Distributed inference with multiple GPUs On distributed setups, you can run inference across multiple GPUs with 🤗 Accelerate or PyTorch Distributed, which is useful for generating with multiple prompts in parallel. This guide will show you how to use 🤗 Accelerate and PyTorch Distributed for distributed inference.  🤗 Accelerate 🤗 Accelerate is a library designed to make it easy to train or run inference across distributed setups. It simplifies the process of setting up the distributed environment, allowing you to focus on your PyTorch code. To begin, create a Python file and initialize an accelerate.PartialState to create a distributed environment; your setup is automatically detected so you don’t need to explicitly define the rank or world_size. Move the DiffusionPipeline to distributed_state.device to assign a GPU to each process. Now use the split_between_processes utility as a context manager to automatically distribute the prompts between the number of processes.   Copied import torch