diff --git a/inference_realesrgan.py b/inference_realesrgan.py
index 0418253..fadec7f 100644
--- a/inference_realesrgan.py
+++ b/inference_realesrgan.py
@@ -1,12 +1,9 @@
 import argparse
 import cv2
 import glob
-import math
-import numpy as np
 import os
-import torch
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from torch.nn import functional as F
+
+from realesrgan import RealESRGANer
 
 
 def main():
@@ -53,61 +50,8 @@ def main():
         imgname, extension = os.path.splitext(os.path.basename(path))
         print('Testing', idx, imgname)
 
-        # ------------------------------ read image ------------------------------ #
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED).astype(np.float32)
-        if np.max(img) > 255:  # 16-bit image
-            max_range = 65535
-            print('\tInput is a 16-bit image')
-        else:
-            max_range = 255
-        img = img / max_range
-        if len(img.shape) == 2:  # gray image
-            img_mode = 'L'
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
-        elif img.shape[2] == 4:  # RGBA image with alpha channel
-            img_mode = 'RGBA'
-            alpha = img[:, :, 3]
-            img = img[:, :, 0:3]
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            if args.alpha_upsampler == 'realesrgan':
-                alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
-        else:
-            img_mode = 'RGB'
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
-        # ------------------- process image (without the alpha channel) ------------------- #
-        upsampler.pre_process(img)
-        if args.tile:
-            upsampler.tile_process()
-        else:
-            upsampler.process()
-        output_img = upsampler.post_process()
-        output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-        output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
-        if img_mode == 'L':
-            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
-
-        # ------------------- process the alpha channel if necessary ------------------- #
-        if img_mode == 'RGBA':
-            if args.alpha_upsampler == 'realesrgan':
-                upsampler.pre_process(alpha)
-                if args.tile:
-                    upsampler.tile_process()
-                else:
-                    upsampler.process()
-                output_alpha = upsampler.post_process()
-                output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-                output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
-                output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
-            else:
-                h, w = alpha.shape[0:2]
-                output_alpha = cv2.resize(alpha, (w * args.scale, h * args.scale), interpolation=cv2.INTER_LINEAR)
-
-            # merge the alpha channel
-            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
-            output_img[:, :, 3] = output_alpha
-
-        # ------------------------------ save image ------------------------------ #
+        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+        output, img_mode = upsampler.enhance(img)
         if args.ext == 'auto':
             extension = extension[1:]
         else:
@@ -115,141 +59,8 @@ def main():
         if img_mode == 'RGBA':  # RGBA images should be saved in png format
             extension = 'png'
         save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
-        if max_range == 65535:  # 16-bit image
-            output = (output_img * 65535.0).round().astype(np.uint16)
-        else:
-            output = (output_img * 255.0).round().astype(np.uint8)
         cv2.imwrite(save_path, output)
 
 
-class RealESRGANer():
-
-    def __init__(self, scale, model_path, tile=0, tile_pad=10, pre_pad=10, half=False):
-        self.scale = scale
-        self.tile_size = tile
-        self.tile_pad = tile_pad
-        self.pre_pad = pre_pad
-        self.mod_scale = None
-        self.half = half
-
-        # initialize model
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale)
-        loadnet = torch.load(model_path)
-        if 'params_ema' in loadnet:
-            keyname = 'params_ema'
-        else:
-            keyname = 'params'
-        model.load_state_dict(loadnet[keyname], strict=True)
-        model.eval()
-        self.model = model.to(self.device)
-        if self.half:
-            self.model = self.model.half()
-
-    def pre_process(self, img):
-        img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
-        self.img = img.unsqueeze(0).to(self.device)
-        if self.half:
-            self.img = self.img.half()
-
-        # pre_pad
-        if self.pre_pad != 0:
-            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
-        # mod pad
-        if self.scale == 2:
-            self.mod_scale = 2
-        elif self.scale == 1:
-            self.mod_scale = 4
-        if self.mod_scale is not None:
-            self.mod_pad_h, self.mod_pad_w = 0, 0
-            _, _, h, w = self.img.size()
-            if (h % self.mod_scale != 0):
