The Role of Text-to-Image Models in Advanced Style Transfer Applications: A Case Study with DALL-E 3

TL;DR

This work investigates incorporating a text-to-image model, DALL·E 3, into neural style transfer by generating style images from natural language prompts and applying them via the Magenta Arbitrary Image Stylization framework. By evaluating using $SSIM$ and $PSNR$ and tracking processing times, the study demonstrates that DALL·E 3–driven styles yield higher perceptual-quality stylizations and greater stylistic diversity, with a net reduction in total processing time of about $2.5$ seconds despite a modest per-run increase in style-transfer time. The approach leverages a decoder-only autoregressive architecture for image generation, and a real-time style transfer model to fuse generated styles with content images, enabling more personalized and varied outputs. The results highlight practical benefits for creative applications while identifying trade-offs related to on-demand generation and high-resolution style images, and point to batch-generation and efficiency optimizations as future directions.

Abstract

While DALL-E 3 has gained popularity for its ability to generate creative and complex images from textual descriptions, its application in the domain of style transfer remains slightly underexplored. This project investigates the integration of DALL-E 3 with traditional neural style transfer techniques to assess the impact of generated style images on the quality of the final output. DALL-E 3 was employed to generate style images based on the descriptions provided and combine these with the Magenta Arbitrary Image Stylization model. This integration is evaluated through metrics such as the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR), as well as processing time assessments. The findings reveal that DALL-E 3 significantly enhances the diversity and artistic quality of stylized images. Although this improvement comes with a slight increase in style transfer time, the data shows that this trade-off is worthwhile because the overall processing time with DALL-E 3 is about 2.5 seconds faster than traditional methods, making it both an efficient and visually superior option.