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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 |