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.

翻译：近年来，几乎所有的图像合成方法——包括扩散模型、掩码标记预测以及下一标记预测——均采用Transformer网络架构。尽管这些方法共享相同的骨干网络，但尚未有在计算量受控条件下直接比较它们对性能与效率影响的系统性研究。本文通过以FLOPs度量的计算预算视角，分析了每种方法的可扩展性。研究发现，在提示跟随能力方面，以下一标记预测为代表的标记预测方法显著优于扩散模型。在图像质量方面，虽然下一标记预测在初期表现更优，但扩展趋势表明其最终会被扩散模型追平。我们进一步比较了各方法的推理计算效率，发现下一标记预测是目前最高效的方法。基于研究结果，我们建议：以图像质量和低延迟为目标的应用宜采用扩散模型；而当提示跟随能力或吞吐量更为关键时，应优先选择下一标记预测方法。