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import os
import io
import warnings
from PIL import Image
from stability_sdk import client
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
import uuid
import gradio as gr
# Our Host URL should not be prepended with "https" nor should it have a trailing slash.
os.environ["STABILITY_HOST"] = "grpc.stability.ai:443"
def get_image(prompt, api_key_stability_ai):
# Sign up for an account at the following link to get an API Key.
# https://platform.stability.ai/
# Click on the following link once you have created an account to be taken to your API Key.
# https://platform.stability.ai/account/keys
# Set up our connection to the API.
if api_key_stability_ai == "":
raise gr.Error("Please add your Stability AI API key ")
else:
try:
stability_api = client.StabilityInference(
key=api_key_stability_ai, # API Key reference.
verbose=True, # Print debug messages.
engine="stable-diffusion-xl-1024-v1-0", # Set the engine to use for generation.
# Check out the following link for a list of available engines: https://platform.stability.ai/docs/features/api-parameters#engine
)
# Set up our initial generation parameters.
answers = stability_api.generate(
prompt=prompt, # The prompt we want to generate an image from.
seed=4253978046, # If a seed is provided, the resulting generated image will be deterministic.
# What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again.
# Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook.
steps=30, # Amount of inference steps performed on image generation. Defaults to 30.
cfg_scale=8.0, # Influences how strongly your generation is guided to match your prompt.
# Setting this value higher increases the strength in which it tries to match your prompt.
# Defaults to 7.0 if not specified.
width=512, # Generation width, defaults to 512 if not included.
height=512, # Generation height, defaults to 512 if not included.
samples=1, # Number of images to generate, defaults to 1 if not included.
sampler=generation.SAMPLER_K_DPMPP_2M, # Choose which sampler we want to denoise our generation with.
# Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers.
# (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m, k_dpmpp_sde)
)
# print("Finish the prompt")
# Set up our warning to print to the console if the adult content classifier is tripped.
# If adult content classifier is not tripped, save generated images.
for resp in answers:
for artifact in resp.artifacts:
# if artifact.finish_reason == generation.FILTER:
# print(artifact.finish_reason)
# print("Warning")
# warnings.warn(
# "Your request activated the API's safety filters and could not be processed."
# "Please modify the prompt and try again.")
if artifact.type == generation.ARTIFACT_IMAGE:
img = Image.open(io.BytesIO(artifact.binary))
unique_filename = str(uuid.uuid4())
img.save(
str(unique_filename) + ".png"
) # Save our generated images with their seed number as the filename.
return unique_filename + ".png"
except Exception as error:
print(str(error))
raise gr.Error(
"An error occurred while generating the image. Please try again."
)
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