Recently, a series of diffusion-aware distillation algorithms have emerged to
alleviate the computational overhead associated with the multi-step inference
process of Diffusion Models (DMs). Current distillation techniques often
dichotomize into two distinct aspects: i) ODE Trajectory Preservation; and ii)
ODE Trajectory Reformulation. However, these approaches suffer from severe
performance degradation or domain shifts. To address these limitations, we
propose Hyper-SD, a novel framework that synergistically amalgamates the
advantages of ODE Trajectory Preservation and Reformulation, while maintaining
near-lossless performance during step compression. Firstly, we introduce
Trajectory Segmented Consistency Distillation to progressively perform
consistent distillation within pre-defined time-step segments, which
facilitates the preservation of the original ODE trajectory from a higher-order
perspective. Secondly, we incorporate human feedback learning to boost the
performance of the model in a low-step regime and mitigate the performance loss
incurred by the distillation process. Thirdly, we integrate score distillation
to further improve the low-step generation capability of the model and offer
the first attempt to leverage a unified LoRA to support the inference process
at all steps. Extensive experiments and user studies demonstrate that Hyper-SD
achieves SOTA performance from 1 to 8 inference steps for both SDXL and SD1.5.
For example, Hyper-SDXL surpasses SDXL-Lightning by +0.68 in CLIP Score and
+0.51 in Aes Score in the 1-step inference.

Hyper-SD is a novel framework that combines the advantages of ODE Trajectory Preservation and Reformulation, achieving state-of-the-art performance in multi-step inference with diffusion models.