diff --git a/extensions-builtin/forge_space_birefnet/forge_app.py b/extensions-builtin/forge_space_birefnet/forge_app.py
index 7a094962..96b686b2 100644
--- a/extensions-builtin/forge_space_birefnet/forge_app.py
+++ b/extensions-builtin/forge_space_birefnet/forge_app.py
@@ -1,3 +1,4 @@
+
 import spaces
 import os
 import gradio as gr
@@ -7,9 +8,10 @@ from transformers import AutoModelForImageSegmentation
 import torch
 from torchvision import transforms
 
-# torch.set_float32_matmul_precision(["high", "highest"][0])
+import glob
+import pathlib
+from PIL import Image
 
-os.environ['HOME'] = spaces.convert_root_path() + 'home'
 
 with spaces.capture_gpu_object() as birefnet_gpu_obj:
     birefnet = AutoModelForImageSegmentation.from_pretrained(
@@ -44,11 +46,69 @@ def fn(image):
     image.putalpha(mask)
     return (image, origin)
 
+@spaces.GPU(gpu_objects=[birefnet_gpu_obj], manual_load=True)
+def batch_process(input_folder, output_folder, save_png, save_flat):
+    # Ensure output folder exists
+    os.makedirs(output_folder, exist_ok=True)
+    
+    # Supported image extensions
+    image_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.webp']
+    
+    # Collect all image files from input folder
+    input_images = []
+    for ext in image_extensions:
+        input_images.extend(glob.glob(os.path.join(input_folder, f'*{ext}')))
+    
+    # Process each image
+    processed_images = []
+    for image_path in input_images:
+        try:
+            # Load image
+            im = load_img(image_path, output_type="pil")
+            im = im.convert("RGB")
+            image_size = im.size
+            image = load_img(im)
+            
+            # Prepare image for processing
+            input_image = transform_image(image).unsqueeze(0).to(spaces.gpu)
+            
+            # Prediction
+            with torch.no_grad():
+                preds = birefnet(input_image)[-1].sigmoid().cpu()
+            
+            pred = preds[0].squeeze()
+            pred_pil = transforms.ToPILImage()(pred)
+            mask = pred_pil.resize(image_size)
+            
+            # Apply mask
+            image.putalpha(mask)
+            
+            # Save processed image
+            output_filename = os.path.join(output_folder, f"{pathlib.Path(image_path).name}")
+
+            if save_flat:
+                background = Image.new('RGBA', image.size, (255, 255, 255))
+                image = Image.alpha_composite(background, image)
+                image = image.convert("RGB")
+            elif output_filename.lower().endswith(".jpg") or output_filename.lower().endswith(".jpeg"):
+                # jpegs don't support alpha channel, so add .png extension (not change, to avoid potential overwrites)
+                output_filename += ".png"
+            if save_png and not output_filename.lower().endswith(".png"):
+                output_filename += ".png"
+
+            image.save(output_filename)
+            
+            processed_images.append(output_filename)
+        
+        except Exception as e:
+            print(f"Error processing {image_path}: {str(e)}")
+    
+    return processed_images
 
 slider1 = ImageSlider(label="birefnet", type="pil")
 slider2 = ImageSlider(label="birefnet", type="pil")
 image = gr.Image(label="Upload an image")
-text = gr.Textbox(label="Paste an image URL")
+text = gr.Textbox(label="URL to image, or local path to image", max_lines=1)
 
 
 chameleon = load_img(spaces.convert_root_path() + "chameleon.jpg", output_type="pil")
@@ -58,11 +118,27 @@ tab1 = gr.Interface(
     fn, inputs=image, outputs=slider1, examples=[chameleon], api_name="image", allow_flagging="never"
 )
 
-tab2 = gr.Interface(fn, inputs=text, outputs=slider2, examples=[url], api_name="text", allow_flagging="never")
+tab2 = gr.Interface(
+    fn, inputs=text, outputs=slider2, examples=[url], api_name="text", allow_flagging="never"
+)
 
+tab3 = gr.Interface(
+    batch_process, 
+    inputs=[
+        gr.Textbox(label="Input folder path", max_lines=1),
+        gr.Textbox(label="Output folder path (will overwrite)", max_lines=1),
+        gr.Checkbox(label="Always save as PNG", value=True),
+        gr.Checkbox(label="Save flat (no mask)", value=False)
+    ], 
+    outputs=gr.File(label="Processed images", type="filepath", file_count="multiple"),
+    api_name="batch", 
+    allow_flagging="never"
+)
 
 demo = gr.TabbedInterface(
-    [tab1, tab2], ["image", "text"], title="birefnet for background removal"
+    [tab1, tab2, tab3], 
+    ["image", "URL", "batch"], 
+    title="birefnet for background removal"
 )
 
 if __name__ == "__main__":