sampling may be even slower than sequential sampling. The two parameters to play with are parallel (batch size) and tolerance. If it fits in memory, for a 1000-step DDPM you can aim for a batch size of around 100 (for example, 8 GPUs and batch_per_device=12 to get parallel=96). A higher batch size may not fit in memory, and lower batch size gives less parallelism. For tolerance, using a higher tolerance may get better speedups but can risk sample quality degradation. If there is quality degradation with the default tolerance, then use a lower tolerance like 0.001. For a 1000-step DDPM on 8 A100 GPUs, you can expect around a 3x speedup from StableDiffusionParadigmsPipeline compared to the StableDiffusionPipeline