Calibri: Enhancing Diffusion Transformers via Parameter-Efficient Calibration

2026-03-25

AI Summary

This work addresses the inefficiency in parameter utilization of Diffusion Transformers for generative tasks, which hampers their denoising performance. To overcome this limitation, the authors propose a lightweight calibration method that introduces approximately 100 learnable scaling parameters and formulates calibration as a black-box reward optimization problem, efficiently solved via an evolutionary algorithm. Requiring only minimal parameter fine-tuning, the approach significantly enhances generation quality and reduces inference steps across various text-to-image diffusion models while preserving high fidelity. This strategy achieves both parameter efficiency and computational efficiency in optimizing Diffusion Transformers, offering a practical and scalable solution for improving generative performance without extensive retraining or architectural modifications.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Diffusion Transformers
• parameter-efficient
• generative quality
• inference steps
• calibration

Innovation

• Diffusion Transformers
• parameter-efficient calibration
• black-box optimization
• evolutionary algorithm
• text-to-image generation