We present Scalable Interpolant Transformers (SiT), a family of generative
models built on the backbone of Diffusion Transformers (DiT). The interpolant
framework, which allows for connecting two distributions in a more flexible way
than standard diffusion models, makes possible a modular study of various
design choices impacting generative models built on dynamical transport: using
discrete vs. continuous time learning, deciding the objective for the model to
learn, choosing the interpolant connecting the distributions, and deploying a
deterministic or stochastic sampler. By carefully introducing the above
ingredients, SiT surpasses DiT uniformly across model sizes on the conditional
ImageNet 256x256 benchmark using the exact same backbone, number of parameters,
and GFLOPs. By exploring various diffusion coefficients, which can be tuned
separately from learning, SiT achieves an FID-50K score of 2.06.

SiT 是一种建立在 DiT 基础之上的生成模型，通过插值框架实现了对各种设计选择的模块化研究，从离散和连续时间学习到模型学习的目标选择，插值连接分布以及确定性或随机采样器等。在相同的基础、参数数量和 GFLOPs 下，通过精心引入上述因素，SiT 在条件 ImageNet 256x256 基准测试中表现出色，并通过探索各种可以单独调整的扩散系数，达到了 2.06 的 FID-50K 分数。