DiRotQ: Rotation-Aware Quantization for 4-bit Diffusion Transformers
Diffusion Transformers (DiTs) achieve state-of-the-art image generation quality but incur substantial memory and computational costs at inference. While aggressive Post-Training Quantization (PTQ) to 4-bit precision offers significant efficiency gains, it typically results in severe quality degradation. Existing approaches, including smoothing-based methods, mixed-precision schemes, rotation techniques, and low-rank residual methods, partially mitigate this issue but still leave a noticeable gap to FP16/BF16 performance. In this work, we introduce DiRotQ, a W4A4 PTQ framework that mitigates this degradation through rotation-aware activation quantization. DiRotQ identifies a low-rank subsp...
| Authors | Sayeh Sharify, Mahsa Salmani, Hesham Mostafa |
|---|---|
| Primary category | cs.CV |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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