OpenNovelty.org

ReDDiT: Rehashing Noise for Discrete Visual Generation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Discrete DiffusionMasked DiffusionImage GenerationNoise Design
Abstract:

In the visual generative area, discrete diffusion models are gaining traction for their efficiency and compatibility. However, pioneered attempts still fall behind their continuous counterparts, which we attribute to noise (absorbing state) design and sampling heuristics. In this study, we propose a rehashing noise approach for discrete diffusion transformer (termed ReDDiT), with the aim to extend absorbing states and improve expressive capacity of discrete diffusion models. ReDDiT enriches the potential paths that latent variables traverse during training with randomized multi-index corruption. The derived rehash sampler, which reverses the randomized absorbing paths, guarantees high diversity and low discrepancy of the generation process. These reformulations lead to more consistent and competitive generation quality, mitigating the need for heavily tuned randomness. Experiments show that ReDDiT significantly outperforms the baseline model (reducing gFID from 6.18 to 1.61) and is on par with the continuous counterparts. The code and models will be publicly available.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes a rehashing noise approach for discrete diffusion transformers, introducing randomized multi-index corruption to extend absorbing states and improve expressive capacity. It resides in the 'Discrete State-Space Diffusion Formulations' leaf, which contains six papers total, indicating a moderately active research direction within the broader discrete diffusion landscape. This leaf focuses on foundational formulations of discrete diffusion processes rather than architectural or application-specific innovations, positioning the work among core theoretical and methodological contributions to discrete state-space design.

The taxonomy reveals that discrete diffusion research spans multiple complementary branches: latent-space methods using vector quantization, conditional generation frameworks, sampling optimizations, and domain-specific applications. The paper's leaf sits within 'Discrete Diffusion Frameworks and Architectures,' which encompasses formulations, hybrid discrete-continuous models, and transformer architectures. Neighboring leaves include 'Hybrid Discrete-Continuous Diffusion Models' and 'Transformer-Based Discrete Diffusion Architectures,' suggesting the field actively explores both theoretical state-space refinements and architectural innovations. The scope note clarifies that this leaf excludes application-specific models, emphasizing the paper's focus on foundational diffusion dynamics rather than downstream tasks.

Among twenty-three candidates examined, the contribution-level analysis reveals mixed novelty signals. The rehashing noise approach examined ten candidates and found one potentially refutable prior work, suggesting some overlap in noise design strategies within the limited search scope. The rehash sampler examined ten candidates with no clear refutations, indicating relatively stronger novelty for the sampling mechanism. The reformulated discrete diffusion dynamics examined three candidates and found one refutable match, though the small sample size limits confidence. These statistics reflect a targeted semantic search rather than exhaustive coverage, meaning additional related work may exist beyond the examined set.

Based on the limited literature search, the work appears to make incremental but substantive refinements to discrete diffusion formulations, particularly in sampling strategies. The taxonomy context shows a moderately populated research direction with active exploration of state-space designs, suggesting the paper contributes to an evolving but not overcrowded subfield. The analysis covers top-ranked semantic matches and does not claim exhaustive prior work coverage, leaving open the possibility of additional relevant studies in adjacent research areas or recent preprints.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
23
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: discrete visual generation with diffusion models. The field has evolved around several complementary directions. At the highest level, researchers explore Discrete Diffusion Frameworks and Architectures that define how to formulate diffusion over categorical or token-based state spaces, often drawing on continuous-time or discrete-time Markov processes. A second major branch, Latent-Space and Vector-Quantized Diffusion, leverages learned codebooks (e.g., Vector Quantized Diffusion[1], Latent Diffusion Models[4]) to compress images into discrete tokens before applying diffusion, balancing efficiency with expressiveness. Conditional and Controllable Discrete Generation addresses how to steer outputs via attributes, layouts, or multimodal signals (e.g., AttriDiffuser[3], HouseDiffusion[9]). Meanwhile, Optimization and Sampling Enhancements focus on accelerating inference or improving training stability through predictor-corrector schemes and novel samplers. Domain-Specific Discrete Diffusion Applications adapt these methods to specialized tasks such as medical imaging, music, or 3D synthesis, and Evaluation and Analysis of Discrete Diffusion examines metrics and theoretical properties unique to discrete spaces. Within the Discrete Diffusion Frameworks and Architectures branch, a particularly active line of work centers on Discrete State-Space Diffusion Formulations, which rigorously define forward and reverse processes for categorical data. ReDDiT[0] sits squarely in this cluster, proposing a novel formulation that refines how noise is injected and reversed in discrete token spaces. Nearby efforts such as Discrete Flow Matching[7] and Discrete State Continuous Time[10] explore alternative parameterizations—flow-based versus continuous-time Markov chains—highlighting trade-offs between mathematical elegance and practical sampling speed. In contrast, works like Authentic Discrete Diffusion[39] and Boundary Conditional Diffusion[45] emphasize conditioning mechanisms or boundary constraints within discrete frameworks. ReDDiT[0] distinguishes itself by focusing on the core state-space dynamics rather than domain-specific conditioning, aligning closely with foundational studies (e.g., Discrete Predictor Corrector[21]) that seek principled noise schedules and transition matrices for categorical variables.

Claimed Contributions

Rehashing noise approach for discrete diffusion transformers

The authors introduce a novel noise design that extends absorbing states from a single mask token to multiple randomized indices. This enriches the potential paths latent variables traverse during training through randomized multi-index corruption, improving the expressive capacity of discrete diffusion models.

10 retrieved papers
Can Refute
Rehash sampler for discrete generation

A principled sampling algorithm is derived from discrete diffusion theory that reverses the randomized corruption paths. This sampler uses multinomial sampling with softmax probabilities instead of Gumbel-max, ensuring high diversity and low discrepancy without requiring heavily tuned randomness parameters.

10 retrieved papers
Reformulated discrete diffusion dynamics with extended absorbing states

The authors reformulate the discrete diffusion process by redefining absorbing states from a single mask token to a set of multiple mask indices. The transition kernel is updated to allow uniform transitions among these extended absorbing states, enabling more flexible and expressive corruption and generation paths.

3 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Rehashing noise approach for discrete diffusion transformers

The authors introduce a novel noise design that extends absorbing states from a single mask token to multiple randomized indices. This enriches the potential paths latent variables traverse during training through randomized multi-index corruption, improving the expressive capacity of discrete diffusion models.

Contribution

Rehash sampler for discrete generation

A principled sampling algorithm is derived from discrete diffusion theory that reverses the randomized corruption paths. This sampler uses multinomial sampling with softmax probabilities instead of Gumbel-max, ensuring high diversity and low discrepancy without requiring heavily tuned randomness parameters.

Contribution

Reformulated discrete diffusion dynamics with extended absorbing states

The authors reformulate the discrete diffusion process by redefining absorbing states from a single mask token to a set of multiple mask indices. The transition kernel is updated to allow uniform transitions among these extended absorbing states, enabling more flexible and expressive corruption and generation paths.

Table of Contents