Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling

Text-to-image diffusion models remain computationally intensive: generating a single image typically requires dozens of passes through large transformer backbones (*e.g.*, SDXL uses ~50 evaluations of a 2.6B-parameter model). Few-step variants reduce the step count to 2–8, but still rely on large, full-precision backbones, making inference impractical on resource-constrained platforms, both on-device (latency/energy) and in data centers with multi-instance GPU (MIG) style GPU partitioning (limited memory/throughput per slice). Existing post-training quantization (PTQ) methods are further hampered by dependence on full-precision calibration. We introduce Q-Sched, a scheduler-level PTQ approach that adapts the diffusion sampler while keeping the quantized weights fixed. By adjusting the few-step sampling trajectory with quantization-aware preconditioning coefficients, Q-Sched matches or surpasses full-precision quality while delivering a $4\times$ reduction in model size and preserving a single reusable checkpoint across bit-widths. To learn these coefficients, we propose a reference-free Joint Alignment–Quality (JAQ) loss, which combines text–image compatibility with an image-quality objective for fine-grained control; JAQ requires only a handful of calibration prompts and avoids any full-precision inference during calibration. Empirically, Q-Sched yields substantial gains: a **15.5%** FID improvement over the FP16 4-step Latent Consistency Model and a **16.6%** improvement over the FP16 8-step Phased Consistency Model, demonstrating that quantization and few-step distillation are complementary for high-fidelity generation. A large-scale user study with **80,000** annotations further validates these results on both FLUX.1[schnell] and SDXL-Turbo. Code will be released.