From 824afea4a7326c6674530af00177c4b8a2aaaa27 Mon Sep 17 00:00:00 2001
From: Jeongin Lee <wjddls7762@gmail.com>
Date: Tue, 3 Sep 2024 18:29:47 +0900
Subject: [PATCH] initial publishing

---
 _images/TEXTUALINVERSION.png         | Bin 0 -> 376348 bytes
 _images/Untitled.png                 | Bin 117225 -> 485301 bytes
 _images/Untitled_10.png              | Bin 0 -> 451843 bytes
 _images/Untitled_2.png               | Bin 0 -> 2766545 bytes
 _images/Untitled_8.png               | Bin 0 -> 626638 bytes
 _images/Untitled_9.png               | Bin 0 -> 430842 bytes
 _images/limitation.png               | Bin 0 -> 228249 bytes
 _sources/docs/review/DreamBooth3D.md | 116 ++++++++++-------
 docs/experiments/js_exp.html         |  38 +++---
 docs/experiments/swjo_exp.html       |  28 ++--
 docs/review/DreamBooth3D.html        | 188 +++++++++++++++++----------
 docs/review/DreamPose.html           |   2 +-
 searchindex.js                       |   2 +-
 13 files changed, 222 insertions(+), 152 deletions(-)
 create mode 100755 _images/TEXTUALINVERSION.png
 create mode 100755 _images/Untitled_10.png
 create mode 100755 _images/Untitled_2.png
 create mode 100755 _images/Untitled_8.png
 create mode 100755 _images/Untitled_9.png
 create mode 100755 _images/limitation.png

diff --git a/_images/TEXTUALINVERSION.png b/_images/TEXTUALINVERSION.png
new file mode 100755
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diff --git a/_sources/docs/review/DreamBooth3D.md b/_sources/docs/review/DreamBooth3D.md
index 3e45b6b3..05fd7d81 100755
--- a/_sources/docs/review/DreamBooth3D.md
+++ b/_sources/docs/review/DreamBooth3D.md
@@ -1,5 +1,5 @@
 # Dream Booth 3D
-```
+``` {admonition} Information
 - **Title:** DreamBooth3D: Subject-Driven Text-to-3D Generation
 
 - **Reference**
@@ -9,11 +9,13 @@
 - **Author:** Jeongin Lee
 
 - **Last updated on {Sep. 3, 2024}**
-```
+```   
 
 ## 0. Abstract
 
-- DreamBooth3D : 피사체의 3-6개의 캐주얼한 촬영 이미지로부터 text-to-3D 생성 모델을 personalization (맞춤화)
+- **DreamBooth3D : 피사체의 3-6개의 캐주얼한 촬영 이미지로부터 text-to-3D 생성 모델을 personalization (맞춤화)**
+
+
 - DreamBooth + DreamFusion 의 결합
     - DreamBooth : personalizing text-to-image models
     - DreamFusion : text-to-3D generation
@@ -40,26 +42,24 @@
 - **해결책**
     - **DreamBooth3D**는 효과적인 3단계 최적화 방식을 제안
     - **Dream Booth** , **Dream Fusion** 사용
-    
-    **[setp 1️⃣]**
-    
+    ---
+    **[STEP 1️⃣]**    
     - **DreamBooth** 모델을 부분적으로 미세 조정
     - **DreamFusion**을 사용하여 **NeRF** 최적화
     - 부분적으로 미세 조정된 DreamBooth 모델은 주어진 대상 뷰에 과적합 되지 않으며 모든 subject별 세부 정보를 캡처하지 않음
     - 결과적으로 생성된 **NeRF** 자산은 3D 일관성이 있지만 subject 에 대한 특성을 완전히 반영하지못함.
     
-    **[setp 2️⃣]**
-    
+    **[STEP 2️⃣]**    
     - **DreamBooth** 모델을 완전히 미세 조정하여 세부 사항을 캡처
     - 1단계에서 학습된 NeRF의 다중 뷰 렌더링을 완전히 학습된 **DreamBooth** 모델에 투입
     - 이를 통해 subject 별로 다중 뷰 가상 이미지 집합을 생성
     
-    **[setp 3️⃣]**
-    
+    **[STEP 3️⃣]**
     - 1단계의 주어진 subject 이미지와 가상(pseudo) 다중 뷰 이미지를 사용하여 **DreamBooth** 모델을 추가로 최적화
     - 추가 최적화한 **DreamBooth** 로 NeRF 3D 볼륨을  최종 최적화
     - 최종 NeRF 최적화시 추가 규제항으로 pseudo 다중 뷰 데이터 세트에 대한 weak reconstruction loss를 사용
     - 3단계에 걸친 NeRF 및 T2I 모델의 합동 최적화는 **DreamBooth** 모델이 subject 의 특정 view point 에 과적합되는 것을 방지하는 동시에 동시에 결과 NeRF 모델이 대상의 정체성에 충실하도록 보장
+    ---
 
 - **결과**
     - 실험 샘플 결과들을 통해 본 접근 방식이 입력 텍스트 프롬프트에 존재하는 컨텍스트를 존중하면서 주어진 대상과 유사성이 높은 현실적인 3D 자산을 생성할 수 있음을 입증
@@ -86,24 +86,24 @@
 - **Textual Inversion**
     입력 개념을 나타내는 사전 학습된 text-to-image 모델의 임베딩 공간에서 새로운 word 를 최적화함으로써 이를 달성
 :::{figure-md} 
-<img src="../../pics/DreamBooth3D/54544834-db8a-49f3-9ac7-d4a530724fd8.png" alt="d4a530724fd8" class="bg-primary mb-1" width="500px">
+<img src="../../pics/DreamBooth3D/54544834-db8a-49f3-9ac7-d4a530724fd8.png" alt="d4a530724fd8" class="bg-primary mb-1" width="800px">
 
 Textual Inversion \  (source: {https://arxiv.org/abs/2208.01618})
 :::
 
 :::{figure-md} 
-<img src="../../pics/DreamBooth3D/Untitled.png" alt="Untitled" class="bg-primary mb-1" width="500px">
+<img src="../../pics/DreamBooth3D/TEXTUALINVERSION.png" alt="TEXTUALINVERSION" class="bg-primary mb-1" width="800px">
 
 Textual Inversion Architecture \  (source: {https://arxiv.org/abs/2208.01618})
 :::
 
-- 이러한 방법은 3D 자산이나 3D 일관성 있는 이미지를 생성 기능을 제공하지 않음.
+**→** 이러한 방법론은 3D asset 을 제공하지 않고 일관성 있는 3D 이미지를 생성할 수 없음.
 
 # **3. Approach**
 
  **Problem setup.**
 :::{figure-md} 
-<img src="../../pics/DreamBooth3D/Untitled_1.png" alt="Untitled_1" class="bg-primary mb-1" width="800px">
+<img src="../../pics/DreamBooth3D/Untitled_1.png" alt="Untitled_1" class="bg-primary mb-1">
 
 Input and Output
 :::
@@ -142,7 +142,7 @@ Input and Output
 
  **Dream Booth T2I Personalization.**
 :::{figure-md} 
- <img src="../../pics/DreamBooth3D/Untitled_4.png" alt="Untitled_4" class="bg-primary mb-1" width="600px">
+ <img src="../../pics/DreamBooth3D/Untitled_4.png" alt="Untitled_4" class="bg-primary mb-1">
 
 특정 피사체에 대한 소수의 이미지 집합 (3-5장) 을 통해 텍스트로 주어지는 Context 에 맞는 맞춤화 이미지 생성 
 :::
@@ -169,26 +169,26 @@ $$
 
 - **(example) over fitting**
 :::{figure-md} 
- <img src="../../pics/DreamBooth3D/Untitled_2.png" alt="Untitled_2" class="bg-primary mb-1" width="500px">
+ <img src="../../pics/DreamBooth3D/Untitled_2.png" alt="Untitled_2" class="bg-primary mb-1" width="800px">
+
 over fitting 
 :::
 
 - **(example) language-drift**
 :::{figure-md} 
- <img src="../../pics/DreamBooth3D/Untitled_3.png" alt="Untitled_3" class="bg-primary mb-1" width="500px">
+ <img src="../../pics/DreamBooth3D/Untitled_3.png" alt="Untitled_3" class="bg-primary mb-1" width="800px">
 
 language-drift
 ::: 
 
 
-
 ---
 
  **DreamFusion** 
 
 - T2I diffusion model을 사용하여 볼륨의 랜덤뷰가 프롬프트  $T$ 에 상응하도록 NeRF $\mathcal{M}_\phi$ ($\phi$ : parameters) 를 통해 표현된 볼륨을 최적화
 :::{figure-md} 
- <img src="../../pics/DreamBooth3D/Untitled_6.png" alt="Untitled_6" class="bg-primary mb-1" width="800px">
+ <img src="../../pics/DreamBooth3D/Untitled_6.png" alt="Untitled_6" class="bg-primary mb-1" >
 
 DreamFusion process / (source : [DreamFusion](https://pseudo-lab.github.io/pseudodiffusers/docs/review/DreamFusion.html))
 ::: 
@@ -216,7 +216,7 @@ DreamFusion process / (source : [DreamFusion](https://pseudo-lab.github.io/pseud
     1. subject에 대해 **T2I model 을 pesonalized(맞춤화)**  
     2. 맞춤화된  T2I model 을 **text-to-3D optimization** 을 위해 사용 
 - **즉, DreamBooth 최적화(personalized) ⇒ DreamFusion 최적화**
-- 그러나, **Naive Dreambooth+Fusion 의 결합은 불만족스러운 결과를 초래**
+- **BUT**, **Naive Dreambooth+Fusion 의 결합은 불만족스러운 결과를 초래**
     
 :::{figure-md} 
  <img src="../../pics/DreamBooth3D/videdocap.png" alt="videdocap" class="bg-primary mb-1" width="600px">
@@ -224,49 +224,56 @@ DreamFusion process / (source : [DreamFusion](https://pseudo-lab.github.io/pseud
 ::: 
 
     
-    **핵심 문제 (KEY Issue)**
+**핵심 문제 (KEY Issue)**
+
+- **Dream Booth**가 **훈련된 뷰에 존재하는 subject 의 뷰에 과적합** 되어 
+**이미지 생성에서 viewpoint 에 대한 다양성이 감소**하는 경향을 보임.
+- 미세 조정 단계가 증가할수록, Subject 유사성 증가 (👍) 
+**BUT**  input exemplar views에 유사하도록 viewpoints 생성 (👎)
     
-    - **Dream Booth**가 **훈련된 뷰에 존재하는 subject 의 뷰에 과적합** 되어 
-    **이미지 생성에서 viewpoint 에 대한 다양성이 감소**하는 경향을 보임.
-    - 미세 조정 단계가 증가할수록, Subject 유사성 증가 (👍) 
-    **BUT**  input exemplar views에 유사하도록 viewpoints 생성 (👎)
-        
-        ⇒ 즉, 다양한 시점에서 이미지를 생성하는 능력이 저하됨. 
+⇒ 즉, 다양한 시점에서 이미지를 생성하는 능력이 저하됨. 
+    
+
+- 이런 DreamBooth 모델 기반의 NeRF SDS 손실은 일관된 3D NeRF 결과물을 얻기에 불충분
+- **DreamBooth+Fusion NeRF** 모델이 **서로 다른 view** 에 걸쳐 학습된 **동일한 대상에 대한 뷰**(예: face of a dog :  다양한 각도에서 본 동일한 dog face)를 가지고 있음.
+    - **"Janus problem"** : 두 가지 상반되거나 연관된 측면을 동시에 다루어야 하는 문제
         
-    - 이런 DreamBooth 모델 기반의 NeRF SDS 손실은 일관된 3D NeRF 결과물을 얻기에 불충분
-    - **DreamBooth+Fusion NeRF** 모델이 **서로 다른 view** 에 걸쳐 학습된 **동일한 대상에 대한 뷰**(예: face of a dog :  다양한 각도에서 본 동일한 dog face )를 가지고 있음.
-        - **"Janus problem"**으로 인한 실패 사례 : 두 가지 상반되거나 연관된 측면을 동시에 다루어야 하는 문제
-            
 
 ### 3.3. Dreambooth3D Optimization
 ---
 :::{figure-md} 
  <img src="../../pics/DreamBooth3D/Untitled_8.png" alt="Untitled_8" class="bg-primary mb-1" width="800px">
+
 DreamBooth3D Overview
 ::: 
 
 - DreamBooth3D Overview 
+
 **stage-1 (왼쪽)**: 먼저 DreamBooth를 부분적으로 훈련시키고, 결과 모델을 사용하여 초기 NeRF를 최적화
+
 **stage-2 (가운데)**: 초기 NeRF에서 랜덤 시점에 따라 다중 시점 이미지를 렌더링한 후, 완전히 훈련된 DreamBooth 모델을 사용하여 이를 가상 다중 시점 subject 이미지로 변환
+
 **stage-3 (오른쪽)**: 다중 시점 이미지를 사용하여 부분적인 DreamBooth를 추가로 미세 조정한 다음, 결과적으로 얻어진 다중 시점 DreamBooth를 사용하여 최종 NeRF 3D 자산을 SDS 손실과 다중 시점 재구성 손실을 통해 최적화
 
 - 위의 문제를 해결하고 성공적인 subject 맞춤 text-to-3D 생성을 위해 효율적인 3단계 최적화 방식을 기반으로 한 Dream-Booth3D 제안
 
+---
+
  **1️⃣Stage-1: 3D with Partial DreamBooth**
 
 :::{figure-md} 
- <img src="../../pics/DreamBooth3D/Untitled_9.png" alt="Untitled_9" class="bg-primary mb-1" width="500px">
+ <img src="../../pics/DreamBooth3D/Untitled_9.png" alt="Untitled_9" class="bg-primary mb-1" width="800px">
+
 Stage-1 :  3D with Partial DreamBooth
 :::                           
 
