Pre-trained diffusion models inherently support image restoration that can be unlocked by optimizing prompt embeddings at the text encoder output using a diffusion bridge formulation, achieving competitive results on models like WAN and FLUX without fine-tuning.
Pnp-flow: Plug-and-play image restoration with flow matching
6 Pith papers cite this work. Polarity classification is still indexing.
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UniEdit-Flow presents tuning-free Uni-Inv and Uni-Edit methods for inversion and editing in flow models that achieve accurate reconstruction and robust region-preserving edits across generative models.
Diffusion-OAMP combines a pre-trained diffusion model with the OAMP algorithm under an SNR-matching rule to enable training-free reconstruction of compressed images transmitted over noisy wireless channels.
SCFlowFR uses data-dependent coupling and shortcut constraints in flow matching to achieve state-of-the-art one-step face restoration with improved perceptual quality and efficiency.
FMPlug adapts foundation flow-matching models into practical priors for inverse problems by combining instance-guided warm-start with sharp Gaussianity regularization, showing superior results on image restoration and scientific tasks with limited samples.
The Transformer is interpreted as discretization of a structured integro-differential equation in continuous domains for tokens and features, unifying attention, feedforward, and normalization via operator and variational views.
citing papers explorer
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Your Pre-trained Diffusion Model Secretly Knows Restoration
Pre-trained diffusion models inherently support image restoration that can be unlocked by optimizing prompt embeddings at the text encoder output using a diffusion bridge formulation, achieving competitive results on models like WAN and FLUX without fine-tuning.
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UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models
UniEdit-Flow presents tuning-free Uni-Inv and Uni-Edit methods for inversion and editing in flow models that achieve accurate reconstruction and robust region-preserving edits across generative models.
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Diffusion-OAMP for Joint Image Compression and Wireless Transmission
Diffusion-OAMP combines a pre-trained diffusion model with the OAMP algorithm under an SNR-matching rule to enable training-free reconstruction of compressed images transmitted over noisy wireless channels.
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Linearized Coupling Flow with Shortcut Constraints for One-Step Face Restoration
SCFlowFR uses data-dependent coupling and shortcut constraints in flow matching to achieve state-of-the-art one-step face restoration with improved perceptual quality and efficiency.
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Saving Foundation Flow-Matching Priors for Inverse Problems
FMPlug adapts foundation flow-matching models into practical priors for inverse problems by combining instance-guided warm-start with sharp Gaussianity regularization, showing superior results on image restoration and scientific tasks with limited samples.
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A Mathematical Explanation of Transformers
The Transformer is interpreted as discretization of a structured integro-differential equation in continuous domains for tokens and features, unifying attention, feedforward, and normalization via operator and variational views.