title: One-Way Ticket:Time-Independent Unified Encoder for Distilling
    Text-to-Image Diffusion Models
summary: 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.
paper_authors._id: 6837dd74ec10479b9605da15
paper_authors.name: Senmao Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:41:51.659Z
paper_authors._id: 637e1cf4f09bf2498c543a73
paper_authors.avatarUrl: /avatars/3c0e4e15602a384839bee0c23029f58a.svg
paper_authors.fullname: Senmao Li
paper_authors.type: user
paper_authors.user: senmaonk
paper_authors._id: 6837dd74ec10479b9605da16
paper_authors.name: Lei Wang
paper_authors._id: 6837dd74ec10479b9605da17
paper_authors.name: Kai Wang
paper_authors._id: 6837dd74ec10479b9605da18
paper_authors.name: Tao Liu
paper_authors._id: 6837dd74ec10479b9605da19
paper_authors.name: Jiehang Xie
paper_authors._id: 6837dd74ec10479b9605da1a
paper_authors.name: Joost van de Weijer
paper_authors._id: 6837dd74ec10479b9605da1b
paper_authors.name: Fahad Shahbaz Khan
paper_authors._id: 6837dd74ec10479b9605da1c
paper_authors.name: Shiqi Yang
paper_authors._id: 6837dd74ec10479b9605da1d
paper_authors.name: Yaxing Wang
paper_authors._id: 6837dd74ec10479b9605da1e
paper_authors.name: Jian Yang
paper_title: One-Way Ticket:Time-Independent Unified Encoder for Distilling
    Text-to-Image Diffusion Models
paper_summary: 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.
paper_ai_summary: Time-independent Unified Encoder TiUE reduces inference time and improves diversity and quality in Text-to-Image diffusion models by sharing encoder features across decoder time steps.
paper_ai_keywords: Text-to-Image diffusion models inference speed image quality distilled T2I models UNet encoders decoders semantic information Time-independent Unified Encoder TiUE loop-free image generation one-pass scheme parallel sampling KL divergence perceptual realism LCM SD-Turbo SwiftBrushv2