Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline
+from PIL import Image
+import time
+import traceback
+from typing import Optional
+
+# ---- Configuration ----
+model_id: str = "runwayml/stable-diffusion-v1-5"
+device: str = "cpu"  # force CPU usage for compatibility
+
+# ---- Load Model ----
+image_generator_pipe: Optional[StableDiffusionPipeline] = None
+
+try:
+    print(f"Loading Stable Diffusion pipeline ({model_id}) on CPU...")
+    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
+    image_generator_pipe = pipe.to(device)
+    print("Stable Diffusion pipeline loaded successfully.")
+except Exception as e:
+    print(f"Failed to load Stable Diffusion model: {e}")
+    traceback.print_exc()
+
+# ---- Core Image Generation Function ----
+def generate_image_sd(prompt: str, negative_prompt: str, guidance_scale: float, num_inference_steps: int) -> Image.Image:
+    if not image_generator_pipe:
+        raise gr.Error("Stable Diffusion pipeline failed to load. Image generation unavailable.")
+
+    if not prompt.strip():
+        raise gr.Error("Prompt cannot be empty.")
+
+    print(f"Generating image with prompt: {prompt[:100]}...")
+    print(f"Negative prompt: {negative_prompt}")
+    print(f"Guidance scale: {guidance_scale}, Steps: {num_inference_steps}")
+
+    start_time = time.time()
+
+    try:
+        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.")
+
+        end_time = time.time()
+        print(f"Image generated in {end_time - start_time:.2f} seconds.")
+        return image
+
+    except Exception as e:
+        print(f"Error generating image: {e}")
+        traceback.print_exc()
+        raise gr.Error(f"Image generation failed: {e}")
+
+# ---- Gradio UI ----
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# Stable Diffusion Image Generator (CPU Mode)")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            prompt = gr.Textbox(label="Prompt", placeholder="A beautiful futuristic city skyline at night")
+            neg_prompt = gr.Textbox(label="Negative Prompt", placeholder="blurry, distorted, watermark")
+            guidance = gr.Slider(1.0, 15.0, value=7.5, step=0.5, label="Guidance Scale")
+            steps = gr.Slider(10, 50, value=25, step=1, label="Inference Steps")
+            generate_btn = gr.Button("Generate Image")
+
+        with gr.Column(scale=1):
+            output_image = gr.Image(label="Generated Image", type="pil")
+
+    generate_btn.click(
+        fn=generate_image_sd,
+        inputs=[prompt, neg_prompt, guidance, steps],
+        outputs=output_image
+    )
+
+# ---- Launch ----
+if __name__ == "__main__":
+    if not image_generator_pipe:
+        print("WARNING: Image generator pipeline is not available. UI will launch, but generation will fail.")
+    demo.launch(server_name="0.0.0.0", server_port=7860)
+