stable-fast
achieves SOTA inference performance on ALL kinds of diffuser models.
And unlike TensorRT
or AITemplate
, which takes dozens of minutes to compile a model, stable-fast
only takes a few seconds to compile a model.
stable-fast
also supports dynamic shape
, LoRA
and ControlNet
out of the box.
Framework | vanilla torch | torch.compile | AITemplate | TensorRT | stable-fast |
---|---|---|---|---|---|
Time | 1897 ms | 1510 ms | 1158 ms | 991 ms | 1029 ms |
NOTE: stable-fast
is currently only in beta stage and is prone to be buggy, feel free to try it out and give suggestions!
stable-fast
is an ultra lightweight inference optimization framework for HuggingFace Diffusers on NVIDIA GPUs.
stable-fast
provides super fast inference optimization by utilizing some key techniques and features:
- CUDNN Convolution Fusion:
stable-fast
implements a series of fully-functional and fully-compatible CUDNN convolution fusion operators for all kinds of combinations ofConv + Bias + Add + Act
computation patterns. - Low Precision & Fused GEMM:
stable-fast
implements a series of fused GEMM operators that compute withfp16
precision, which is fast than PyTorch's defaults (read & write withfp16
while compute withfp32
). - NHWC & Fused GroupNorm:
stable-fast
implements a highly optimized fused NHWCGroupNorm + GELU
operator with OpenAI'sTriton
, which eliminates the need of memory format permutation operators. - Fully Traced Model:
stable-fast
improves thetorch.jit.trace
interface to make it more proper for tracing complex models. Nearly every part ofStableDiffusionPipeline
can be traced and converted to TorchScript. It is more stable thantorch.compile
and has a significantly lower CPU overhead thantorch.compile
and supports ControlNet and LoRA. - CUDA Graph:
stable-fast
can capture the UNet structure into CUDA Graph format, which can reduce the CPU overhead when the batch size is small. - Fused Multihead Attention:
stable-fast
just uses xformers and make it compatible with TorchScript.
My next goal is to keep stable-fast
as one of the fastest inference optimization frameworks for diffusers
and also
provide both speedup and VRAM reduction for transformers
.
In fact, I already use stable-fast
to optimize LLMs and achieve a significant speedup.
But I still need to do some work to make it more stable and easy to use and provide a stable user interface.
- Fast:
stable-fast
is specialy optimized for HuggingFace Diffusers. It achieves a high performance across many libraries. And it provides a very fast compilation speed within only a few seconds. It is significantly faster thantorch.compile
,TensorRT
andAITemplate
in compilation time. - Minimal:
stable-fast
works as a plugin framework forPyTorch
. It utilizes existingPyTorch
functionality and infrastructures and is compatible with other acceleration techniques, as well as popular fine-tuning techniques and deployment solutions. - Maximum Compatibility:
stable-fast
is compatible with all kinds ofHuggingFace Diffusers
andPyTorch
versions. It is also compatible withControlNet
andLoRA
.
NOTE: stable-fast
is currently only tested on Linux
and WSL2 in Windows
.
You need to install PyTorch with CUDA support at first (versions from 1.12 to 2.1 are suggested).
I only test stable-fast
with torch==2.1.0
, xformers==0.0.22
and triton==2.1.0
on CUDA 12.1
and Python 3.10
.
Other versions might build and run successfully but that's not guaranteed.
Download the wheel corresponding to your system from the Releases Page and install it with pip3 install <wheel file>
.
Currently both Linux and Windows wheels are available.
# Change cu121 to your CUDA version and <wheel file> to the path of the wheel file.
# And make sure the wheel file is compatible with your PyTorch version.
pip3 install --index-url https://download.pytorch.org/whl/cu121 'diffusers>=0.19.3' 'xformers>=0.0.20' 'torch>=1.12.0' '<wheel file>'
# Make sure you have CUDNN/CUBLAS installed.
# https://developer.nvidia.com/cudnn
# https://developer.nvidia.com/cublas
# Install PyTorch with CUDA and other packages at first
pip3 install 'torch>=1.12.0' 'diffusers>=0.19.3' 'xformers>=0.0.20' 'triton>=2.1.0'
# Windows user: Triton might be not available, you could skip it.
# (Optional) Makes the build much faster
pip3 install ninja
# Set TORCH_CUDA_ARCH_LIST if running and building on different GPU types
# You can also install the latest stable release from PyPI
# pip3 install -v -U stable-fast
pip3 install -v -U git+https://github.com/chengzeyi/stable-fast.git@main#egg=stable-fast
# (this can take dozens of minutes)
NOTE: Any usage outside sfast.compilers
is not guaranteed to be backward compatible.
