DICE：支持多项式扩散与掩码生成模型可控编辑的离散反演方法
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models
AI总结：
针对离散扩散模型可控编辑受限的问题，本文提出离散反演方法DICE，通过记录逆扩散过程的噪声与掩码模式实现精准重建编辑，在多模型多域验证中兼顾保真度与编辑能力。
摘要：
离散扩散模型已在图像生成、掩码语言建模等任务中取得成功，但在可控内容编辑方面存在局限。本文提出DICE（面向可控编辑的离散反演），这是首个可为离散扩散模型（包括多项式扩散和掩码生成模型）实现精确反演的方法。通过记录逆扩散过程中的噪声序列与掩码模式，DICE无需预定义掩码或注意力操控，即可实现离散数据的精准重建与灵活编辑。本文在图像和文本两个领域验证了DICE的有效性，在VQ-Diffusion、Paella、RoBERTa等模型上开展评估。结果表明，DICE在提升编辑能力的同时可保持较高的数据保真度，为离散空间中的细粒度内容操控提供了新的可能。
英文摘要：
Discrete diffusion models have achieved success in tasks like image generation and masked language modeling but face limitations in controlled content editing. We introduce DICE (Discrete Inversion for Controllable Editing), the first approach to enable precise inversion for discrete diffusion models, including multinomial diffusion and masked generative models. By recording noise sequences and masking patterns during the reverse diffusion process, DICE enables accurate reconstruction and flexible editing of discrete data without the need for predefined masks or attention manipulation. We demonstrate the effectiveness of DICE across both image and text domains, evaluating it on models such as VQ-Diffusion, Paella, and RoBERTa. Our results show that DICE preserves high data fidelity while enhancing editing capabilities, offering new opportunities for fine-grained content manipulation in discrete spaces.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
