  The Diffusion models, widely used for image generation, face significant
challenges related to their broad applicability due to prolonged inference
times and high memory demands. Efficient Post-Training Quantization (PTQ) is
crucial to address these issues. However, unlike traditional models, diffusion
models critically rely on the time-step for the multi-round denoising.
Typically, each time-step is encoded into a hypersensitive temporal feature by
several modules. Despite this, existing PTQ methods do not optimize these
modules individually. Instead, they employ unsuitable reconstruction objectives
and complex calibration methods, leading to significant disturbances in the
temporal feature and denoising trajectory, as well as reduced compression
efficiency. To address these challenges, we introduce a novel quantization
framework that includes three strategies: 1) TIB-based Maintenance: Based on
our innovative Temporal Information Block (TIB) definition, Temporal
Information-aware Reconstruction (TIAR) and Finite Set Calibration (FSC) are
developed to efficiently align original temporal features. 2) Cache-based
Maintenance: Instead of indirect and complex optimization for the related
modules, pre-computing and caching quantized counterparts of temporal features
are developed to minimize errors. 3) Disturbance-aware Selection: Employ
temporal feature errors to guide a fine-grained selection between the two
maintenance strategies for further disturbance reduction. This framework
preserves most of the temporal information and ensures high-quality end-to-end
generation. Extensive testing on various datasets, diffusion models and
hardware confirms our superior performance and acceleration..
