Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot
  Subject-Driven Image Generator
Authors: name: Chaehun Shin, name: Jooyoung Choi, name: Heeseung Kim, name: Sungroh Yoon

Subject-driven text-to-image generation aims to produce images of a new
subject within a desired context by accurately capturing both the visual
characteristics of the subject and the semantic content of a text prompt.
Traditional methods rely on time- and resource-intensive fine-tuning for
subject alignment, while recent zero-shot approaches leverage on-the-fly image
prompting, often sacrificing subject alignment. In this paper, we introduce
Diptych Prompting, a novel zero-shot approach that reinterprets as an
inpainting task with precise subject alignment by leveraging the emergent
property of diptych generation in large-scale text-to-image models. Diptych
Prompting arranges an incomplete diptych with the reference image in the left
panel, and performs text-conditioned inpainting on the right panel. We further
prevent unwanted content leakage by removing the background in the reference
image and improve fine-grained details in the generated subject by enhancing
attention weights between the panels during inpainting. Experimental results
confirm that our approach significantly outperforms zero-shot image prompting
methods, resulting in images that are visually preferred by users.
Additionally, our method supports not only subject-driven generation but also
stylized image generation and subject-driven image editing, demonstrating
versatility across diverse image generation applications. Project page:
https://diptychprompting.github.io/