6 accepted papers
Step-level caching offers a promising avenue for accelerating diffusion models by exploiting temporal redundancy. However, existing strategies predominantly rely on heuristic, threshold-based metrics to trigger cache updates. This reactive paradigm is inherently myopic as it optimizes only for local…
Estimating single-cell responses across various perturbations facilitates the identification of key genes and enhances drug screening, significantly boosting experimental efficiency. However, single-cell sequencing is a destructive process, making it impossible to capture the same cell's phenotype b…
Diffusion and flow matching models generate high-fidelity data by simulating paths defined by Ordinary or Stochastic Differential Equations (ODEs/SDEs), starting from a tractable prior distribution. The probability flow ODE formulation enables the use of advanced numerical solvers to accelerate samp
Diffusion models generate synthetic images through an iterative refinement process. However, the misalignment between the simulation-free objective and the iterative process often causes accumulated gradient error along the sampling trajectory, which leads to unsatisfactory results and a failure to
Video generation has advanced rapidly, but current models remain limited to short clips, far from the length and complexity of real-world narratives. Long video generation is thus both important and challenging. Existing approaches either attempt to extend the modeling length of video diffusion mode…
Diffusion-based generative processes, formulated as differential equation solving, frequently balance computational speed with sample quality. Our theoretical investigation of ODE- and SDE-based solvers reveals complementary weaknesses: ODE solvers accumulate irreducible gradient error along de…