DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models

Figure 1. We introduce DistriFusion, a training-free algorithm to harness multiple GPUs to accelerate diffusion model inference without sacrificing image quality. Naïve Patch (Figure 2(b)) suffers from the fragmentation issue due to the lack of patch interaction. Our DistriFusion removes artifacts and avoids the communication overhead by reusing the features from the previous steps. Setting: SDXL with 50-step Euler sampler, 1280 × 1920 resolution. Latency is measured on A100s.