  Nearly every recent image synthesis approach, including diffusion,
masked-token prediction, and next-token prediction, uses a Transformer network
architecture. Despite this common backbone, there has been no direct, compute
controlled comparison of how these approaches affect performance and
efficiency. We analyze the scalability of each approach through the lens of
compute budget measured in FLOPs. We find that token prediction methods, led by
next-token prediction, significantly outperform diffusion on prompt following.
On image quality, while next-token prediction initially performs better,
scaling trends suggest it is eventually matched by diffusion. We compare the
inference compute efficiency of each approach and find that next token
prediction is by far the most efficient. Based on our findings we recommend
diffusion for applications targeting image quality and low latency; and
next-token prediction when prompt following or throughput is more important.
