🌘w🌖

app.py

@@ -6,26 +6,30 @@ from transformers import AutoModelForImageSegmentation
 import torch
 from torchvision import transforms
 from PIL import Image
-torch.set_float32_matmul_precision(['high', 'highest'][0])
 
-birefnet = AutoModelForImageSegmentation.from_pretrained('ZhengPeng7/BiRefNet', trust_remote_code=True)
-birefnet.to("cuda")
-transform_image = transforms.Compose([
- transforms.Resize((1024, 1024)),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-])
+torch.set_float32_matmul_precision(["high", "highest"][0])
 
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+ "ZhengPeng7/BiRefNet", trust_remote_code=True
+)
+birefnet.to("cuda")
+transform_image = transforms.Compose(
+ [
+ transforms.Resize((1024, 1024)),
+ transforms.ToTensor(),
+ transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+ ]
+)
 
 
 @spaces.GPU
 def fn(image):
- im = load_img(image,output_type="pil")
- im = im.convert('RGB') 
+ im = load_img(image, output_type="pil")
+ im = im.convert("RGB")
  image_size = im.size
  origin = im.copy()
  image = load_img(im)
- input_images = transform_image(image).unsqueeze(0).to('cuda')
+ input_images = transform_image(image).unsqueeze(0).to("cuda")
  # Prediction
  with torch.no_grad():
  preds = birefnet(input_images)[-1].sigmoid().cpu()
@@ -33,7 +37,8 @@ def fn(image):
  pred_pil = transforms.ToPILImage()(pred)
  mask = pred_pil.resize(image_size)
  image.putalpha(mask)
- return (image , origin)
+ return (image, origin)
+
 
 slider1 = ImageSlider(label="birefnet", type="pil")
 slider2 = ImageSlider(label="birefnet", type="pil")
@@ -41,11 +46,19 @@ image = gr.Image(label="Upload an image")
 text = gr.Textbox(label="Paste an image URL")
 
 chameleon = Image.open("chameleon.jpg")
+cool = Image.open("cool kid.jpg")
+
 url = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"
-tab1 = gr.Interface(fn,inputs= image, outputs= slider1,examples=[chameleon], api_name="image")
-tab2 = gr.Interface(fn,inputs= text, outputs= slider2,examples=[url], api_name="text")
+tab1 = gr.Interface(
+ fn, inputs=image, outputs=slider1, examples=[[chameleon], [cool]], api_name="image"
+)
+
+tab2 = gr.Interface(fn, inputs=text, outputs=slider2, examples=[url], api_name="text")
+
 
-demo = gr.TabbedInterface([tab1,tab2],["image","text"],title="birefnet with image slider")
+demo = gr.TabbedInterface(
+ [tab1, tab2], ["image", "text"], title="birefnet for background removal"
+)
 
 if __name__ == "__main__":
- demo.launch()
+ demo.launch()