Sprint: Sparse-Dense Residual Fusion for Efficient Diffusion
  Transformers
Authors: name: Dogyun Park, name: Moayed Haji-Ali, name: Yanyu Li, name: Willi Menapace, name: Sergey Tulyakov, name: Hyunwoo J. Kim, name: Aliaksandr Siarohin, name: Anil Kag
Organization: Snap Research

Diffusion Transformers (DiTs) deliver state-of-the-art generative performance
but their quadratic training cost with sequence length makes large-scale
pretraining prohibitively expensive. Token dropping can reduce training cost,
yet na\"ive strategies degrade representations, and existing methods are either
parameter-heavy or fail at high drop ratios. We present SPRINT, Sparse--Dense
Residual Fusion for Efficient Diffusion Transformers, a simple method that
enables aggressive token dropping (up to 75%) while preserving quality. SPRINT
leverages the complementary roles of shallow and deep layers: early layers
process all tokens to capture local detail, deeper layers operate on a sparse
subset to cut computation, and their outputs are fused through residual
connections. Training follows a two-stage schedule: long masked pre-training
for efficiency followed by short full-token fine-tuning to close the
train--inference gap. On ImageNet-1K 256x256, SPRINT achieves 9.8x training
savings with comparable FID/FDD, and at inference, its Path-Drop Guidance (PDG)
nearly halves FLOPs while improving quality. These results establish SPRINT as
a simple, effective, and general solution for efficient DiT training.