_test.py

import time
import torch
from diffusers import (StableDiffusionPipeline,
                       EulerAncestralDiscreteScheduler)
from sfast.compilers.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.
# But it can increase the amount of GPU memory used.
# For StableVideoDiffusionPipeline it is not needed.
config.enable_cuda_graph = True

model = compile(model, config)