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.
Abstract (translated)
尽管DALL-E 3因其能够根据文本描述生成创意和复杂的图像而受到欢迎，但在风格迁移领域的应用仍稍显不足。本项目研究了将DALL-E 3与传统的神经风格迁移技术相结合，以评估生成的风格图像对最终输出质量的影响。使用DALL-E 3基于提供的描述生成风格图像，并将其与Magenta任意图像样式化模型结合。这种整合通过结构相似性指数测量（SSIM）和峰值信噪比（PSNR）等指标以及处理时间评估来进行评价。研究结果表明，DALL-E 3显著增强了风格化图像的多样性和艺术质量。虽然这种方法在风格迁移时会稍微增加一些时间，但数据表明这种权衡是值得的，因为使用DALL-E 3的整体处理时间比传统方法快约2.5秒，使其成为既高效又视觉上更优的选择。
URL
https://arxiv.org/abs/2412.05325
