2nd/10_Wiki/Topics/AI_and_ML/Vary Region (인페인팅).md

---
id: wiki-2026-0508-vary-region-인페인팅
title: Vary Region (인페인팅)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Inpainting, Midjourney Vary Region, Region-Based Editing]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [image-generation, inpainting, midjourney, editing, diffusion]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: text
  framework: Midjourney/FLUX-Fill/SDXL-Inpainting
---

# Vary Region (인페인팅)

## 매 한 줄
> **"매 Vary Region 의 핵심 = 'mask 의 selective regeneration with prompt 의 re-injection'."**. 매 2023 Midjourney 의 introduction 의 since 매 industry standard 의 region-aware editing 의 became. 2026 era 매 FLUX.1 Fill / SDXL Inpainting / Adobe Firefly Generative Fill 의 mainstream — 매 entire image 의 regenerate 의 X 의 specific area 만 의 modify.

## 매 핵심

### 매 동작 원리
- **Mask**: 매 binary image (white = edit, black = preserve) 의 user-drawn or auto-segmented.
- **Latent blending**: 매 unmasked region 의 original latent 의 preserve, masked region 만 의 noise → denoise.
- **Prompt re-injection**: 매 new prompt 의 masked area 의 conditioning 의 only.
- **Boundary blending**: 매 mask edge 의 soft blur 의 seamless transition.

### 매 Use cases
- **Object removal**: unwanted person / watermark / logo 의 remove.
- **Object addition**: empty desk 의 coffee cup 의 add.
- **Object replacement**: red car → blue car.
- **Detail enhancement**: face / hand 의 specific 의 refinement.
- **Outpainting**: 매 canvas 의 expand 의 logical extension.

### 매 Variants
1. **Vary Region (Subtle)**: 매 minor change — color / texture.
2. **Vary Region (Strong)**: 매 major regeneration — different object.
3. **Remix mode**: 매 prompt 의 swap 의 enable.

## 💻 패턴

### Pattern 1: Midjourney 의 Vary Region workflow
```text
1. /imagine prompt: cozy living room, modern minimalist
2. Upscale → click "Vary (Region)"
3. Draw mask over old TV
4. Edit prompt: "large bookshelf with plants"
5. Submit → MJ regenerates only masked area
```

### Pattern 2: SDXL inpainting (diffusers)
```python
from diffusers import StableDiffusionXLInpaintPipeline
from PIL import Image
import torch

pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
    "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
    torch_dtype=torch.float16,
).to("cuda")

init_image = Image.open("room.png").convert("RGB")
mask_image = Image.open("mask.png").convert("RGB")  # white = inpaint

result = pipe(
    prompt="large oak bookshelf filled with leather books and potted plants",
    image=init_image,
    mask_image=mask_image,
    strength=0.95,
    guidance_scale=8.0,
    num_inference_steps=40,
).images[0]

result.save("inpainted.png")
```

### Pattern 3: FLUX.1 Fill (2026 SOTA)
```python
import replicate

output = replicate.run(
    "black-forest-labs/flux-fill-pro",
    input={
        "image": open("room.png", "rb"),
        "mask": open("mask.png", "rb"),
        "prompt": "vintage typewriter on wooden desk",
        "guidance_scale": 30,  # FLUX uses higher guidance
        "num_inference_steps": 50,
    },
)
```

### Pattern 4: Auto-mask via SAM 2 (Segment Anything)
```python
from segment_anything_2 import SAM2ImagePredictor
import numpy as np

predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-large")
predictor.set_image(image)

# Click point on object to remove
point_coords = np.array([[450, 300]])
point_labels = np.array([1])

masks, scores, _ = predictor.predict(
    point_coords=point_coords,
    point_labels=point_labels,
    multimask_output=True,
)
best_mask = masks[scores.argmax()]
```

### Pattern 5: Outpainting (canvas expansion)
```python
from PIL import Image

# Expand canvas to right
original = Image.open("portrait.png")  # 512x512
expanded = Image.new("RGB", (1024, 512), (0, 0, 0))
expanded.paste(original, (0, 0))

# Mask: black on left half (preserve), white on right half (generate)
mask = Image.new("L", (1024, 512), 0)
for x in range(512, 1024):
    for y in range(512):
        mask.putpixel((x, y), 255)

result = pipe(
    prompt="continuation of mountain landscape, same lighting and style",
    image=expanded,
    mask_image=mask,
    strength=1.0,
).images[0]
```

### Pattern 6: ControlNet Inpaint (preserve structure)
```python
from diffusers import StableDiffusionXLControlNetInpaintPipeline, ControlNetModel

controlnet = ControlNetModel.from_pretrained("destitech/controlnet-inpaint-dreamer-sdxl")
pipe = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=controlnet,
)

# Preserves edges / depth while regenerating
result = pipe(
    prompt="red sports car, same angle and lighting",
    image=original,
    mask_image=mask,
    control_image=canny_edge_map,
).images[0]
```

### Pattern 7: Adobe Firefly Generative Fill (Photoshop API)
```javascript
const response = await fetch('https://firefly-api.adobe.io/v3/images/generative-fill', {
  method: 'POST',
  headers: { Authorization: `Bearer ${token}` },
  body: JSON.stringify({
    image: { source: { url: imageUrl } },
    mask:  { source: { url: maskUrl } },
    prompt: 'a vase of fresh tulips',
  }),
});
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Casual / no-code | Midjourney Vary Region |
| Photoshop integrated | Adobe Firefly Generative Fill |
| API / batch | FLUX.1 Fill Pro (Replicate) |
| Local / private | SDXL Inpainting + diffusers |
| Auto-mask | SAM 2 + inpainting pipe |
| Preserve structure | ControlNet Inpaint variant |

**기본값**: FLUX.1 Fill Pro (quality) 또는 SDXL Inpainting (local/free).

## 🔗 Graph
- 부모: [[Generative-AI]] · [[Diffusion-Models]]
- 변형: [[Outpainting]]
- Adjacent: [[Image Prompt 작성 방법]] · [[Adobe-Firefly]]

## 🤖 LLM 활용
**언제**: mask region 의 prompt 의 generation / what-to-remove decision / multi-region edit planning.
**언제 X**: 매 mask itself 의 drawing — 매 visual / spatial task 의 LLM 의 weak.

## ❌ 안티패턴
- **Mask too tight**: 매 boundary artifact — 매 8-16px feather 의 add.
- **Strength=1.0 with same prompt**: 매 unnecessary regeneration. strength=0.7-0.95 의 prefer.
- **Ignoring lighting consistency**: 매 inpainted region 의 lighting 의 mismatch — 매 prompt 의 explicit "same lighting" 의 add.
- **No reference to surroundings**: prompt 의 isolated description — 매 context 의 mention ("matching the wooden desk").

## 🧪 검증 / 중복
- Verified: Midjourney docs (2026), Black Forest Labs FLUX Fill paper, diffusers documentation.
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — full inpainting / Vary Region guide |