Create app.py

app.py

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+import streamlit as st
+import torch
+from diffusers import StableDiffusionPipeline
+from PIL import Image
+import io
+import base64
+
+# Page configuration
+st.set_page_config(
+    page_title="Text to Image Generator",
+    page_icon="🎨",
+    layout="wide",
+)
+
+# Custom CSS for better UI
+st.markdown("""
+<style>
+    .main {
+        background-color: #f5f7f9;
+    }
+    .stApp {
+        max-width: 1200px;
+        margin: 0 auto;
+    }
+    .css-18e3th9 {
+        padding-top: 2rem;
+    }
+    .css-1d391kg {
+        padding: 3rem 1rem;
+    }
+    .stButton>button {
+        background-color: #4CAF50;
+        color: white;
+        padding: 10px 24px;
+        border: none;
+        border-radius: 4px;
+        cursor: pointer;
+        font-size: 16px;
+        transition: all 0.3s;
+    }
+    .stButton>button:hover {
+        background-color: #45a049;
+        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
+    }
+    .download-link {
+        display: inline-block;
+        margin-top: 10px;
+        padding: 10px 15px;
+        background-color: #3498db;
+        color: white;
+        text-decoration: none;
+        border-radius: 4px;
+        text-align: center;
+    }
+    h1 {
+        color: #2C3E50;
+        margin-bottom: 30px;
+    }
+    .stSlider {
+        padding-top: 1rem;
+        padding-bottom: 1rem;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Header
+st.title("✨ Text to Image Generator")
+st.markdown("Turn your imagination into images using AI! 🖼️")
+
+@st.cache_resource
+def load_model():
+    model_id = "runwayml/stable-diffusion-v1-5"
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_id, 
+        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+    )
+    if torch.cuda.is_available():
+        pipe = pipe.to("cuda")
+    return pipe
+
+# Create columns for the layout
+col1, col2 = st.columns([1, 1])
+
+with col1:
+    # Text input for the prompt
+    prompt = st.text_area(
+        "Enter your prompt",
+        placeholder="A beautiful sunset over the ocean with palm trees in the foreground",
+        height=100
+    )
+
+    # Advanced options in an expander
+    with st.expander("Advanced Options"):
+        num_inference_steps = st.slider(
+            "Quality (Steps)", 
+            min_value=1, 
+            max_value=100, 
+            value=50,
+            help="Higher values produce more detailed images but take longer"
+        )
+        
+        guidance_scale = st.slider(
+            "Creativity", 
+            min_value=1.0, 
+            max_value=20.0, 
+            value=7.5, 
+            step=0.5,
+            help="Higher values make the image more closely follow the prompt"
+        )
+        
+        seed = st.number_input(
+            "Random Seed", 
+            min_value=-1, 
+            max_value=2147483647, 
+            value=-1,
+            help="Set a specific seed for reproducible results, or -1 for random"
+        )
+        
+        image_width = st.select_slider(
+            "Image Width",
+            options=[512, 576, 640, 704, 768],
+            value=512
+        )
+        
+        image_height = st.select_slider(
+            "Image Height",
+            options=[512, 576, 640, 704, 768],
+            value=512
+        )
+
+    # Generate button
+    generate_button = st.button("Generate Image")
+
+# Function to get a download link
+def get_image_download_link(img, filename="generated_image.png", text="Download Image"):
+    buffered = io.BytesIO()
+    img.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode()
+    href = f'<a href="data:image/png;base64,{img_str}" download="{filename}" class="download-link">{text}</a>'
+    return href
+
+with col2:
+    # Display the generated image and provide a download link
+    st.markdown("### Generated Image")
+    
+    result_container = st.empty()
+    
+    if generate_button and prompt:
+        try:
+            with st.spinner("Generating your image..."):
+                # Load the model
+                pipe = load_model()
+                
+                # Set the seed for reproducibility
+                generator = None
+                if seed != -1:
+                    generator = torch.Generator().manual_seed(seed)
+                
+                # Generate the image
+                image = pipe(
+                    prompt=prompt,
+                    num_inference_steps=num_inference_steps,
+                    guidance_scale=guidance_scale,
+                    generator=generator,
+                    width=image_width,
+                    height=image_height
+                ).images[0]
+                
+                # Display the image
+                result_container.image(image, caption=prompt, use_column_width=True)
+                
+                # Provide a download link
+                st.markdown(get_image_download_link(image), unsafe_allow_html=True)
+                
+                # Display the parameters used
+                st.markdown("### Parameters Used")
+                st.text(f"Steps: {num_inference_steps}")
+                st.text(f"Guidance Scale: {guidance_scale}")
+                st.text(f"Seed: {seed}")
+                st.text(f"Dimensions: {image_width}x{image_height}")
+                
+        except Exception as e:
+            result_container.error(f"An error occurred: {str(e)}")
+    else:
+        result_container.markdown("Your image will appear here after generation.")
+
+# Add footer
+st.markdown("---")
+st.markdown("Powered by Hugging Face Diffusers and Streamlit")