ion T2I diffusion models such as Stable Diffusion
XL (SDXL), and their use of classifier-free guidance (CFG) introduces a
persistent trade-off between text-image alignment and generation diversity. We
address these challenges by optimizing Score identity Distillation (SiD) -- a
data-free, one-step distillation framework -- for few-step generation. Backed
by theoretical analysis that justifies matching a uniform mixture of outputs
from all generation steps to the data distribution, our few-step distillation
