Update app.py

app.py

@@ -1,9 +1,14 @@
 import streamlit as st
+# Set the page layout to 'wide'
+st.set_page_config(layout="wide")
 import requests
 from PIL import Image
 from io import BytesIO
 # from IPython.display import display
 import base64
+import time
+
+
 
 # helper decoder
 def decode_base64_image(image_string):
@@ -19,66 +24,162 @@ def display_image(image=None,width=500,height=500):
 # API Gateway endpoint URL
 api_url = 'https://a02q342s5b.execute-api.us-east-2.amazonaws.com/reinvent-demo-inf2-sm-20231114'
 
+# Define the CSS to change the text input background color
+input_field_style = """
+<style>
+    /* Customize the text input field background and text color */
+    .stTextInput input {
+        background-color: #fbd8bf; /* 'Rind' color */
+        color: #232F3E; /* Dark text color */
+    }
+    /* You might also want to change the color for textarea if you're using it */
+    .stTextArea textarea {
+        background-color: #fbd8bf; /* 'Rind' color */
+        color: #232F3E; /* Dark text color */
+    }
+</style>
+"""
+
+# Inject custom styles into the Streamlit app
+st.markdown(input_field_style, unsafe_allow_html=True)
+
+
+# Creating Tabs
+tab1, tab2 = st.tabs(["Image Generation", "Text Generation"])
+
+with tab1:
+    # Create two columns for layout
+    left_column, right_column = st.columns(2)
+    # ===========
+    with left_column:
+        # Define Streamlit UI elements
+        st.title('Stable Diffusion XL Image Generation with AWS Inferentia')
+
+        prompt_one = st.text_area("Enter your prompt:", 
+                            f"Raccoon astronaut in space, sci-fi, future, cold color palette, muted colors, detailed, 8k")
+
+        # Number of inference steps
+        num_inference_steps_one = st.slider("Number of Inference Steps", 
+                                    min_value=1, 
+                                    max_value=100, 
+                                    value=30, 
+                                    help="More steps might improve quality, with diminishing marginal returns. 30-50 seems best, but your mileage may vary.")
+
+        # Create an expandable section for optional parameters
+        with st.expander("Optional Parameters"):
+            # Random seed input
+            seed_one = st.number_input("Random seed", 
+                                value=555, 
+                                help="Set to the same value to generate the same image if other inputs are the same, change to generate a different image for same inputs.")
+
+            # Negative prompt input
+            negative_prompt_one = st.text_area("Enter your negative prompt:", 
+                            "cartoon, graphic, text, painting, crayon, graphite, abstract glitch, blurry")
+
+        
+
+
 
-# ===========
-# Define Streamlit UI elements
-st.title('Stable Diffusion XL with Refiner Image Generation')
 
 
+    if st.button('Generate Image'):
+        with st.spinner(f'Generating Image with {num_inference_steps_one} iterations'):
+            with right_column:
+                start_time = time.time()
+                # ===============
+                # Example input data
+                prompt_input_one = {
+                    "prompt": prompt_one,
+                    "parameters": {
+                        "num_inference_steps": num_inference_steps_one,
+                        "seed": seed_one,
+                        "negative_prompt": negative_prompt_one
+                    }
+                }
 
-prompt = st.text_area("Enter your prompt:", 
-                      "Manatee astronaut in space, sci-fi, future, cold color palette, muted colors, detailed, 8k")
+                # Make API request
+                response_one = requests.post(api_url, json=prompt_input_one)
 
-negative_prompt = st.text_area("Enter your negative prompt:", 
-                      "anime, cartoon, graphic, text, painting, crayon, graphite, abstract glitch, blurry")
+                # Process and display the response
+                if response_one.status_code == 200:
+                    result_one = response_one.json()
+                    # st.success(f"Prediction result: {result}")
+                    image_one = display_image(decode_base64_image(result_one["generated_images"][0]))
+                    st.image(image_one, 
+                        caption=f"{prompt_one}") 
+                    end_time = time.time()
+                    total_time = round(end_time - start_time, 2)
+                    st.text(f"Prompt: {prompt_one}")
+                    st.text(f"Number of Iterations: {num_inference_steps_one}")
+                    st.text(f"Random Seed: {seed_one}")
+                    st.text(f'Total time taken: {total_time} seconds')
+                    # Calculate and display the time per iteration in milliseconds
+                    time_per_iteration_ms = (total_time / num_inference_steps_one)
+                    st.text(f'Time per iteration: {time_per_iteration_ms:.2f} seconds')
+                else:
+                    st.error(f"Error: {response_one.text}")
 
-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", 
+with tab2:        
+    # ===========
+    # Define Streamlit UI elements
+    st.title('Stable Diffusion XL Image Generation')
+
+
+
+    prompt = st.text_area("Enter your prompt:", 
+                        "Raccoons astronaut in space, sci-fi, future, cold color palette, muted colors, detailed, 8k")
+
+    # Number of inference steps
+    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}")
+                                value=40, 
+                                help="More steps might improve quality, with diminishing marginal returns. 30-50 seems best, but your mileage may vary.")
 
-        
+    # Create an expandable section for optional parameters
+    with st.expander("Optional Parameters"):
+        # Random seed input
+        seed = st.number_input("Random seed", 
+                            value=42, 
+                            help="Set to the same value to generate the same image if other inputs are the same, change to generate a different image for same inputs.")
 
+        # Negative prompt input
+        negative_prompt = st.text_area("Enter your negative prompt:", 
+                        "anime, cartoon, graphic, text, painting, crayon, graphite, abstract glitch, blurry")
+
+    
+
+
+
+
+
+    if st.button('Generate Image:'):
+        with st.spinner(f'Generating Image with {num_inference_steps} iterations'):
+            # ===============
+            # Example input data
+            prompt_input = {
+                "prompt": prompt,
+                "parameters": {
+                    "num_inference_steps": num_inference_steps,
+                    "seed": seed,
+                    "negative_prompt": negative_prompt
+                }
+            }
+
+            # 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}")
+                image2 = display_image(decode_base64_image(result["generated_images"][0]))
+                st.header("SDXL Base")
+                st.image(image2, 
+                    caption=f"SDXL Base, {num_inference_steps} iterations") 
+            else:
+                st.error(f"Error: {response.text}")
         
         
-        
+