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DragFlow: Unleashing DiT Priors with Region-Based Supervision for Drag Editing

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Image EditingDrag EditingDiffusion Models
Abstract:

Drag-based image editing has long suffered from distortions in the target region, largely because the priors of earlier base models, Stable Diffusion, are insufficient to project optimized latents back onto the natural image manifold. With the shift from UNet-based DDPMs to more scalable DiT with flow matching (e.g., SD3.5, FLUX), generative priors have become significantly stronger, enabling advances across diverse editing tasks. However, drag-based editing has yet to benefit from these stronger priors. This work introduces DragFlow, the first framework to effectively harness FLUX’s rich prior via region-based supervision, enabling full use of its finer-grained, spatially precise features for drag-based editing and achieving substantial improvements over existing baselines. We first show that directly applying point-based drag editing to DiTs performs poorly: unlike the highly compressed features of UNets, DiT features are insufficiently structured to provide reliable guidance for point-wise motion supervision. To overcome this limitation, DragFlow introduces a region-based editing paradigm, where affine transformations enable richer and more consistent feature supervision. Additionally, we integrate pretrained open-domain personalization adapters (e.g., IP-Adapter) to enhance subject consistency, while preserving background fidelity through gradient mask-based hard constraints. Multimodal large language models (MLLMs) are further employed to resolve task ambiguities. For evaluation, we curate a novel Region-based Dragging benchmark (ReD Bench) featuring region-level dragging instructions. Extensive experiments on DragBench-DR and ReD Bench show that DragFlow surpasses both point-based and region-based baselines, setting a new state-of-the-art in drag-based image editing. Code and datasets will be publicly available upon publication.

Disclaimer
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.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces DragFlow, a framework for drag-based image editing using diffusion transformers (DiTs) with region-based supervision rather than point-based handles. It resides in the Region-Based Drag Editing leaf of the taxonomy, which contains only two papers total: DragFlow itself and one sibling (RegionDrag). This represents a relatively sparse research direction within the broader drag-based editing landscape, suggesting the region-based paradigm for DiT architectures remains underexplored compared to the more populated Point-Based Drag Editing leaf, which includes five papers addressing UNet-based diffusion models.

The taxonomy reveals that DragFlow sits within Core Drag-Based Editing Frameworks, adjacent to Point-Based Drag Editing methods like DragDiffusion and DragonDiffusion that rely on sparse handle-target pairs. Neighboring branches include Optimization and Efficiency Enhancements (adapter-based methods, fast inference techniques) and Correspondence-Driven Editing (explicit feature matching approaches). The scope note for Region-Based Editing explicitly excludes point-based and correspondence-driven techniques, positioning DragFlow as addressing a distinct supervision paradigm: affine transformations over regions rather than point-wise motion constraints or explicit feature alignment.

Among 26 candidates examined, the DragFlow framework contribution (Contribution A) shows no clear refutation across 7 candidates reviewed, suggesting novelty in applying region-based supervision specifically to DiT architectures. However, the ReD Bench benchmark (Contribution B) encounters 3 refutable candidates among 9 examined, indicating prior work on region-based evaluation or benchmarking exists. The adapter-enhanced inversion method (Contribution C) examined 10 candidates with no refutations, though this reflects the limited search scope rather than exhaustive coverage. The statistics suggest the core framework appears more novel than the benchmark component within the examined literature.

Based on top-26 semantic matches, DragFlow occupies a sparsely populated taxonomy leaf and introduces a region-based supervision approach not clearly anticipated by the examined prior work on DiT drag editing. The analysis does not cover the full breadth of diffusion transformer editing literature, and the refutable benchmark candidates suggest some overlap in evaluation methodology. The framework's novelty appears strongest in bridging DiT architectures with region-level spatial control, though the limited search scope leaves open questions about related work in broader image manipulation or transformer-based editing domains.

