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| import streamlit as st | |
| import requests | |
| from PIL import Image | |
| from io import BytesIO | |
| # from IPython.display import display | |
| import base64 | |
| # helper decoder | |
| def decode_base64_image(image_string): | |
| base64_image = base64.b64decode(image_string) | |
| buffer = BytesIO(base64_image) | |
| return Image.open(buffer) | |
| # display PIL images as grid | |
| def display_image(image=None,width=500,height=500): | |
| img = image.resize((width, height)) | |
| return img | |
| # API Gateway endpoint URL | |
| api_url = 'https://a02q342s5b.execute-api.us-east-2.amazonaws.com/reinvent-demo-inf2-sm-20231114' | |
| # =========== | |
| # Define Streamlit UI elements | |
| st.title('Stable Diffusion XL with Refiner Image Generation') | |
| prompt = st.text_area("Enter your prompt:", | |
| "Manatee astronaut in space, sci-fi, future, cold color palette, muted colors, detailed, 8k") | |
| negative_prompt = st.text_area("Enter your negative prompt:", | |
| "anime, cartoon, graphic, text, painting, crayon, graphite, abstract glitch, blurry") | |
| seed = st.number_input("Random seed", value=555, placeholder="Type a number...", help="set to same value to generate same image, if other inputs are the same, change to generate a different image for same inputs") | |
| # seed = 555 | |
| num_inference_steps = st.slider("Number of Inference Steps", | |
| min_value=1, | |
| max_value=100, | |
| value=20, | |
| help="more steps might improve quality, with diminishing marginal returns. 30-50 seems best, but your mileage may vary.") | |
| denoising_start = st.slider("Denoising Start", | |
| min_value=0.0, | |
| max_value=1.0, | |
| value=0.8, | |
| help="when to stop modifying the overall image and start refining the details") | |
| if st.button('Generate Image'): | |
| with st.spinner(f'Generating Image with {num_inference_steps} iterations, beginning to refine around iteration {int(num_inference_steps * denoising_start)}...'): | |
| # =============== | |
| # Example input data | |
| prompt_input = { | |
| "prompt": prompt, | |
| "parameters": { | |
| "num_inference_steps": num_inference_steps, | |
| "seed": seed, | |
| "negative_prompt": negative_prompt | |
| # "denoising_start": denoising_start | |
| } | |
| } | |
| # Make API request | |
| response = requests.post(api_url, json=prompt_input) | |
| # Process and display the response | |
| if response.status_code == 200: | |
| result = response.json() | |
| # st.success(f"Prediction result: {result}") | |
| image = display_image(decode_base64_image(result["generated_images"][0])) | |
| st.header("SDXL Base + Refiner") | |
| st.image(image, | |
| caption=f"SDXL Base + Refiner, {num_inference_steps} iterations, beginning to refine around iteration {int(num_inference_steps * denoising_start)}") | |
| else: | |
| st.error(f"Error: {response.text}") | |