TextDiffuser consists of two stages: first, a Transformer model generates the layout of keywords extracted from text prompts, and then diffusion models generate images conditioned on the text prompt and the generated layout.

To address these problems, we propose T extCtrl, a diffusion-based method that edits text with prior guidance control.

Training models with longer in-context lengths is a significant challenge for multi-modal machine learning due to substantial GPU memory and computational costs.

'The proposed tool can have a "There is a paradigm shift happening from datasets to This tool is aligned with that shift and might be broadly useful.". "V ery well written and structured.' R1: "one is left wondering whether this insight generalizes beyond the specifics of this experiments/dataset?" In the general case, one should always be careful on how scientific findings can generalize to other setups. "It is difficult to characterize what new scientific understanding or knowledge was presented in this paper ." We agree, many of the presented results are part of the wisdom of the more experimented researchers. R1: "The value of such tools is often clear only in hindsight...

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 integrity. 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 utilized in the first stage to enhance the model's ability to leverage image information during translation. Subsequently, a diffusion model trained for style-consistent text-image generation ensures uniformity in text style within images and preserves background details. A dataset comprising 400,000 style-consistent pseudo text-image pairs is curated for model training. 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.