Update app.py

app.py

@@ -1,23 +1,44 @@
 import gradio as gr
-from diffusers import StableDiffusionPipeline
+from diffusers import DiffusionPipeline
 import torch
 
-# Load model (smaller for faster loading)
-model = StableDiffusionPipeline.from_pretrained(
-    "OFA-Sys/small-stable-diffusion-v0",  # Lightweight model
-    torch_dtype=torch.float32
-)
+# 1. USE A SMALLER MODEL (CPU-FRIENDLY)
+model_id = "OFA-Sys/small-stable-diffusion-v0"  # Lightweight model
 
-def generate_image(prompt):
-    return model(prompt).images[0]
+# 2. SIMPLIFIED PIPELINE FOR CPU
+pipe = DiffusionPipeline.from_pretrained(model_id)
+pipe = pipe.to("cpu")  # Force CPU usage
 
-# Create Gradio interface
-demo = gr.Interface(
-    fn=generate_image,
-    inputs=gr.Textbox(label="Describe your image"),
-    outputs=gr.Image(label="Generated Image"),
-    title="🎨 AI Image Generator",
-    examples=[["a cat astronaut"], ["colorful jellyfish in space"]]
-)
+# 3. FASTER GENERATION SETTINGS
+def generate_image(prompt, negative_prompt="", steps=15):
+    return pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=steps,
+        guidance_scale=7.5
+    ).images[0]
+
+# 4. STREAMLINED UI
+with gr.Blocks() as demo:
+    gr.Markdown("# ⚡ Lightning-Fast AI Image Generator")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Your Prompt", value="a cat astronaut")
+            negative = gr.Textbox(label="Avoid (Optional)", value="blurry, deformed")
+            steps = gr.Slider(1, 30, value=10, label="Quality Steps")
+            btn = gr.Button("Generate →")
+        
+        output = gr.Image(label="Result", height=400)
+    
+    btn.click(fn=generate_image, inputs=[prompt, negative, steps], outputs=output)
+    
+    gr.Examples(
+        examples=[
+            ["cyberpunk cityscape at night, neon lights", "people, text", 12],
+            ["watercolor painting of a forest", "photorealistic, humans", 8]
+        ],
+        inputs=[prompt, negative, steps]
+    )
 
 demo.launch()