Aspect-Ratio Bucketing

Key Insight

Most training pipelines center-crop every image to a square, which throws away the edges of tall portraits and wide landscapes — so the model never learns to compose anything but squares. Aspect-ratio bucketing fixes this by sorting images into groups by shape and building each batch from a single group, since a batch must share one tensor shape. After training this way the model generates correctly-framed portraits and landscapes on demand, and you can visibly see composition improve on non-square test prompts.