-                self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
-            if (w % self.mod_scale != 0):
-                self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
-            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
-
-    def process(self):
-        try:
-            # inference
-            with torch.no_grad():
-                self.output = self.model(self.img)
-        except Exception as error:
-            print('Error', error)
-
-    def tile_process(self):
-        """Modified from: https://github.com/ata4/esrgan-launcher
-        """
-        batch, channel, height, width = self.img.shape
-        output_height = height * self.scale
-        output_width = width * self.scale
-        output_shape = (batch, channel, output_height, output_width)
-
-        # start with black image
-        self.output = self.img.new_zeros(output_shape)
-        tiles_x = math.ceil(width / self.tile_size)
-        tiles_y = math.ceil(height / self.tile_size)
-
-        # loop over all tiles
-        for y in range(tiles_y):
-            for x in range(tiles_x):
-                # extract tile from input image
-                ofs_x = x * self.tile_size
-                ofs_y = y * self.tile_size
-                # input tile area on total image
-                input_start_x = ofs_x
-                input_end_x = min(ofs_x + self.tile_size, width)
-                input_start_y = ofs_y
-                input_end_y = min(ofs_y + self.tile_size, height)
-
-                # input tile area on total image with padding
-                input_start_x_pad = max(input_start_x - self.tile_pad, 0)
-                input_end_x_pad = min(input_end_x + self.tile_pad, width)
-                input_start_y_pad = max(input_start_y - self.tile_pad, 0)
-                input_end_y_pad = min(input_end_y + self.tile_pad, height)
-
-                # input tile dimensions
-                input_tile_width = input_end_x - input_start_x
-                input_tile_height = input_end_y - input_start_y
-                tile_idx = y * tiles_x + x + 1
-                input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
-
-                # upscale tile
-                try:
-                    with torch.no_grad():
-                        output_tile = self.model(input_tile)
-                except Exception as error:
-                    print('Error', error)
-                print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
-
-                # output tile area on total image
-                output_start_x = input_start_x * self.scale
-                output_end_x = input_end_x * self.scale
-                output_start_y = input_start_y * self.scale
-                output_end_y = input_end_y * self.scale
-
-                # output tile area without padding
-                output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
-                output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
-                output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
-                output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
-
-                # put tile into output image
-                self.output[:, :, output_start_y:output_end_y,
-                            output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
-                                                                       output_start_x_tile:output_end_x_tile]
-
-    def post_process(self):
-        # remove extra pad
-        if self.mod_scale is not None:
-            _, _, h, w = self.output.size()
-            self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
-        # remove prepad
-        if self.pre_pad != 0:
-            _, _, h, w = self.output.size()
-            self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
-        return self.output
-
-
 if __name__ == '__main__':
     main()
diff --git a/realesrgan/__init__.py b/realesrgan/__init__.py
index 36731a0..4ccac57 100644
--- a/realesrgan/__init__.py
+++ b/realesrgan/__init__.py
@@ -2,4 +2,5 @@
 from .archs import *
 from .data import *
 from .models import *
+from .utils import *
 from .version import __gitsha__, __version__
diff --git a/realesrgan/archs/__init__.py b/realesrgan/archs/__init__.py
index 4ec725e..f3fbbf3 100644
--- a/realesrgan/archs/__init__.py
+++ b/realesrgan/archs/__init__.py
@@ -7,4 +7,4 @@ from os import path as osp
 arch_folder = osp.dirname(osp.abspath(__file__))
 arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
 # import all the arch modules
-_arch_modules = [importlib.import_module(f'archs.{file_name}') for file_name in arch_filenames]
+_arch_modules = [importlib.import_module(f'realesrgan.archs.{file_name}') for file_name in arch_filenames]
diff --git a/realesrgan/data/__init__.py b/realesrgan/data/__init__.py
index 3b8afa6..a3f8fdd 100644
--- a/realesrgan/data/__init__.py
+++ b/realesrgan/data/__init__.py
@@ -7,4 +7,4 @@ from os import path as osp
 data_folder = osp.dirname(osp.abspath(__file__))
 dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
