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 subspace capturing dominant activation variance via Principal Component Analysis (PCA), preserving coefficients in this subspace at higher precision while quantizing the remaining components to 4-bit. Activations are rotated into the PCA basis at inference time using calibration-derived orthogonal transformations, while the inverse rotation is fused into the layer weights offline. Combined with GPTQ-based weight quantization, DiRotQ achieves an FID $(\downarrow)$ of $15.9$ and PSNR $(\uparrow)$ of $19.1$ dB on PixArt-$\Sigma$ over the MJHQ-30K dataset, outperforming the prior state-of-the-art SVDQuant (FID $18.9$, PSNR $17.6$) under the same INT W4A4 setting. Beyond standard metrics, we introduce a *VLM-as-a-Judge* evaluation protocol for diffusion model quantization, the first such evaluation in this setting, providing a more holistic assessment of perceptual quality and prompt alignment under aggressive compression. On the systems side, we implement a *Triton-based custom kernel* to enable efficient end-to-end inference, reducing memory usage of the 12B FLUX.1-dev model by $2.1\times$ and delivering $2.3\times$ speedup over the BF16 baseline, on a 24GB RTX 4090 GPU. Our anonymous codebase is available [here](https://anonymous.4open.science/r/DiRotQ-5BF6/).