Diffusers documentation

Distributed inference with multiple GPUs

You are viewing main version, which requires installation from source. If you'd like regular pip install, checkout the latest stable version (v0.29.2).
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

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.

import torch
from accelerate import PartialState
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
distributed_state = PartialState()

with distributed_state.split_between_processes(["a dog", "a cat"]) as prompt:
    result = pipeline(prompt).images[0]

Use the --num_processes argument to specify the number of GPUs to use, and call accelerate launch to run the script:

accelerate launch run_distributed.py --num_processes=2

To learn more, take a look at the Distributed Inference with 🤗 Accelerate guide.

PyTorch Distributed

PyTorch supports DistributedDataParallel which enables data parallelism.

To start, create a Python file and import torch.distributed and torch.multiprocessing to set up the distributed process group and to spawn the processes for inference on each GPU. You should also initialize a DiffusionPipeline:

import torch
import torch.distributed as dist
import torch.multiprocessing as mp

from diffusers import DiffusionPipeline

sd = DiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True

You’ll want to create a function to run inference; init_process_group handles creating a distributed environment with the type of backend to use, the rank of the current process, and the world_size or the number of processes participating. If you’re running inference in parallel over 2 GPUs, then the world_size is 2.

Move the DiffusionPipeline to rank and use get_rank to assign a GPU to each process, where each process handles a different prompt:

def run_inference(rank, world_size):
    dist.init_process_group("nccl", rank=rank, world_size=world_size)


    if torch.distributed.get_rank() == 0:
        prompt = "a dog"
    elif torch.distributed.get_rank() == 1:
        prompt = "a cat"

    image = sd(prompt).images[0]

To run the distributed inference, call mp.spawn to run the run_inference function on the number of GPUs defined in world_size:

def main():
    world_size = 2
    mp.spawn(run_inference, args=(world_size,), nprocs=world_size, join=True)

if __name__ == "__main__":

Once you’ve completed the inference script, use the --nproc_per_node argument to specify the number of GPUs to use and call torchrun to run the script:

torchrun run_distributed.py --nproc_per_node=2

You can use device_map within a DiffusionPipeline to distribute its model-level components on multiple devices. Refer to the Device placement guide to learn more.

< > Update on GitHub