Calibri: Enhancing Diffusion Transformers via Parameter-Efficient Calibration
In this paper, we uncover the hidden potential of Diffusion Transformers (DiTs) to significantly enhance generative tasks. Through an in-depth analysis of the denoising process, we demonstrate that introducing a single learned scaling parameter can significantly improve the performance of DiT blocks. Building on this insight, we propose Calibri, a parameter-efficient approach that optimally calibrates DiT components to elevate generative quality. Calibri frames DiT calibration as a black-box reward optimization problem, which is efficiently solved using an evolutionary algorithm and modifies just ~100 parameters. Experimental results reveal that despite its lightweight design, Calibri consistently improves performance across various text-to-image models. Notably, Calibri also reduces the inference steps required for image generation, all while maintaining high-quality outputs.
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Danil Tokhchukov
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2026-03-27T11:32:40.146Z
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Danil Tokhchukov
makriot
user
makriot
69c6340e2de2ee20094eddbf
Aysel Mirzoeva
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Andrey Kuznetsov
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Konstantin Sobolev
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2026-03-27T11:33:21.230Z
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Konstantin Sobolev
k-sobolev
user
k-sobolev
Calibri: Enhancing Diffusion Transformers via Parameter-Efficient Calibration
In this paper, we uncover the hidden potential of Diffusion Transformers (DiTs) to significantly enhance generative tasks. Through an in-depth analysis of the denoising process, we demonstrate that introducing a single learned scaling parameter can significantly improve the performance of DiT blocks. Building on this insight, we propose Calibri, a parameter-efficient approach that optimally calibrates DiT components to elevate generative quality. Calibri frames DiT calibration as a black-box reward optimization problem, which is efficiently solved using an evolutionary algorithm and modifies just ~100 parameters. Experimental results reveal that despite its lightweight design, Calibri consistently improves performance across various text-to-image models. Notably, Calibri also reduces the inference steps required for image generation, all while maintaining high-quality outputs.
Diffusion Transformers can be enhanced through a parameter-efficient calibration approach that improves generative quality while reducing inference steps.
Diffusion Transformers denoising process learned scaling parameter parameter-efficient approach DiT blocks generative quality black-box reward optimization evolutionary algorithm