chore: end try

app.py

@@ -7,7 +7,6 @@ import trimesh
 import mcubes
 import imageio
 from torchvision.utils import save_image
-from torchvision.transforms import ToPILImage
 from PIL import Image
 from transformers import AutoModel, AutoConfig
 from rembg import remove, new_session
@@ -184,10 +183,6 @@ def generate_mesh(image, source_size=512, render_size=384, mesh_size=512, export
 
     with torch.no_grad():
         planes = model_wrapper.forward(image, source_camera)
-        planes_pil_image = np.concatenate([
-            np.concatenate([planes[0][1], planes[0][2]], axis=1),
-            np.concatenate([planes[0][0], planes[0][0]], axis=1),
-        ], axis=0)
 
         if export_mesh:
             grid_out = model_wrapper.model.synthesizer.forward_grid(planes=planes, grid_size=mesh_size)
@@ -201,7 +196,7 @@ def generate_mesh(image, source_size=512, render_size=384, mesh_size=512, export
             mesh_path = "xiaoxis_mesh.obj"
             mesh.export(mesh_path, 'obj')
 
-            return planes_pil_image, mesh_path
+            return None, mesh_path
 
         if export_video:
             render_cameras = _default_render_cameras(batch_size=1).to(model_wrapper.device)
@@ -225,23 +220,23 @@ def generate_mesh(image, source_size=512, render_size=384, mesh_size=512, export
             video_path = "xiaoxis_video.mp4"
             imageio.mimwrite(video_path, frames, fps=fps)
 
-            return planes_pil_image, video_path
+            return None, video_path
 
-        return planes_pil_image, None
+        return planes, None
 
     return None, None
 
 def step_1_generate_planes(image):
-    planes_pil_image, _ = generate_mesh(image)
-    return planes_pil_image
+    planes, _ = generate_mesh(image)
+    return planes
 
 def step_2_generate_obj(image):
-    planes_pil_image, mesh_path = generate_mesh(image, export_mesh=True)
-    return planes_pil_image, mesh_path, mesh_path
+    _, mesh_path = generate_mesh(image, export_mesh=True)
+    return mesh_path, mesh_path
 
 def step_3_generate_video(image):
-    planes_pil_image, video_path = generate_mesh(image, export_video=True)
-    return planes_pil_image, video_path
+    _, video_path = generate_mesh(image, export_video=True)
+    return video_path
 
 # 从 assets 文件夹中设置示例文件，并限制最多读取 10 个文件
 example_folder = "assets"
@@ -261,15 +256,9 @@ with gr.Blocks() as demo:
         with gr.Column():
             img_input = gr.Image(type="pil", label="输入图像")
             examples_component = gr.Examples(examples=examples, inputs=img_input, outputs=None, examples_per_page=5)
-            generate_planes_button = gr.Button("生成平面图")
             generate_mesh_button = gr.Button("生成模型")
             generate_video_button = gr.Button("生成视频")
         with gr.Column():
-            planes_output = gr.Image(
-                label="平面图",
-                type="pil",
-                interactive=False
-            )
             model_output = LitModel3D(
                 clear_color=[0, 0, 0, 0],  # 可调整背景颜色，以获得更好的对比度
                 label="模型可视化",
@@ -294,15 +283,14 @@ with gr.Blocks() as demo:
     def clear_model_viewer():
         """在加载新模型前重置 Gradio。"""
         update_output = gr.update(value=None)
-        return update_output, update_output
+        return update_output
 
     # 首先清除输出的数据
-    img_input.change(clear_model_viewer, inputs=None, outputs=[planes_output, model_output])
+    img_input.change(clear_model_viewer, inputs=None, outputs=model_output)
 
     # 然后生成模型和视频
-    generate_planes_button.click(step_1_generate_planes, inputs=img_input, outputs=planes_output)
-    generate_mesh_button.click(step_2_generate_obj, inputs=img_input, outputs=[planes_output, obj_file_output, model_output])
-    generate_video_button.click(step_3_generate_video, inputs=img_input, outputs=[planes_output, video_file_output])
+    generate_mesh_button.click(step_2_generate_obj, inputs=img_input, outputs=[obj_file_output, model_output])
+    generate_video_button.click(step_3_generate_video, inputs=img_input, outputs=video_file_output)
 
 demo.launch(
     auth=(os.environ.get('AUTH_USERNAME'), os.environ.get('AUTH_PASSWORD'))