xymfei/stable-fast

快速稳定扩散，一个超轻量级的推理性能优化库An ultra lightweight inference performance optimization library for HuggingFace Diffusers on NVIDIA GPUs.

Stable Fast

Introduction

NOTE: stable-fast is 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 library 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 the best performance over all 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

A100 SXM 80GB (SD v1.5, 512x512, fp16)

Framework	Performance
Vanilla PyTorch	23 it/s
AITemplate	44 it/s
TensorRT	52 it/s
OneFlow	55 it/s
Stable Fast (with xformers & triton)	60 it/s

RTX 3090 Ti (SD v1.5, 512x512, fp16)

Framework	Performance
Vanilla PyTorch	16 it/s
AITemplate	31 it/s
TensorRT	33 it/s
OneFlow	39 it/s
Stable Fast (with xformers & triton)	38 it/s

Usage

Installation

NOTE: stable-fast is currently only tested on Linux. You need to install PyTorch with CUDA support at first (versions from 1.12 to 2.1 are suggested).

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
pip install torch diffusers xformers 'triton>=2.1.0'

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

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

Optimize StableDiffusionPipeline

import torch
from diffusers import StableDiffusionPipeline
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.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')
try:
    import triton
    config.enable_triton = True
except ImportError:
    print('triton not installed, skip')
# CUDA Graph is suggested for small batch sizes.
# After capturing, the model only accepts one fixed image size.
# If you want the model to be dynamic, don't enable 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',
    height=512,
    width=512,
    num_inference_steps=50,
    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]