Ensuring Consistency for In-Image Translation

The in-image machine translation task involves translating text embedded within images, with the translated results presented in image format. While this task has numerous applications in various scenarios such as film poster translation and everyday scene image translation, existing methods frequently neglect the aspect of consistency throughout this process. We propose the need to uphold two types of consistency in this task: translation consistency and image generation consistency. The former entails incorporating image information during translation, while the latter involves maintaining consistency between the style of the text image and the original image, ensuring background coherence. To address these consistency requirements, we introduce a novel two-stage framework named HCIIT (High-Consistency In-Image Translation), which involves text image translation using a multimodal multilingual large language model in the first stage and image backfilling with a diffusion model in the second stage. Chain-of-thought learning is employed in the first stage to enhance the model’s ability to effectively leverage visual information during translation. Subsequently, a diffusion model trained for style-consistent text–image generation is adopted. We further modify the structural network of the conventional diffusion model by introducing a style latent module, which ensures uniformity of text style within images while preserving fine-grained background details. The results obtained on both curated test sets and authentic image test sets validate the effectiveness of our framework in ensuring consistency and producing high-quality translated images.