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import os
import openai
import io
import warnings
from PIL import Image
from stability_sdk import client
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
import streamlit as st
STABILITY_KEY = st.secrets["STABILITY_KEY"]
OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]
MODEL = st.secrets["MODEL"]
MODEL2 = st.secrets["MODEL2"]
openai.api_key = OPENAI_API_KEY
st.title('Image Generator App')
# Initialize session state if it doesn't exist
if 'prompts' not in st.session_state:
st.session_state['prompts'] = []
if 'selected_prompt' not in st.session_state:
st.session_state['selected_prompt'] = ""
if 'edited_prompt' not in st.session_state:
st.session_state['edited_prompt'] = ""
st.session_state['edited_prompt'] = st.text_input(r'Input Prompt:', value=st.session_state['selected_prompt'])
def generateImageViaStabilityai(prompt):
os.environ['STABILITY_HOST'] = 'grpc.stability.ai:443'
stability_api = client.StabilityInference(
key=STABILITY_KEY, # API Key reference.
verbose=True, # Print debug messages.
engine="stable-diffusion-xl-1024-v1-0",
)
# Set up our initial generation parameters.
answers = stability_api.generate(
prompt=prompt,
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=50, # 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=1024, # Generation width, defaults to 512 if not included.
height=1024, # Generation height, defaults to 512 if not included.
style_preset="photographic",
samples=5, # 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)
)
# Set up our warning to print to the console if the adult content classifier is tripped.
for resp in answers:
for artifact in resp.artifacts:
if artifact.finish_reason == generation.FILTER:
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))
#img.save(str(artifact.seed)+ ".png") # Save our generated images with their seed number as the filename.
st.image(img, caption=f'Seed {artifact.seed}', use_column_width=True)
# Button to generate the image
if st.button(r'generate image'):
generateImageViaStabilityai(prompt=st.session_state['edited_prompt'])
st.session_state['prompt_generated'] = False |