Update app.py

app.py

@@ -1,33 +1,25 @@
 import streamlit as st
-from transformers import pipeline
-from diffusers import StableDiffusionPipeline
 import torch
+from diffusers import StableDiffusionPipeline
 
-# Load the pre-trained Stable Diffusion model
 @st.cache_resource
 def load_model():
-    # You can load any Stable Diffusion model here
-    model = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v-1-4-original")
-    model.to("cpu")  # Ensure it uses CPU for inference
-    return model
+    model_id = "CompVis/stable-diffusion-v1-4"  # Correct model ID
 
-# Initialize Streamlit components
-st.title("Text-to-Image Generator")
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_id,
+        torch_dtype=torch.float32  # Ensure CPU compatibility
+    )
 
-st.write("Enter a description and generate an image!")
+    pipe.to("cpu")  # Force CPU execution
+    return pipe
 
-# User input for text prompt
-prompt = st.text_input("Enter your text prompt here:")
+st.title("Text-to-Image Generator")
+prompt = st.text_input("Enter a prompt:")
 
 if prompt:
     st.write("Generating image... Please wait.")
-
-    # Load model once (from the Hugging Face model hub)
     model = load_model()
-
-    # Generate the image from the text prompt
     with torch.no_grad():
         image = model(prompt).images[0]
-
-    # Show the generated image in the app
     st.image(image, caption="Generated Image", use_column_width=True)