Dynamic resolution priors enable faster diffusion-based image restoration by operating in lower-dimensional subspaces, with adapted methods outperforming prior DM approaches on most tasks.
Upsample what matters: Region-adaptive latent sampling for accelerated diffusion transformers
3 Pith papers cite this work. Polarity classification is still indexing.
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SpecEdit accelerates diffusion-based image editing up to 10x by using a low-resolution draft to identify edit-relevant tokens via semantic discrepancies for selective high-resolution denoising.
A commutator-zero condition enables training-free generation of perceptually consistent low-resolution previews for high-resolution diffusion model outputs, achieving up to 33% computation reduction.
citing papers explorer
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SpecEdit: Training-Free Acceleration for Diffusion based Image Editing via Semantic Locking
SpecEdit accelerates diffusion-based image editing up to 10x by using a low-resolution draft to identify edit-relevant tokens via semantic discrepancies for selective high-resolution denoising.
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Training-free, Perceptually Consistent Low-Resolution Previews with High-Resolution Image for Efficient Workflows of Diffusion Models
A commutator-zero condition enables training-free generation of perceptually consistent low-resolution previews for high-resolution diffusion model outputs, achieving up to 33% computation reduction.