Diffusers documentation

Memory and speed

You are viewing main version, which requires installation from source. If you'd like regular pip install, checkout the latest stable version (v0.14.0).
Join the Hugging Face community

to get started

# Memory and speed

We present some techniques and ideas to optimize 🤗 Diffusers inference for memory or speed. As a general rule, we recommend the use of xFormers for memory efficient attention, please see the recommended installation instructions.

We’ll discuss how the following settings impact performance and memory.

Latency Speedup
original 9.50s x1
cuDNN auto-tuner 9.37s x1.01
fp16 3.61s x2.63
channels last 3.30s x2.88
traced UNet 3.21s x2.96
memory efficient attention 2.63s x3.61
obtained on NVIDIA TITAN RTX by generating a single image of size 512x512 from the prompt "a photo of an astronaut riding a horse on mars" with 50 DDIM steps.

## Enable cuDNN auto-tuner

NVIDIA cuDNN supports many algorithms to compute a convolution. Autotuner runs a short benchmark and selects the kernel with the best performance on a given hardware for a given input size.

Since we’re using convolutional networks (other types currently not supported), we can enable cuDNN autotuner before launching the inference by setting:

import torch

torch.backends.cudnn.benchmark = True

### Use tf32 instead of fp32 (on Ampere and later CUDA devices)

On Ampere and later CUDA devices matrix multiplications and convolutions can use the TensorFloat32 (TF32) mode for faster but slightly less accurate computations. By default PyTorch enables TF32 mode for convolutions but not matrix multiplications, and unless a network requires full float32 precision we recommend enabling this setting for matrix multiplications, too. It can significantly speed up computations with typically negligible loss of numerical accuracy. You can read more about it here. All you need to do is to add this before your inference:

import torch

torch.backends.cuda.matmul.allow_tf32 = True

## Half precision weights

To save more GPU memory and get more speed, you can load and run the model weights directly in half precision. This involves loading the float16 version of the weights, which was saved to a branch named fp16, and telling PyTorch to use the float16 type when loading them:

pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",

torch_dtype=torch.float16,
)
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
It is strongly discouraged to make use of [torch.autocast](https://pytorch.org/docs/stable/amp.html#torch.autocast) in any of the pipelines as it can lead to black images and is always slower than using pure float16 precision.

## Sliced attention for additional memory savings

For even additional memory savings, you can use a sliced version of attention that performs the computation in steps instead of all at once.

Attention slicing is useful even if a batch size of just 1 is used - as long as the model uses more than one attention head. If there is more than one attention head the *QK^T* attention matrix can be computed sequentially for each head which can save a significant amount of memory.

To perform the attention computation sequentially over each head, you only need to invoke enable_attention_slicing() in your pipeline before inference, like here:

import torch
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",

torch_dtype=torch.float16,
)
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
pipe.enable_attention_slicing()
image = pipe(prompt).images[0]

There’s a small performance penalty of about 10% slower inference times, but this method allows you to use Stable Diffusion in as little as 3.2 GB of VRAM!

## Sliced VAE decode for larger batches

To decode large batches of images with limited VRAM, or to enable batches with 32 images or more, you can use sliced VAE decode that decodes the batch latents one image at a time.

You likely want to couple this with enable_attention_slicing() or enable_xformers_memory_efficient_attention() to further minimize memory use.

To perform the VAE decode one image at a time, invoke enable_vae_slicing() in your pipeline before inference. For example:

import torch
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",

torch_dtype=torch.float16,
)
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
pipe.enable_vae_slicing()
images = pipe([prompt] * 32).images

You may see a small performance boost in VAE decode on multi-image batches. There should be no performance impact on single-image batches.

## Tiled VAE decode and encode for large images

Tiled VAE processing makes it possible to work with large images on limited VRAM. For example, generating 4k images in 8GB of VRAM. Tiled VAE decoder splits the image into overlapping tiles, decodes the tiles, and blends the outputs to make the final image.

You want to couple this with enable_attention_slicing() or enable_xformers_memory_efficient_attention() to further minimize memory use.

To use tiled VAE processing, invoke enable_vae_tiling() in your pipeline before inference. For example:

import torch
from diffusers import StableDiffusionPipeline, UniPCMultistepScheduler

pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
prompt = "a beautiful landscape photograph"
pipe.enable_vae_tiling()
pipe.enable_xformers_memory_efficient_attention()

image = pipe([prompt], width=3840, height=2224, num_inference_steps=20).images[0]

The output image will have some tile-to-tile tone variation from the tiles having separate decoders, but you shouldn’t see sharp seams between the tiles. The tiling is turned off for images that are 512x512 or smaller.

For additional memory savings, you can offload the weights to CPU and only load them to GPU when performing the forward pass.

import torch
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",

torch_dtype=torch.float16,
)

prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]

And you can get the memory consumption to < 3GB.

Note that this method works at the submodule level, not on whole models. This is the best way to minimize memory consumption, but inference is much slower due to the iterative nature of the process. The UNet component of the pipeline runs several times (as many as num_inference_steps); each time, the different submodules of the UNet are sequentially onloaded and then offloaded as they are needed, so the number of memory transfers is large.

Consider using model offloading as another point in the optimization space: it will be much faster, but memory savings won't be as large.

It is also possible to chain offloading with attention slicing for minimal memory consumption (< 2GB).

import torch
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",

torch_dtype=torch.float16,
)

prompt = "a photo of an astronaut riding a horse on mars"
pipe.enable_attention_slicing(1)

image = pipe(prompt).images[0]

Note: When using enable_sequential_cpu_offload(), it is important to not move the pipeline to CUDA beforehand or else the gain in memory consumption will only be minimal. See this issue for more information.

