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
+
+# Stable Diffusion模型相关设置
+model_id: str = "runwayml/stable-diffusion-v1-5"
+device: str = "cpu"  # force CPU usage for compatibility
+
+image_generator_pipe: Optional[StableDiffusionPipeline] = None
+
+try:
+    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
+    image_generator_pipe = pipe.to(device)
+except Exception as e:
+    print(f"Failed to load Stable Diffusion model: {e}")
+
+# 提示词优化函数（简单版）
+def optimize_prompt_simple(short_description: str) -> str:
+    optimized_prompt = f"Generate a high-quality, detailed image based on the following description: {short_description}"
+    return optimized_prompt
+
+# 图像生成函数
+def generate_image_sd(short_description: str,
+                      negative_prompt: str,
+                      guidance_scale: float,
+                      num_inference_steps: int) -> Image.Image:
+    optimized_prompt = optimize_prompt_simple(short_description)
+
+    try:
+        with torch.no_grad():
+            if image_generator_pipe is None:
+                raise RuntimeError("Stable Diffusion pipeline is not available.")
+
+            output = image_generator_pipe(
+                prompt=optimized_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.")
+
+        return image
+    except Exception as e:
+        traceback.print_exc()
+        raise gr.Error(f"Image generation failed: {str(e)}")
+# Gradio界面
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    with gr.Row():
+        with gr.Column(scale=1):
+            short_description = gr.Textbox(label="Short Description", placeholder="A magical treehouse in the sky")
+            optimized_prompt_display = gr.Textbox(label="Optimized Prompt", interactive=False)
+            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")
+
+    # 当用户输入简短描述时，自动优化提示词并显示
+    short_description.input(
+        fn=lambda x: optimize_prompt_simple(x),
+        inputs=short_description,
+        outputs=optimized_prompt_display
+    )
+
+    generate_btn.click(
+        fn=generate_image_sd,
+        inputs=[short_description, neg_prompt, guidance, steps],
+        outputs=output_image
+    )
+
+if __name__ == "__main__":
+    if not image_generator_pipe:
+        print("WARNING: Stable Diffusion pipeline is not available. UI will launch, but generation will fail.")
+
+    demo.launch(server_name="0.0.0.0", server_port=7860)