2-Bit Autoregressive Image Generation on Mobile: An End-to-End Framework via Complex-Valued QAT

Image generation is one of the most valuable applications of generative models. As users become increasingly sensitive to data sovereignty and privacy, migrating inference from the cloud to the edge (on-device) has become an urgent necessity. Deploying generative models on mobile hardware requires low storage footprint, low inference latency, and low energy consumption. Model quantization has emerged as the predominant technical trajectory to address these challenges. Quantization-aware training (QAT) embeds quantization operators into the training process, allowing model parameters to actively adapt to low-level representation, thus exceeding the performance of post-training quantization (PTQ) in the three dimensions of storage, latency and energy consumption. This paper proposes a complex-valued QAT framework for autoregressive image generation that addresses channel pairing and layer-wise quantization sensitivity, and supports on-device deployment. We firstly introduce complex-valued quantization methods to the field of image generation. Complex-valued QAT maps weights to the set $\{\pm 1,\pm i\}$, offering superior representation capacity compared to real-valued methods under equivalent storage budgets. And we propose Weight Channel Reorder strategy, which forces complex projection points toward the coordinate axes to significantly suppress quantization error. Extensive experiments on LlamaGen and VAR show that our 2-bit quantized models approach full-precision performance (e.g., FID 2.83 vs. 2.62 on LlamaGen-XL), achieving lower FID than all compared 2-bit QAT baselines and most 6-bit PTQ methods. We further develop a GGUF export pipeline and deploy the model through llama.cpp with a Metal GPU backend, achieving fully offline AR image generation on iPhone. Code and demo are available at https://anonymous.4open.science/r/AR_Quant/.