Update app.py

app.py

@@ -1,34 +1,34 @@
 import streamlit as st
-from diffusers import StableDiffusionPipeline
+from diffusers import AutoPipelineForText2Image
 import torch
 from PIL import Image
 import io
 import os
 
-# Force CPU usage on Spaces
+# Force CPU usage
 os.environ["CUDA_VISIBLE_DEVICES"] = ""
 
 @st.cache_resource
 def load_model():
-    pipe = StableDiffusionPipeline.from_pretrained(
-        "runwayml/stable-diffusion-v1-5",
-        torch_dtype=torch.float32,
+    pipe = AutoPipelineForText2Image.from_pretrained(
+        "stabilityai/sd-turbo", 
+        torch_dtype=torch.float32  # CPU-compatible
     )
-    pipe.to("cpu")  # Force CPU usage
+    pipe.to("cpu")
     return pipe
 
-# Streamlit UI
-st.title("🎨 AI Image Generator (CPU - Hugging Face Spaces)")
+st.title("⚡ Fast AI Image Generator (under 1 minute)")
 
 prompt = st.text_input("Enter your prompt:", 
-                       "A multi-dimensional futuristic city with glowing lights, fractals, 8K")
+                       "A glowing alien city with floating islands and neon rivers, concept art, 8K")
 
-guidance = st.slider("Creativity (Guidance Scale)", 1.0, 20.0, 7.5)
+guidance = st.slider("Guidance scale (higher = more faithful to prompt)", 1.0, 10.0, 3.0)
 
-if st.button("Generate"):
-    with st.spinner("Generating image on CPU... please wait ⏳"):
+if st.button("Generate Image"):
+    with st.spinner("Generating image (approx. 20–40 seconds on CPU)..."):
         pipe = load_model()
-        image = pipe(prompt, guidance_scale=guidance).images[0]
+        result = pipe(prompt, guidance_scale=guidance, num_inference_steps=20)
+        image = result.images[0]
 
         st.image(image, caption="Generated Image", use_column_width=True)