NOTE: To get the best performance, xformers
and OpenAI's triton>=2.1.0
need to be installed and enabled.
You might need to build xformers
from source to make it compatible with your PyTorch
.
stable-fast
is able to optimize StableDiffusionPipeline
and StableDiffusionPipelineXL
directly.
import time
import torch
from diffusers import (StableDiffusionPipeline,
EulerAncestralDiscreteScheduler)
from sfast.compilers.stable_diffusion_pipeline_compiler import (
compile, CompilationConfig)
def load_model():
model = StableDiffusionPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5',
torch_dtype=torch.float16)
model.scheduler = EulerAncestralDiscreteScheduler.from_config(
model.scheduler.config)
model.safety_checker = None
model.to(torch.device('cuda'))
return model
model = load_model()
config = CompilationConfig.Default()
# xformers and Triton are suggested for achieving best performance.
try:
import xformers
config.enable_xformers = True
except ImportError:
print('xformers not installed, skip')
try:
import triton
config.enable_triton = True
except ImportError:
print('Triton not installed, skip')
# CUDA Graph is suggested for small batch sizes and small resolutions to reduce CPU overhead.
config.enable_cuda_graph = True
model = compile(model, config)
kwarg_inputs = dict(
prompt=
'(masterpiece:1,2), best quality, masterpiece, best detail face, a beautiful girl',
height=512,
width=512,
num_inference_steps=30,
num_images_per_prompt=1,
)
# NOTE: Warm it up.
# The initial calls will trigger compilation and might be very slow.
# After that, it should be very fast.
for _ in range(3):
output_image = model(**kwarg_inputs).images[0]
# Let's see it!
# Note: Progress bar might work incorrectly due to the async nature of CUDA.
begin = time.time()
output_image = model(**kwarg_inputs).images[0]
print(f'Inference time: {time.time() - begin:.3f}s')
# Let's view it in terminal!
from sfast.utils.term_image import print_image
print_image(output_image, max_width=80)
Refer to examples/optimize_stable_diffusion_pipeline.py for more details.
stable-fast
is able to optimize the newest latent consistency model
pipeline and achieve a significant speedup.
Refer to examples/optimize_lcm_pipeline.py for more details.
Switching LoRA dynamically is supported but you need to do some extra work.
It is possible because the compiled graph and CUDA Graph
share the same
underlaying data (pointers) with the original UNet model. So all you need to do
is to update the original UNet model's parameters inplace.
The following code assumes you have already load a LoRA and compiled the model, and you want to switch to another LoRA.
# load_state_dict with assign=True requires torch >= 2.1.0
def update_state_dict(dst, src):
for key, value in src.items():
# Do inplace copy.
# As the traced forward function shares the same underlaying data (pointers),
# this modification will be reflected in the traced forward function.
dst[key].copy_(value)
# Switch "another" LoRA into UNet
def switch_lora(unet, lora):
# Store the original UNet parameters
state_dict = unet.state_dict()
# Load another LoRA into unet
unet.load_attn_procs(lora)
# Inplace copy current UNet parameters to the original unet parameters
update_state_dict(state_dict, unet.state_dict())
# Load the original UNet parameters back.
# We use assign=True because we still want to hold the references
# of the original UNet parameters
unet.load_state_dict(state_dict, assign=True)
switch_lora(compiled_model.unet, lora_b_path)
stable-fast
extends PyTorch's quantize_dynamic
functionality and provides a fast quantized linear operator.
By enabling it, you could get a slight VRAM reduction for diffusers
and significant VRAM reduction for transformers
,
and cound get a potential speedup.
However, since diffusers
implements its own Linear
layer as LoRACompatibleLinear
,
you need to do some hacks to make it work and it is a little complex and tricky.
Refer to tests/compilers/test_stable_diffusion_pipeline_compiler.py to see how to do it.
# TCMalloc is highly suggested to reduce CPU overhead
# https://github.com/google/tcmalloc
LD_PRELOAD=/path/to/libtcmalloc.so python3 ...
import packaging.version
import torch
if packaging.version.parse(torch.__version__) >= packaging.version.parse('1.12.0'):
torch.backends.cuda.matmul.allow_tf32 = True
Performance varies very greatly across different hardware/software/platform/driver configurations.
It is very hard to benchmark accurately. And preparing the environment for benchmarking is also a hard job.
I have tested on some platforms before but the results may still be inaccurate.
