  Attention mechanism has been crucial for image diffusion models, however,
their quadratic computational complexity limits the sizes of images we can
process within reasonable time and memory constraints. This paper investigates
the importance of dense attention in generative image models, which often
contain redundant features, making them suitable for sparser attention
mechanisms. We propose a novel training-free method ToDo that relies on token
downsampling of key and value tokens to accelerate Stable Diffusion inference
by up to 2x for common sizes and up to 4.5x or more for high resolutions like
2048x2048. We demonstrate that our approach outperforms previous methods in
balancing efficient throughput and fidelity.
