Distributed inference

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

with distributed_state.split_between_processes(["a dog", "a cat"]) as prompt:
    result = pipeline(prompt).images[0]
    result.save(f"result_{distributed_state.process_index}.png")

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

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(
    "stable-diffusion-v1-5/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)

    sd.to(rank)

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

    image = sd(prompt).images[0]
    image.save(f"./{'_'.join(prompt)}.png")

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__":
    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

Model sharding

Modern diffusion systems such as Flux are very large and have multiple models. For example, Flux.1-Dev is made up of two text encoders - T5-XXL and CLIP-L - a diffusion transformer, and a VAE. With a model this size, it can be challenging to run inference on consumer GPUs.

Model sharding is a technique that distributes models across GPUs when the models don’t fit on a single GPU. The example below assumes two 16GB GPUs are available for inference.

Start by computing the text embeddings with the text encoders. Keep the text encoders on two GPUs by setting device_map="balanced". The balanced strategy evenly distributes the model on all available GPUs. Use the max_memory parameter to allocate the maximum amount of memory for each text encoder on each GPU.

from diffusers import FluxPipeline
import torch

prompt = "a photo of a dog with cat-like look"

pipeline = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    transformer=None,
    vae=None,
    device_map="balanced",
    max_memory={0: "16GB", 1: "16GB"},
    torch_dtype=torch.bfloat16
)
with torch.no_grad():
    print("Encoding prompts.")
    prompt_embeds, pooled_prompt_embeds, text_ids = pipeline.encode_prompt(
        prompt=prompt, prompt_2=None, max_sequence_length=512
    )

Once the text embeddings are computed, remove them from the GPU to make space for the diffusion transformer.

import gc 

def flush():
    gc.collect()
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

del pipeline.text_encoder
del pipeline.text_encoder_2
del pipeline.tokenizer
del pipeline.tokenizer_2
del pipeline

flush()

Load the diffusion transformer next which has 12.5B parameters. This time, set device_map="auto" to automatically distribute the model across two 16GB GPUs. The auto strategy is backed by Accelerate and available as a part of the Big Model Inference feature. It starts by distributing a model across the fastest device first (GPU) before moving to slower devices like the CPU and hard drive if needed. The trade-off of storing model parameters on slower devices is slower inference latency.

from diffusers import FluxTransformer2DModel
import torch 

transformer = FluxTransformer2DModel.from_pretrained(
    "black-forest-labs/FLUX.1-dev", 
    subfolder="transformer",
    device_map="auto",
    torch_dtype=torch.bfloat16
)

Add the transformer model to the pipeline for denoising, but set the other model-level components like the text encoders and VAE to None because you don’t need them yet.

pipeline = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    text_encoder=None,
    text_encoder_2=None,
    tokenizer=None,
    tokenizer_2=None,
    vae=None,
    transformer=transformer,
    torch_dtype=torch.bfloat16
)

print("Running denoising.")
height, width = 768, 1360
latents = pipeline(
    prompt_embeds=prompt_embeds,
    pooled_prompt_embeds=pooled_prompt_embeds,
    num_inference_steps=50,
    guidance_scale=3.5,
    height=height,
    width=width,
    output_type="latent",
).images

Remove the pipeline and transformer from memory as they’re no longer needed.

del pipeline.transformer
del pipeline

flush()

Finally, decode the latents with the VAE into an image. The VAE is typically small enough to be loaded on a single GPU.

from diffusers import AutoencoderKL
from diffusers.image_processor import VaeImageProcessor
import torch 

vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder="vae", torch_dtype=torch.bfloat16).to("cuda")
vae_scale_factor = 2 ** (len(vae.config.block_out_channels))
image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor)

with torch.no_grad():
    print("Running decoding.")
    latents = FluxPipeline._unpack_latents(latents, height, width, vae_scale_factor)
    latents = (latents / vae.config.scaling_factor) + vae.config.shift_factor

    image = vae.decode(latents, return_dict=False)[0]
    image = image_processor.postprocess(image, output_type="pil")
    image[0].save("split_transformer.png")

By selectively loading and unloading the models you need at a given stage and sharding the largest models across multiple GPUs, it is possible to run inference with large models on consumer GPUs.