title: DICE: Discrete Inversion Enabling Controllable Editing for Multinomial
    Diffusion and Masked Generative Models
summary: 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. For project webpage, see
  https://hexiaoxiao-cs.github.io/DICE/.
paper_authors._id: 6708860e9e319cca02bb57dc
paper_authors.name: Xiaoxiao He
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paper_authors.fullname: Arist He
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paper_authors.name: Ligong Han
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paper_authors.fullname: Ligong Han
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paper_authors.name: Quan Dao
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paper_authors.fullname: Quan Dao
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paper_authors.name: Song Wen
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paper_authors.fullname: Song Wen
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paper_authors.name: Minhao Bai
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paper_authors.name: Di Liu
paper_authors._id: 6708860e9e319cca02bb57e2
paper_authors.name: Han Zhang
paper_authors._id: 6708860e9e319cca02bb57e3
paper_authors.name: Martin Renqiang Min
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paper_authors.name: Felix Juefei-Xu
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paper_authors.fullname: Xu
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paper_authors.user: FelixXu
paper_authors._id: 6708860e9e319cca02bb57e5
paper_authors.name: Chaowei Tan
paper_authors._id: 6708860e9e319cca02bb57e6
paper_authors.name: Bo Liu
paper_authors._id: 6708860e9e319cca02bb57e7
paper_authors.name: Kang Li
paper_authors._id: 6708860e9e319cca02bb57e8
paper_authors.name: Hongdong Li
paper_authors._id: 6708860e9e319cca02bb57e9
paper_authors.name: Junzhou Huang
paper_authors._id: 6708860e9e319cca02bb57ea
paper_authors.name: Faez Ahmed
paper_authors._id: 6708860e9e319cca02bb57eb
paper_authors.name: Akash Srivastava
paper_authors.status: admin_assigned
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paper_authors.fullname: Akash Srivastava
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paper_authors.user: akashsri
paper_authors._id: 6708860e9e319cca02bb57ec
paper_authors.name: Dimitris Metaxas
paper_title: DICE: Discrete Inversion Enabling Controllable Editing for Multinomial
    Diffusion and Masked Generative Models
paper_summary: 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. For project webpage, see
  https://hexiaoxiao-cs.github.io/DICE/.
paper_ai_keywords: discrete diffusion models image generation masked language modeling DICE (Discrete Inversion for Controllable ReCON) multinomial diffusion masked generative models reverse diffusion process noise sequences masking patterns accurate reconstruction flexible editing predefined masks attention manipulation VQ-Diffusion Paired generative models RoBERT data fidelity fine-grained content manipulation discrete spaces