DICE: Discrete Inversion Enabling Controllable Editing for Multinomial
  Diffusion and Masked Generative Models
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/.
6708860e9e319cca02bb57dc
Xiaoxiao He
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2024-10-11T07:43:46.191Z
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Arist He
user
AristHe
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Ligong Han
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2024-10-11T10:30:47.517Z
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Ligong Han
user
ligongh
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Quan Dao
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2024-10-11T10:30:54.192Z
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Quan Dao
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quandao10
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Song Wen
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2024-12-04T09:47:01.319Z
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Song Wen
user
swen9
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Minhao Bai
6708860e9e319cca02bb57e1
Di Liu
6708860e9e319cca02bb57e2
Han Zhang
6708860e9e319cca02bb57e3
Martin Renqiang Min
6708860e9e319cca02bb57e4
Felix Juefei-Xu
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2024-10-11T10:32:32.170Z
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Xu
user
FelixXu
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Chaowei Tan
6708860e9e319cca02bb57e6
Bo Liu
6708860e9e319cca02bb57e7
Kang Li
6708860e9e319cca02bb57e8
Hongdong Li
6708860e9e319cca02bb57e9
Junzhou Huang
6708860e9e319cca02bb57ea
Faez Ahmed
6708860e9e319cca02bb57eb
Akash Srivastava
admin_assigned
2024-10-11T10:31:40.129Z
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Akash Srivastava
user
akashsri
6708860e9e319cca02bb57ec
Dimitris Metaxas
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial
  Diffusion and Masked Generative Models
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/.
DICE enables precise inversion and flexible editing for discrete diffusion models, enhancing content manipulation in image and text domains.
discrete diffusion models DICE multinomial diffusion masked generative models reverse diffusion process VQ-Diffusion Paella RoBERTa high data fidelity fine-grained content manipulation