Hybrid parallelism framework achieves 2.3× speedup on diffusion models using conditional guidance scheduling.
Diffusion models have achieved remarkable progress in high-fidelity image, video, and audio generation, yet inference remains computationally expensive. Nevertheless, current diffusion acceleration methods based on distributed parallelism suffer from noticeable generation artifacts and fail to achieve substantial acceleration proportional to the number of GPUs. Therefore, we propose a hybrid parallelism framework that combines a novel data parallel strategy, condition-based partitioning, with an optimal pipeline scheduling method, adaptive parallelism switching, to reduce generation latency and achieve high generation quality in conditional diffusion models. The key ideas are to (i) leverage the conditional and unconditional denoising paths as a new data-partitioning perspective and (ii) adaptively enable optimal pipeline parallelism according to the denoising discrepancy between these two paths. Our framework achieves 2.31times and 2.07times latency reductions on SDXL and SD3, respectively, using two NVIDIA RTX~3090 GPUs, while preserving image quality. This result confirms the generality of our approach across U-Net-based diffusion models and DiT-based flow-matching architectures. Our approach also outperforms existing methods in acceleration under high-resolution synthesis settings. Code is available at https://github.com/kaist-dmlab/Hybridiff.
Hybrid parallelism combining condition-based data partitioning and adaptive pipeline switching achieves 2.31× speedup on two GPUs.
Exploiting classifier-free guidance's dual paths eliminates patch boundary artifacts while preserving global image consistency.
Denoising discrepancy metric automatically determines optimal switching points between serial and parallel execution phases.
Method requires classifier-free guidance architecture, limiting applicability to some diffusion model variants.
Speedup gains tested primarily on two GPUs; scalability to larger GPU clusters remains unclear.
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