update

app.py

@@ -35,13 +35,17 @@ with gr.Blocks(
                 with gr.Tab(
                     label="Image(auto seg)", id=0
                 ) as single_image_input_tab:
-                    raw_image_cache = gr.State()
+                    raw_image_cache = gr.Image(
+                        format="png",
+                        image_mode="RGB",
+                        type="pil",
+                    )
                     image_prompt = gr.Image(
                         label="Input Image",
                         format="png",
                         image_mode="RGBA",
                         type="pil",
-                        height=500,
+                        height=400,
                     )
                     gr.Markdown(
                         """
@@ -439,4 +443,4 @@ with gr.Blocks(
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(server_name="10.34.8.82", server_port=8084)

common.py

@@ -193,8 +193,8 @@ def render_video(
 
 @spaces.GPU
 def preprocess_image_fn(
-    image: str | np.ndarray | Image.Image,
-) -> Image.Image:
+    image: str | np.ndarray | Image.Image
+) -> tuple[Image.Image, Image.Image]:
     if isinstance(image, str):
         image = Image.open(image)
     elif isinstance(image, np.ndarray):
@@ -210,8 +210,8 @@ def preprocess_image_fn(
 
 @spaces.GPU
 def preprocess_sam_image_fn(
-    image: Image.Image, req: gr.Request
-) -> Image.Image:
+    image: Image.Image
+) -> tuple[Image.Image, Image.Image]:
     if isinstance(image, np.ndarray):
         image = Image.fromarray(image)
 
@@ -359,11 +359,10 @@ def image_to_3d(
     if isinstance(seg_image, np.ndarray):
         seg_image = Image.fromarray(seg_image)
 
-    print("raw_image_cache", raw_image_cache)
-    print("seg_image", seg_image)
-    os.makedirs(f"{TMP_DIR}/{req.session_hash}", exist_ok=True)
-    seg_image.save(f"{TMP_DIR}/{req.session_hash}/seg_image.png")
-    raw_image_cache.save(f"{TMP_DIR}/{req.session_hash}/raw_image.png")
+    output_root = os.path.join(TMP_DIR, str(req.session_hash))
+    os.makedirs(output_root, exist_ok=True)
+    seg_image.save(f"{output_root}/seg_image.png")
+    raw_image_cache.save(f"{output_root}/raw_image.png")
     PIPELINE.cuda()
     outputs = PIPELINE.run(
         seg_image,
@@ -386,9 +385,8 @@ def image_to_3d(
     mesh_model = outputs["mesh"][0]
     color_images = render_video(gs_model)["color"]
     normal_images = render_video(mesh_model)["normal"]
-    output_root = TMP_DIR
-    if req is not None:
-        output_root = os.path.join(output_root, str(req.session_hash))
+    
+    
     video_path = os.path.join(output_root, "gs_mesh.mp4")
     merge_images_video(color_images, normal_images, video_path)
     state = pack_state(gs_model, mesh_model)

requirements.txt

@@ -1,9 +1,9 @@
 --extra-index-url https://download.pytorch.org/whl/cu121
 
 
-torch==2.4.0
-torchvision==0.19.0
-xformers==0.0.27.post2
+torch==2.4.0+cu121
+torchvision==0.19.0+cu121
+xformers==0.0.27.post2+cu121
 pytorch-lightning==2.4.0
 spconv-cu120==2.3.6
 dataclasses_json