arXiv:2607.02461v1 Announce Type: cross Abstract: Diffusion transformers (DiTs) achieve state-of-the-art image and video generation, but their multi-step sampling and growing parameter count make inference expensive. Post-training quantization (PTQ) is the natural remedy, yet DiT activations shift across timesteps, prompts, and guidance branches, forcing prior methods to re-fit calibration data for every new checkpoint or modality. We present OrbitQuant, a data-agnostic weight-activation quantizer that bypasses range estimation by quantizing in a normalized, rotated basis. In this basis, a ran
The increasing complexity and computational demands of state-of-the-art AI models like Diffusion Transformers necessitate new methods for efficient inference, making quantization research highly relevant now.
Efficient AI inference directly translates to lower operational costs, broader accessibility of advanced models, and reduced energy consumption, crucial for scaling AI applications.
This data-agnostic quantization method simplifies the deployment and optimization of Diffusion Transformers, removing a significant bottleneck in their widespread use across diverse hardware and applications.
- · AI model developers
- · Cloud computing providers
- · Edge AI hardware manufacturers
- · AI application users
- · Companies reliant on brute-force compute for competitive advantage
- · Inefficient AI inference solutions
Reduced computational cost and energy footprint of deploying complex generative AI models.
Accelerated adoption and integration of Diffusion Transformers in a wider range of industries, including personalized content generation and robotics.
Increased global competition in generative AI development as barriers to entry related to inference cost are lowered.
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Read at arXiv cs.LG