Prompt-Based Learning for Image Variation Using Single Image Multi-Scale Diffusion Models

In this paper, we propose a novel technique for a multi-scale framework with text-based learning using a single image to perform variations and text-based editing of the input image. Our approach captures the detailed internal information of a single image, enabling numerous variations while preserving the original features. In addition, text-conditioned learning provides a method to combine text and images to effectively perform text-based editing based on a single image. We propose a technique that integrates the diffusion U-Net structure within a multi-scale framework to accurately capture the quality and internal structure of an image from a single image and perform diverse variations while maintaining the features of the original image. Additionally, we utilized a pre-trained Bootstrapped Language-Image Pretraining (BLIP) model to generate various prompts for effective text-based editing, and we fed the prompts that most closely resembled the input image into the training process using Contrastive Language-Image Pretraining (CLIP)'s prior knowledge. To improve accuracy during the image editing stage, we designed a contrastive loss function to enhance the relevance between the prompt and the image. As a result, we improved the performance of learning between text and images, and through various experiments, we demonstrated its effectiveness on text-based image editing tasks. Our experiments show that the proposed method significantly improves the performance of single-image-based generative models and presents new possibilities in the field of text-based image editing.