Create app.py

app.py

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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 (set to same value to generate same image, if other inputs are the same, change to generate a different image for same inputs)", value=None, placeholder="Type a number...")
+# seed = 555
+
+num_inference_steps = st.slider("Number of Inference Steps (more steps might improve quality, with diminishing marginal returns. 30-50 seems best, but your mileage may vary.)", 
+                                min_value=1, 
+                                max_value=100, 
+                                value=20)
+denoising_start = st.slider("Denoising Start (when to stop modifying the overall image and start refining the details)", 
+                            min_value=0.0, 
+                            max_value=1.0, 
+                            value=0.8)
+
+
+
+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}")
+
+        
+
+        
+        
+