Refactor app.py for improved error handling and simplify Gradio interface; downgrade gradio version in requirements.txt

app.py

@@ -30,27 +30,28 @@ except Exception as e:
 from diffusers import DiffusionPipeline
 import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-logger.info(f"Using device: {device}")
-logger.info(f"Loading model: {model_repo_id}")
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
+# 使用 try/except 避免在导入模块时出错
 try:
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model_repo_id = "stabilityai/sdxl-turbo"
+    
+    logger.info(f"Using device: {device}")
+    logger.info(f"Loading model: {model_repo_id}")
+    
+    if torch.cuda.is_available():
+        torch_dtype = torch.float16
+    else:
+        torch_dtype = torch.float32
+    
     pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
     pipe = pipe.to(device)
     logger.info("Model loaded successfully")
+    
+    MAX_SEED = np.iinfo(np.int32).max
+    MAX_IMAGE_SIZE = 1024
 except Exception as e:
-    logger.error(f"Error loading model: {str(e)}")
-    raise
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+    logger.error(f"Error during setup: {str(e)}")
+    # 不立即抛出异常，让 Gradio 界面可以加载
 
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
@@ -88,113 +89,69 @@ def infer(
         return image, seed
     except Exception as e:
         logger.error(f"Error in inference: {str(e)}")
-        raise gr.Error(f"Error generating image: {str(e)}")
+        return None, seed  # 返回 None 而不是抛出异常
 
+# 定义示例
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
     "A delicious ceviche cheesecake slice",
 ]
 
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-try:
-    with gr.Blocks(css=css) as demo:
-        with gr.Column(elem_id="col-container"):
-            gr.Markdown(" # Text-to-Image Gradio Template")
+# 简化 CSS
+css = "#col-container { margin: 0 auto; max-width: 640px; }"
 
+# 创建简化版的 Gradio 界面
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# Text-to-Image Generator")
+        
+        # 主输入区域
+        prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt")
+        run_button = gr.Button("Generate Image")
+        
+        # 结果显示
+        result = gr.Image(label="Generated Image")
+        seed_text = gr.Number(label="Seed Used")
+        
+        # 高级设置（折叠）
+        with gr.Accordion("Advanced Settings", open=False):
+            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What to exclude from the image")
+            
+            # 种子设置
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+            
+            # 尺寸设置
             with gr.Row():
-                prompt = gr.Text(
-                    label="Prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                    container=False,
-                )
-
-                run_button = gr.Button("Run", scale=0, variant="primary")
-
-            result = gr.Image(label="Result", show_label=False)
-
-            with gr.Accordion("Advanced Settings", open=False):
-                negative_prompt = gr.Text(
-                    label="Negative prompt",
-                    max_lines=1,
-                    placeholder="Enter a negative prompt",
-                    visible=True, # 改为可见
-                )
-
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=MAX_SEED,
-                    step=1,
-                    value=0,
-                )
-
-                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-                with gr.Row():
-                    width = gr.Slider(
-                        label="Width",
-                        minimum=256,
-                        maximum=MAX_IMAGE_SIZE,
-                        step=32,
-                        value=1024,
-                    )
-
-                    height = gr.Slider(
-                        label="Height",
-                        minimum=256,
-                        maximum=MAX_IMAGE_SIZE,
-                        step=32,
-                        value=1024,
-                    )
-
-                with gr.Row():
-                    guidance_scale = gr.Slider(
-                        label="Guidance scale",
-                        minimum=0.0,
-                        maximum=10.0,
-                        step=0.1,
-                        value=0.0,
-                    )
-
-                    num_inference_steps = gr.Slider(
-                        label="Number of inference steps",
-                        minimum=1,
-                        maximum=50,
-                        step=1,
-                        value=2,
-                    )
-
-            gr.Examples(examples=examples, inputs=[prompt])
-        gr.on(
-            triggers=[run_button.click, prompt.submit],
-            fn=infer,
-            inputs=[
-                prompt,
-                negative_prompt,
-                seed,
-                randomize_seed,
-                width,
-                height,
-                guidance_scale,
-                num_inference_steps,
-            ],
-            outputs=[result, seed],
-        )
+                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
+                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
+            
+            # 生成参数
+            with gr.Row():
+                guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=0.0)
+                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=2)
+        
+        # 示例
+        gr.Examples(examples, inputs=prompt)
     
-    logger.info("Gradio interface created successfully")
-except Exception as e:
-    logger.error(f"Error creating Gradio interface: {str(e)}")
-    raise
-
+    # 绑定事件处理
+    run_button.click(
+        fn=infer,
+        inputs=[
+            prompt,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+        ],
+        outputs=[result, seed_text],
+    )
+
+# 启动应用
 if __name__ == "__main__":
     try:
         logger.info("Starting Gradio app")

requirements.txt

@@ -4,4 +4,4 @@ invisible_watermark
 torch
 transformers
 xformers
-gradio==3.45.0
+gradio==3.34.0