Note that when benchmarking, the progress bar showed by tqdm
may be inaccurate because of the asynchronous nature of CUDA.
To solve this problem, I have to add torch.cuda.synchronize()
after every inference step, which will slow down the inference,
so the results might not be very accurate and might be slower than the actual performance.
stable-fast
is expected to work better on newer GPUs and newer CUDA versions.
On older GPUs, the performance increase might be limited.
During benchmarking, the progress bar might work incorrectly because of the asynchronous nature of CUDA.
This is my personal gaming PC😄. It has a more powerful CPU than those from cloud server providers.
Framework | SD 1.5 | SD 2.1 | SD XL (1024x1024) |
---|---|---|---|
Vanilla PyTorch (2.1.0+cu118) | 29.5 it/s | 32.4 it/s | 4.6 it/s |
torch.compile (2.1.0+cu118, NHWC UNet) | 40.0 it/s | 44.0 it/s | 6.1 it/s |
AITemplate | 44.2 it/s | untested | untested |
OneFlow | 50.3 it/s | untested | untested |
AUTO1111 WebUI | 17.2 it/s | 15.2 it/s | 3.6 it/s |
AUTO1111 WebUI (with SDPA) | 24.5 it/s | 26.1 it/s | 4.3 it/s |
TensorRT (AUTO1111 WebUI) | 40.8 it/s | untested | untested |
TensorRT Official Demo | 52.6 it/s | untested | untested |
Stable Fast (with xformers & Triton) | 50.5 it/s | 53.3 it/s | 8.3 it/s |
Framework | SD 1.5 | SD 2.1 | SD 1.5 ControlNet |
---|---|---|---|
Vanilla PyTorch (2.1.0+cu118) | 24.9 it/s | 27.1 it/s | 18.9 it/s |
torch.compile (2.1.0+cu118, NHWC UNet) | 33.5 it/s | 38.2 it/s | 22.7 it/s |
AITemplate | 65.7 it/s | 71.6 it/s | untested |
OneFlow | 60.1 it/s | 12.9 it/s (??) | untested |
TensorRT | untested | untested | untested |
Stable Fast (with xformers & Triton) | 61.8 it/s | 61.6 it/s | 42.3 it/s |
(??): OneFlow seems to be not working well with SD 2.1
Framework | SD 1.5 | SD 2.1 | SD 1.5 ControlNet |
---|---|---|---|
Vanilla PyTorch (2.1.0+cu118) | 19.3 it/s | 20.4 it/s | 13.8 it/s |
torch.compile (2.1.0+cu118, NHWC UNet) | 24.4 it/s | 26.9 it/s | 17.7 it/s |
AITemplate | untested | untested | untested |
OneFlow | 32.8 it/s | 8.82 it/s (??) | untested |
TensorRT | untested | untested | untested |
Stable Fast (with xformers & Triton) | 28.1 it/s | 30.2 it/s | 20.0 it/s |
(??): OneFlow seems to be not working well with SD 2.1
Framework | SD 1.5 |
---|---|
Vanilla PyTorch (2.1.0+cu118) | 22.5 it/s |
torch.compile (2.1.0+cu118, NHWC UNet) | 25.3 it/s |
AITemplate | 34.6 it/s |
OneFlow | 38.8 it/s |
TensorRT | untested |
Stable Fast (with xformers & Triton) | 31.5 it/s |
Thanks for @Consceleratus's help, I have tested speed on H100.
Detailed benchmarking results will be available soon.
Thanks for @SuperSecureHuman's help, benchmarking on A100 PCIe 40GB is available now.
Framework | SD 1.5 | SD 2.1 | SD 1.5 ControlNet | SD XL |
---|---|---|---|---|
Vanilla PyTorch (2.1.0+cu118) | 23.8 it/s | 23.8 it/s | 15.7 it/s | 10.0 it/s |
torch.compile (2.1.0+cu118, NHWC UNet) | 37.7 it/s | 42.7 it/s | 24.7 it/s | 20.9 it/s |
Stable Fast (with xformers & Triton) | 53.2 it/s | 55.9 it/s | 37.1 it/s | 29.6 it/s |
Model | Supported |
---|---|
Hugging Face Diffusers (1.5/2.1/XL) | Yes |
With ControlNet | Yes |
With LoRA | Yes |
Dynamic Shape | Yes |
Latent Consistency Model | Yes |
UI Framework | Supported | Link |
---|---|---|
AUTOMATIC1111 | WIP | |
SD Next | Yes | SD Next |
ComfyUI | Yes | ComfyUI_stable_fast |
Refer to doc/troubleshooting.md for more details.