DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models

The introduction of DICE marks a significant advancement in the field of AI, particularly in controlled content editing for discrete diffusion models. This innovation aligns with recent developments in instruction-based image editing, as seen in SliderEdit, which allows for complex edits through multi-instruction prompts. Both DICE and SliderEdit emphasize the importance of precise control in editing processes, showcasing a trend towards enhancing user capabilities in image and text manipulation. Furthermore, the challenges posed by backdoor attacks on large vision-language models, highlighted in MTAttack, underline the necessity for robust editing frameworks like DICE that can withstand potential vulnerabilities while offering advanced editing functionalities.