🚀Stable Fast

Introduction

NOTE: stable-fast is currently only in beta stage and is prone to be buggy, feel free to try it out and give suggestions!

What is this?

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 of Conv + Bias + Add + Act computation patterns.
Low Precision & Fused GEMM: stable-fast implements a series of fused GEMM operators that compute with fp16 precision, which is fast than PyTorch's defaults (read & write with fp16 while compute with fp32).
NHWC & Fused GroupNorm: stable-fast implements a highly optimized fused NHWC GroupNorm + GELU operator with OpenAI's Triton, which eliminates the need of memory format permutation operators.
Fully Traced Model: stable-fast improves the torch.jit.trace interface to make it more proper for tracing complex models. Nearly every part of StableDiffusionPipeline can be traced and converted to TorchScript. It is more stable than torch.compile and has a significantly lower CPU overhead than torch.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.

Differences With Other Acceleration Libraries

Fast: stable-fast is specialy optimized for HuggingFace Diffusers. It achieves a high performance across many libraries.
Minimal: stable-fast works as a plugin framework for PyTorch. It utilizes existing PyTorch functionality and infrastructures and is compatible with other acceleration techniques, as well as popular fine-tuning techniques and deployment solutions.

Performance Comparison

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.

RTX 4080 (512x512, batch size 1, fp16, tcmalloc enabled, in WSL2)

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
Stable Fast (with xformers & Triton)	49.7 it/s	52.5 it/s	8.1 it/s

RTX 4090 (512x512, batch size 1, fp16, tcmalloc enabled)

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

RTX 3080 Ti (512x512, batch size 1, fp16, tcmalloc enabled)

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

RTX 3090 (512x512, batch size 1, fp16, tcmalloc enabled)

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

A100

Sorry, currently A100 is hard and expensive to rent from cloud server providers in my region. A few months ago I have tested this framework on A100 and the speed is around 61 it/s for SD 1.5. Detailed benchmark results will be available when I have the access to A100 again.

Compatibility

Model	Supported
Hugging Face Diffusers (1.5/2.1/XL)	Yes
With ControlNet	Yes
With LoRA	Yes
Dynamic Shape	Yes

UI Framework	Supported	Link
AUTOMATIC1111	WIP
SD Next	WIP
ComfyUI	Yes	`ComfyUI_stable_fast`

Usage

Optimize StableDiffusionPipeline

import torch
from diffusers import (StableDiffusionPipeline, EulerAncestralDiscreteScheduler)
from sfast.compilers.stable_diffusion_pipeline_compiler import (compile,
                                                                CompilationConfig
                                                                )

def load_model():
    # NOTE:
    # You could change to StableDiffusionXLPipeline to load SDXL model.
    # If the resolution is high (1024x1024),
    # ensure you VRAM is sufficient, especially when you are on Windows or WSL,
    # where the GPU driver may choose to allocate from "shared VRAM" when OOM would occur.
    # Or the performance might regress.
    # from diffusers import StableDiffusionXLPipeline
    #
    # model = StableDiffusionXLPipeline.from_pretrained(
    #     'stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16)

    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.
# It might be slow for Triton to generate, compile and fine-tune kernels.
try:
    import xformers
    config.enable_xformers = True
except ImportError:
    print('xformers not installed, skip')
# NOTE:
# When GPU VRAM is insufficient or the architecture is too old, Triton might be slow.
# Disable Triton if you encounter this problem.
try:
    import triton
    config.enable_triton = True
except ImportError:
    print('Triton not installed, skip')
# NOTE:
# CUDA Graph is suggested for small batch sizes and small resolutions to reduce CPU overhead.
# My implementation can handle dynamic shape with increased need for GPU memory.
# But when your GPU VRAM is insufficient or the image resolution is high,
# CUDA Graph could cause less efficient VRAM utilization and slow down the inference,
# especially when on Windows or WSL which has the "shared VRAM" mechanism.
# If you meet problems related to it, you should disable it.
config.enable_cuda_graph = True

compiled_model = compile(model, config)

kwarg_inputs = dict(
    prompt=
    '(masterpiece:1,2), best quality, masterpiece, best detail face, lineart, monochrome, a beautiful girl',
    # NOTE: If you use SDXL, you should use a higher resolution to improve the generation quality.
    height=512,
    width=512,
    num_inference_steps=30,
    num_images_per_prompt=1,
)

# NOTE: Warm it up.
# The first call will trigger compilation and might be very slow.
# After the first call, it should be very fast.
output_image = compiled_model(**kwarg_inputs).images[0]

# Let's see the second call!
output_image = compiled_model(**kwarg_inputs).images[0]

Installation

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. Other versions might build and run successfully but that's not guaranteed.

Install From Source

# 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'

# (Optional) Makes the build much faster
pip3 install ninja

# Set TORCH_CUDA_ARCH_LIST if running and building on different GPU types
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.

Some Common Methods To Speed Up PyTorch

# 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

Trouble Shooting

Compilation Is SO SLOW. How To Improve It?

Dynamic code generation is usually the cause for slow compilation. You could disable features related to it to speed up compilation. But this might slow down your inference.

Disable JIT Optimized Execution

# Wrap your code in this context manager
with torch.jit.optimized_execution(False):
    # Do your things

Disable Triton

config.enable_triton = False

Inference Is SO SLOW. What's Wrong?

Disable CUDA Graph When GPU VRAM Is Insufficient

When your GPU VRAM is insufficient or the image resolution is high, CUDA Graph could cause less efficient VRAM utilization and slow down the inference.

config.enable_cuda_graph = False

skirsten / stable-fast