 - 입력된 Subject 이미지를 사용하여 DreamBooth 모델 $\hat{\mathcal{D}}_\theta$ 를 훈련
 
-<aside>
+---
 🌟 **DreamBoothT2I 모델의 초기 체크포인트가 (=부분적으로 파인튜닝한 결과) 주어진 subject view에 과적합되지 않음을 확인**
 
 **⇒ partial DreamBooth (부분적으로 파인튜닝한 Dreambooth)**
-
-</aside>
+---
 
 - **partial DreamBooth** 모델 하에 DreamFusion은 더 일관된 3D NeRF를 생성가능
 - NeRF 최적화시 SDS 손실 사용 :
@@ -275,25 +282,28 @@ Stage-1 :  3D with Partial DreamBooth
         - SDS 손실을 사용하여 주어진 텍스트 프롬프트에 대한 초기 NeRF 자산을 최적화
 - **partial DreamBooth** 모델과 NeRF 결과물은 입력된 subject 와 완전히 유사하지 않음
 
-<aside>
+---
+
 🌟 즉,  **1️⃣ Stage-1 에서의 초기 NeRF** 는 **주어진 subject 와  부분적으로ㅁ 유사**하면서, 
 **주어진 텍스트 프롬프트에 충실한**  **subject class 3D 모델**
 
-</aside>
+---
 
  **2️⃣Stage-2: Multi-view Data Generation**
 
 :::{figure-md} 
- <img src="../../pics/DreamBooth3D/Untitled_10.png" alt="Untitled_10" class="bg-primary mb-1" width="500px">
+ <img src="../../pics/DreamBooth3D/Untitled_10.png" alt="Untitled_10" class="bg-primary mb-1" width="800px">
+
 Stage-2: Multi-view Data Generation
 :::                           
 
-<aside>
+---
+
 🌟 **Stage-2 Multi-view Data Generation : 본 접근법의 가장 중요한 부분**
 
 일관성을 갖춘 **3D initial NeRF** 와 **fully-trained DreamBooth** 를 사용하여 **pseudo multi-view subject** 이미지들을 생성
 
-</aside>
+---
 
 1. **Initial NeRF** 로부터 다양한 랜덤 viewpoints  $\{v\}$을 따라 여러 이미지$\left\{\hat{I}v \in \mathbb{R}^{n \times 3}\right\}$ 를 렌더링하여 다중 시점 렌더링을 생성
 2. 각 **렌더링에 고정된 양의 노이즈를 추가하는 forward diffusion** 과정을 통해 $t_{pseudo}$로 전환
@@ -306,17 +316,19 @@ Stage-2: Multi-view Data Generation
         ⇒ 가상(pseudo) 다중 시점 이미지 집합 **(collection of pseudo multi-view images)**
         
 
-<aside>
+---
+
 🔑 **Key insight**
 1. 초기 NeRF 이미지가 unseen views 에 가까울 경우, 
     DreamBooth가 Subject 의 unseen views를 효과적으로 생성 가능 
 
 2. 입력 이미지에 비해 Subject 와 더 유사한 출력 이미지를 효과적으로 생성가능**
 
-</aside>
+---
 
 :::{figure-md} 
- <img src="../../pics/DreamBooth3D/Untitled_11.png" alt="Untitled_11" class="bg-primary mb-1" width="500px">
+ <img src="../../pics/DreamBooth3D/Untitled_11.png" alt="Untitled_11" class="bg-primary mb-1" width="800px">
+
 :::                  
 
 - 위 그림을 통해 체크할 부분
@@ -327,13 +339,13 @@ Stage-2: Multi-view Data Generation
  **3️⃣Stage-3: Final NeRF with Multi-view DreamBooth**
 
 :::{figure-md} 
- <img src="../../pics/DreamBooth3D/Untitled_12.png" alt="Untitled_12" class="bg-primary mb-1" width="600px">
+ <img src="../../pics/DreamBooth3D/Untitled_12.png" alt="Untitled_12" class="bg-primary mb-1" width="800px">
 
 Stage-3 : Final NeRF with Multi-view DreamBooth
 SDS와 multi-view reconstruction 손실을 사용한 최종 NeRF 최적화
 :::  
 
-**새로운 데이터 $I^{\text{aug}}$ 생성**
+> **새로운 데이터 $I^{\text{aug}}$ 생성**
 
 - 가상 다중 시점 이미지 $\left\{I_v^{\text {pseudo }}\right\}$, 입력 Subject 이미지 $\left\{I_i\right\}$ 의 결합을 통해 생성
 
@@ -341,13 +353,13 @@ $$
 I^{\text {aug }}=\left\{I_v^{\text {pseudo }}\right\} \cup\left\{I_i\right\}
 $$
 
-  **$I^{\text {aug}}$ 를 사용하여 최종 Multi-view DreamBooth 모델을 최적화** 
+> **$I^{\text {aug}}$ 를 사용하여 최종 Multi-view DreamBooth 모델을 최적화** 
 
 1. 1단계에서 **partial DreamBooth** $\hat{\mathcal{D}}_{\theta^*}$ 준비 
 2. 위의 증강 데이터 $I^{\text {aug}}$  를 사용하여 $\hat{\mathcal{D}}_{\theta^*}$ 에 대한 **파인튜닝**을 추가 진행
 3. **Multi-view DreamBooth** $\hat{\mathcal{D}}_\theta^{\mathrm{multi}}$ 를  생성
 
-  $\hat{\mathcal{D}}_\theta^{\text {multi }}$ **모델을 사용하여 DreamFusion SDS Loss 와 함께 NeRF 3D assets 를 최적화** 
+> $\hat{\mathcal{D}}_\theta^{\text {multi }}$ **모델을 사용하여 DreamFusion SDS Loss 와 함께 NeRF 3D assets 를 최적화** 
 
 - 1단계의 **partial DreamBooth**에 비해 **multi-view DreamBooth** 의 뷰 일반화와 subject 보존 능력이 더 우수하기 때문에 **subject idendtity가 상당히 향상된 NeRF 모델 생성 가능**
 - **BUT** SDS 손실만 사용시 최적화된 NeRF assets 이
@@ -360,6 +372,8 @@ $$
         - 색상 값을 잘못 예측하여 비현실적인 색상 표현이 나타난 경우 발생
         - 다양한 시점에서 일관된 색상 표현을 유지하지 못한 경우 발생
 
+---
+
 **Reconstruction loss** 
 
 - $\left\{I_v^{\mathrm{pseudo}}\right\}$ 가 생성된 카메라 매개변수 $\left\{P_v\right\}$ 정보를 알고 있으므로,
@@ -430,6 +444,7 @@ $$
 
 :::{figure-md} 
  <img src="../../pics/DreamBooth3D/Untitled_13.png" alt="Untitled_13" class="bg-primary mb-1" width="800px">
+
 :::  
 
 - **비교 결과**: DreamBooth3D, Latent-NeRF, DreamBooth+Fusion 기준 모델의 비교
@@ -441,12 +456,14 @@ $$
 
 :::{figure-md} 
  <img src="../../pics/DreamBooth3D/Untitled_14.png" alt="Untitled_14" class="bg-primary mb-1" width="800px">
+
 :::  
 
  **Initial vs. Final NeRF**
 
 :::{figure-md} 
  <img src="../../pics/DreamBooth3D/Untitled_15.png" alt="Untitled_15" class="bg-primary mb-1" width="800px">
+
 :::  
 
 - 1단계와 3단계에서 생성된 초기 NeRF와 최종 NeRF 결과
@@ -457,6 +474,7 @@ $$
  **User Study.**
 :::{figure-md} 
  <img src="../../pics/DreamBooth3D/Untitled_16.png" alt="Untitled_16" class="bg-primary mb-1" width="800px">
+
 :::  
 
 **→ DreamBooth3D와 비교 모델들을 세가지측면에 대해 아래의 질문에 대한 답변으로 평가** 
@@ -474,6 +492,7 @@ $$
 ## 4.2. Sample Applications
 :::{figure-md} 
  <img src="../../pics/DreamBooth3D/Untitled_17.png" alt="Untitled_17" class="bg-primary mb-1" width="800px">
+
 :::  
 
 - **Recontextualization. (재문맥화)**
@@ -483,6 +502,7 @@ $$
 
 :::{figure-md} 
  <img src="../../pics/DreamBooth3D/19.png" alt="19" class="bg-primary mb-1" width="600px">
+
 ::: 
 
 - **Color/Material Editing.**
@@ -492,6 +512,7 @@ $$
  
 :::{figure-md} 
  <img src="../../pics/DreamBooth3D/20.png" alt="20" class="bg-primary mb-1" width="600px">
+
 ::: 
 
 - **Stylization**
@@ -505,6 +526,7 @@ $$
 ---
 :::{figure-md} 
  <img src="../../pics/DreamBooth3D/limitation.png" alt="limitation" class="bg-primary mb-1" width="600px">
+
 limitations 
 :::
 
