Update app.py

app.py

@@ -3,25 +3,27 @@ from diffusers import StableDiffusionPipeline
 import gradio as gr
 
 def load_pipeline():
-    
-   if torch.cuda.is_available():
-        device = "cuda"
+    # Auto-detect any available GPU backend or fallback to CPU
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
     elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
-        device = "mps"
+        device = torch.device("mps")
     else:
-        device = "cpu"
+        device = torch.device("cpu")
 
-    # Use float16 on any accelerator, float32 on CPU
-    dtype = torch.float16 if device != "cpu" else torch.float32
+    # Use float16 precision on any GPU/MPS, float32 on CPU
+    dtype = torch.float16 if device.type != "cpu" else torch.float32
     print(f"Using device: {device}, dtype: {dtype}")
 
-
+    # Load weights & configs from HF at runtime
     pipe = StableDiffusionPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5",
         torch_dtype=dtype
-    )
-    return pipe.to(device)
+    ).to(device)
+
+    return pipe
 
+# Initialize pipeline once
 pipe = load_pipeline()
 
 def generate(prompt: str, steps: int, scale: float):
@@ -29,17 +31,18 @@ def generate(prompt: str, steps: int, scale: float):
     out = pipe(prompt, num_inference_steps=steps, guidance_scale=scale)
     return out.images[0]
 
+# Build and launch Gradio UI
 demo = gr.Interface(
     fn=generate,
     inputs=[
-        gr.Textbox(lines=1, placeholder="a steampunk robot in a lush jungle", label="Prompt"),
+        gr.Textbox(lines=1, placeholder="Enter prompt…", label="Prompt"),
         gr.Slider(1, 100, value=50, step=1, label="Inference Steps"),
         gr.Slider(1.0, 15.0, value=7.5, step=0.1, label="Guidance Scale"),
     ],
     outputs=gr.Image(type="pil"),
-    title="Stable Diffusion image generator",
-    description="Generates images using Stable Diffusion."
+    title="Stable Diffusion Image Generator",
+    description="Generates images based on your prompt!."
 )
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch()