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.

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...