diff --git a/docs/experiments/js_exp.html b/docs/experiments/js_exp.html
index ebc13c9e..d90955d3 100755
--- a/docs/experiments/js_exp.html
+++ b/docs/experiments/js_exp.html
@@ -529,7 +529,7 @@ <h2>1. 개요<a class="headerlink" href="#id1" title="Permalink to this heading"
 <figure class="align-default" id="id3">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/4classes.png"><img alt="4classes" class="bg-primary mb-1" src="../../_images/4classes.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 649 </span><span class="caption-text">4 classes of leaves</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 654 </span><span class="caption-text">4 classes of leaves</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -550,7 +550,7 @@ <h2>2. Baseline 구축<a class="headerlink" href="#baseline" title="Permalink to
 <figure class="align-default" id="id4">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/multiple_ex.png"><img alt="multiple_ex" class="bg-primary mb-1" src="../../_images/multiple_ex.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 650 </span><span class="caption-text">4 classes of leaves</span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 655 </span><span class="caption-text">4 classes of leaves</span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
@@ -562,7 +562,7 @@ <h2>3. Stable diffusion fine tuning<a class="headerlink" href="#stable-diffusion
 <figure class="align-default" id="id5">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/multiple_sd.png"><img alt="multiple_sd" class="bg-primary mb-1" src="../../_images/multiple_sd.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 651 </span><span class="caption-text">prompt: “a photo of leaves with multiple diseases</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 656 </span><span class="caption-text">prompt: “a photo of leaves with multiple diseases</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -573,7 +573,7 @@ <h2>3. Stable diffusion fine tuning<a class="headerlink" href="#stable-diffusion
 <figure class="align-default" id="id6">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/multiple_db.png"><img alt="multiple_db" class="bg-primary mb-1" src="../../_images/multiple_db.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 652 </span><span class="caption-text">prompt: “a photo of a &lt;diseaes-leaf&gt; leaf”</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 657 </span><span class="caption-text">prompt: “a photo of a &lt;diseaes-leaf&gt; leaf”</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -584,13 +584,13 @@ <h2>3. Stable diffusion fine tuning<a class="headerlink" href="#stable-diffusion
 <figure class="align-default" id="id7">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/leaf_sd.png"><img alt="leaf_sd" class="bg-primary mb-1" src="../../_images/leaf_sd.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 653 </span><span class="caption-text">fine tuning 전</span><a class="headerlink" href="#id7" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 658 </span><span class="caption-text">fine tuning 전</span><a class="headerlink" href="#id7" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="id8">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/leaf_db.png"><img alt="leaf_db" class="bg-primary mb-1" src="../../_images/leaf_db.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 654 </span><span class="caption-text">fine tuning 후</span><a class="headerlink" href="#id8" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 659 </span><span class="caption-text">fine tuning 후</span><a class="headerlink" href="#id8" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -600,13 +600,13 @@ <h2>3. Stable diffusion fine tuning<a class="headerlink" href="#stable-diffusion
 <figure class="align-default" id="id9">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/photo_sd.png"><img alt="photo_sd" class="bg-primary mb-1" src="../../_images/photo_sd.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 655 </span><span class="caption-text">fine tuning 전</span><a class="headerlink" href="#id9" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 660 </span><span class="caption-text">fine tuning 전</span><a class="headerlink" href="#id9" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="id10">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/photo_db.png"><img alt="photo_db" class="bg-primary mb-1" src="../../_images/photo_db.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 656 </span><span class="caption-text">fine tuning 후</span><a class="headerlink" href="#id10" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 661 </span><span class="caption-text">fine tuning 후</span><a class="headerlink" href="#id10" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -616,13 +616,13 @@ <h2>3. Stable diffusion fine tuning<a class="headerlink" href="#stable-diffusion
 <figure class="align-default" id="id11">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/multiple_pp.png"><img alt="multiple_pp" class="bg-primary mb-1" src="../../_images/multiple_pp.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 657 </span><span class="caption-text">multiple diseases class 이미지 생성 결과, prompt: “&lt;diseaes-leaf&gt; leaf”</span><a class="headerlink" href="#id11" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 662 </span><span class="caption-text">multiple diseases class 이미지 생성 결과, prompt: “&lt;diseaes-leaf&gt; leaf”</span><a class="headerlink" href="#id11" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="id12">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/leaf_pp.png"><img alt="leaf_pp" class="bg-primary mb-1" src="../../_images/leaf_pp.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 658 </span><span class="caption-text">leaf 생성 결과, prompt: “leaf”</span><a class="headerlink" href="#id12" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 663 </span><span class="caption-text">leaf 생성 결과, prompt: “leaf”</span><a class="headerlink" href="#id12" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -631,7 +631,7 @@ <h2>3. Stable diffusion fine tuning<a class="headerlink" href="#stable-diffusion
 <figure class="align-default" id="id13">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/photo_pp.png"><img alt="photo_pp" class="bg-primary mb-1" src="../../_images/photo_pp.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 659 </span><span class="caption-text">photo 생성 결과, prompt: “photo”</span><a class="headerlink" href="#id13" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 664 </span><span class="caption-text">photo 생성 결과, prompt: “photo”</span><a class="headerlink" href="#id13" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -652,7 +652,7 @@ <h2>4. 성능 비교<a class="headerlink" href="#id2" title="Permalink to this h
 <figure class="align-default" id="id14">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/result_base.png"><img alt="result_base" class="bg-primary mb-1" src="../../_images/result_base.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 660 </span><span class="caption-text">result_base</span><a class="headerlink" href="#id14" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 665 </span><span class="caption-text">result_base</span><a class="headerlink" href="#id14" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>생성한 이미지를 추가 데이터로 활용한 경우</p>
@@ -662,7 +662,7 @@ <h2>4. 성능 비교<a class="headerlink" href="#id2" title="Permalink to this h
 <figure class="align-default" id="id15">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/result_new.png"><img alt="result_new" class="bg-primary mb-1" src="../../_images/result_new.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 661 </span><span class="caption-text">result_now</span><a class="headerlink" href="#id15" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 666 </span><span class="caption-text">result_now</span><a class="headerlink" href="#id15" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -688,37 +688,37 @@ <h2>6. Appendix<a class="headerlink" href="#appendix" title="Permalink to this h
 <figure class="align-default" id="id16">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/cat.png"><img alt="cat" class="bg-primary mb-1" src="../../_images/cat.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 662 </span><span class="caption-text">cat 생성 결과, prompt: “cat”</span><a class="headerlink" href="#id16" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 667 </span><span class="caption-text">cat 생성 결과, prompt: “cat”</span><a class="headerlink" href="#id16" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="id17">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/sea.png"><img alt="sea" class="bg-primary mb-1" src="../../_images/sea.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 663 </span><span class="caption-text">sea 생성 결과, prompt: “sea”</span><a class="headerlink" href="#id17" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 668 </span><span class="caption-text">sea 생성 결과, prompt: “sea”</span><a class="headerlink" href="#id17" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="id18">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/pirate.png"><img alt="pirate" class="bg-primary mb-1" src="../../_images/pirate.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 664 </span><span class="caption-text">pirate 생성 결과, prompt: “pirate”</span><a class="headerlink" href="#id18" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 669 </span><span class="caption-text">pirate 생성 결과, prompt: “pirate”</span><a class="headerlink" href="#id18" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="id19">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/illustration.png"><img alt="illustration" class="bg-primary mb-1" src="../../_images/illustration.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 665 </span><span class="caption-text">illustration 생성 결과, prompt: “illustration”</span><a class="headerlink" href="#id19" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 670 </span><span class="caption-text">illustration 생성 결과, prompt: “illustration”</span><a class="headerlink" href="#id19" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="id20">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/animation.png"><img alt="animation" class="bg-primary mb-1" src="../../_images/animation.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 666 </span><span class="caption-text">animation 생성 결과, prompt: “animation”</span><a class="headerlink" href="#id20" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 671 </span><span class="caption-text">animation 생성 결과, prompt: “animation”</span><a class="headerlink" href="#id20" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="id21">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/wallpaper.png"><img alt="wallpaper" class="bg-primary mb-1" src="../../_images/wallpaper.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 667 </span><span class="caption-text">wallpaper 생성 결과, prompt: “wallpaper”</span><a class="headerlink" href="#id21" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 672 </span><span class="caption-text">wallpaper 생성 결과, prompt: “wallpaper”</span><a class="headerlink" href="#id21" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
diff --git a/docs/experiments/swjo_exp.html b/docs/experiments/swjo_exp.html
index 405238cd..3589ce0f 100755
--- a/docs/experiments/swjo_exp.html
+++ b/docs/experiments/swjo_exp.html
@@ -528,7 +528,7 @@ <h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to t
 <figure class="align-default" id="id1">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_01.png"><img alt="swjo_exp_01" class="bg-primary mb-1" src="../../_images/swjo_exp_01.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 668 </span><span class="caption-text">Training Data</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 673 </span><span class="caption-text">Training Data</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>DreamBooth 를 실험하면서 대표적으로 instance prompt, guidance scale, negative prompt, 그리고 마지막으로 prior preservation loss 를 반영하는 정도를 조절하는 prior_loss_weight 를 바꿔가면서 학습해보았습니다. 사전학습된 text-to-image 모델로 처음에는 <em>hakurei/waifu-diffusion</em> 모델을 시도해봤지만 결과가 만족스럽지 못해 <em>runwayml/stable-diffusion-v1-5</em> 모델로 fine-tuning 작업을 진행했습니다.</p>
@@ -603,7 +603,7 @@ <h3>Prior Preservation Loss<a class="headerlink" href="#prior-preservation-loss"
 <figure class="align-default" id="id2">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_02.png"><img alt="swjo_exp_02" class="bg-primary mb-1" src="../../_images/swjo_exp_02.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 669 </span><span class="caption-text">With Prior Preservation Loss</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 674 </span><span class="caption-text">With Prior Preservation Loss</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -612,7 +612,7 @@ <h3>Prior Preservation Loss<a class="headerlink" href="#prior-preservation-loss"
 <figure class="align-default" id="id3">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_03.png"><img alt="swjo_exp_03" class="bg-primary mb-1" src="../../_images/swjo_exp_03.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 670 </span><span class="caption-text">Without Prior Preservation Loss</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 675 </span><span class="caption-text">Without Prior Preservation Loss</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
@@ -625,7 +625,7 @@ <h3>Negative Prompt<a class="headerlink" href="#negative-prompt" title="Permalin
 <figure class="align-default" id="id4">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_03.png"><img alt="swjo_exp_03" class="bg-primary mb-1" src="../../_images/swjo_exp_03.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 671 </span><span class="caption-text">Without Negative Prompt</span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 676 </span><span class="caption-text">Without Negative Prompt</span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul>
@@ -636,7 +636,7 @@ <h3>Negative Prompt<a class="headerlink" href="#negative-prompt" title="Permalin
 <figure class="align-default" id="id5">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_04.png"><img alt="swjo_exp_04" class="bg-primary mb-1" src="../../_images/swjo_exp_04.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 672 </span><span class="caption-text">With Negative Prompt</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 677 </span><span class="caption-text">With Negative Prompt</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
@@ -710,7 +710,7 @@ <h3>Instance Prompt / Guidance Scale<a class="headerlink" href="#instance-prompt
 <figure class="align-default" id="id6">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_05.png"><img alt="swjo_exp_05" class="bg-primary mb-1" src="../../_images/swjo_exp_05.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 673 </span><span class="caption-text">Instance Prompt</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 678 </span><span class="caption-text">Instance Prompt</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Inference step 을 늘려가면서 추론된 인물 이미지의 퀄리티가 상승하는 부분도 확인할 수 있었습니다. 또한, guidance scale 에 대한 실험도 진행했는데 guidance scale 이 작을수록 prompt 와 무관한 random 한 이미지들을 생성하게 됩니다. 최종적으로 num_inference steps 와 guidance scale 의 값은 각각 100 과 7.5 로 설정하였습니다.</p>
@@ -720,7 +720,7 @@ <h3>Instance Prompt / Guidance Scale<a class="headerlink" href="#instance-prompt
 <figure class="align-default" id="id7">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_06.png"><img alt="swjo_exp_06" class="bg-primary mb-1" src="../../_images/swjo_exp_06.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 674 </span><span class="caption-text">Increasing Number of Inference Steps</span><a class="headerlink" href="#id7" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 679 </span><span class="caption-text">Increasing Number of Inference Steps</span><a class="headerlink" href="#id7" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul>
@@ -731,7 +731,7 @@ <h3>Instance Prompt / Guidance Scale<a class="headerlink" href="#instance-prompt
 <figure class="align-default" id="id8">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_07.png"><img alt="swjo_exp_07" class="bg-primary mb-1" src="../../_images/swjo_exp_07.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 675 </span><span class="caption-text">Increasing Number of Inference Steps / Negative Prompt</span><a class="headerlink" href="#id8" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 680 </span><span class="caption-text">Increasing Number of Inference Steps / Negative Prompt</span><a class="headerlink" href="#id8" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul>
@@ -743,7 +743,7 @@ <h3>Instance Prompt / Guidance Scale<a class="headerlink" href="#instance-prompt
 <figure class="align-default" id="id9">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_08.png"><img alt="swjo_exp_08" class="bg-primary mb-1" src="../../_images/swjo_exp_08.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 676 </span><span class="caption-text">Guidance Scale</span><a class="headerlink" href="#id9" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 681 </span><span class="caption-text">Guidance Scale</span><a class="headerlink" href="#id9" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>동일한 inference prompt 로 prior-preservation loss 를 제외해본 결과, 생성된 남자의 머리카락이 더 길어지고 더 여성스러운 생김새를 가지는 놀라운 사실도 발견했습니다.</p>
@@ -755,7 +755,7 @@ <h3>Instance Prompt / Guidance Scale<a class="headerlink" href="#instance-prompt
 <figure class="align-default" id="id10">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_09.png"><img alt="swjo_exp_09" class="bg-primary mb-1" src="../../_images/swjo_exp_09.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 677 </span><span class="caption-text">Without Prior Preservation Loss</span><a class="headerlink" href="#id10" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 682 </span><span class="caption-text">Without Prior Preservation Loss</span><a class="headerlink" href="#id10" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
@@ -771,7 +771,7 @@ <h2>Appendix<a class="headerlink" href="#appendix" title="Permalink to this head
 <figure class="align-default" id="id11">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_10.png"><img alt="swjo_exp_10" class="bg-primary mb-1" src="../../_images/swjo_exp_10.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 678 </span><span class="caption-text">Appendix 1</span><a class="headerlink" href="#id11" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 683 </span><span class="caption-text">Appendix 1</span><a class="headerlink" href="#id11" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul>
@@ -782,7 +782,7 @@ <h2>Appendix<a class="headerlink" href="#appendix" title="Permalink to this head
 <figure class="align-default" id="id12">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_11.png"><img alt="swjo_exp_11" class="bg-primary mb-1" src="../../_images/swjo_exp_11.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 679 </span><span class="caption-text">Appendix 2</span><a class="headerlink" href="#id12" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 684 </span><span class="caption-text">Appendix 2</span><a class="headerlink" href="#id12" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul>
@@ -793,7 +793,7 @@ <h2>Appendix<a class="headerlink" href="#appendix" title="Permalink to this head
 <figure class="align-default" id="id13">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_12.png"><img alt="swjo_exp_12" class="bg-primary mb-1" src="../../_images/swjo_exp_12.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 680 </span><span class="caption-text">Appendix 3</span><a class="headerlink" href="#id13" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 685 </span><span class="caption-text">Appendix 3</span><a class="headerlink" href="#id13" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>마지막으로 하단의 좌측과 우측 사진은 각각 “A photo of <em>sks</em> girl” 그리고 “A photo of a girl in the style of <em>sks</em>” 이라는 prompt 로 DreamBooth 모델을 각각 학습한 후, 나비를 생성하라는 동일한 prompt 로 추론해본 결과입니다. <em>sks</em> 가 수식하는 명사가 girl 이 아닌 style 이도록 prompt 를 수정함으로써, butterfly 사진을 생성할때 조금이나마 더 프리드로우 웹툰의 그림체를 반영할 수 있었던 부분도 확인할 수 있었습니다.</p>
@@ -803,7 +803,7 @@ <h2>Appendix<a class="headerlink" href="#appendix" title="Permalink to this head
 <figure class="align-default" id="id14">
 <a class="bg-primary mb-1 reference internal image-reference" href="../../_images/swjo_exp_13.png"><img alt="swjo_exp_13" class="bg-primary mb-1" src="../../_images/swjo_exp_13.png" style="width: 700px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 681 </span><span class="caption-text">Appendix 4</span><a class="headerlink" href="#id14" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 686 </span><span class="caption-text">Appendix 4</span><a class="headerlink" href="#id14" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
diff --git a/docs/review/DreamBooth3D.html b/docs/review/DreamBooth3D.html
index 324b4910..8ec2042c 100755
--- a/docs/review/DreamBooth3D.html
+++ b/docs/review/DreamBooth3D.html
@@ -505,6 +505,7 @@ <h2> Contents </h2>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#preliminaries">3.1. Preliminaries</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#failure-of-naive-dreambooth-fusion">3.2 Failure of Naive Dreambooth+Fusion</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#dreambooth3d-optimization">3.3. Dreambooth3D Optimization</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#partial-dreambooth-dreambooth"><strong>⇒ partial DreamBooth (부분적으로 파인튜닝한 Dreambooth)</strong></a></li>
 </ul>
 </li>
 <li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#experiments">4. Experiments</a><ul class="visible nav section-nav flex-column">
@@ -528,21 +529,24 @@ <h2> Contents </h2>
                   
   <section class="tex2jax_ignore mathjax_ignore" id="dream-booth-3d">
 <h1>Dream Booth 3D<a class="headerlink" href="#dream-booth-3d" title="Permalink to this heading">#</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="o">**</span><span class="n">Title</span><span class="p">:</span><span class="o">**</span> <span class="n">DreamBooth3D</span><span class="p">:</span> <span class="n">Subject</span><span class="o">-</span><span class="n">Driven</span> <span class="n">Text</span><span class="o">-</span><span class="n">to</span><span class="o">-</span><span class="mi">3</span><span class="n">D</span> <span class="n">Generation</span>
-
-<span class="o">-</span> <span class="o">**</span><span class="n">Reference</span><span class="o">**</span>
-    <span class="o">-</span> <span class="n">Paper</span><span class="p">:</span> <span class="p">[</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">arxiv</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="nb">abs</span><span class="o">/</span><span class="mf">2303.13508</span><span class="p">](</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">arxiv</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="nb">abs</span><span class="o">/</span><span class="mf">2303.13508</span><span class="p">)</span>
-    <span class="o">-</span> <span class="n">Project</span> <span class="n">Page</span> <span class="p">:</span> <span class="p">[</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">dreambooth3d</span><span class="o">.</span><span class="n">github</span><span class="o">.</span><span class="n">io</span><span class="o">/</span><span class="p">](</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">dreambooth3d</span><span class="o">.</span><span class="n">github</span><span class="o">.</span><span class="n">io</span><span class="o">/</span><span class="p">)</span>
-
-<span class="o">-</span> <span class="o">**</span><span class="n">Author</span><span class="p">:</span><span class="o">**</span> <span class="n">Jeongin</span> <span class="n">Lee</span>
-
-<span class="o">-</span> <span class="o">**</span><span class="n">Last</span> <span class="n">updated</span> <span class="n">on</span> <span class="p">{</span><span class="n">Sep</span><span class="o">.</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2024</span><span class="p">}</span><span class="o">**</span>
-</pre></div>
+<div class="admonition-information admonition">
+<p class="admonition-title">Information</p>
+<ul class="simple">
+<li><p><strong>Title:</strong> DreamBooth3D: Subject-Driven Text-to-3D Generation</p></li>
+<li><p><strong>Reference</strong></p>
+<ul>
+<li><p>Paper: <a class="reference external" href="https://arxiv.org/abs/2303.13508">https://arxiv.org/abs/2303.13508</a></p></li>
+<li><p>Project Page : <a class="reference external" href="https://dreambooth3d.github.io/">https://dreambooth3d.github.io/</a></p></li>
+</ul>
+</li>
+<li><p><strong>Author:</strong> Jeongin Lee</p></li>
+<li><p><strong>Last updated on {Sep. 3, 2024}</strong></p></li>
+</ul>
 </div>
 <section id="abstract">
 <h2>0. Abstract<a class="headerlink" href="#abstract" title="Permalink to this heading">#</a></h2>
 <ul class="simple">
-<li><p>DreamBooth3D : 피사체의 3-6개의 캐주얼한 촬영 이미지로부터 text-to-3D 생성 모델을 personalization (맞춤화)</p></li>
+<li><p><strong>DreamBooth3D : 피사체의 3-6개의 캐주얼한 촬영 이미지로부터 text-to-3D 생성 모델을 personalization (맞춤화)</strong></p></li>
 <li><p>DreamBooth + DreamFusion 의 결합</p>
 <ul>
 <li><p>DreamBooth : personalizing text-to-image models</p></li>
@@ -579,20 +583,21 @@ <h2>1. Introduction<a class="headerlink" href="#introduction" title="Permalink t
 <li><p><strong>DreamBooth3D</strong>는 효과적인 3단계 최적화 방식을 제안</p></li>
 <li><p><strong>Dream Booth</strong> , <strong>Dream Fusion</strong> 사용</p></li>
 </ul>
-<p><strong>[setp 1️⃣]</strong></p>
+<hr class="docutils" />
+<p><strong>[STEP 1️⃣]</strong></p>
 <ul class="simple">
 <li><p><strong>DreamBooth</strong> 모델을 부분적으로 미세 조정</p></li>
 <li><p><strong>DreamFusion</strong>을 사용하여 <strong>NeRF</strong> 최적화</p></li>
 <li><p>부분적으로 미세 조정된 DreamBooth 모델은 주어진 대상 뷰에 과적합 되지 않으며 모든 subject별 세부 정보를 캡처하지 않음</p></li>
 <li><p>결과적으로 생성된 <strong>NeRF</strong> 자산은 3D 일관성이 있지만 subject 에 대한 특성을 완전히 반영하지못함.</p></li>
 </ul>
-<p><strong>[setp 2️⃣]</strong></p>
+<p><strong>[STEP 2️⃣]</strong></p>
 <ul class="simple">
 <li><p><strong>DreamBooth</strong> 모델을 완전히 미세 조정하여 세부 사항을 캡처</p></li>
 <li><p>1단계에서 학습된 NeRF의 다중 뷰 렌더링을 완전히 학습된 <strong>DreamBooth</strong> 모델에 투입</p></li>
 <li><p>이를 통해 subject 별로 다중 뷰 가상 이미지 집합을 생성</p></li>
 </ul>
-<p><strong>[setp 3️⃣]</strong></p>
+<p><strong>[STEP 3️⃣]</strong></p>
 <ul class="simple">
 <li><p>1단계의 주어진 subject 이미지와 가상(pseudo) 다중 뷰 이미지를 사용하여 <strong>DreamBooth</strong> 모델을 추가로 최적화</p></li>
 <li><p>추가 최적화한 <strong>DreamBooth</strong> 로 NeRF 3D 볼륨을  최종 최적화</p></li>
@@ -600,6 +605,7 @@ <h2>1. Introduction<a class="headerlink" href="#introduction" title="Permalink t
 <li><p>3단계에 걸친 NeRF 및 T2I 모델의 합동 최적화는 <strong>DreamBooth</strong> 모델이 subject 의 특정 view point 에 과적합되는 것을 방지하는 동시에 동시에 결과 NeRF 모델이 대상의 정체성에 충실하도록 보장</p></li>
 </ul>
 </li>
+<hr class="docutils" />
 <li><p><strong>결과</strong></p>
 <ul class="simple">
 <li><p>실험 샘플 결과들을 통해 본 접근 방식이 입력 텍스트 프롬프트에 존재하는 컨텍스트를 존중하면서 주어진 대상과 유사성이 높은 현실적인 3D 자산을 생성할 수 있음을 입증</p></li>
@@ -646,27 +652,25 @@ <h2>2. Related Work<a class="headerlink" href="#related-work" title="Permalink t
 입력 개념을 나타내는 사전 학습된 text-to-image 모델의 임베딩 공간에서 새로운 word 를 최적화함으로써 이를 달성</p></li>
 </ul>
 <figure class="align-default" id="id1">
-<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/54544834-db8a-49f3-9ac7-d4a530724fd8.png"><img alt="d4a530724fd8" class="bg-primary mb-1" src="../../_images/54544834-db8a-49f3-9ac7-d4a530724fd8.png" style="width: 500px;" /></a>
+<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/54544834-db8a-49f3-9ac7-d4a530724fd8.png"><img alt="d4a530724fd8" class="bg-primary mb-1" src="../../_images/54544834-db8a-49f3-9ac7-d4a530724fd8.png" style="width: 800px;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 642 </span><span class="caption-text">Textual Inversion \  (source: {<a class="reference external" href="https://arxiv.org/abs/2208.01618">https://arxiv.org/abs/2208.01618</a>})</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="id2">
-<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled.png"><img alt="Untitled" class="bg-primary mb-1" src="../../_images/Untitled.png" style="width: 500px;" /></a>
+<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/TEXTUALINVERSION.png"><img alt="TEXTUALINVERSION" class="bg-primary mb-1" src="../../_images/TEXTUALINVERSION.png" style="width: 800px;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 643 </span><span class="caption-text">Textual Inversion Architecture \  (source: {<a class="reference external" href="https://arxiv.org/abs/2208.01618">https://arxiv.org/abs/2208.01618</a>})</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
-<ul class="simple">
-<li><p>이러한 방법은 3D 자산이나 3D 일관성 있는 이미지를 생성 기능을 제공하지 않음.</p></li>
-</ul>
+<p><strong>→</strong> 이러한 방법론은 3D asset 을 제공하지 않고 일관성 있는 3D 이미지를 생성할 수 없음.</p>
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="approach">
 <h1><strong>3. Approach</strong><a class="headerlink" href="#approach" title="Permalink to this heading">#</a></h1>
 <p><strong>Problem setup.</strong></p>
 <figure class="align-default" id="id3">
-<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled_1.png"><img alt="Untitled_1" class="bg-primary mb-1" src="../../_images/Untitled_1.png" style="width: 800px;" /></a>
+<img alt="Untitled_1" class="bg-primary mb-1" src="../../_images/Untitled_1.png" />
 <figcaption>
 <p><span class="caption-number">Fig. 644 </span><span class="caption-text">Input and Output</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
 </figcaption>
@@ -712,7 +716,7 @@ <h2>3.1. Preliminaries<a class="headerlink" href="#preliminaries" title="Permali
 <hr class="docutils" />
 <p><strong>Dream Booth T2I Personalization.</strong></p>
 <figure class="align-default" id="id4">
-<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled_4.png"><img alt="Untitled_4" class="bg-primary mb-1" src="../../_images/Untitled_4.png" style="width: 600px;" /></a>
+<img alt="Untitled_4" class="bg-primary mb-1" src="../../_images/Untitled_4.png" />
 <figcaption>
 <p><span class="caption-number">Fig. 645 </span><span class="caption-text">특정 피사체에 대한 소수의 이미지 집합 (3-5장) 을 통해 텍스트로 주어지는 Context 에 맞는 맞춤화 이미지 생성</span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
 </figcaption>
@@ -745,13 +749,19 @@ <h2>3.1. Preliminaries<a class="headerlink" href="#preliminaries" title="Permali
 <ul class="simple">
 <li><p><strong>(example) over fitting</strong></p></li>
 </ul>
+<figure class="align-default" id="id5">
+<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled_2.png"><img alt="Untitled_2" class="bg-primary mb-1" src="../../_images/Untitled_2.png" style="width: 800px;" /></a>
+<figcaption>
+<p><span class="caption-number">Fig. 646 </span><span class="caption-text">over fitting</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
+</figcaption>
+</figure>
 <ul class="simple">
 <li><p><strong>(example) language-drift</strong></p></li>
 </ul>
-<figure class="align-default" id="id5">
-<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled_3.png"><img alt="Untitled_3" class="bg-primary mb-1" src="../../_images/Untitled_3.png" style="width: 500px;" /></a>
+<figure class="align-default" id="id6">
+<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled_3.png"><img alt="Untitled_3" class="bg-primary mb-1" src="../../_images/Untitled_3.png" style="width: 800px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 646 </span><span class="caption-text">language-drift</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 647 </span><span class="caption-text">language-drift</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <hr class="docutils" />
@@ -759,10 +769,10 @@ <h2>3.1. Preliminaries<a class="headerlink" href="#preliminaries" title="Permali
 <ul class="simple">
 <li><p>T2I diffusion model을 사용하여 볼륨의 랜덤뷰가 프롬프트  <span class="math notranslate nohighlight">\(T\)</span> 에 상응하도록 NeRF <span class="math notranslate nohighlight">\(\mathcal{M}_\phi\)</span> (<span class="math notranslate nohighlight">\(\phi\)</span> : parameters) 를 통해 표현된 볼륨을 최적화</p></li>
 </ul>
-<figure class="align-default" id="id6">
-<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled_6.png"><img alt="Untitled_6" class="bg-primary mb-1" src="../../_images/Untitled_6.png" style="width: 800px;" /></a>
+<figure class="align-default" id="id7">
+<img alt="Untitled_6" class="bg-primary mb-1" src="../../_images/Untitled_6.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 647 </span><span class="caption-text">DreamFusion process / (source : <a class="reference external" href="https://pseudo-lab.github.io/pseudodiffusers/docs/review/DreamFusion.html">DreamFusion</a>)</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 648 </span><span class="caption-text">DreamFusion process / (source : <a class="reference external" href="https://pseudo-lab.github.io/pseudodiffusers/docs/review/DreamFusion.html">DreamFusion</a>)</span><a class="headerlink" href="#id7" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul>
@@ -802,41 +812,59 @@ <h2>3.2 Failure of Naive Dreambooth+Fusion<a class="headerlink" href="#failure-o
 </ol>
 </li>
 <li><p><strong>즉, DreamBooth 최적화(personalized) ⇒ DreamFusion 최적화</strong></p></li>
-<li><p>그러나, <strong>Naive Dreambooth+Fusion 의 결합은 불만족스러운 결과를 초래</strong></p></li>
+<li><p><strong>BUT</strong>, <strong>Naive Dreambooth+Fusion 의 결합은 불만족스러운 결과를 초래</strong></p></li>
+</ul>
+<p><strong>핵심 문제 (KEY Issue)</strong></p>
+<ul class="simple">
+<li><p><strong>Dream Booth</strong>가 <strong>훈련된 뷰에 존재하는 subject 의 뷰에 과적합</strong> 되어
+<strong>이미지 생성에서 viewpoint 에 대한 다양성이 감소</strong>하는 경향을 보임.</p></li>
+<li><p>미세 조정 단계가 증가할수록, Subject 유사성 증가 (👍)
+<strong>BUT</strong>  input exemplar views에 유사하도록 viewpoints 생성 (👎)</p></li>
+</ul>
+<p>⇒ 즉, 다양한 시점에서 이미지를 생성하는 능력이 저하됨.</p>
+<ul class="simple">
+<li><p>이런 DreamBooth 모델 기반의 NeRF SDS 손실은 일관된 3D NeRF 결과물을 얻기에 불충분</p></li>
+<li><p><strong>DreamBooth+Fusion NeRF</strong> 모델이 <strong>서로 다른 view</strong> 에 걸쳐 학습된 <strong>동일한 대상에 대한 뷰</strong>(예: face of a dog :  다양한 각도에서 본 동일한 dog face)를 가지고 있음.</p>
+<ul>
+<li><p><strong>“Janus problem”</strong> : 두 가지 상반되거나 연관된 측면을 동시에 다루어야 하는 문제</p></li>
+</ul>
+</li>
 </ul>
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>**핵심 문제 (KEY Issue)**
-
-- **Dream Booth**가 **훈련된 뷰에 존재하는 subject 의 뷰에 과적합** 되어 
-**이미지 생성에서 viewpoint 에 대한 다양성이 감소**하는 경향을 보임.
-- 미세 조정 단계가 증가할수록, Subject 유사성 증가 (👍) 
-**BUT**  input exemplar views에 유사하도록 viewpoints 생성 (👎)
-    
-    ⇒ 즉, 다양한 시점에서 이미지를 생성하는 능력이 저하됨. 
-    
-- 이런 DreamBooth 모델 기반의 NeRF SDS 손실은 일관된 3D NeRF 결과물을 얻기에 불충분
-- **DreamBooth+Fusion NeRF** 모델이 **서로 다른 view** 에 걸쳐 학습된 **동일한 대상에 대한 뷰**(예: face of a dog :  다양한 각도에서 본 동일한 dog face )를 가지고 있음.
-    - **&quot;Janus problem&quot;**으로 인한 실패 사례 : 두 가지 상반되거나 연관된 측면을 동시에 다루어야 하는 문제
-</pre></div>
-</div>
 </section>
 <section id="dreambooth3d-optimization">
 <h2>3.3. Dreambooth3D Optimization<a class="headerlink" href="#dreambooth3d-optimization" title="Permalink to this heading">#</a></h2>
 <hr class="docutils" />
+<figure class="align-default" id="id8">
+<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled_8.png"><img alt="Untitled_8" class="bg-primary mb-1" src="../../_images/Untitled_8.png" style="width: 800px;" /></a>
+<figcaption>
+<p><span class="caption-number">Fig. 649 </span><span class="caption-text">DreamBooth3D Overview</span><a class="headerlink" href="#id8" title="Permalink to this image">#</a></p>
+</figcaption>
+</figure>
+<ul class="simple">
+<li><p>DreamBooth3D Overview</p></li>
+</ul>
+<p><strong>stage-1 (왼쪽)</strong>: 먼저 DreamBooth를 부분적으로 훈련시키고, 결과 모델을 사용하여 초기 NeRF를 최적화</p>
+<p><strong>stage-2 (가운데)</strong>: 초기 NeRF에서 랜덤 시점에 따라 다중 시점 이미지를 렌더링한 후, 완전히 훈련된 DreamBooth 모델을 사용하여 이를 가상 다중 시점 subject 이미지로 변환</p>
+<p><strong>stage-3 (오른쪽)</strong>: 다중 시점 이미지를 사용하여 부분적인 DreamBooth를 추가로 미세 조정한 다음, 결과적으로 얻어진 다중 시점 DreamBooth를 사용하여 최종 NeRF 3D 자산을 SDS 손실과 다중 시점 재구성 손실을 통해 최적화</p>
 <ul class="simple">
-<li><p>DreamBooth3D Overview
-<strong>stage-1 (왼쪽)</strong>: 먼저 DreamBooth를 부분적으로 훈련시키고, 결과 모델을 사용하여 초기 NeRF를 최적화
-<strong>stage-2 (가운데)</strong>: 초기 NeRF에서 랜덤 시점에 따라 다중 시점 이미지를 렌더링한 후, 완전히 훈련된 DreamBooth 모델을 사용하여 이를 가상 다중 시점 subject 이미지로 변환
-<strong>stage-3 (오른쪽)</strong>: 다중 시점 이미지를 사용하여 부분적인 DreamBooth를 추가로 미세 조정한 다음, 결과적으로 얻어진 다중 시점 DreamBooth를 사용하여 최종 NeRF 3D 자산을 SDS 손실과 다중 시점 재구성 손실을 통해 최적화</p></li>
 <li><p>위의 문제를 해결하고 성공적인 subject 맞춤 text-to-3D 생성을 위해 효율적인 3단계 최적화 방식을 기반으로 한 Dream-Booth3D 제안</p></li>
 </ul>
+<hr class="docutils" />
 <p><strong>1️⃣Stage-1: 3D with Partial DreamBooth</strong></p>
+<figure class="align-default" id="id9">
+<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled_9.png"><img alt="Untitled_9" class="bg-primary mb-1" src="../../_images/Untitled_9.png" style="width: 800px;" /></a>
+<figcaption>
+<p><span class="caption-number">Fig. 650 </span><span class="caption-text">Stage-1 :  3D with Partial DreamBooth</span><a class="headerlink" href="#id9" title="Permalink to this image">#</a></p>
+</figcaption>
+</figure>
 <ul class="simple">
 <li><p>입력된 Subject 이미지를 사용하여 DreamBooth 모델 <span class="math notranslate nohighlight">\(\hat{\mathcal{D}}_\theta\)</span> 를 훈련</p></li>
 </ul>
-<aside>
-🌟 **DreamBoothT2I 모델의 초기 체크포인트가 (=부분적으로 파인튜닝한 결과) 주어진 subject view에 과적합되지 않음을 확인**
-<p><strong>⇒ partial DreamBooth (부분적으로 파인튜닝한 Dreambooth)</strong></p>
-</aside>
+<hr class="docutils" />
+<p>🌟 <strong>DreamBoothT2I 모델의 초기 체크포인트가 (=부분적으로 파인튜닝한 결과) 주어진 subject view에 과적합되지 않음을 확인</strong></p>
+</section>
+<section id="partial-dreambooth-dreambooth">
+<h2><strong>⇒ partial DreamBooth (부분적으로 파인튜닝한 Dreambooth)</strong><a class="headerlink" href="#partial-dreambooth-dreambooth" title="Permalink to this heading">#</a></h2>
 <ul class="simple">
 <li><p><strong>partial DreamBooth</strong> 모델 하에 DreamFusion은 더 일관된 3D NeRF를 생성가능</p></li>
 <li><p>NeRF 최적화시 SDS 손실 사용 :</p>
@@ -851,15 +879,21 @@ <h2>3.3. Dreambooth3D Optimization<a class="headerlink" href="#dreambooth3d-opti
 </li>
 <li><p><strong>partial DreamBooth</strong> 모델과 NeRF 결과물은 입력된 subject 와 완전히 유사하지 않음</p></li>
 </ul>
-<aside>
-🌟 즉,  **1️⃣ Stage-1 에서의 초기 NeRF** 는 **주어진 subject 와  부분적으로ㅁ 유사**하면서, 
-**주어진 텍스트 프롬프트에 충실한**  **subject class 3D 모델**
-</aside>
+<hr class="docutils" />
+<p>🌟 즉,  <strong>1️⃣ Stage-1 에서의 초기 NeRF</strong> 는 <strong>주어진 subject 와  부분적으로ㅁ 유사</strong>하면서,
+<strong>주어진 텍스트 프롬프트에 충실한</strong>  <strong>subject class 3D 모델</strong></p>
+<hr class="docutils" />
 <p><strong>2️⃣Stage-2: Multi-view Data Generation</strong></p>
-<aside>
-🌟 **Stage-2 Multi-view Data Generation : 본 접근법의 가장 중요한 부분**
+<figure class="align-default" id="id10">
+<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled_10.png"><img alt="Untitled_10" class="bg-primary mb-1" src="../../_images/Untitled_10.png" style="width: 800px;" /></a>
+<figcaption>
+<p><span class="caption-number">Fig. 651 </span><span class="caption-text">Stage-2: Multi-view Data Generation</span><a class="headerlink" href="#id10" title="Permalink to this image">#</a></p>
+</figcaption>
+</figure>
+<hr class="docutils" />
+<p>🌟 <strong>Stage-2 Multi-view Data Generation : 본 접근법의 가장 중요한 부분</strong></p>
 <p>일관성을 갖춘 <strong>3D initial NeRF</strong> 와 <strong>fully-trained DreamBooth</strong> 를 사용하여 <strong>pseudo multi-view subject</strong> 이미지들을 생성</p>
-</aside>
+<hr class="docutils" />
 <ol class="arabic">
 <li><p><strong>Initial NeRF</strong> 로부터 다양한 랜덤 viewpoints  <span class="math notranslate nohighlight">\(\{v\}\)</span>을 따라 여러 이미지<span class="math notranslate nohighlight">\(\left\{\hat{I}v \in \mathbb{R}^{n \times 3}\right\}\)</span> 를 렌더링하여 다중 시점 렌더링을 생성</p></li>
 <li><p>각 <strong>렌더링에 고정된 양의 노이즈를 추가하는 forward diffusion</strong> 과정을 통해 <span class="math notranslate nohighlight">\(t_{pseudo}\)</span>로 전환</p></li>
@@ -874,14 +908,14 @@ <h2>3.3. Dreambooth3D Optimization<a class="headerlink" href="#dreambooth3d-opti
 </ul>
 </li>
 </ol>
-<aside>
-🔑 **Key insight**
-1. 초기 NeRF 이미지가 unseen views 에 가까울 경우, 
-    DreamBooth가 Subject 의 unseen views를 효과적으로 생성 가능 
-<ol class="arabic simple" start="2">
+<hr class="docutils" />
+<p>🔑 <strong>Key insight</strong></p>
+<ol class="arabic simple">
+<li><p>초기 NeRF 이미지가 unseen views 에 가까울 경우,
+DreamBooth가 Subject 의 unseen views를 효과적으로 생성 가능</p></li>
 <li><p>입력 이미지에 비해 Subject 와 더 유사한 출력 이미지를 효과적으로 생성가능**</p></li>
 </ol>
-</aside>
+<hr class="docutils" />
 <ul class="simple">
 <li><p>위 그림을 통해 체크할 부분</p>
 <ul>
@@ -892,14 +926,16 @@ <h2>3.3. Dreambooth3D Optimization<a class="headerlink" href="#dreambooth3d-opti
 </li>
 </ul>
 <p><strong>3️⃣Stage-3: Final NeRF with Multi-view DreamBooth</strong></p>
-<figure class="align-default" id="id7">
-<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled_12.png"><img alt="Untitled_12" class="bg-primary mb-1" src="../../_images/Untitled_12.png" style="width: 600px;" /></a>
+<figure class="align-default" id="id11">
+<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/Untitled_12.png"><img alt="Untitled_12" class="bg-primary mb-1" src="../../_images/Untitled_12.png" style="width: 800px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 648 </span><span class="caption-text">Stage-3 : Final NeRF with Multi-view DreamBooth
-SDS와 multi-view reconstruction 손실을 사용한 최종 NeRF 최적화</span><a class="headerlink" href="#id7" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 652 </span><span class="caption-text">Stage-3 : Final NeRF with Multi-view DreamBooth
+SDS와 multi-view reconstruction 손실을 사용한 최종 NeRF 최적화</span><a class="headerlink" href="#id11" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
-<p><strong>새로운 데이터 <span class="math notranslate nohighlight">\(I^{\text{aug}}\)</span> 생성</strong></p>
+<blockquote>
+<div><p><strong>새로운 데이터 <span class="math notranslate nohighlight">\(I^{\text{aug}}\)</span> 생성</strong></p>
+</div></blockquote>
 <ul class="simple">
 <li><p>가상 다중 시점 이미지 <span class="math notranslate nohighlight">\(\left\{I_v^{\text {pseudo }}\right\}\)</span>, 입력 Subject 이미지 <span class="math notranslate nohighlight">\(\left\{I_i\right\}\)</span> 의 결합을 통해 생성</p></li>
 </ul>
@@ -907,13 +943,17 @@ <h2>3.3. Dreambooth3D Optimization<a class="headerlink" href="#dreambooth3d-opti
 \[
 I^{\text {aug }}=\left\{I_v^{\text {pseudo }}\right\} \cup\left\{I_i\right\}
 \]</div>
-<p><strong><span class="math notranslate nohighlight">\(I^{\text {aug}}\)</span> 를 사용하여 최종 Multi-view DreamBooth 모델을 최적화</strong></p>
+<blockquote>
+<div><p><strong><span class="math notranslate nohighlight">\(I^{\text {aug}}\)</span> 를 사용하여 최종 Multi-view DreamBooth 모델을 최적화</strong></p>
+</div></blockquote>
 <ol class="arabic simple">
 <li><p>1단계에서 <strong>partial DreamBooth</strong> <span class="math notranslate nohighlight">\(\hat{\mathcal{D}}_{\theta^*}\)</span> 준비</p></li>
 <li><p>위의 증강 데이터 <span class="math notranslate nohighlight">\(I^{\text {aug}}\)</span>  를 사용하여 <span class="math notranslate nohighlight">\(\hat{\mathcal{D}}_{\theta^*}\)</span> 에 대한 <strong>파인튜닝</strong>을 추가 진행</p></li>
 <li><p><strong>Multi-view DreamBooth</strong> <span class="math notranslate nohighlight">\(\hat{\mathcal{D}}_\theta^{\mathrm{multi}}\)</span> 를  생성</p></li>
 </ol>
-<p><span class="math notranslate nohighlight">\(\hat{\mathcal{D}}_\theta^{\text {multi }}\)</span> <strong>모델을 사용하여 DreamFusion SDS Loss 와 함께 NeRF 3D assets 를 최적화</strong></p>
+<blockquote>
+<div><p><span class="math notranslate nohighlight">\(\hat{\mathcal{D}}_\theta^{\text {multi }}\)</span> <strong>모델을 사용하여 DreamFusion SDS Loss 와 함께 NeRF 3D assets 를 최적화</strong></p>
+</div></blockquote>
 <ul class="simple">
 <li><p>1단계의 <strong>partial DreamBooth</strong>에 비해 <strong>multi-view DreamBooth</strong> 의 뷰 일반화와 subject 보존 능력이 더 우수하기 때문에 <strong>subject idendtity가 상당히 향상된 NeRF 모델 생성 가능</strong></p></li>
 <li><p><strong>BUT</strong> SDS 손실만 사용시 최적화된 NeRF assets 이</p>
@@ -932,6 +972,7 @@ <h2>3.3. Dreambooth3D Optimization<a class="headerlink" href="#dreambooth3d-opti
 </ul>
 </li>
 </ul>
+<hr class="docutils" />
 <p><strong>Reconstruction loss</strong></p>
 <ul>
 <li><p><span class="math notranslate nohighlight">\(\left\{I_v^{\mathrm{pseudo}}\right\}\)</span> 가 생성된 카메라 매개변수 <span class="math notranslate nohighlight">\(\left\{P_v\right\}\)</span> 정보를 알고 있으므로,
@@ -1099,6 +1140,12 @@ <h2>4.2. Sample Applications<a class="headerlink" href="#sample-applications" ti
 <section id="limitations">
 <h2>4.3. Limitations<a class="headerlink" href="#limitations" title="Permalink to this heading">#</a></h2>
 <hr class="docutils" />
+<figure class="align-default" id="id12">
+<a class="bg-primary mb-1 reference internal image-reference" href="../../_images/limitation.png"><img alt="limitation" class="bg-primary mb-1" src="../../_images/limitation.png" style="width: 600px;" /></a>
+<figcaption>
+<p><span class="caption-number">Fig. 653 </span><span class="caption-text">limitations</span><a class="headerlink" href="#id12" title="Permalink to this image">#</a></p>
+</figcaption>
+</figure>
 <ol class="arabic simple">
 <li><p>최적화된 3D 표현이 때때로 과도하게 포화되고 매끄럽게 처리되는 경우가 존재</p>
 <ol class="arabic simple">
@@ -1197,6 +1244,7 @@ <h1>5. Conclusion<a class="headerlink" href="#conclusion" title="Permalink to th
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#preliminaries">3.1. Preliminaries</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#failure-of-naive-dreambooth-fusion">3.2 Failure of Naive Dreambooth+Fusion</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#dreambooth3d-optimization">3.3. Dreambooth3D Optimization</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#partial-dreambooth-dreambooth"><strong>⇒ partial DreamBooth (부분적으로 파인튜닝한 Dreambooth)</strong></a></li>
 </ul>
 </li>
 <li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#experiments">4. Experiments</a><ul class="visible nav section-nav flex-column">
diff --git a/docs/review/DreamPose.html b/docs/review/DreamPose.html
index 8f81572b..812c789e 100755
--- a/docs/review/DreamPose.html
+++ b/docs/review/DreamPose.html
@@ -714,7 +714,7 @@ <h1>3. Background<a class="headerlink" href="#background" title="Permalink to th
 </li>
 <li><p>Latent diffusion model (ex. Stable Diffusion)</p>
 <figure class="align-default" id="id2">
-<img alt="latent diffusion" class="bg-primary mb-1" src="../../_images/Untitled1.png" />
+<img alt="latent diffusion" class="bg-primary mb-1" src="../../_images/Untitled.png" />
 <figcaption>
 <p><span class="caption-number">Fig. 573 </span><span class="caption-text">Latent Diffusion Model</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
 </figcaption>
diff --git a/searchindex.js b/searchindex.js
index d96035d2..a91b5243 100755
--- a/searchindex.js
+++ b/searchindex.js
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39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50], "\uc18d\ub3c4\uc640": [2, 22], "\ubcf4\uc5ec\uc8fc\uc5b4": 2, "\uace0\uc218\uc900\uc758": 2, "\ubdf0": [2, 17, 18, 29], "\ud569\uc131\uc5d0": [2, 36, 45], "\ub300\ud574": [2, 3, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 35, 36, 37, 38, 39, 40, 43, 44, 46, 47, 48, 49, 50], "\ucc98\uc74c\uc73c\ub85c": 2, "\ub80c\ub354\ub9c1\uc744": [2, 17], "\uc81c\uacf5\ud55c\ub2e4": [2, 27, 47], "normal": [2, 3, 7, 12, 13, 14, 17, 18, 20, 29, 32, 34, 39, 40, 44, 45], "\ud45c\uba74": 2, "\ubc95\uc120": 2, "\uc5c6\ub294": [2, 5, 7, 15, 16, 17, 19, 25, 31, 32, 33, 40, 42, 43, 45], "\ud3ec\uc778\ud2b8\ub4e4\uc758": 2, "sparse\ud55c": 2, "\uc14b\uc744": [2, 29, 32, 33, 36, 37], "initi": [2, 7, 14, 17, 18, 26, 39], "point\ub85c": [2, 32], "\ud558\uc5ec": [2, 3, 6, 8, 32, 37, 40, 45], "\uace0\ud488\uc9c8\uc758": [2, 4, 17, 47], "novel": [2, 24, 29, 32, 43], "view": [2, 4, 17, 18, 32, 34, 38, 43, 44, 50], 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"\uc608\uce21\ub41c": [6, 14, 18, 19, 22, 28, 29], "\ub178\uc774\uc988": [6, 17, 19, 22, 27, 31, 32, 43], "\ud3c9\uade0\uac12\uc774\uba70": 6, "tild": [6, 13, 15, 19, 20, 22, 23, 37, 47, 48, 50], "\ub178\uc774\uc988\uc758": [6, 37], "\uc2e0\uacbd\ub9dd": [6, 9, 22], "\uc608\uc804": 6, "\uac19\uc558\uc74c": 6, "\uc774\ub85c\ubd80\ud130": 6, "elbo": [6, 13, 41], "term": [6, 7, 23, 44, 50], "\uace7": [6, 11], "\ub418\ubbc0\ub85c": [6, 9, 14, 32], "\ubca0\uc774\uc988": 6, "\uc774\ub860\uacfc": 6, "markov": [6, 12, 13, 44], "chain": [6, 11, 13, 42, 43, 44], "properti": [6, 28, 39, 43], "\ub3c4\ucd9c": 6, "markovian": [6, 13, 15, 28, 42, 48], "\uc131\ub9bd\ub428\uc744": 6, "\uc815\ub9ac\ub428": 6, "\ud1b5\ud569\ud558\uace0": 6, "reparameter": [6, 41, 47], "mu_t": 6, "\ubcc0\ud615\ud560": 6, "\ubcc0\ud615": [6, 27], "\uc608\uce21\ud558\ub294": [6, 7, 13, 15, 21, 22, 28, 32, 37, 47, 48], "\uc608\uce21\ud558\ub3c4\ub85d": [6, 13, 19, 23, 28, 29, 48], "\ud559\uc2b5\ub428": 6, "\ub0b4\uc6a9\uc744": [6, 8, 11, 16, 22, 27], "\uc2dd\uc5d0": [6, 39, 47], "\uba85\uc2dc\ud558\uae30": 6, "\uba85\uc2dc\ub41c": 6, "\uc368\ubcfc": 6, "\uc368\ubcf4\ub294": 6, "\uac83\uc784": 6, "\uadf8\ub7f0\ub370": [6, 9, 34], "\ucc38\uace0\ud574\ubcf4\uba74": 6, "\uc6b0\ub9ac\ub294": [6, 11, 29, 50], "\uadfc\uc0ac\ud558\ub3c4\ub85d": 6, "\ud559\uc2b5\uc2dc\ucf1c\uc57c\ud568": 6, "mu": [6, 13, 18, 34, 39, 42, 47, 48, 50], "_t": [6, 15, 19, 24, 39, 47, 49], "\uc815\ub9ac\ub41c": 6, "\ud559\uc2b5\ub418\uc5b4\uc57c\ud558\ub294": 6, "kl": [6, 13, 18, 25, 32, 44, 50], "arg": [6, 38, 43, 50], "min_": [6, 50], "c_": [6, 8, 10, 17, 19, 20, 31, 33, 39, 43, 47], "\ub2e8\uc21c\ud654\ub420": 6, "algorithm": [2, 6, 13, 29, 38, 39], "\uc815\ub9ac": [6, 40], "\ub9c8\uce58": [6, 11, 24], "\uadf8\ub7ec\ud588\ub4ef\uc774": 6, "\ube60\uc9c4": 6, "acceler": [6, 12, 42, 43], "\uac00\uc18d\uc2dc\ud0ac": 6, "\uae38\uc774\ub97c": [6, 27], "\ub450\uc5c8\uc744": 6, "varibal": 6, "subset": [6, 19], "\uc815\uc758\ub428": 6, "\uac12\uc758": [6, 14, 19, 23, 47], "\ub450\uc5c8\uc74c": 6, "experi": [2, 6, 51], "setup": [6, 17, 30], "\ud558\uc774\ud37c\ub9c8\ub77c\ubbf8\ud130": 6, "\ud504\ub808\uc784\uc6cc\ud06c\ub294": 6, "\uc774\ub8e8\uc5b4\uc9d0": 6, "\uc0ac\uc6a9\ub41c": [6, 7, 8, 17, 19, 40, 48], "stage": [6, 11, 16, 17, 24, 32, 49], "fid": [6, 7, 11, 13, 