One-Way Ticket:Time-Independent Unified Encoder for Distilling
  Text-to-Image Diffusion Models
Authors: name: Senmao Li, name: Lei Wang, name: Kai Wang, name: Tao Liu, name: Jiehang Xie, name: Joost van de Weijer, name: Fahad Shahbaz Khan, name: Shiqi Yang, name: Yaxing Wang, name: Jian Yang

Text-to-Image (T2I) diffusion models have made remarkable advancements in
generative modeling; however, they face a trade-off between inference speed and
image quality, posing challenges for efficient deployment. Existing distilled
T2I models can generate high-fidelity images with fewer sampling steps, but
often struggle with diversity and quality, especially in one-step models. From
our analysis, we observe redundant computations in the UNet encoders. Our
findings suggest that, for T2I diffusion models, decoders are more adept at
capturing richer and more explicit semantic information, while encoders can be
effectively shared across decoders from diverse time steps. Based on these
observations, we introduce the first Time-independent Unified Encoder TiUE for
the student model UNet architecture, which is a loop-free image generation
approach for distilling T2I diffusion models. Using a one-pass scheme, TiUE
shares encoder features across multiple decoder time steps, enabling parallel
sampling and significantly reducing inference time complexity. In addition, we
incorporate a KL divergence term to regularize noise prediction, which enhances
the perceptual realism and diversity of the generated images. Experimental
results demonstrate that TiUE outperforms state-of-the-art methods, including
LCM, SD-Turbo, and SwiftBrushv2, producing more diverse and realistic results
while maintaining the computational efficiency.