title: CoRe^2: Collect, Reflect and Refine to Generate Better and Faster
summary: Making text-to-image (T2I) generative model sample both fast and well
  represents a promising research direction. Previous studies have typically
  focused on either enhancing the visual quality of synthesized images at the
  expense of sampling efficiency or dramatically accelerating sampling without
  improving the base model's generative capacity. Moreover, nearly all inference
  methods have not been able to ensure stable performance simultaneously on both
  diffusion models (DMs) and visual autoregressive models (ARMs). In this paper,
  we introduce a novel plug-and-play inference paradigm, CoRe^2, which comprises
  three subprocesses: Collect, Reflect, and Refine. CoRe^2 first collects
  classifier-free guidance (CFG) trajectories, and then use collected data to
  train a weak model that reflects the easy-to-learn contents while reducing
  number of function evaluations during inference by half. Subsequently, CoRe^2
  employs weak-to-strong guidance to refine the conditional output, thereby
  improving the model's capacity to generate high-frequency and realistic
  content, which is difficult for the base model to capture. To the best of our
  knowledge, CoRe^2 is the first to demonstrate both efficiency and effectiveness
  across a wide range of DMs, including SDXL, SD3.5, and FLUX, as well as ARMs
  like LlamaGen. It has exhibited significant performance improvements on HPD v2,
  Pick-of-Pic, Drawbench, GenEval, and T2I-Compbench. Furthermore, CoRe^2 can be
  seamlessly integrated with the state-of-the-art Z-Sampling, outperforming it by
  0.3 and 0.16 on PickScore and AES, while achieving 5.64s time saving using
  SD3.5.Code is released at https://github.com/xie-lab-ml/CoRe/tree/main.
paper_authors._id: 67d3daf40034469b0d6cc872
paper_authors.name: Shitong Shao
paper_authors._id: 67d3daf40034469b0d6cc873
paper_authors.name: Zikai Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-16T21:14:14.209Z
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paper_authors.fullname: Zikai Zhou
paper_authors.type: user
paper_authors.user: Klayand
paper_authors._id: 67d3daf40034469b0d6cc874
paper_authors.name: Dian Xie
paper_authors._id: 67d3daf40034469b0d6cc875
paper_authors.name: Yuetong Fang
paper_authors._id: 67d3daf40034469b0d6cc876
paper_authors.name: Tian Ye
paper_authors._id: 67d3daf40034469b0d6cc877
paper_authors.name: Lichen Bai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T10:43:18.852Z
paper_authors._id: 6424772d956c16097c2745b4
paper_authors.avatarUrl: /avatars/469af721009b9825ae6ac49112f58fdb.svg
paper_authors.fullname: Bai LiChen
paper_authors.type: user
paper_authors.user: indulgeBai
paper_authors._id: 67d3daf40034469b0d6cc878
paper_authors.name: Zeke Xie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-16T21:14:16.493Z
paper_authors._id: 67a5fb17baac478d94c59fec
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/gMtdNiD8ztyAjB_weexcK.png
paper_authors.fullname: Z.X.
paper_authors.type: user
paper_authors.user: xLeaF
paper_title: CoRe^2: Collect, Reflect and Refine to Generate Better and Faster
paper_summary: Making text-to-image (T2I) generative model sample both fast and well
  represents a promising research direction. Previous studies have typically
  focused on either enhancing the visual quality of synthesized images at the
  expense of sampling efficiency or dramatically accelerating sampling without
  improving the base model's generative capacity. Moreover, nearly all inference
  methods have not been able to ensure stable performance simultaneously on both
  diffusion models (DMs) and visual autoregressive models (ARMs). In this paper,
  we introduce a novel plug-and-play inference paradigm, CoRe^2, which comprises
  three subprocesses: Collect, Reflect, and Refine. CoRe^2 first collects
  classifier-free guidance (CFG) trajectories, and then use collected data to
  train a weak model that reflects the easy-to-learn contents while reducing
  number of function evaluations during inference by half. Subsequently, CoRe^2
  employs weak-to-strong guidance to refine the conditional output, thereby
  improving the model's capacity to generate high-frequency and realistic
  content, which is difficult for the base model to capture. To the best of our
  knowledge, CoRe^2 is the first to demonstrate both efficiency and effectiveness
  across a wide range of DMs, including SDXL, SD3.5, and FLUX, as well as ARMs
  like LlamaGen. It has exhibited significant performance improvements on HPD v2,
  Pick-of-Pic, Drawbench, GenEval, and T2I-Compbench. Furthermore, CoRe^2 can be
  seamlessly integrated with the state-of-the-art Z-Sampling, outperforming it by
  0.3 and 0.16 on PickScore and AES, while achieving 5.64s time saving using
  SD3.5.Code is released at https://github.com/xie-lab-ml/CoRe/tree/main.
paper_ai_keywords: diffusion models (DMs) visual autoregressive models (ARMs) classifier-free guidance (CFG) HPD v2 Pick-of-Pic Drawbench GenEval T2I-Compbench PickScore AES Z-Sampling SDXL SD3.5 FLUX LlamaGen