Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline
+from PIL import Image
+import traceback
+from typing import Optional
+
+model_id: str = "runwayml/stable-diffusion-v1-5"
+device: str = "cpu"  # force CPU usage for compatibility
+
+image_generator_pipe: Optional[StableDiffusionPipeline] = None
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
+image_generator_pipe = pipe.to(device)
+
+
+def generate_image_sd(prompt: str, negative_prompt: str, guidance_scale: float, num_inference_steps: int) -> Image.Image:
+    with torch.no_grad():
+        output = image_generator_pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps
+        )
+        image = output.images[0] if output.images else None
+
+    if not image:
+        raise RuntimeError("No image was returned from the generation pipeline.")
+
+    print(f"Image generated in {end_time - start_time:.2f} seconds.")
+    return image