14, 21, 23, 25, 28, 31, 34, 39, 41, 42, 45, 47, 48, 49], "\uc0dd\uc131\ubb3c\uc758": [6, 8], "\ud3c9\uac00\ud558\uae30": [6, 11, 24, 27, 32, 35], "5\uac1c\uc758": [6, 8, 19, 20, 36], "\uc0d8\ud50c\uc744": [6, 7, 17, 21, 22, 27, 29, 36, 40, 50], "\ub9c8\ub2e4\uc758": 6, "\ud45c\uc900\ud3b8\ucc28\uc758": 6, "\ub300\ud574\uc11c": [6, 11, 18, 19, 21, 22, 24, 25, 27, 29, 34, 35, 36, 38, 39, 40, 43, 44, 48], "\ud3c9\uade0": [6, 7, 8, 13, 16, 27, 32, 43, 50], "\ub0c4": [6, 28, 41], "\uc2e4\ud5d8\ud568": 6, "\ub2a5\ub825": [6, 11], "celebamask": 6, "hq": [6, 15, 22, 25], "layout": [6, 24, 25], "\uc8fc\uace0": [6, 16, 21], "photorealist": 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10, 11, 13, 17, 19, 20, 24, 25, 26, 29, 35, 38, 39, 40, 41, 42, 43, 44, 47, 48, 49], "\uc601\ud5a5": [6, 42], "\ubc1b\uc74c": [6, 45], "\uc904": [6, 15, 21, 31, 32, 40, 42, 45], "rregular": 6, "occlus": [6, 19], "\ub098\ud0c0\ub098\ub294\ub370": 6, "\uc9c1\uc811\uc801\uc778": [6, 44], "\ud558\ubbc0\ub85c": [6, 13, 15, 32, 50], "\ubb38\uc81c\ub85c\ubd80\ud130": 6, "\uc790\uc720\ub85c\uc6c0": 6, "\ud2b9\uc131\uc73c\ub85c": 6, "\uc0dd\uc131\ud574\ub0c4": 6, "\uc2e4\ud5d8\uc5d0\uc11c": [6, 21, 32, 40, 50], "\uae30\ub85d\ud588\uc73c\uba70": 6, "\uae30\ub85d\ud568": [6, 41], "\uc131\ub2a5\uc740": [6, 19, 23, 35, 38], "\ub6f0\uc5b4\ub0a8": 6, "\uc810\uc218\uac00": [6, 7, 14, 41, 45], "\ub6f0\uc5b4\ub0ac\uc74c": [6, 14], "\uc2e4\ud5d8\ud588\uc74c": 6, "\uc5d0\uc11c\ub3c4": [6, 40, 43], "campar": 6, "\uae30": 6, "\ub85d\ud568": 6, "\uc0c9\uc0c1\ud654": 6, "\ub4f1\uc758": [6, 13, 19, 22, 29, 36, 39, 40, 47, 48], "studi": [6, 8, 10, 17, 22, 33, 48], "factor": [6, 14, 25], "\ud588\uc74c": [6, 42], 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8, "\uae30\ubc1c\ud55c": 8, "\uc811\uadfc": [8, 17, 18, 19, 22, 29, 36], "\ubc29\ubc95\uc774": [8, 15, 17, 18, 19, 21, 22, 24, 27, 35, 40, 47], "xu": 8, "set": [8, 13, 16, 17, 18, 20, 26, 38, 40, 43, 45, 48], "evolut": 8, "\ud65c\uc6a9\ud574": [8, 11, 16, 31, 37, 38, 40, 42, 49], "\ud615\ud0dc\uc758": [8, 29, 31, 32, 36, 43], "\uc81c\uc548\ud588\uc2b5\ub2c8\ub2e4": [8, 34], "elgamm": 8, "gan\uc758": [8, 15, 25, 36], "\ud0d0\uad6c\ud558\uba70": 8, "\uc2a4\ud0c0\uc77c\uc5d0\uc11c\uc758": 8, "\ud3b8\ucc28\ub97c": 8, "\uadf9\ub300\ud654\ud558\ub294": 8, "\ud559\uc2b5\ud588\uc2b5\ub2c8\ub2e4": [8, 11], "sbai": 8, "\uc190\uc2e4": [8, 17, 41], "\ub3c4\ub9bd\ud588\uc2b5\ub2c8\ub2e4": 8, "\uce74\ud14c\uace0\ub9ac\uc640": 8, "\uc77c\uce58\ud558\ub3c4\ub85d": [8, 36], "\ucd5c\uc801\ud654\ud558\uba74\uc11c\ub3c4": 8, "\uce74\ud14c\uace0\ub9ac\uc758": 8, "\uac1c\ub150\ub4e4\uacfc": 8, "\ucc3e\ub294": [8, 15, 19, 36], "\uc811\uadfc\ud588\uc2b5\ub2c8\ub2e4": 8, "\uac1c\ub150\ub4e4\uc740": 8, "\ud63c\ud569\ub420": 8, "\uc720\uc5f0\ud55c": 8, "\uac16\uac8c\ub429\ub2c8\ub2e4": [8, 34], "bottom": 8, "model\uc5d0\uc11c\ub294": [8, 36], "\uc624\ud1a0\uc778\ucf54\ub354\uc758": [8, 19], "\ub0b4\uc5d0\uc11c": [8, 22, 29, 31, 36, 37, 46], "\uc9c4\ud589\ub429\ub2c8\ub2e4": [8, 48], "\uba3c\uc800": [8, 9, 17, 22, 24, 27, 29, 34, 35, 38, 41, 44], "\uc778\uace0\ub354": 8, "\ub9e4\ud551\ud558\ub294": [8, 40], "d\ub294": [8, 15], "\uc7ac\uad6c\uc131\ud558\ub3c4\ub85d": 8, "ddpm\uc758": [8, 12, 21, 23, 33, 42], "\uc190\uc2e4\uc744": [8, 17, 19, 22], "\ucd5c\uc18c\ud654\ud558\ub3c4\ub85d": [8, 22, 39], "\ud559\uc2b5\ud569\ub2c8\ub2e4": [8, 40, 44, 48, 49], "\uc7a0\uc7ac": [8, 24, 35], "\ucf54\ub4dc": [8, 27, 34, 38], "zt": 8, "\uc7a1\uc74c": 8, "\uc81c\uac70\ud569\ub2c8\ub2e4": 8, "\ud604\uc7ac": [8, 11, 12, 18, 33, 35], "t\uc640": 8, "\uc870\uac74": [8, 17, 19, 21], "\ubca1\ud130": [8, 14, 19, 25, 29], "c\ub3c4": 8, "\uace0\ub824\ub429\ub2c8\ub2e4": 8, "model\uc740": [8, 13, 15, 20, 21, 24, 25, 26, 28, 32, 36, 47], "\uc778\ucf54\ub529\uc5d0\uc11c": 8, "\ud30c\uc0dd\ub41c": [8, 19, 36], "\ud504\ub86c\ud504\ud2b8": [8, 17, 18, 24, 27], "decompos": [8, 22], "\ud504\ub86c\ud504\ud2b8\ub85c\ubd80\ud130": 8, "\uc608\uce21\ud569\ub2c8\ub2e4": [8, 22, 40], "\ub2e4\uc74c\uc73c\ub85c": [8, 22], "\ubd80\uc5ec\ud558\uc5ec": 8, "decoder\ub85c": 8, "\ubcf4\ub0b4\uc9d1\ub2c8\ub2e4": 8, "\ub3c5\ub9bd\uc801\uc778": [8, 12, 39], "\uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4": 8, "l_": [8, 18, 23, 30, 32, 37, 39, 42, 47, 49], "\ub2e4\uc591\uc131\uc744": [8, 16, 17, 35], "\ud5a5\uc0c1\uc2dc\ud0a4\uba70": 8, "\uc811\uadfc\ud558\uace0": 8, "\uacf5\uac04\uc5d0\uc11c": [8, 14, 17, 29, 36], "conceptlab\uc740": 8, "\ub300\ud45c\ud558\ub294": 8, "v_": [8, 20], "\ucd5c\uc801\ud654\ud569\ub2c8\ub2e4": 8, "\uce74\ud14c\uace0\ub9ac\uc5d0": [8, 27], "\uc720\uc0ac\ud558\uba74\uc11c\ub3c4": [8, 36], "\uba64\ubc84\ub4e4\uacfc": 8, "\uac00\uc9c0\ub3c4\ub85d": [8, 32, 35], "\uc9d1\ud569\uc744": [8, 17, 29], "\uacc4\uc0b0\ud569\ub2c8\ub2e4": [8, 39, 43], "\ud6c8\ub828\ud558\ub294": [8, 19, 24], "\ubc14\ud0d5\uc73c\ub85c": [8, 25, 29, 40, 48], "contraints\ub97c": 8, "\ub354\ud558\uae30": 8, "blip": [8, 11], "vqa": 8, "\uae0d\uc815\uc801": 8, "\uc81c\uc57d": 8, "po": 8, "\uac00\uc9c0\ub97c": 8, "\uc870\uac74\uc740": 8, "\uc815\uc758\ub429\ub2c8\ub2e4": 8, "\ub450\uac00\uc9c0": [8, 32, 42], "\uce21\uc815\ud569\ub2c8\ub2e4": [8, 11, 48], "\uc6b0\uc120": [8, 9, 11, 21, 24, 31, 43, 48], "\ub2e8\uc5b4": [8, 27, 33, 36], "\ubb34\uc791\uc704": [8, 22], "\uc0d8\ud50c\ub9c1\ub41c": [8, 29], "y\uc5d0": 8, "\ud1b5\ud569\ud569\ub2c8\ub2e4": 8, "\ubb38\uc7a5\uc740": 8, "\uc778\ucf54\ub529\ub418\uba70": 8, "\uc774\uac83\uc774": [8, 11, 40], "\uc815\uc758\ud569\ub2c8\ub2e4": [8, 39, 43, 46, 48, 49, 50], "\ud1b5\uacfc\uc2dc\ud0a4\uba74": 8, "\ud504\ub86c\ud504\ud2b8\uc758": [8, 19], "\uc778\uc2a4\ud134\uc2a4\uac00": 8, "\uc870\uac74\uacfc": 8, "\uae0d\uc815": 8, "\ubd80\uc815": 8, "\uad11\ubc94\uc704\ud558\uac8c": 8, "\uc720\uc9c0\ud558\uace0\uc790": [8, 43], "\ud1b5\uacfc\ud558\uc9c0": 8, "\uc54a\uc2b5\ub2c8\ub2e4": [8, 11, 39, 40], "\uc5f0\uad6c\uc5d0\uc11c\uc758": 8, "\ud568\uc218\ub294": [8, 25, 29], "langl": 8, "rangl": 8, "lambda": [8, 13, 17, 24, 39, 40, 43], "v\uc5d0\uc11c": 8, "\uc870\uac74\uc5d0\uc11c": 8, "\uba40\uc5b4\uc9c0\uace0": 8, "\uc81c\uc57d\uc870\uac74\uc5d0": 8, "\uac00\uae4c\uc6cc\uc9c0\ub3c4\ub85d": [8, 15], "\uc815\uaddc\ud654\ub294": 8, "\uc9d1\ud569\uc774": 8, "\ud074": [8, 35], "\uba64\ubc84\ub85c\uc758": 8, "collapsing\uc744": 8, "\ubc29\uc9c0\ud558\ub294": [8, 17, 18, 19], "\uc0ac\uc6a9\ub429\ub2c8\ub2e4": [8, 9, 22], "\uc81c\uc57d\uc5d0": 8, "s_": [8, 18, 19, 20, 33, 39], "max_": 8, "\ubc29\uc2dd\uc740": [8, 10, 22, 26, 29, 32, 36, 47, 48, 49], "\ud568\uc218\uc5d0": 8, "\ud1b5\ud569\ub418\uba70": 8, "\ub0c4\uc73c\ub85c\uc368": 8, "\uc870\uac74\uc5d0": 8, "\ud328\ub110\ud2f0\ub97c": [8, 40], "\ubd80\uc5ec\ud569\ub2c8\ub2e4": 8, "\uacfc\uc815": [8, 10, 13, 23, 27, 28, 29, 32, 36], "\uac1c\ub150\uc5d0": [8, 17, 36], "\ub2e8\uc5b4\ub97c": [8, 36], "\ucd94\ub860\ud558\uace0": [8, 38], "\uac70\uce69\ub2c8\ub2e4": 8, "\uc218\ub3d9\uc73c\ub85c": 8, "\uc801\uc6a9\ud558\ub294": [8, 13, 15, 26, 39, 43, 47, 48, 49], "\ud798\ub4e4\uace0": 8, "\uad11\ubc94\uc704\ud55c": [8, 17], "\uba64\ubc84\ub4e4\uc744": 8, "\ub300\ud45c\ud558\uc9c0": 8, "\ubabb\ud560": [8, 22, 31], "\ud655\uc7a5\ud558\ub294": [8, 27], "scheme\uc744": 8, "\ud6c8\ub828\ub41c": [8, 9, 17, 19, 22, 24, 35, 36], "\uc9c8\uc758\ud558\uc5ec": 8, "\uc774\ubbf8\uc9c0\uc5d0": [8, 10, 12, 13, 17, 19, 20, 23, 27, 28, 29, 30, 33, 35, 36, 38, 40, 41], "\uc874\uc7ac\ud558\ub294": [8, 11, 17, 29, 38], "\uba64\ubc84\uac00": 8, "\ubb34\uc5c7\uc778\uc9c0": [8, 25, 43, 50], "\uc2dd\ubcc4\ud558\ub3c4\ub85d": 8, "\ub098\uc628": [8, 15, 21, 22, 39, 41, 42, 45, 47, 48, 49, 50], "\uc778\uc2a4\ud134\uc2a4\ub97c": 8, "\ud6c8\ub828\uc758": 8, "\ucd94\uac00\ud569\ub2c8\ub2e4": 8, "\ub2e8\uacc4\uc5d0": [8, 39], "\uac78\uccd0": [8, 17, 27, 36], "\ubcf4\uc5ec\uc90d\ub2c8\ub2e4": [8, 9, 40, 43, 49, 50], "\uc870\uac74\uc774": [8, 19, 22], "\uc9c0\uc18d\uc801\uc73c\ub85c": [8, 25, 35], "\uc870\uc815\ub418\uace0": 8, "\ud655\uc7a5\ub418\uc5c8\uc74c\uc744": 8, "\uac1c\ub150": [8, 36, 43, 47], "\uc14b\uc5d0": [8, 19], "\ud63c\ud569": [8, 35], "\uac1c\ub150\uc5d0\uc11c": 8, "im": 8, "\ub9cc\ub4ed\ub2c8\ub2e4": [8, 40], "\ud1b5\uacfc\ud558\uc5ec": [8, 32], "\uc0dd\uc131\ud569\ub2c8\ub2e4": [8, 11, 22, 39, 49, 50], "\uc801\uc6a9\ud569\ub2c8\ub2e4": [8, 11, 40], "\uac1c\ub150\uc774\ub098": 8, "\uc801\uc6a9\ub420": 8, "\uacc4\uce35\u3147\uc801": 8, "\ubc18\ubcf5\uc801\uc73c\ub85c": [8, 19, 29], "\uacb0\uacfc\ubb3c\uc5d0": 8, "\uac1c\ub150\uc758": 8, "\uc81c\uc5b4\ud558\uae30": [8, 16, 17, 22], "\uac00\uc911\uce58": [8, 17, 19, 22, 40, 45], "\ud56d\ubaa9\uc774": [8, 17], "\ucd94\uac00\uc801\uc73c\ub85c": [8, 9, 11, 15, 17, 21, 24, 31, 32, 35, 39, 48], "\uadf8\ub9bc\uc5d0\ub294": 8, "\uac1c\ub150\uc774": 8, "\uc67c\ucabd\uc5d0": [8, 27], "\ud45c\uc2dc\ub418\uc5b4": 8, "\uc0dd\uc131\ud588\ub294\uc9c0\ub97c": 8, "\ud65c\uc6a9\ud588\uc2b5\ub2c8\ub2e4": [8, 22, 48], "conceptlab\uc774": 8, "\uc81c\uc548\ud55c": [8, 15, 19, 25, 34, 35], "\ud504\ub86c\ud504\ud2b8\uc640": [8, 17, 27], "\uc801\uc6a9\ud588\uc2b5\ub2c8\ub2e4": [8, 49], "\uac1c\ub150\ub4e4\uc744": 8, "\ud63c\ud569\ud558\uc5ec": [8, 19, 47], "\ub3c5\ud2b9\ud55c": 8, "\ucc3d\uc870\ubb3c\uc744": 8, "\uc717\uc904\uc5d0\uc11c\ub294": 8, "\uc774\uc5b4\uc9c0\ub294": [8, 36], "\uc904\uc5d0\uc11c\ub294": 8, "\uc5bb\uc5b4\uc9c4": [8, 17], "conceptlab\uc758": 8, "\uc785\uc99d\ud558\uae30": 8, "\uc815\uc131\uc801": [8, 10, 