fix

app.py

@@ -67,6 +67,7 @@ def get_random_seed(randomize_seed, seed):
     return seed
 
 # process image
+@spaces.GPU(duration=10)
 def process_image(input_image):
     input_image = np.array(input_image) # uint8
     # bg removal if there is no alpha channel
@@ -74,12 +75,12 @@ def process_image(input_image):
         input_image = rembg.remove(input_image, session=bg_remover)  # [H, W, 4]
     mask = input_image[..., -1] > 0
     image = recenter_foreground(input_image, mask, border_ratio=0.1)
-    image = cv2.resize(image, (518, 518), interpolation=cv2.INTER_AREA)
+    image = cv2.resize(image, (518, 518), interpolation=cv2.INTER_LINEAR)
     return image
 
 # process generation
 @spaces.GPU(duration=60)
-def process_3d(input_image, num_steps=50, cfg_scale=7.0, grid_res=384, seed=42, simplify_mesh=False, target_num_faces=100000):
+def process_3d(input_image, num_steps=50, cfg_scale=7, grid_res=384, seed=42, simplify_mesh=False, target_num_faces=100000):
 
     # seed
     kiui.seed_everything(seed)
@@ -89,10 +90,8 @@ def process_3d(input_image, num_steps=50, cfg_scale=7.0, grid_res=384, seed=42,
     output_glb_path = f"output/partpacker_{datetime.now().strftime('%Y%m%d_%H%M%S')}.glb"
 
     # input image (assume processed to RGBA uint8)
-    image = np.array(input_image)
-    image = image.astype(np.float32) / 255.0
+    image = input_image.astype(np.float32) / 255.0
     image = image[..., :3] * image[..., 3:4] + (1 - image[..., 3:4])  # white background
-    image = (image * 255).astype(np.uint8)
     image_tensor = torch.from_numpy(image).permute(2, 0, 1).contiguous().unsqueeze(0).float().cuda()
 
     data = {"cond_images": image_tensor}
@@ -162,8 +161,8 @@ with block:
         with gr.Column(scale=1):
             with gr.Row():
                 # input image
-                input_image = gr.Image(label="Input Image")
-                seg_image = gr.Image(label="Segmentation Result", type="pil", format="png", interactive=False, image_mode="RGBA")
+                input_image = gr.Image(label="Input Image", type="numpy")
+                seg_image = gr.Image(label="Segmentation Result", type="numpy", format="png", interactive=False, image_mode="RGBA")
             with gr.Accordion("Settings", open=True):
                 # inference steps
                 num_steps = gr.Slider(label="Inference steps", minimum=1, maximum=100, step=1, value=50)