DiTo shifts token reduction in DiTs to output token similarity, reusing prior-step matches across timesteps with PMR scheduling and frequency-aware penalties to raise PSNR at given speedups.
Machine Intelligence Research22(4), 730–751 (Jun 2025)
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A covariance-aware extension of DDIM sampling for pixel-space diffusion models that uses Tweedie's formula and Fourier decomposition to model reverse-process covariance and improves sample quality at low NFE.
Z²-Sampling implicitly realizes zero-cost zigzag trajectories for curvature-aware semantic alignment in diffusion models by reducing multi-step paths via operator dualities and temporal caching while synthesizing a directional derivative penalty.
A lightweight transformer module learns quality-aware vectors from timestep and prompt embeddings to modulate adaptive LayerNorm in DiT blocks, yielding consistent image quality gains over baseline diffusion transformers.
A training-free global-local skipping strategy accelerates 3D diffusion-based PET denoising by over an order of magnitude while maintaining or improving image quality across multiple tracers.
TCC calibrates cached representations in diffusion sampling via an offline iterative procedure that accounts for trajectory shifts, improving FID from 29.83 to 27.35 on PixArt-alpha while preserving reuse policies.
Distilled one-step consistency model from optimal-transport flow-matching teacher reconstructs high-fidelity dynamical system flows from low-fidelity data with 12x speedup, half the parameters, and 23.1% better SSIM than scratch-trained baselines.
A denoising diffusion model trained on transformed JWST observations generates multi-band galaxy images that match key statistical properties of real galaxies for Roman weak lensing simulations.
TEMPO-Diffusion is a targeted backdoor attack framework for diffusion models that uses time-conditioned triggers to poison class-specific synthetic data, achieving high attack success in downstream classifiers.
Diffusion model improves GFS/GEFS ensemble CAPE forecasts and incorporates aerosol optical depths for additional gains.
Focused Forcing is a training-free per-frame KV selection method that combines attention scores with diversity metrics and head-importance estimation to accelerate autoregressive video diffusion up to 1.48x while improving quality.
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