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Attention is a general reasoning mechanism than can flexibly deal with imageinformation, but its memory requirements had made it so far impractical forhigh resolution image generation. We present Grid Partitioned Attention (GPA),a new approximate attention algorithm that leverages a sparse inductive biasfor higher computational and memory efficiency in image domains: queries attendonly to few keys, spatially close queries attend to close keys due tocorrelations. Our paper introduces the new attention layer, analyzes itscomplexity and how the trade-off between memory usage and model power can betuned by the hyper-parameters.We will show how such attention enables noveldeep learning architectures with copying modules that are especially useful forconditional image generation tasks like pose morphing. Our contributions are(i) algorithm and code1of the novel GPA layer, (ii) a novel deepattention-copying architecture, and (iii) new state-of-the art experimentalresults in human pose morphing generation benchmarks.

Grid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail Generation