Taxonomy

Core-task Taxonomy Papers
31
3
Claimed Contributions
26
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: drag-based image editing with diffusion transformers. This field centers on enabling users to manipulate images by dragging handle points to target locations, leveraging diffusion models to propagate these edits naturally across the image. The taxonomy reveals several main branches: Core Drag-Based Editing Frameworks establish foundational methods for point-driven manipulation, often through iterative optimization in latent or feature space (e.g., DragDiffusion[2], DragonDiffusion[8]). Optimization and Efficiency Enhancements focus on accelerating convergence and reducing computational overhead, with works like InstantDrag[16] and LightningDrag[27] pursuing faster inference. Correspondence-Driven Editing emphasizes tracking and matching features to guide deformations, while Multimodal and Semantic Integration incorporates text or semantic cues (e.g., CLIPDrag[24]) to enrich control. Extensions to 3D and Video Domains broaden the paradigm beyond static 2D images, as seen in MVDrag3D[5] and DragVideo[28], and Specialized Applications target domain-specific scenarios such as automotive design or hand pose editing. A particularly active line of work explores region-based strategies that move beyond single-point handles to manipulate entire areas or semantic segments, offering more intuitive control for complex edits. DragFlow[0] sits within this Region-Based Drag Editing cluster, alongside RegionDrag[1], both emphasizing how to propagate user-specified region deformations coherently through diffusion transformer architectures. Compared to earlier point-centric methods like DragDiffusion[2] or Drag Your Noise[3], these region-focused approaches address the challenge of editing larger, semantically meaningful structures rather than isolated pixels. Meanwhile, efficiency-oriented works such as InstantDrag[16] and adaptive scheduling methods like AdaptiveDrag[23] tackle orthogonal concerns of speed and convergence stability. DragFlow[0] thus represents a shift toward more expressive, region-aware editing interfaces, balancing the flexibility of diffusion transformers with the practical need for user-friendly, semantically grounded manipulation tools.

Claimed Contributions

DragFlow framework with region-based supervision for DiT drag editing

DragFlow is a novel framework that pioneers drag-based image editing using Diffusion Transformers (DiTs) with flow matching. It replaces traditional point-based supervision with region-level affine transformations to better leverage the finer-grained features of DiT models like FLUX, achieving substantial improvements over existing baselines.

7 retrieved papers
Region-based Dragging benchmark (ReD Bench)

The authors introduce ReD Bench, a new benchmark dataset designed for evaluating region-based drag editing methods. Each sample includes point-to-region alignment, explicit task tags (relocation, deformation, rotation), and contextual descriptions that clarify user intent, providing richer supervision than existing benchmarks.

9 retrieved papers
Can Refute
Adapter-enhanced inversion for subject consistency in CFG-distilled models

The framework incorporates pretrained personalization adapters to extract subject representations and inject them into the base model's prior. This technique addresses the larger inversion drift in CFG-distilled DiT models, markedly improving subject fidelity during drag edits without requiring additional fine-tuning.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

DragFlow framework with region-based supervision for DiT drag editing

DragFlow is a novel framework that pioneers drag-based image editing using Diffusion Transformers (DiTs) with flow matching. It replaces traditional point-based supervision with region-level affine transformations to better leverage the finer-grained features of DiT models like FLUX, achieving substantial improvements over existing baselines.

Contribution

Region-based Dragging benchmark (ReD Bench)

The authors introduce ReD Bench, a new benchmark dataset designed for evaluating region-based drag editing methods. Each sample includes point-to-region alignment, explicit task tags (relocation, deformation, rotation), and contextual descriptions that clarify user intent, providing richer supervision than existing benchmarks.

Contribution

Adapter-enhanced inversion for subject consistency in CFG-distilled models

The framework incorporates pretrained personalization adapters to extract subject representations and inject them into the base model's prior. This technique addresses the larger inversion drift in CFG-distilled DiT models, markedly improving subject fidelity during drag edits without requiring additional fine-tuning.

Table of Contents

DragFlow: Unleashing DiT Priors with Region-Based Supervision for Drag Editing | Novelty Validation