diff --git a/mainscripts/FacesetResizer.py b/mainscripts/FacesetResizer.py
new file mode 100644
index 0000000..f2ec391
--- /dev/null
+++ b/mainscripts/FacesetResizer.py
@@ -0,0 +1,147 @@
+import multiprocessing
+import shutil
+
+from core import pathex
+from core.cv2ex import *
+from core.interact import interact as io
+from core.joblib import Subprocessor
+from DFLIMG import *
+from facelib import LandmarksProcessor, FaceType
+
+
+class FacesetResizerSubprocessor(Subprocessor):
+
+    #override
+    def __init__(self, image_paths, output_dirpath, image_size):
+        self.image_paths = image_paths
+        self.output_dirpath = output_dirpath
+        self.image_size = image_size
+        self.result = []
+
+        super().__init__('FacesetResizer', FacesetResizerSubprocessor.Cli, 600)
+
+    #override
+    def on_clients_initialized(self):
+        io.progress_bar (None, len (self.image_paths))
+
+    #override
+    def on_clients_finalized(self):
+        io.progress_bar_close()
+
+    #override
+    def process_info_generator(self):
+        base_dict = {'output_dirpath':self.output_dirpath, 'image_size':self.image_size}
+
+        for device_idx in range( min(8, multiprocessing.cpu_count()) ):
+            client_dict = base_dict.copy()
+            device_name = f'CPU #{device_idx}'
+            client_dict['device_name'] = device_name
+            yield device_name, {}, client_dict
+
+    #override
+    def get_data(self, host_dict):
+        if len (self.image_paths) > 0:
+            return self.image_paths.pop(0)
+
+    #override
+    def on_data_return (self, host_dict, data):
+        self.image_paths.insert(0, data)
+
+    #override
+    def on_result (self, host_dict, data, result):
+        io.progress_bar_inc(1)
+        if result[0] == 1:
+            self.result +=[ (result[1], result[2]) ]
+
+    #override
+    def get_result(self):
+        return self.result
+
+    class Cli(Subprocessor.Cli):
+
+        #override
+        def on_initialize(self, client_dict):
+            self.output_dirpath = client_dict['output_dirpath']
+            self.image_size = client_dict['image_size']
+            self.log_info (f"Running on { client_dict['device_name'] }")
+
+        #override
+        def process_data(self, filepath):
+            try:
+                dflimg = DFLIMG.load (filepath)
+                if dflimg is None or not dflimg.has_data():
+                    self.log_err (f"{filepath.name} is not a dfl image file")
+                else:
+                    dfl_dict = dflimg.get_dict()
+
+                    img = cv2_imread(filepath)
+                    h,w = img.shape[:2]
+                    if h != w:
+                        raise Exception(f'w != h in {filepath}')
+                    
+                    image_size = self.image_size
+                    scale = w / image_size
+                    
+                    img = cv2.resize(img, (image_size, image_size), interpolation=cv2.INTER_LANCZOS4)                    
+
+                    output_filepath = self.output_dirpath / filepath.name
+                    cv2_imwrite ( str(output_filepath), img, [int(cv2.IMWRITE_JPEG_QUALITY), 100] )
+
+                    dflimg = DFLIMG.load (output_filepath)
+                    dflimg.set_dict(dfl_dict)
+                    
+                    
+                    lmrks = dflimg.get_landmarks()                    
+                    lmrks /= scale
+                    dflimg.set_landmarks(lmrks)
+                    
+                    seg_ie_polys = dflimg.get_seg_ie_polys()
+                    seg_ie_polys.mult_points( 1.0 / scale)
+                    dflimg.set_seg_ie_polys(seg_ie_polys)
+                    
+                    mat = dflimg.get_image_to_face_mat()
+                    if mat is not None:
+                        face_type = FaceType.fromString ( dflimg.get_face_type() )
+                        mat = LandmarksProcessor.get_transform_mat ( dflimg.get_source_landmarks(), image_size, face_type )
+                        dflimg.set_image_to_face_mat(mat)
+                    dflimg.save()
+
+                    return (1, filepath, output_filepath)
+            except:
+                self.log_err (f"Exception occured while processing file {filepath}. Error: {traceback.format_exc()}")
+
+            return (0, filepath, None)
+
+def process_folder ( dirpath):
+    
+    image_size = io.input_int(f"New image size", 512, valid_range=[256,2048])
+
+
+    output_dirpath = dirpath.parent / (dirpath.name + '_resized')
+    output_dirpath.mkdir (exist_ok=True, parents=True)
+
+    dirpath_parts = '/'.join( dirpath.parts[-2:])
+    output_dirpath_parts = '/'.join( output_dirpath.parts[-2:] )
+    io.log_info (f"Resizing faceset in {dirpath_parts}")
+    io.log_info ( f"Processing to {output_dirpath_parts}")
+
+    output_images_paths = pathex.get_image_paths(output_dirpath)
+    if len(output_images_paths) > 0:
+        for filename in output_images_paths:
+            Path(filename).unlink()
+
+    image_paths = [Path(x) for x in pathex.get_image_paths( dirpath )]
+    result = FacesetResizerSubprocessor ( image_paths, output_dirpath, image_size).run()
+
+    is_merge = io.input_bool (f"\r\nMerge {output_dirpath_parts} to {dirpath_parts} ?", True)
+    if is_merge:
+        io.log_info (f"Copying processed files to {dirpath_parts}")
+
+        for (filepath, output_filepath) in result:
+            try:
+                shutil.copy (output_filepath, filepath)
+            except:
+                pass
+
+        io.log_info (f"Removing {output_dirpath_parts}")
+        shutil.rmtree(output_dirpath)