🌘w🌖

README.md

@@ -1,6 +1,6 @@
 ---
 title: Background Removal
-emoji: 🏃
+emoji: 🌘w🌖
 colorFrom: purple
 colorTo: indigo
 sdk: gradio

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+from gradio_imageslider import ImageSlider
+from loadimg import load_img
+import spaces
+from transformers import AutoModelForImageSegmentation
+import torch
+from torchvision import transforms
+
+# torch.set_float32_matmul_precision(['high', 'highest'][0])
+
+birefnet = AutoModelForImageSegmentation.from_pretrained('zhengpeng7/BiRefNet', trust_remote_code=True,device="auto",torch_dtype=torch.float16)
+
+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)
+    im = im.convert('RGB') 
+    image = load_img(im)
+    input_images = transform_image(image).unsqueeze(0).to('cuda')
+    # Prediction
+    with torch.no_grad():
+        preds = birefnet(input_images)[-1].sigmoid().cpu()
+    pred = preds[0].squeeze()
+    pred_pil = transforms.ToPILImage()(pred)
+    out = (pred_pil , im)
+    return out
+
+slider1 = ImageSlider(label="birefnet", type="pil")
+slider2 = ImageSlider(label="RMBG", type="pil")
+image = gr.Image(label="Upload an image")
+text = gr.Textbox(label="Paste an image URL")
+
+
+tab1 = gr.Interface(fn,inputs= image, outputs= slider1, api_name="image")
+tab2 = gr.Interface(fn,inputs= text, outputs= slider2, api_name="text")
+
+demo = gr.TabbedInterface([tab1,tab2],["image","text"],title="RMBG with image slider")
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+accelerate
+opencv-python
+spaces
+pillow
+numpy
+timm
+kornia
+prettytable
+typing
+scikit-image
+huggingface_hub
+transformers>=4.39.1
+gradio
+gradio_imageslider
+loadimg>=0.1.1