put the init part out of defined function

app.py

@@ -377,30 +377,7 @@ class ImageGenerator:
         return images_list
 
 
-def prepare_infer_func():
-    # 模型仓库ID（如："bert-base-uncased"）
-    model_repo = "stepfun-ai/Step1X-Edit"
-    # 本地保存路径
-    model_path = "./model_weights"
-    os.makedirs(model_path, exist_ok=True)
-
-
-    # 下载模型（包括所有文件）
-    snapshot_download(
-        repo_id=model_repo,
-        local_dir=model_path,
-        local_dir_use_symlinks=False  # 避免使用符号链接
-    )
-
-
-    image_edit = ImageGenerator(
-        ae_path=os.path.join(model_path, 'vae.safetensors'),
-        dit_path=os.path.join(model_path, "step1x-edit-i1258.safetensors"),
-        qwen2vl_model_path='Qwen/Qwen2.5-VL-7B-Instruct',
-        max_length=640,
-    )
 
-    return image_edit.generate_image
 
 @spaces.GPU(duration=240)
 def inference(prompt, ref_images, seed, size_level, infer_func=None):
@@ -428,46 +405,64 @@ def inference(prompt, ref_images, seed, size_level, infer_func=None):
     return image, random_seed
 
 
-def create_demo():
-    inference_func = prepare_infer_func()
-    with gr.Blocks() as demo:
-        gr.Markdown(
-            """
-            # Step1X-Edit
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                prompt = gr.Textbox(
-                    label="编辑指令",
-                    value='Remove the person from the image.',
-                )
-                init_image = gr.Image(label="Input Image", type='pil')
-
-                random_seed = gr.Number(label="Random Seed", value=-1, minimum=-1)
-
-                size_level = gr.Number(label="size level (recommend 512, 768, 1024, min 512)", value=512, minimum=512)
-
-                generate_btn = gr.Button("Generate")
-
-            with gr.Column():
-                output_image = gr.Image(label="Generated Image",type='pil',image_mode='RGB')
-                output_random_seed = gr.Textbox(label="Used Seed", lines=5)
-        from functools import partial
-        generate_btn.click(
-            fn=partial(inference, infer_func=inference_func),
-            inputs=[
-                prompt, 
-                init_image,
-                random_seed,
-                size_level,
-            ],
-            outputs=[output_image, output_random_seed],
-        )
-    
-    return demo
+# 模型仓库ID（如："bert-base-uncased"）
+model_repo = "stepfun-ai/Step1X-Edit"
+# 本地保存路径
+model_path = "./model_weights"
+os.makedirs(model_path, exist_ok=True)
 
 
-if __name__ == "__main__":
-    demo = create_demo()
-    demo.launch(server_name='0.0.0.0',server_port=7860)
+# 下载模型（包括所有文件）
+snapshot_download(
+    repo_id=model_repo,
+    local_dir=model_path,
+    local_dir_use_symlinks=False  # 避免使用符号链接
+)
+
+
+image_edit = ImageGenerator(
+    ae_path=os.path.join(model_path, 'vae.safetensors'),
+    dit_path=os.path.join(model_path, "step1x-edit-i1258.safetensors"),
+    qwen2vl_model_path='Qwen/Qwen2.5-VL-7B-Instruct',
+    max_length=640,
+)
+
+inference_func = image_edit.generate_image
+
+    # inference_func = prepare_infer_func()
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Step1X-Edit
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="编辑指令",
+                value='Remove the person from the image.',
+            )
+            init_image = gr.Image(label="Input Image", type='pil')
+
+            random_seed = gr.Number(label="Random Seed", value=-1, minimum=-1)
+
+            size_level = gr.Number(label="size level (recommend 512, 768, 1024, min 512)", value=512, minimum=512)
+
+            generate_btn = gr.Button("Generate")
+
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image",type='pil',image_mode='RGB')
+            output_random_seed = gr.Textbox(label="Used Seed", lines=5)
+    from functools import partial
+    generate_btn.click(
+        fn=partial(inference, infer_func=inference_func),
+        inputs=[
+            prompt, 
+            init_image,
+            random_seed,
+            size_level,
+        ],
+        outputs=[output_image, output_random_seed],
+    )
+
+demo.launch()