Budgeted Block–Time Cache Scheduling for Diffusion Transformers

Diffusion models have demonstrated remarkable performance in image and video generation, while their multi-step denoising process incurs substantial computational overhead. Feature caching accelerates sampling by reusing intermediate activations across timesteps, making the caching schedule critical to both efficiency and generation quality. Existing scheduling methods primarily operate at the timestep level, imposing a shared execution decision on all Transformer blocks within a timestep and thereby overlooking the heterogeneous cache sensitivity and computational redundancy across network depth. We propose BTCache, which models cacheability jointly across denoising steps and Transformer blocks. By predicting the quality impact of block–time reuse, BTCache identifies critical computations and allocates a prescribed compute budget across the joint block–time space. At each denoising step, a deterministic selector executes the budget-feasible action of lowest predicted deviation from full computation, selectively recomputing sensitive blocks while reusing redundant features within the same timestep. Extensive experiments on FLUX, HunyuanVideo, and Wan2.1 demonstrate that BTCache preserves high visual quality under prescribed compute budgets.