.png --cw 100 \
  --sref 2934852919 --sw 200 --ar 16:9 --v 7
```

### Pattern 2 — Storyboard batch (Python)
```python
from itertools import product

CHAR = "https://cdn.example.com/char.png"
STYLE = 2934852919
SCENES = [
    "climbing a snowy mountain at dawn",
    "fighting a wolf in deep forest",
    "drinking tea in candle-lit tavern",
    "riding a horse across plains under storm",
]
ARS = ["16:9"]

for scene, ar in product(SCENES, ARS):
    print(f"/imagine the same character {scene} "
          f"--cref {CHAR} --cw 100 --sref {STYLE} --sw 200 --ar {ar} --v 7")
```

### Pattern 3 — FLUX IP-Adapter character lock
```python
from diffusers import FluxPipeline
from ip_adapter_flux import IPAdapterFluxPlus
import torch

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev",
                                    torch_dtype=torch.bfloat16).to("cuda")
adapter = IPAdapterFluxPlus(pipe, "ip-adapter-plus_flux.bin")

ref = load_image("char_sheet.png")
scenes = ["mountain summit at sunset", "neon-lit alley, rain", "tavern, candlelight"]

for i, scene in enumerate(scenes):
    out = adapter.generate(image=ref,
                           prompt=f"the same character, {scene}, cinematic, 35mm",
                           num_inference_steps=30, guidance_scale=4.0,
                           seed=42 + i).images[0]
    out.save(f"shot_{i:02d}.png")
```

### Pattern 4 — InstantID face lock (ComfyUI)
```python
# ComfyUI workflow (JSON snippet)
{
  "InstantIDLoader": {"face_image": "ref.png"},
  "ControlNet":      {"model": "instantid_controlnet"},
  "KSampler":        {"steps": 28, "cfg": 5.0, "seed": 1234},
  "CLIPTextEncode":  {"text": "the same person, standing on a beach, golden hour"}
}
# Repeat with different prompt → identity stays locked.
```

### Pattern 5 — LoRA training (Replicate)
```bash
# Train character LoRA
cog predict r8.im/ostris/flux-dev-lora-trainer \
  -i input_images=@char_dataset.zip \
  -i trigger_word=AYAKO \
  -i steps=2000 -i lora_rank=16
# → flux-lora-AYAKO.safetensors

# Use:
/imagine AYAKO walking in Tokyo neon street at night --v flux-lora-AYAKO --ar 16:9
```

### Pattern 6 — Outfit / wardrobe consistency
```text
# V8 outfit lock with --ow
/imagine the same character on a beach in same outfit \
  --cref char.png --cw 100 --ow 100 \
  --sref 2934852919 --sw 200 --ar 16:9 --v 8
```

### Pattern 7 — Series QA grid
```python
# Build a contact-sheet of 12 shots for visual QA
from PIL import Image
shots = [Image.open(f"shot_{i:02d}.png") for i in range(12)]
W, H = 1024, 576
sheet = Image.new("RGB", (W*4, H*3))
for i, im in enumerate(shots):
    sheet.paste(im.resize((W,H)), ((i%4)*W, (i//4)*H))
sheet.save("series_qa.png")
```

### Pattern 8 — Video extension (Runway / Kling)
```python
# After image series approved → animate
import requests
for i in range(12):
    r = requests.post("https://api.runwayml.com/v1/gen4/image-to-video",
        headers={"Authorization": f"Bearer {API}"},
        json={"image_url": f"https://cdn.example.com/shot_{i:02d}.png",
              "prompt": "subtle camera push-in, 5s, cinematic",
              "duration": 5, "model": "gen-4-turbo"})
    print(r.json()["video_url"])
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Quick 5-shot storyboard | Midjourney cref + sref |
| Photo-real, 20+ shots, character | FLUX + InstantID |
| 100+ shot, very specific person | LoRA train + FLUX |
| Same outfit, multiple poses | V8 cref + ow |
| Stylized illustration series | sref + cref + Niji V7 |

**기본값**: 매 5–20 shots → Midjourney cref+sref. 매 100+ → LoRA.

## 🔗 Graph
- 부모: [[AI 이미지 생성 (AI Image Generation)]] · [[Brand Consistency Maintenance|Character Consistency]]
- 변형: [[Brand Consistency Maintenance|Character_Reference]] · [[InstantID]] · [[IP-Adapter]]
- 응용: [[상업용 브랜드 이미지 및 디자인 시스템 구축]]
- Adjacent: [[스타일 코드]] · [[사후 편집 (Post-editing)]]

## 🤖 LLM 활용
**언제**: 매 storyboard scene list 의 generation, 매 prompt batch 의 expansion (장면 변형).
**언제 X**: 매 visual continuity 의 final judgment — 매 art director 의 eye 가 필요.

## ❌ 안티패턴
- **No reference sheet**: 매 매 batch 마다 character drift.
- **Too high cw + free prompt**: 매 character pose freeze (overfit).
- **Multiple cref 동시**: 매 face blend → 매 third person.
- **Style 의 mid-series 변경**: 매 final compilation 의 jarring.

## 🧪 검증 / 중복
- Verified (Midjourney V7/V8 docs, FLUX IP-Adapter docs, Replicate LoRA trainer).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — multi-shot pipeline + 8 patterns |