TreeQ: Pushing the Quantization Boundary of Diffusion Transformer via Tree-Structured Mixed-Precision Search

• URL: http://arxiv.org/abs/2512.06353v1
• Date: Sat, 06 Dec 2025 08:59:12 GMT
• Title: TreeQ: Pushing the Quantization Boundary of Diffusion Transformer via Tree-Structured Mixed-Precision Search
• Authors: Kaicheng Yang, Kaisen Yang, Baiting Wu, Xun Zhang, Qianrui Yang, Haotong Qin, He Zhang, Yulun Zhang,
• Abstract summary: Diffusion Transformers (DiTs) have emerged as a highly scalable and effective backbone for image generation.<n>Mixed-Precision Quantization (MPQ) has demonstrated remarkable success in advancing U-Net quantization to sub-4bit settings.<n>We propose TreeQ, a unified framework addressing key challenges in DiT quantization.
• Score: 35.93578975066986
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Diffusion Transformers (DiTs) have emerged as a highly scalable and effective backbone for image generation, outperforming U-Net architectures in both scalability and performance. However, their real-world deployment remains challenging due to high computational and memory demands. Mixed-Precision Quantization (MPQ), designed to push the limits of quantization, has demonstrated remarkable success in advancing U-Net quantization to sub-4bit settings while significantly reducing computational and memory overhead. Nevertheless, its application to DiT architectures remains limited and underexplored. In this work, we propose TreeQ, a unified framework addressing key challenges in DiT quantization. First, to tackle inefficient search and proxy misalignment, we introduce Tree Structured Search (TSS). This DiT-specific approach leverages the architecture's linear properties to traverse the solution space in O(n) time while improving objective accuracy through comparison-based pruning. Second, to unify optimization objectives, we propose Environmental Noise Guidance (ENG), which aligns Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) configurations using a single hyperparameter. Third, to mitigate information bottlenecks in ultra-low-bit regimes, we design the General Monarch Branch (GMB). This structured sparse branch prevents irreversible information loss, enabling finer detail generation. Through extensive experiments, our TreeQ framework demonstrates state-of-the-art performance on DiT-XL/2 under W3A3 and W4A4 PTQ/PEFT settings. Notably, our work is the first to achieve near-lossless 4-bit PTQ performance on DiT models. The code and models will be available at https://github.com/racoonykc/TreeQ

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