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This approach shares a lot in common with the idea of multivariate interpolation over scattered data. Multivariate interpolation attempts to estimate values at unknown points within an existing data set and is often used in fields such as geostatistics or for geophysical analysis like elevation modelling. We can think of our colour palette as the set of variables we want to interpolate from, and our input colour as the unknown we’re trying to estimate. We can borrow some ideas from multivariate interpolation to develop more effective dithering algorithms.

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Standard forward pass. The model's forward() method must be a standard tensor-in, logits-out computation. No problem-specific control flow (for-loops over digits, explicit carry variables, string manipulation) inside forward(). The autoregressive generation loop lives outside the model, exactly as it would for any language model.

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