Diffusion models produce high quality images but inference is costly due to many denoising steps and heavy matrix operations. We present DiffPro, a post-training, hardware-faithful framework that works with the exact integer kernels used in deployment and jointly tunes timesteps and per-layer precision in Diffusion Transformers (DiTs) to reduce latency and memory without any training. DiffPro combines three parts: a manifold-aware sensitivity metric to allocate weight bits, dynamic activation quantization to stabilize activations across timesteps, and a budgeted timestep selector guided by teacher-student drift. In experiments DiffPro achieves up to 6.25x model compression, fifty percent fewer timesteps, and 2.8x faster inference with Delta FID <= 10 on standard benchmarks, demonstrating practical efficiency gains. DiffPro unifies step reduction and precision planning into a single budgeted deployable plan for real-time energy-aware diffusion inference.
@article{arxiv.2511.11446,
title = {DiffPro: Joint Timestep and Layer-Wise Precision Optimization for Efficient Diffusion Inference},
author = {Farhana Amin and Sabiha Afroz and Kanchon Gharami and Mona Moghadampanah and Dimitrios S. Nikolopoulos},
journal= {arXiv preprint arXiv:2511.11446},
year = {2025}
}