Text-Guided Local Control for Regional Editing

In the realm of text-to-image generation, a significant leap has been made in producing images that are both visually realistic and semantically coherent. However, achieving precise local control of images, guided by textual descriptions without compromising the original content, presents a formidable challenge. To tackle this, we introduce a novel text-based approach that enables localized control and generation of single-target images within the scope of general natural language images. Our method encompasses a mask matching module coupled with a fusion-diffusion technique. It processes the original image along with user-provided textual cues by utilizing a parser within the mask matching module to create a mask that corresponds with the textual description. This mask directs the diffusion process, which ensures a harmonious blend of the edited and unedited parts of the image. Besides, an innovative resampling strategy is proposed to expedite semantic transitions during the diffusion process. The method's efficacy is underscored by its enhanced semantic consistency and visual appeal, offering an advancement in the field of image generation and editing.