REVIEW 13 cited by
FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
abstract
Though Rectified Flows (ReFlows) with distillation offers a promising way for fast sampling, its fast inversion transforms images back to structured noise for recovery and following editing remains unsolved. This paper introduces FireFlow, a simple yet effective zero-shot approach that inherits the startling capacity of ReFlow-based models (such as FLUX) in generation while extending its capabilities to accurate inversion and editing in $8$ steps. We first demonstrate that a carefully designed numerical solver is pivotal for ReFlow inversion, enabling accurate inversion and reconstruction with the precision of a second-order solver while maintaining the practical efficiency of a first-order Euler method. This solver achieves a $3\times$ runtime speedup compared to state-of-the-art ReFlow inversion and editing techniques, while delivering smaller reconstruction errors and superior editing results in a training-free mode. The code is available at $\href{https://github.com/HolmesShuan/FireFlow}{this URL}$.
Forward citations
Cited by 13 Pith papers
-
Improving Robotic Generalist Policies via Flow Reversal Steering
Flow Reversal Steering steers flow matching generalist policies by reversing suboptimal actions to nearby better modes, enabling improved zero-shot control, quick distillation, and RL bootstrapping in robotic manipulation.
-
Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
MPFM models flow matching velocity as a Gaussian mixture prior per normal class plus a mutual information regularizer to improve open-set anomaly detection over unimodal prototypes.
-
Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
MPFM uses flow matching with a Gaussian mixture prior on the velocity field and a mutual information maximizer to improve open-set anomaly detection over unimodal prototype methods.
-
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...
-
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...
-
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...
-
VAGS: Velocity Adaptive Guidance Scale for Image Editing and Generation
VAGS adapts the CFG scale at each ODE step using velocity alignment signals to raise structural fidelity in editing and sample quality in generation over fixed-scale baselines.
-
Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
MPFM transforms normal features into a structured Gaussian mixture prototype space via a mixture velocity field and mutual information regularization to achieve state-of-the-art open-set supervised anomaly detection.
-
BiFM: Bidirectional Flow Matching for Few-Step Image Editing and Generation
A single flow-matching model can learn bidirectional average velocities under a shared instantaneous field and a consistency loss, improving few-step image editing and generation over prior few-step baselines.
-
FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image Generation
FreeGraftor performs subject-driven text-to-image generation without training by cross-image feature grafting via semantic matching, position-constrained attention fusion, and a noise initialization strategy that pres...
-
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.
-
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.
-
On the Controllability-Fidelity Frontier in Diffusion Editing
A study deriving mathematical formulations and bounds for diffusion editing objectives while empirically comparing methods on fidelity and control metrics and discussing ethical issues.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.