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The passage around this formula

For images the dominant answer is patchification, introduced at scale by the Vision Transformer: cut the image into a grid of non-overlapping square patches, flatten each, and project it linearly into the model’s embedding dimension, so that a pure transformer applied directly to sequences of image patches performs competitively with convolutional networks when pre-trained on enough data [ 2 ] . The token count follows immediately from the geometry, $N_{\mathrm{tok}} = \frac{H}{P} \cdot \frac{W}{P}$. for an image of height H and width W at patch size P . The quadratic relationship is the entire practical story of image tokenisation. Halving the patch size quadruples the sequence, and attention cost grows faster still.…

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