17, 19, 25, 41], "\uc815\ub7c9\uc801": [8, 10, 17, 19, 22, 28, 41], "\ud3c9\uac00\ub97c": [8, 18, 19, 22, 24, 27, 31, 40, 41, 44, 45, 47], "\uadf8\ub9bc\ub4e4\uc5d0\uc11c": 8, "\uc788\ub4ef\uc774": [8, 15, 35], "negative\ub97c": 8, "\uc801\uc6a9\ud558\uc600\uace0": 8, "\uc2dc\ub4dc\ub97c": 8, "\ub2ec\ub9ac\ud558\uba70": 8, "\ub2a5\ub825\uc774": [8, 11, 17, 24, 38], "\uc7a5\uba74\uc5d0": [8, 29, 36], "\ubc30\uce58\ud560": 8, "\uc0dd\uc131\ubb3c\ub4e4\uc740": 8, "\ubc30\uacbd": [8, 17, 18, 19, 20, 29, 31, 36], "\ucc3d\uc870\ub4f1": 8, "\ub2e4\uc591\ud558\uac8c": [8, 25, 46, 48], "\uac00\ub2a5\ud569\ub2c8\ub2e4": [8, 11, 22, 39, 43, 48, 49], "conceptlab\uc744": 8, "mixing\uc758": 8, "mixing\uc740": 8, "\uac1c\ub150\ub4e4\uc758": 8, "\ud569\uccd0": 8, "\ud558\uc774\ube0c\ub9ac\ub4dc": 8, "\ud615\uc131\ud558\ub294": 8, "\uc870\uac74\ub9cc\uc744": 8, "\uc904\uc5d0\ub294": 8, "\ub78d\uc2a4\ud130\uc758": 8, "\uc0dd\uc0c1\uacfc": 8, "\uc9d1\uac8c\ubc1c": 8, "\uac70\ubd81\uc774\uc758": 8, "\ub4f1\uaecd\uc9c8": 8, "\uc735\ud569\ud558\ub294": 8, "conceptlab\uc5d0": 8, "\uac1c\ub150\ub4e4\uc774": 8, "\uc138\ub300": 8, "\uc5b4\ub5bb\uac8c": [8, 9, 11, 13, 25, 27, 28, 35, 36], "\ubc1c\uc804\ud558\ub294\uc9c0": 8, "diffusion2\uc640": 8, "kandinski": 8, "\ud3c9\uac00\ud588\uc2b5\ub2c8\ub2e4": [8, 44], "kandinsky\uc758": 8, "\uc720\ub9ac\ud55c": 8, "\ud504\ub86c\ud504\ud2b8\ub294": [8, 22, 27], "model\uc774": [8, 15, 18, 21, 23, 24, 25], "\ud1a0\uadfc\uacfc": 8, "\ubaa8\ub450\uc5d0": 8, "\ub9de\ucdb0\uc9c8": 8, "\ub2e4\uc911": [8, 17, 31], "\ucc98\ub9ac\ud558\uace0": 8, "\ud45c\ud604\uc744": [8, 16, 17, 19, 29], "\uac16\ucd94\uace0": [8, 11, 15], "\ud3ec\ud568\ud558\uba70": 8, "\uac1c\ub150\uacfc": 8, "\ub2ee\uc9c0": 8, "\uce21\uc815\ud588\uc2b5\ub2c8\ub2e4": 8, "\ud3c9\uac00\uc5d0\ub294": 8, "\uc560\uc644\ub3d9\ubb3c": 8, "\uc2dd\ubb3c": 8, "\uacfc\uc77c": 8, "\uac00\uad6c": 8, "\uc545\uae30\uc758": 8, "5\uac00\uc9c0": [8, 27, 49], "\ub3c4\uba54\uc778\uc5d0": 8, "\uace0\ub824\ud558\uace0": [8, 19], "\uc870\ud569\uc5d0": 8, "\ub79c\ub364": [8, 13, 17, 18, 19, 27, 32], "\uc2dc\ub4dc\ub85c": 8, "\ud6c8\ub828\ud558\uc5ec": 8, "\ucd1d": [8, 11, 14, 16, 20, 25, 29, 33, 34, 35, 36, 40, 48], "75\uac1c\uc758": 8, 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"\uc5f0\uc0b0\uc774": [7, 14, 25, 47], "\ud14d\uc2a4\ud2b8\uc5d0\uc11c": [7, 22, 27], "\ubc29\ubc95\uc758": [7, 8, 24, 36], "probability\ub97c": 7, "\ube84\uc148\ud558\ub294": 7, "\uc5f0\uc0b0\uacfc": 7, "\ube44\uc2b7\ud558\ub2e4": [7, 31], "ms": [7, 20, 21, 41, 45], "coco": [7, 18, 21, 28, 32, 41, 45, 49], "30k": 7, "\uce21\uc815\ud588\ub2e4": [7, 38], "\ud6a8\uc728\uc131\uc774": 7, "\ucd94\ub860\uc5d0\uc11c": 7, "1\uac1c": [7, 41], "2\uac1c\ub85c": 7, "\uc608\uc81c\ub85c": 7, "\ub3d9\uc791\ud560": [7, 35], "\uc6b0\uc218\ud55c": [7, 12, 14, 17, 19, 22, 27, 31, 40, 41], "\uae30\ub85d\ud588\ub2e4": [7, 33], "\uace0\ud488\uc9c8": [7, 11, 16, 19, 22, 31], "\uac80\uc0c9\uc758": 7, "\uc911\uc694\uc131\uc744": [7, 14, 22, 40], "figure5": 7, "llm\uc5d0\uc11c": 7, "\ub2e8\uacc4\uc774\ub2e4": [7, 24], "\uba85\ub839\uc5b4": [7, 18], "\uc774\ud574\ud558\ub294": 7, "\ub3c4\uc640\uc8fc\uba70": 7, "task\uc5d0\uc11c\ub3c4": 7, "\ud29c\ub2dd\uc774": 7, "\ub208\uc5d0": [7, 19], "\ub744\uac8c": [7, 19], 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"\ud65c\uc6a9\ud574\uc11c": [8, 11, 42, 50], "generation\uc5d0\uc11c\uc758": 8, "\uc774\uc810\uc744": [8, 25, 29], "\ubcf4\uc785\ub2c8\ub2e4": [8, 40], "\uc785\ub825\ub41c": [8, 17, 19, 32, 36, 46], "\ud574\ub2f9\ud558\ub294": [8, 9, 19, 21, 28, 29, 46], "\uc784\ubca0\ub529\uc73c\ub85c": [8, 19], "\ub9e4\ud551\ud569\ub2c8\ub2e4": 8, "\ub514\ucf54\ub354\ub294": 8, "\uc784\ubca0\ub529\uc5d0": [8, 19, 31], "condition\uc774": 8, "\ubd80\uc5ec\ub41c": 8, "\uc0dd\uc131\ud558\uae30": [8, 17, 22, 32, 36, 39, 40, 47], "\ud6c8\ub828\ub429\ub2c8\ub2e4": 8, "personalization\uc740": 8, "\ub9e5\ub77d\uc5d0\uc11c": 8, "\uc0ac\uc6a9\uc790\uac00": [8, 17, 30, 31, 36], "\uc8fc\uc81c\ub098": 8, "\uc2a4\ud0c0\uc77c\uc744": [8, 19, 22, 33, 35], "\uc77c\ubc18\uc801\uc73c\ub85c": [8, 17, 18, 19, 29, 32, 35, 36, 39, 47], "\ud559\uc2b5\uc2dc\ud0a4\uae30": [8, 18, 32], "\ucd5c\uc801\ud654\ud558\uac70\ub098": 8, "\ud65c\uc6a9\ud569\ub2c8\ub2e4": [8, 17, 39], "\ucd08\ucca8\uc744": [8, 36], "\ub9de\ucd94\uace0": 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"\ud63c\ud569\ub420": 8, "\uc720\uc5f0\ud55c": 8, "\uac16\uac8c\ub429\ub2c8\ub2e4": [8, 34], "bottom": 8, "model\uc5d0\uc11c\ub294": [8, 36], "\uc624\ud1a0\uc778\ucf54\ub354\uc758": [8, 19], "\ub0b4\uc5d0\uc11c": [8, 22, 29, 31, 36, 37, 46], "\uc9c4\ud589\ub429\ub2c8\ub2e4": [8, 48], "\uba3c\uc800": [8, 9, 17, 22, 24, 27, 29, 34, 35, 38, 41, 44], "\uc778\uace0\ub354": 8, "\ub9e4\ud551\ud558\ub294": [8, 40], "d\ub294": [8, 15], "\uc7ac\uad6c\uc131\ud558\ub3c4\ub85d": 8, "ddpm\uc758": [8, 12, 21, 23, 33, 42], "\uc190\uc2e4\uc744": [8, 17, 19, 22], "\ucd5c\uc18c\ud654\ud558\ub3c4\ub85d": [8, 22, 39], "\ud559\uc2b5\ud569\ub2c8\ub2e4": [8, 40, 44, 48, 49], "\uc7a0\uc7ac": [8, 24, 35], "\ucf54\ub4dc": [8, 27, 34, 38], "zt": 8, "\uc7a1\uc74c": 8, "\uc81c\uac70\ud569\ub2c8\ub2e4": 8, "\ud604\uc7ac": [8, 11, 12, 18, 33, 35], "t\uc640": 8, "\uc870\uac74": [8, 17, 19, 21], "\ubca1\ud130": [8, 14, 19, 25, 29], "c\ub3c4": 8, "\uace0\ub824\ub429\ub2c8\ub2e4": 8, "model\uc740": [8, 13, 15, 20, 21, 24, 25, 26, 28, 32, 36, 47], "\uc778\ucf54\ub529\uc5d0\uc11c": 8, "\ud30c\uc0dd\ub41c": [8, 19, 36], "\ud504\ub86c\ud504\ud2b8": [8, 17, 18, 24, 27], "decompos": [8, 22], "\ud504\ub86c\ud504\ud2b8\ub85c\ubd80\ud130": 8, "\uc608\uce21\ud569\ub2c8\ub2e4": [8, 22, 40], "\ub2e4\uc74c\uc73c\ub85c": [8, 22], "\ubd80\uc5ec\ud558\uc5ec": 8, "decoder\ub85c": 8, "\ubcf4\ub0b4\uc9d1\ub2c8\ub2e4": 8, "\ub3c5\ub9bd\uc801\uc778": [8, 12, 39], "\uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4": 8, "l_": [8, 18, 23, 30, 32, 37, 39, 42, 47, 49], "\ub2e4\uc591\uc131\uc744": [8, 16, 17, 35], "\ud5a5\uc0c1\uc2dc\ud0a4\uba70": 8, "\uc811\uadfc\ud558\uace0": 8, "\uacf5\uac04\uc5d0\uc11c": [8, 14, 17, 29, 36], "conceptlab\uc740": 8, "\ub300\ud45c\ud558\ub294": 8, "v_": [8, 20], "\ucd5c\uc801\ud654\ud569\ub2c8\ub2e4": 8, "\uce74\ud14c\uace0\ub9ac\uc5d0": [8, 27], "\uc720\uc0ac\ud558\uba74\uc11c\ub3c4": [8, 36], "\uba64\ubc84\ub4e4\uacfc": 8, "\uac00\uc9c0\ub3c4\ub85d": [8, 32, 35], "\uc9d1\ud569\uc744": [8, 17, 29], "\uacc4\uc0b0\ud569\ub2c8\ub2e4": [8, 39, 43], "\ud6c8\ub828\ud558\ub294": [8, 19, 24], "\ubc14\ud0d5\uc73c\ub85c": [8, 25, 29, 40, 48], "contraints\ub97c": 8, "\ub354\ud558\uae30": 8, "blip": [8, 11], "vqa": 8, "\uae0d\uc815\uc801": 8, "\uc81c\uc57d": 8, "po": 8, "\uac00\uc9c0\ub97c": 8, "\uc870\uac74\uc740": 8, "\uc815\uc758\ub429\ub2c8\ub2e4": 8, "\ub450\uac00\uc9c0": [8, 32, 42], "\uce21\uc815\ud569\ub2c8\ub2e4": [8, 11, 48], "\uc6b0\uc120": [8, 9, 11, 21, 24, 31, 43, 48], "\ub2e8\uc5b4": [8, 27, 33, 36], "\ubb34\uc791\uc704": [8, 22], "\uc0d8\ud50c\ub9c1\ub41c": [8, 29], "y\uc5d0": 8, "\ud1b5\ud569\ud569\ub2c8\ub2e4": 8, "\ubb38\uc7a5\uc740": 8, "\uc778\ucf54\ub529\ub418\uba70": 8, "\uc774\uac83\uc774": [8, 11, 40], "\uc815\uc758\ud569\ub2c8\ub2e4": [8, 39, 43, 46, 48, 49, 50], "\ud1b5\uacfc\uc2dc\ud0a4\uba74": 8, "\ud504\ub86c\ud504\ud2b8\uc758": [8, 19], "\uc778\uc2a4\ud134\uc2a4\uac00": 8, "\uc870\uac74\uacfc": 8, "\uae0d\uc815": 8, "\ubd80\uc815": 8, "\uad11\ubc94\uc704\ud558\uac8c": 8, "\uc720\uc9c0\ud558\uace0\uc790": [8, 43], "\ud1b5\uacfc\ud558\uc9c0": 8, "\uc54a\uc2b5\ub2c8\ub2e4": [8, 11, 39, 40], "\uc5f0\uad6c\uc5d0\uc11c\uc758": 8, "\ud568\uc218\ub294": [8, 25, 29], "langl": 8, "rangl": 8, "lambda": [8, 13, 17, 24, 39, 40, 43], "v\uc5d0\uc11c": 8, "\uc870\uac74\uc5d0\uc11c": 8, "\uba40\uc5b4\uc9c0\uace0": 8, "\uc81c\uc57d\uc870\uac74\uc5d0": 8, "\uac00\uae4c\uc6cc\uc9c0\ub3c4\ub85d": [8, 15], "\uc815\uaddc\ud654\ub294": 8, "\uc9d1\ud569\uc774": 8, "\ud074": [8, 35], "\uba64\ubc84\ub85c\uc758": 8, "collapsing\uc744": 8, "\ubc29\uc9c0\ud558\ub294": [8, 17, 18, 19], "\uc0ac\uc6a9\ub429\ub2c8\ub2e4": [8, 9, 22], "\uc81c\uc57d\uc5d0": 8, "s_": [8, 18, 19, 20, 33, 39], "max_": 8, "\ubc29\uc2dd\uc740": [8, 10, 22, 26, 29, 32, 36, 47, 48, 49], "\ud568\uc218\uc5d0": 8, "\ud1b5\ud569\ub418\uba70": 8, "\ub0c4\uc73c\ub85c\uc368": 8, "\uc870\uac74\uc5d0": 8, "\ud328\ub110\ud2f0\ub97c": [8, 40], "\ubd80\uc5ec\ud569\ub2c8\ub2e4": 8, "\uacfc\uc815": [8, 10, 13, 23, 27, 28, 29, 32, 36], "\uac1c\ub150\uc5d0": [8, 17, 36], "\ub2e8\uc5b4\ub97c": [8, 36], "\ucd94\ub860\ud558\uace0": [8, 38], "\uac70\uce69\ub2c8\ub2e4": 8, "\uc218\ub3d9\uc73c\ub85c": 8, "\uc801\uc6a9\ud558\ub294": [8, 13, 15, 26, 39, 43, 47, 48, 49], "\ud798\ub4e4\uace0": 8, "\uad11\ubc94\uc704\ud55c": [8, 17], "\uba64\ubc84\ub4e4\uc744": 8, "\ub300\ud45c\ud558\uc9c0": 8, "\ubabb\ud560": [8, 22, 31], "\ud655\uc7a5\ud558\ub294": [8, 27], "scheme\uc744": 8, "\ud6c8\ub828\ub41c": [8, 9, 17, 19, 22, 24, 35, 36], "\uc9c8\uc758\ud558\uc5ec": 8, "\uc774\ubbf8\uc9c0\uc5d0": [8, 10, 12, 13, 17, 19, 20, 23, 27, 28, 29, 30, 33, 35, 36, 38, 40, 41], "\uc874\uc7ac\ud558\ub294": [8, 11, 17, 29, 38], "\uba64\ubc84\uac00": 8, "\ubb34\uc5c7\uc778\uc9c0": [8, 25, 43, 50], "\uc2dd\ubcc4\ud558\ub3c4\ub85d": 8, "\ub098\uc628": [8, 15, 21, 22, 39, 41, 42, 45, 47, 48, 49, 50], "\uc778\uc2a4\ud134\uc2a4\ub97c": 8, "\ud6c8\ub828\uc758": 8, "\ucd94\uac00\ud569\ub2c8\ub2e4": 8, 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