CoRe^2: Collect, Reflect and Refine to Generate Better and Faster
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.
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Shitong Shao
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ShanShan
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Zikai Zhou
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Zikai Zhou
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Dian Xie
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Yuetong Fang
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Tian Ye
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Lichen Bai
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Bai LiChen
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Zeke Xie
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Z.X.
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CoRe^2: Collect, Reflect and Refine to Generate Better and Faster
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.
CoRe^2, a new plug-and-play inference paradigm, enhances both the efficiency and quality of text-to-image generative models by leveraging classifier-free guidance and weak-to-strong refinement.
text-to-image generative model sampling efficiency diffusion models visual autoregressive models classifier-free guidance inference paradigm weak-to-strong guidance HPD Pick-of-Pic Drawbench GenEval T2I-Compbench Z-Sampling PickScore AES