Joint Post-Training Quantization of Vision Transformers with Learned Prompt-Guided Data Generation

• URL: http://arxiv.org/abs/2602.18861v1
• Date: Sat, 21 Feb 2026 15:02:21 GMT
• Title: Joint Post-Training Quantization of Vision Transformers with Learned Prompt-Guided Data Generation
• Authors: Shile Li, Markus Karmann, Onay Urfalioglu,
• Abstract summary: We present a framework for joint quantization of Vision Transformers trained on ImageNet.<n>We achieve state-of-the-art W4A4 and W3A3 accuracies on ImageNet.<n>We also introduce a data-free calibration strategy that synthesizes diverse, label-free samples.
• Score: 5.75627633588113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a framework for end-to-end joint quantization of Vision Transformers trained on ImageNet for the purpose of image classification. Unlike prior post-training or block-wise reconstruction methods, we jointly optimize over the entire set of all layers and inter-block dependencies without any labeled data, scaling effectively with the number of samples and completing in just one hour on a single GPU for ViT-small. We achieve state-of-the-art W4A4 and W3A3 accuracies on ImageNet and, to the best of our knowledge, the first PTQ results that maintain strong accuracy on ViT, DeiT, and Swin-T models under extremely low-bit settings (W1.58A8), demonstrating the potential for efficient edge deployment. Furthermore, we introduce a data-free calibration strategy that synthesizes diverse, label-free samples using Stable Diffusion Turbo guided by learned multi-mode prompts. By encouraging diversity in both the learned prompt embeddings and the generated image features, our data-free approach achieves performance on par with real-data ImageNet calibration and surpasses simple text-prompt baselines such as "a <adjective> photo of <adjective> <cls>".

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