这篇论文打破常规：不用多步扩散，只用单步卷积网络就在ImageNet上跑出FID 2.56，思路极简但效果惊艳，适合想了解生成模型新方向的你。
ROMS-IMLE 提出了一种极简的单步生成模型，仅使用隐式极大似然估计（IMLE）作为训练目标，并采用中等规模卷积网络，摒弃了迭代去噪和Transformer。在ImageNet 256分辨率上，该模型以单步生成达到FID 2.56，同时保持较好的精确率和召回率。实验表明，多步扩散并非高性能生成的必要条件，简洁设计也能实现竞争性结果。
ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling
Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching. Along the way, the underlying techniques have become more complicated and various beliefs about what drives strong empirical performance have taken hold. Due to the success of diffusion models and flow matching, one of the more common beliefs is the importance of transforming the noise distribution to the data distribution gradually through many small transformations. We ask whether this is truly necessary, and take a minimalist approach to designing a competitive generative model. We start with the bare-bones essentials, namely just a training objective and a model. We purposefully make both simple. For the training objective, we choose Implicit Maximum Likelihood Estimation (IMLE), and eschew more complicated alternatives such as variational inference, adversarial training and numerical integration. For the model, we eschew transformers and instead choose a moderately sized convolutional network. Then we judiciously added elements that are truly essential, which surprisingly do not include iterative denoising. The result is a single-step parameter-efficient generative model that produces high quality samples at fast speed: it achieves an FID of 2.56 on ImageNet 256 and simultaneously attains good precision and recall.