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EVCtrl: Efficient Control Adapter for Visual Generation

ICLR 2026 Conference Withdrawn SubmissionZixiang Yang, Yue Ma, Yinhan Zhang, Shanhui Mo, Dongrui Liu, Linfeng Zhang
OpenReview Score: 5.5Download Report PDF
Visual GenerationDiffusion ModelsControl Adapter
Abstract:

Visual generation includes both image and video generation, training probabilistic models to create coherent, diverse, and semantically faithful content from scratch. While early research focused on unconditional sampling, practitioners now demand controllable generation that allows precise specification of layout, pose, motion, or style. While ControlNet grants precise spatial-temporal control, its auxiliary branch markedly increases latency and introduces redundant computation in both uncontrolled regions and denoising steps, especially for video. To address this problem, we introduce EVCtrl, a lightweight, plug-and-play control adapter that slashes overhead without retraining the model. Specifically, we propose a spatio-temporal dual caching strategy for sparse control information. For spatial redundancy, we first profile how each layer of DiT-ControlNet responds to fine-grained control, then partition the network into global and local functional zones. A locality-aware cache focuses computation on the local zones that truly need the control signal, skipping the bulk of redundant computation in global regions. For temporal redundancy, we selectively omit unnecessary denoising steps to improve efficiency. Extensive experiments on CogVideo-Controlnet, Wan2.1-Controlnet, and Flux demonstrate that our method is effective in image and video control generation without the need for training. For example, it achieves 2.16 and 2.05 times speedups on CogVideo-Controlnet and Wan2.1-Controlnet, respectively, with almost no degradation in generation quality.Codes are available in the supplementary materials.

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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 introduces EVCtrl, a lightweight control adapter designed to reduce computational overhead in controllable visual generation without retraining. It resides in the 'Inference Acceleration and Distillation' leaf within the 'Architectural Efficiency and Acceleration' branch, alongside two sibling papers. This leaf represents a moderately populated research direction focused on accelerating sampling through distillation, step reduction, or adaptive computation. The taxonomy contains fifty papers across approximately thirty-six topics, suggesting EVCtrl occupies a well-established but not overcrowded niche within the broader field of efficient controllable diffusion models.

The taxonomy reveals neighboring research directions that contextualize EVCtrl's positioning. Adjacent leaves include 'Latent Space Compression and Efficiency' and 'Backbone Architecture Optimization', both addressing computational efficiency through different mechanisms—compressed representations versus core architectural redesign. The 'Spatial and Structural Control Mechanisms' branch, particularly 'General Spatial Conditioning Frameworks' containing ControlNet-related work, represents the control paradigm EVCtrl seeks to optimize. The taxonomy's scope note explicitly excludes control mechanisms from the efficiency branch, clarifying that EVCtrl bridges these domains by making existing control methods more efficient rather than introducing novel control modalities.

Among twenty-nine candidates examined across three contributions, the analysis reveals mixed novelty signals. The core EVCtrl adapter concept examined ten candidates with zero refutations, suggesting reasonable distinctiveness within the limited search scope. Local Focused Caching similarly showed no refutations across ten candidates. However, Denoising Step Skipping encountered two refutable candidates among nine examined, indicating more substantial prior work in temporal redundancy reduction. These statistics reflect a targeted semantic search, not exhaustive coverage, meaning the absence of refutations does not guarantee absolute novelty but suggests the approach diverges from the most semantically similar recent work.

Based on the limited search scope of twenty-nine candidates, EVCtrl appears to offer a reasonably distinct contribution by combining spatial and temporal efficiency strategies specifically for controllable generation. The analysis captures top-K semantic matches and does not encompass the full literature on diffusion acceleration or control mechanisms. The two refutations for temporal step skipping warrant closer examination to assess whether EVCtrl's specific implementation differs substantively from prior temporal redundancy techniques.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
29
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Efficient controllable visual generation with diffusion models. The field has evolved into several major branches addressing distinct challenges. Architectural Efficiency and Acceleration focuses on reducing computational costs through distillation, pruning, and faster sampling strategies, exemplified by works like Flash Diffusion[45] and approaches building on Latent Diffusion Models[18]. Spatial and Structural Control Mechanisms encompasses methods for precise spatial guidance, including landmark-based control (ControlNet[13], Uni-ControlNet[6]) and layout-driven generation (LayoutDiffusion[35]). Compositional and Multi-Condition Control tackles the challenge of combining multiple guidance signals, as seen in Composable Diffusion[5] and Composer[17]. Conditional Generation Theory and Methodology explores foundational sampling and guidance techniques (ILVR[4], Conditional Sampling Diffusion[3]), while Temporal and Video Generation extends these ideas to dynamic content (Photorealistic Video Diffusion[1], Longer Image Animation[8]). Domain-Specific Applications and Editing branches address specialized use cases and post-generation refinement. Within the Architectural Efficiency branch, a central tension exists between generation quality and computational cost. Many studies explore distillation and acceleration without sacrificing controllability, while others investigate efficient adapter architectures like Ctrl-Adapter[27]. EVCtrl[0] situates itself squarely in the Inference Acceleration and Distillation cluster, emphasizing efficient control during the sampling process. Compared to Flash Diffusion[45], which prioritizes raw speed through aggressive distillation, EVCtrl[0] appears to balance acceleration with maintaining fine-grained control capabilities. The broader landscape reveals ongoing questions about whether efficiency gains should come from architectural redesign, training-time distillation, or inference-time optimization—with EVCtrl[0] contributing to the latter direction by demonstrating that controllable generation need not require prohibitive computational resources.

Claimed Contributions

EVCtrl: Efficient Control Adapter for Visual Generation

The authors propose EVCtrl, a training-free control adapter designed to reduce computational overhead in controllable image and video generation. It addresses spatial and temporal redundancies in ControlNet-based methods without requiring model retraining.

10 retrieved papers
Local Focused Caching (LFoC) for Spatial Redundancy

The authors introduce a spatial caching strategy that identifies and updates only tokens encoding fine-grained control information (such as edges), while reusing cached features for regions without control signals. This reduces redundant computation in spatially sparse control conditions.

10 retrieved papers
Denoising Step Skipping (DSS) for Temporal Redundancy

The authors propose a temporal strategy that selectively performs full computation only on critical denoising steps that significantly affect the control signal, while maintaining periodic caching for other steps. This exploits the observation that adjacent timesteps exhibit high similarity in the control branch.

9 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

EVCtrl: Efficient Control Adapter for Visual Generation

The authors propose EVCtrl, a training-free control adapter designed to reduce computational overhead in controllable image and video generation. It addresses spatial and temporal redundancies in ControlNet-based methods without requiring model retraining.

Contribution

Local Focused Caching (LFoC) for Spatial Redundancy

The authors introduce a spatial caching strategy that identifies and updates only tokens encoding fine-grained control information (such as edges), while reusing cached features for regions without control signals. This reduces redundant computation in spatially sparse control conditions.

Contribution

Denoising Step Skipping (DSS) for Temporal Redundancy

The authors propose a temporal strategy that selectively performs full computation only on critical denoising steps that significantly affect the control signal, while maintaining periodic caching for other steps. This exploits the observation that adjacent timesteps exhibit high similarity in the control branch.

Table of Contents