← Back to all news
arXiv Computer Vision By Federico Putamorsi, Leonardo Zini, Marcella Cornia, Lorenzo Baraldi

SPARK: Input-Conditioned Sparse Activation Modulation for Frozen DiT-based Super-Resolution

Read the original on arXiv Computer Vision →

The paper introduces SPARK, a lightweight input‑conditioned controller that modulates only a few dominant channels in frozen Diffusion Transformer (DiT) based super‑resolution models. By predicting bounded per‑channel affine transformations for selected channels, SPARK improves reconstruction fidelity and perceptual quality without fine‑tuning the backbone or adding adapters. Experiments on three DiT‑based SR backbones across DIV2K, RealSR, and DRealSR demonstrate consistent gains while modulating only eight channels per stream and block.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Sep 11

FreeTransformSR: Efficient Lightweight Image Super-Resolution via Free Low-Rank Learnable Transform

FreeTransformSR is a lightweight image super‑resolution network that uses a channel‑wise free low‑rank learnable transform to adaptively modulate features with minimal parameters. It adds a local feature modulation branch with depthwise convolution and a soft complexity adaptive module that fuses local convolution and window self‑attention based on texture characteristics. The model also employs an adaptive intensity modulation strategy and achieves competitive PSNR/SSIM on five benchmark datasets while using only 595K parameters and running faster than competing methods.

By Hongji Li, Yunhui Li
efficiencybenchmarks
More like this →