  Advanced diffusion models (DMs) perform impressively in image
super-resolution (SR), but the high memory and computational costs hinder their
deployment. Binarization, an ultra-compression algorithm, offers the potential
for effectively accelerating DMs. Nonetheless, due to the model structure and
the multi-step iterative attribute of DMs, existing binarization methods result
in significant performance degradation. In this paper, we introduce a novel
binarized diffusion model, BI-DiffSR, for image SR. First, for the model
structure, we design a UNet architecture optimized for binarization. We propose
the consistent-pixel-downsample (CP-Down) and consistent-pixel-upsample (CP-Up)
to maintain dimension consistent and facilitate the full-precision information
transfer. Meanwhile, we design the channel-shuffle-fusion (CS-Fusion) to
enhance feature fusion in skip connection. Second, for the activation
difference across timestep, we design the timestep-aware redistribution (TaR)
and activation function (TaA). The TaR and TaA dynamically adjust the
distribution of activations based on different timesteps, improving the
flexibility and representation alability of the binarized module. Comprehensive
experiments demonstrate that our BI-DiffSR outperforms existing binarization
methods. Code is available at https://github.com/zhengchen1999/BI-DiffSR.
