[paper]
A training-free scheduler adaptation for quantized diffusion models. Use an off-the-shelf pre-trained model and adapt it's scheduler for faster inference.
- [2025-09-01] 🔥 Q-Sched is publicly released!
Text-to-image diffusion models are computationally intensive, often requiring dozens of forward passes through large transformer backbones. For instance, Stable Diffusion XL generates high-quality images with 50 evaluations of a 2.6B-parameter model, an expensive process even for a single batch. Few-step diffusion models reduce this cost to 2-8 denoising steps but still depend on large, uncompressed U-Net or diffusion transformer backbones, which are often too costly for full-precision inference without datacenter GPUs. These requirements also limit existing post-training quantization methods that rely on full-precision calibration. We introduce Q-Sched, a new paradigm for post-training quantization that modifies the diffusion model scheduler rather than model weights. By adjusting the few-step sampling trajectory, Q-Sched achieves full-precision accuracy with a 4x reduction in model size. To learn quantization-aware pre-conditioning coefficients, we propose the JAQ loss, which combines text-image compatibility with an image quality metric for fine-grained optimization. JAQ is reference-free and requires only a handful of calibration prompts, avoiding full-precision inference during calibration. Q-Sched delivers 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, showing that quantization and few-step distillation are complementary for high-fidelity generation. A large-scale user study with more than 80,000 annotations further confirms Q-Sched’s effectiveness on both FLUX.1[schnell] and SDXL-Turbo. Code will be released upon publication.
Our current environment is compatible with Nvidia RTX A6000s with CUDA 12.2, driver version 535.230.02.
conda env create -f env.ymlDownload the sDCI .yaml file to use as a calibration dataset. Name it as q-sched/dci_prompts.yaml.
See scripts folder. First, find new coefficients using source scripts/qsched.sh and generate images with the new schedule using source scripts/collect_images.sh.