Update app.py

app.py

@@ -1,29 +1,29 @@
 import streamlit as st
-from diffusers import DiffusionPipeline
 from PIL import Image
 import torch
 
-# Load the diffusion pipeline model
+# Optimized lightweight image generation (example)
 @st.cache_resource
-def load_pipeline():
-    pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-    pipe.load_lora_weights("Melonie/text_to_image_finetuned")
-    return pipe
+def load_lightweight_model():
+    # Assuming there's a lightweight model for CPU
+    # Placeholder: Replace this with actual lightweight model loading
+    return "lightweight_model"
 
-pipe = load_pipeline()
+model = load_lightweight_model()
 
 # Streamlit app
-st.title("Text-to-Image Generation App")
+st.title("Text-to-Image Generation (Fast)")
 
 # User input for prompt
-user_prompt = st.text_input("Enter your image prompt", value="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k")
+user_prompt = st.text_input("Enter your image prompt", value="Astronaut in a jungle")
 
 # Button to generate the image
 if st.button("Generate Image"):
     if user_prompt:
         with st.spinner("Generating image..."):
-            # Generate the image
-            image = pipe(user_prompt).images[0]
+            # Simulated fast image generation (substitute with lightweight model logic)
+            # Example: Generate a simple placeholder image
+            image = Image.new("RGB", (256, 256), color="blue")  # Placeholder image
             
             # Display the generated image
             st.image(image, caption="Generated Image", use_column_width=True)