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FlowAlign: Trajectory-Regularized, Inversion-Free Flow-based Image Editing
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Recent inversion-free, flow-based image editing methods such as FlowEdit leverages a pre-trained noise-to-image flow model such as Stable Diffusion 3, enabling text-driven manipulation by solving an ordinary differential equation (ODE). While the lack of exact latent inversion is a core advantage of these methods, it often results in unstable editing trajectories and poor source consistency. To address this limitation, we propose {\em FlowAlign}, a novel inversion-free flow-based framework for consistent image editing with optimal control-based trajectory control. Specifically, FlowAlign introduces source similarity at the terminal point as a regularization term to promote smoother and more consistent trajectories during the editing process. Notably, our terminal point regularization is shown to explicitly balance semantic alignment with the edit prompt and structural consistency with the source image along the trajectory. Furthermore, FlowAlign naturally supports reverse editing by simply reversing the ODE trajectory, highliting the reversible and consistent nature of the transformation. Extensive experiments demonstrate that FlowAlign outperforms existing methods in both source preservation and editing controllability.
Forward citations
Cited by 18 Pith papers
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DirectEdit: Step-Level Accurate Inversion for Flow-Based Image Editing
DirectEdit achieves step-level accurate inversion for flow-based image editing by directly aligning forward paths, using attention feature injection and mask-guided noise blending to balance fidelity and editability w...
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FlowAnchor: Stabilizing the Editing Signal for Inversion-Free Video Editing
FlowAnchor stabilizes editing signals in flow-based inversion-free video editing via spatial-aware attention refinement and adaptive magnitude modulation for improved faithfulness and temporal coherence.
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Exploring Cross-Modal Flows for Few-Shot Learning
FMA introduces flow matching for multi-step cross-modal feature alignment in few-shot learning, using fixed coupling, noise augmentation, and early-stopping to outperform one-step PEFT methods.
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h-Flow: Flexible Flow-based Image Editing via Doob's h-Transform
h-Flow extends Doob's h-transform to deterministic rectified flows via an equivalent SDE, yielding closed-form reconstruction guidance plus orthogonal velocity editing for controllable text-based image editing.
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ElasticTTT: Prior-Preserving Test-Time Tuning for Video Editing
A test-time tuning framework with three regularization techniques that preserves the generative prior of a video diffusion model during one-shot editing, achieving state-of-the-art results on the authors' benchmark.
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ElasticTTT: Prior-Preserving Test-Time Tuning for Video Editing
Test-time tuning of video diffusion models collapses generation toward the source video; ElasticTTT counters this with noisy targets, contrastive source-prompt guidance, and asynchronous region-wise noise scheduling, ...
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Bridging the Manifold Gap: Riemannian Residual Line Search for One-Step Image Editing
Riemannian Residual Line Search improves one-step diffusion image editing by curvature-based residual path construction and CLIP-based candidate selection, reporting SOTA on 700-sample PIE-Bench++ across 10 edit types.
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Flow-based Policy Adaptation without Policy Updates
GLOVES learns flow models from limited expert demonstrations to selectively correct actions from non-expert policies or operators toward expert distributions using reverse-flow OOD detection as an intervention gate.
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StreamEdit: Training-Free Video Editing via Few-Step Streaming Video Generation
StreamGVE enables high-quality training-free video editing by converting the task to noise-to-data streaming generation with dual-branch fast sampling, self-attention bridges, cross-attention grounding, source-oriente...
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StreamEdit: Training-Free Video Editing via Few-Step Streaming Video Generation
StreamEdit enables high-quality training-free video editing by adapting streaming video generation models with dual-branch fast sampling, self-attention bridge, cross-attention grounding, source-oriented guidance, and...
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Semantic Granularity Navigation in Image Editing
NaviEdit reallocates fixed step budgets in diffusion rollouts to intermediate scales for improved semantic editability while preserving fidelity via a self-consistency contract.
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LimeCross: Context-Conditioned Layered Image Editing with Structural Consistency
LimeCross enables text-guided editing of individual layers in composite images by conditioning on cross-layer context via bi-stream attention while preserving layer integrity and introducing the LayerEditBench benchmark.
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Wavelet-Guided Semantic Signal Compensation for Inversion-Free Image Editing
Proposes a frequency-aware semantic compensation strategy using wavelets to strengthen text-conditioned signals in early diffusion steps for better global editing without inversion.
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Bridging the Manifold Gap: Riemannian Residual Line Search for One-Step Image Editing
Second-order curvature-corrected residual line search over energy-field transport candidates yields SOTA one-step text-guided image editing on PIE-Bench++.
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DirectEdit: Step-Level Accurate Inversion for Flow-Based Image Editing
DirectEdit eliminates reconstruction error in flow-based image editing by aligning forward paths and applying attention feature injection with mask-guided noise blending.
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FlowSteer: Conditioning Flow Field for Consistent Image Restoration
A sparse mid-to-late schedule of null-space fidelity updates lets a frozen text-to-image flow model restore images with high measurement consistency.
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Semantic Granularity Navigation in Image Editing
NaviEdit is a training-free inference-time controller that decouples edit progress from model scale traversal in diffusion-based image editing via self-consistency, reporting average gains across editors and backbones.
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