Sequential CPU offloading, as discussed in the previous section, preserves a lot of memory but makes inference slower, because submodules are moved to GPU as needed, and immediately returned to CPU when a new module runs.

Full-model offloading is an alternative that moves whole models to the GPU, instead of handling each model’s constituent modules. This results in a negligible impact on inference time (compared with moving the pipeline to cuda), while still providing some memory savings.

In this scenario, only one of the main components of the pipeline (typically: text encoder, unet and vae) will be in the GPU while the others wait in the CPU. Components like the UNet that run for multiple iterations will stay on GPU until they are no longer needed.

This feature can be enabled by invoking enable_model_cpu_offload() on the pipeline, as shown below.

import torch
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
)

prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]

This is also compatible with attention slicing for additional memory savings.

import torch
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
)

prompt = "a photo of an astronaut riding a horse on mars"
pipe.enable_attention_slicing(1)

image = pipe(prompt).images[0]
This feature requires accelerate version 0.17.0 or larger.

## Using Channels Last memory format

Channels last memory format is an alternative way of ordering NCHW tensors in memory preserving dimensions ordering. Channels last tensors ordered in such a way that channels become the densest dimension (aka storing images pixel-per-pixel). Since not all operators currently support channels last format it may result in a worst performance, so it’s better to try it and see if it works for your model.

For example, in order to set the UNet model in our pipeline to use channels last format, we can use the following:

print(pipe.unet.conv_out.state_dict()["weight"].stride())  # (2880, 9, 3, 1)
pipe.unet.to(memory_format=torch.channels_last)  # in-place operation
print(
pipe.unet.conv_out.state_dict()["weight"].stride()
)  # (2880, 1, 960, 320) having a stride of 1 for the 2nd dimension proves that it works

## Tracing

Tracing runs an example input tensor through your model, and captures the operations that are invoked as that input makes its way through the model’s layers so that an executable or ScriptFunction is returned that will be optimized using just-in-time compilation.

To trace our UNet model, we can use the following:

import time
import torch
from diffusers import StableDiffusionPipeline
import functools

# set variables
n_experiments = 2
unet_runs_per_experiment = 50

def generate_inputs():
sample = torch.randn(2, 4, 64, 64).half().cuda()
timestep = torch.rand(1).half().cuda() * 999
encoder_hidden_states = torch.randn(2, 77, 768).half().cuda()
return sample, timestep, encoder_hidden_states

pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
).to("cuda")
unet = pipe.unet
unet.eval()
unet.to(memory_format=torch.channels_last)  # use channels_last memory format
unet.forward = functools.partial(unet.forward, return_dict=False)  # set return_dict=False as default

# warmup
for _ in range(3):
with torch.inference_mode():
inputs = generate_inputs()
orig_output = unet(*inputs)

# trace
print("tracing..")
unet_traced = torch.jit.trace(unet, inputs)
unet_traced.eval()
print("done tracing")

# warmup and optimize graph
for _ in range(5):
with torch.inference_mode():
inputs = generate_inputs()
orig_output = unet_traced(*inputs)

# benchmarking
with torch.inference_mode():
for _ in range(n_experiments):
torch.cuda.synchronize()
start_time = time.time()
for _ in range(unet_runs_per_experiment):
orig_output = unet_traced(*inputs)
torch.cuda.synchronize()
print(f"unet traced inference took {time.time() - start_time:.2f} seconds")
for _ in range(n_experiments):
torch.cuda.synchronize()
start_time = time.time()
for _ in range(unet_runs_per_experiment):
orig_output = unet(*inputs)
torch.cuda.synchronize()
print(f"unet inference took {time.time() - start_time:.2f} seconds")

# save the model
unet_traced.save("unet_traced.pt")

Then we can replace the unet attribute of the pipeline with the traced model like the following

from diffusers import StableDiffusionPipeline
import torch
from dataclasses import dataclass

@dataclass
class UNet2DConditionOutput:
sample: torch.FloatTensor

pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
).to("cuda")

# use jitted unet

# del pipe.unet
class TracedUNet(torch.nn.Module):
def __init__(self):
super().__init__()
self.in_channels = pipe.unet.in_channels
self.device = pipe.unet.device

def forward(self, latent_model_input, t, encoder_hidden_states):
sample = unet_traced(latent_model_input, t, encoder_hidden_states)[0]
return UNet2DConditionOutput(sample=sample)

pipe.unet = TracedUNet()

with torch.inference_mode():
image = pipe([prompt] * 1, num_inference_steps=50).images[0]

## Memory Efficient Attention

Recent work on optimizing the bandwitdh in the attention block has generated huge speed ups and gains in GPU memory usage. The most recent being Flash Attention from @tridao: code, paper.

Here are the speedups we obtain on a few Nvidia GPUs when running the inference at 512x512 with a batch size of 1 (one prompt):

GPU Base Attention FP16 Memory Efficient Attention FP16
NVIDIA Tesla T4 3.5it/s 5.5it/s
NVIDIA 3060 RTX 4.6it/s 7.8it/s
NVIDIA A10G 8.88it/s 15.6it/s
NVIDIA RTX A6000 11.7it/s 21.09it/s
NVIDIA TITAN RTX 12.51it/s 18.22it/s
A100-SXM4-40GB 18.6it/s 29.it/s
A100-SXM-80GB 18.7it/s 29.5it/s

To leverage it just make sure you have:

from diffusers import StableDiffusionPipeline
import torch

pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
).to("cuda")

pipe.enable_xformers_memory_efficient_attention()

with torch.inference_mode():
sample = pipe("a small cat")

# optional: You can disable it via
# pipe.disable_xformers_memory_efficient_attention()