Update app.py

app.py

@@ -6,11 +6,11 @@ from transformers import AutoModelForImageSegmentation
 import torch
 from torchvision import transforms
 
-# 모델을 전역으로 로드 (기본적으로 CPU에 로드)
+# 모델 로딩 및 GPU 환경 설정
 birefnet = AutoModelForImageSegmentation.from_pretrained(
     "ZhengPeng7/BiRefNet", trust_remote_code=True
 )
-# GPU 환경에서만 모델을 GPU로 이동
+birefnet.to("cuda")  # ZeroGPU 환경에서는 GPU("cuda") 사용
 
 transform_image = transforms.Compose(
     [
@@ -22,20 +22,16 @@ transform_image = transforms.Compose(
 
 @spaces.GPU
 def fn(image):
-    # GPU 할당 시 모델을 CUDA로 이동
-    birefnet.to("cuda")
     im = load_img(image, output_type="pil")
     im = im.convert("RGB")
     origin = im.copy()
     processed_image = process(im)
-    # 작업 완료 후 모델을 CPU로 이동
-    birefnet.to("cpu")
     return (processed_image, origin)
 
 def process(image):
     image_size = image.size
-    input_images = transform_image(image).unsqueeze(0).to("cuda")
-    # Prediction
+    input_images = transform_image(image).unsqueeze(0).to("cuda")  # 입력도 GPU로 전달
+    # 예측 수행
     with torch.no_grad():
         preds = birefnet(input_images)[-1].sigmoid().cpu()
     pred = preds[0].squeeze()
@@ -46,13 +42,11 @@ def process(image):
 
 @spaces.GPU
 def process_file(f):
-    birefnet.to("cuda")
     name_path = f.rsplit(".", 1)[0] + ".png"
     im = load_img(f, output_type="pil")
     im = im.convert("RGB")
     transparent = process(im)
     transparent.save(name_path)
-    birefnet.to("cpu")
     return name_path
 
 slider1 = ImageSlider(label="Processed Image", type="pil")
@@ -62,7 +56,7 @@ image_file_upload = gr.Image(label="Upload an image", type="filepath")
 url_input = gr.Textbox(label="Paste an image URL")
 output_file = gr.File(label="Output PNG File")
 
-# 예시 이미지
+# Example images
 chameleon = load_img("butterfly.jpg", output_type="pil")
 url_example = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"