ResetEdit embeds a recoverable discrepancy signal during image generation in diffusion models to reconstruct an approximate original latent for high-fidelity text-guided editing.
Improving tuning-free real image editing with proximal guidance
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3verdicts
UNVERDICTED 3representative citing papers
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.
Near-reversible Runge-Kutta ODE solvers combined with vector-field smoothing deliver more stable and higher-fidelity text-guided edits in diffusion models than exactly reversible schemes.
citing papers explorer
-
ResetEdit: Precise Text-guided Editing of Generated Image via Resettable Starting Latent
ResetEdit embeds a recoverable discrepancy signal during image generation in diffusion models to reconstruct an approximate original latent for high-fidelity text-guided editing.
-
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.
-
Stable and Near-Reversible Diffusion ODE Solvers for Image Editing
Near-reversible Runge-Kutta ODE solvers combined with vector-field smoothing deliver more stable and higher-fidelity text-guided edits in diffusion models than exactly reversible schemes.