From following these steps: https://towardsdatascience.com/how-to-run-a-stable-diffusion-server-on-google-cloud-platform-gcp-c879357808bf.

inference.py

# all the imports
import torch, time, math, os

# import specified modules
from torch import autocast
from diffusers import StableDiffusionPipeline

# confirm GPU supports the NVIDIA machine learning toolkit
assert torch.cuda.is_available()
# Stable Diffusion v1.4: CompVis/stable-diffusion-v1-4
# Stable Diffusion v1.5: runwayml/stable-diffusion-v1-5
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token=True).to("cuda")

# the prompt which will be used to create the image
prompt = "Masterpiece, Stunning, Best quality, a captivating space-themed artwork featuring the ethereal beauty of swirling nebulas and twinkling stars with an astronaut in it dark, blue themed"
# get first 20 chars of the prompt to use as the file name
first_20_chars = prompt[0:20]
# sanitize the file name by removing any "," characters
sanitized_chars = first_20_chars.replace(",", "")
sanitized_chars = sanitized_chars.replace(" ", "-")

with autocast("cuda"):
    image = pipe(prompt).images[0]
    
# save newly generated image assets to the generate-assets/ directory
timestamp = math.ceil(time.time())
title = f"{sanitized_chars}-{timestamp}"

parent_dir = "/path/to/generated-assets"
# each time inference.py is run a new directory should be created which is named
# the current timestamp. This new directory is where the newly generated 
# image should be saved
directory = title
file_path = os.path.join(parent_dir, directory)
# set permissions
mode = 0o744
os.mkdir(file_path, mode)

file_name = f"{title}.png"
file_path_and_name = f"{file_path}/{file_name}"
image.save(file_path_and_name)

print(f"{file_path_and_name} created!")
# success 
# ✨ 😎 ✨ 
sparkle = "\U00002728"
sunglasses = "\U0001F60E"
print (f"{sparkle} {sunglasses} {sparkle}")
print ("Winning")