Earticle

초록

영어

This work presents a novel fine-tuning scheme for enhancing the quality of Subject Driven Image Generation. Motivated by recent works on fine-tuning pre-trained diffusion models, we extract information from visual patch embedding to optimize the performance of the image encoder in our proposed method. Additionally, the loss function of the conventional Unet model is replaced with Masked Diffusion Loss. During inference time, the model can control degree of similarity between result image and reference image by using Classifier - Free Guidance method. Experimental results indicate that the proposed model exhibits improved image generation quality in comparison to the previous schemes.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. METHOD
A. Image Encoder
B. Model Training Strategy
IV. EXPERIMENT
V. RESULTS AND DISCUSSION
VI. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Beomjo Kim [ Department of Artificial Intelligence Graduate school of Ajou University ]
• Sangjin Ahn [ Department of Artificial Intelligence Graduate school of Ajou University ]
• Kyung-Ah Sohn [ Department of Artificial Intelligence Graduate school of Ajou University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004