AI RESEARCH
RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
arXiv CS.CV
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ArXi:2509.23582v2 Announce Type: replace Diffusion Transformers (DiTs) have recently emerged as a powerful backbone for image generation, nstrating superior scalability and performance over U-Net architectures. However, their practical deployment is hindered by substantial computational and memory costs. While Quantization-Aware