#
xDiT

xDiT is an inference engine designed for the large scale parallel deployment of Diffusion Transformers (DiTs). xDiT provides a suite of efficient parallel approaches for Diffusion Models, as well as GPU kernel accelerations.

There are four parallel methods supported in xDiT, including Unified Sequence Parallelism, PipeFusion, CFG parallelism and data parallelism. The four parallel methods in xDiT can be configured in a hybrid manner, optimizing communication patterns to best suit the underlying network hardware.

Optimization orthogonal to parallelization focuses on accelerating single GPU performance. In addition to utilizing well-known Attention optimization libraries, we leverage compilation acceleration technologies such as torch.compile and onediff.

The overview of xDiT is shown as follows.

You can install xDiT using the following command:

    pip install xfuser

Here’s an example of using xDiT to accelerate inference of a Diffusers model.

     import torch
     from diffusers import StableDiffusion3Pipeline

     from xfuser import xFuserArgs, xDiTParallel
     from xfuser.config import FlexibleArgumentParser
     from xfuser.core.distributed import get_world_group

     def main():
    +    parser = FlexibleArgumentParser(description="xFuser Arguments")
    +    args = xFuserArgs.add_cli_args(parser).parse_args()
    +    engine_args = xFuserArgs.from_cli_args(args)
    +    engine_config, input_config = engine_args.create_config()

         local_rank = get_world_group().local_rank
         pipe = StableDiffusion3Pipeline.from_pretrained(
             pretrained_model_name_or_path=engine_config.model_config.model,
             torch_dtype=torch.float16,
         ).to(f"cuda:{local_rank}")

    # do anything you want with pipeline here

    +    pipe = xDiTParallel(pipe, engine_config, input_config)

         pipe(
             height=input_config.height,
             width=input_config.height,
             prompt=input_config.prompt,
             num_inference_steps=input_config.num_inference_steps,
             output_type=input_config.output_type,
             generator=torch.Generator(device="cuda").manual_seed(input_config.seed),
         )

    +    if input_config.output_type == "pil":
    +        pipe.save("results", "stable_diffusion_3")

    if __name__ == "__main__":
        main()

As you can see, we only need to use xFuserArgs from xDiT to get configuration parameters, and pass these parameters along with the pipeline object from the Diffusers library into xDiTParallel to complete the parallelization of a specific pipeline in Diffusers.

xDiT runtime parameters can be viewed in the command line using `-h`, and you can refer to this usage example for more details.

xDiT needs to be launched using torchrun to support its multi-node, multi-GPU parallel capabilities. For example, the following command can be used for 8-GPU parallel inference:

    torchrun --nproc_per_node=8 ./inference.py --model models/FLUX.1-dev --data_parallel_degree 2 --ulysses_degree 2 --ring_degree 2 --prompt "A snowy mountain" "A small dog" --num_inference_steps 50

##
Supported models

A subset of Diffusers models are supported in xDiT, such as Flux.1, Stable Diffusion 3, etc. The latest supported models can be found here.

##
Benchmark

We tested different models on various machines, and here is some of the benchmark data.

###
Flux.1-schnell

###
Stable Diffusion 3

###
HunyuanDiT

More detailed performance metric can be found on our github page.

##
Reference

xDiT-project

USP: A Unified Sequence Parallelism Approach for Long Context Generative AI

PipeFusion: Displaced Patch Pipeline Parallelism for Inference of Diffusion Transformer Models

Update on GitHub