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Efficient Zero-shot Inpainting with Decoupled Diffusion Guidance

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Diffusion modelszero-shotguidance
Abstract:

Diffusion models have emerged as powerful priors for image editing tasks such as inpainting and local modification, where the objective is to generate realistic content that remains consistent with observed regions. In particular, zero-shot approaches that leverage a pretrained diffusion model, without any retraining, have been shown to achieve highly effective reconstructions. However, state-of-the-art zero-shot methods typically rely on a sequence of surrogate likelihood functions, whose scores are used as proxies for the ideal score. This procedure however requires vector-Jacobian products through the denoiser at every reverse step, introducing significant memory and runtime overhead. To address this issue, we propose a new likelihood surrogate that yields simple and efficient to sample Gaussian posterior transitions, sidestepping the backpropagation through the denoiser network. Our extensive experiments show that our method achieves strong observation consistency compared with fine-tuned baselines and produces coherent, high-quality reconstructions, all while significantly reducing inference cost.

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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 vector-Jacobian-product-free framework for zero-shot diffusion-based inpainting, introducing a new likelihood surrogate that yields Gaussian posterior transitions without backpropagation through the denoiser. It resides in the Null-Space and Range-Space Guidance leaf, which contains only three papers total. This leaf sits within the broader Diffusion Model Adaptation Mechanisms branch, indicating a moderately crowded research direction focused on steering pretrained models without retraining. The small leaf size suggests this specific projection-based guidance approach represents a focused subfield rather than a saturated research area.

The taxonomy reveals that neighboring leaves explore alternative guidance mechanisms: Gradient and Attention Guidance manipulates sampling through optimization or attention, Latent Space Optimization regularizes representations during diffusion, and Stochastic Sampling modifies noise schedules. The paper's null-space approach differs fundamentally by decomposing the generation process to preserve observed pixels while hallucinating missing content, contrasting with gradient-based methods that iteratively refine outputs. This positioning suggests the work builds on a distinct lineage of projection-based techniques rather than gradient or attention manipulation, though all share the zero-shot adaptation goal.

Among nineteen candidates examined, the VJP-free framework contribution shows one refutable candidate from five examined, indicating some prior work addresses computational efficiency in zero-shot inpainting. The decoupled twisting function examined four candidates with none refutable, suggesting this theoretical formulation may be more novel. The DING method examined ten candidates without refutation, though this larger search scope does not guarantee exhaustive coverage. The limited search scale means these statistics reflect top-semantic-match overlap rather than comprehensive field assessment, leaving open whether deeper literature contains additional relevant work.

Based on the constrained search of nineteen papers, the work appears to occupy a moderately explored niche within zero-shot diffusion adaptation. The efficiency focus and theoretical decomposition show partial novelty, though the single refutable pair for the core framework suggests some computational concerns have been addressed previously. The analysis covers top-semantic matches and citation expansion but does not claim exhaustive field coverage, particularly for recent preprints or domain-specific efficiency techniques outside the main inpainting literature.

Taxonomy

Core-task Taxonomy Papers
36
3
Claimed Contributions
19
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Zero-shot image inpainting using pretrained diffusion models. The field has organized itself around several complementary directions. Diffusion Model Adaptation Mechanisms explores how to steer pretrained models without retraining, often through guidance strategies that manipulate the denoising process to respect masked regions while generating coherent content. Multimodal and Conditional Inpainting extends these ideas by incorporating text prompts, depth maps, or other modalities to control what gets synthesized. Training-Based and Hybrid Approaches blend zero-shot flexibility with lightweight fine-tuning or test-time optimization to improve quality or domain fit. Domain-Specific Inpainting Applications targets specialized use cases such as face completion, document restoration, or video layer matting, while Diffusion Model Enhancements and Efficiency focuses on accelerating sampling or reducing computational overhead. Together, these branches reflect a tension between leveraging off-the-shelf generative priors and adapting them to diverse constraints and modalities. Within Diffusion Model Adaptation Mechanisms, a particularly active line of work centers on null-space and range-space guidance, where methods like Null-Space Model[2] and Pretrained Latent Inpainting[1] decompose the generation process to preserve known pixels while freely hallucinating missing content. Decoupled Diffusion Guidance[0] sits squarely in this cluster, proposing a refined decomposition that separates constraints from creative synthesis more cleanly than earlier approaches. Compared to Null-Space Model[2], which introduced the foundational projection idea, Decoupled Diffusion Guidance[0] emphasizes decoupling guidance signals to reduce artifacts at mask boundaries. Meanwhile, works like Pretrained Latent Inpainting[1] operate in latent space for efficiency, raising questions about how best to balance pixel-level fidelity with computational cost. Across these studies, the central challenge remains achieving seamless blending and semantic coherence without task-specific training, a goal that continues to drive innovation in guidance design and sampling strategies.

Claimed Contributions

VJP-free framework for zero-shot inpainting with diffusion priors

The authors introduce a framework that eliminates the need for vector-Jacobian product evaluations and backpropagation through the denoiser network, addressing the computational and memory overhead of existing zero-shot methods.

5 retrieved papers
Can Refute
Decoupled twisting function with closed-form mixture distribution

The method modifies the twisting function by evaluating the denoiser at an independent draw from the pretrained transition, breaking the dependency and enabling exact sampling from posterior transitions without VJP computations.

4 retrieved papers
DING method for efficient zero-shot inpainting

The authors develop DING, which achieves superior trade-offs between fidelity and realism while being faster and more memory-efficient than competing approaches, even outperforming fine-tuned models without task-specific training.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

VJP-free framework for zero-shot inpainting with diffusion priors

The authors introduce a framework that eliminates the need for vector-Jacobian product evaluations and backpropagation through the denoiser network, addressing the computational and memory overhead of existing zero-shot methods.

Contribution

Decoupled twisting function with closed-form mixture distribution

The method modifies the twisting function by evaluating the denoiser at an independent draw from the pretrained transition, breaking the dependency and enabling exact sampling from posterior transitions without VJP computations.

Contribution

DING method for efficient zero-shot inpainting

The authors develop DING, which achieves superior trade-offs between fidelity and realism while being faster and more memory-efficient than competing approaches, even outperforming fine-tuned models without task-specific training.

Table of Contents

Efficient Zero-shot Inpainting with Decoupled Diffusion Guidance | Novelty Validation