TurboQuantConv: Channel-Isolated Incoherence Processing for 3D Convolutions in Video Generative AI

Three-dimensional Convolutional Variational Autoencoders (3D-VAEs) serve as the fundamental compression backbone of modern video generative models, yet their deep 3D convolutions (Conv3D) impose prohibitive computational and memory burdens. Pushing post-training quantization (PTQ) down to 4-bit weights and activations (W4A4) typically causes substantial quality degradation due to non-stationary spatiotemporal activation bursts. In this paper, we propose TurboQuantConv, a channel-isolated incoherence processing and quantization framework for deep multi-dimensional convolutions. By harmonizing outlier suppression with convolutional receptive field geometry, TurboQuantConv effectively suppresses spatiotemporal bursts and achieves high-fidelity quantization without requiring fine-tuning or retraining. Under W4A4 compression, TurboQuantConv outperforms prior state-of-the-art PTQ methods by  4 to 8 dB PSNR across 3D-VAE benchmarks. In downstream 81-frame video generation with the 5B Wan 2.2 Diffusion Transformer, TurboQuantConv achieves the lowest FVD $152.6$ while closely preserving unquantized visual dynamics, avoiding the severe motion freeze and chromatic collapse from which existing PTQ methods suffer. These results establish a practical foundation for efficient edge deployment of video generative models.