 # import all the dataset modules
-_dataset_modules = [importlib.import_module(f'data.{file_name}') for file_name in dataset_filenames]
+_dataset_modules = [importlib.import_module(f'realesrgan.data.{file_name}') for file_name in dataset_filenames]
diff --git a/realesrgan/models/__init__.py b/realesrgan/models/__init__.py
index a7ce9a2..0be7105 100644
--- a/realesrgan/models/__init__.py
+++ b/realesrgan/models/__init__.py
@@ -7,4 +7,4 @@ from os import path as osp
 model_folder = osp.dirname(osp.abspath(__file__))
 model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
 # import all the model modules
-_model_modules = [importlib.import_module(f'models.{file_name}') for file_name in model_filenames]
+_model_modules = [importlib.import_module(f'realesrgan.models.{file_name}') for file_name in model_filenames]
diff --git a/realesrgan/utils.py b/realesrgan/utils.py
new file mode 100644
index 0000000..06b2261
--- /dev/null
+++ b/realesrgan/utils.py
@@ -0,0 +1,226 @@
+import cv2
+import math
+import numpy as np
+import os
+import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from torch.hub import download_url_to_file, get_dir
+from torch.nn import functional as F
+from urllib.parse import urlparse
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+class RealESRGANer():
+
+    def __init__(self, scale, model_path, tile=0, tile_pad=10, pre_pad=10, half=False):
+        self.scale = scale
+        self.tile_size = tile
+        self.tile_pad = tile_pad
+        self.pre_pad = pre_pad
+        self.mod_scale = None
+        self.half = half
+
+        # initialize model
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale)
+
+        if model_path.startswith('https://'):
+            model_path = load_file_from_url(
+                url=model_path, model_dir='realesrgan/weights', progress=True, file_name=None)
+        loadnet = torch.load(model_path)
+        if 'params_ema' in loadnet:
+            keyname = 'params_ema'
+        else:
+            keyname = 'params'
+        model.load_state_dict(loadnet[keyname], strict=True)
+        model.eval()
+        self.model = model.to(self.device)
+        if self.half:
+            self.model = self.model.half()
+
+    def pre_process(self, img):
+        img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
+        self.img = img.unsqueeze(0).to(self.device)
+        if self.half:
+            self.img = self.img.half()
+
+        # pre_pad
+        if self.pre_pad != 0:
+            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
+        # mod pad
+        if self.scale == 2:
+            self.mod_scale = 2
+        elif self.scale == 1:
+            self.mod_scale = 4
+        if self.mod_scale is not None:
+            self.mod_pad_h, self.mod_pad_w = 0, 0
+            _, _, h, w = self.img.size()
+            if (h % self.mod_scale != 0):
+                self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
+            if (w % self.mod_scale != 0):
+                self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
+            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
+
+    def process(self):
+        try:
+            # inference
+            with torch.no_grad():
+                self.output = self.model(self.img)
+        except Exception as error:
+            print('Error', error)
+
+    def tile_process(self):
+        """Modified from: https://github.com/ata4/esrgan-launcher
+        """
+        batch, channel, height, width = self.img.shape
+        output_height = height * self.scale
+        output_width = width * self.scale
+        output_shape = (batch, channel, output_height, output_width)
+
+        # start with black image
+        self.output = self.img.new_zeros(output_shape)
+        tiles_x = math.ceil(width / self.tile_size)
+        tiles_y = math.ceil(height / self.tile_size)
+
+        # loop over all tiles
+        for y in range(tiles_y):
+            for x in range(tiles_x):
+                # extract tile from input image
+                ofs_x = x * self.tile_size
+                ofs_y = y * self.tile_size
+                # input tile area on total image
+                input_start_x = ofs_x
+                input_end_x = min(ofs_x + self.tile_size, width)
+                input_start_y = ofs_y
+                input_end_y = min(ofs_y + self.tile_size, height)
+
+                # input tile area on total image with padding
+                input_start_x_pad = max(input_start_x - self.tile_pad, 0)
+                input_end_x_pad = min(input_end_x + self.tile_pad, width)
+                input_start_y_pad = max(input_start_y - self.tile_pad, 0)
+                input_end_y_pad = min(input_end_y + self.tile_pad, height)
+
+                # input tile dimensions
+                input_tile_width = input_end_x - input_start_x
+                input_tile_height = input_end_y - input_start_y
+                tile_idx = y * tiles_x + x + 1
+                input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
+
+                # upscale tile
+                try:
+                    with torch.no_grad():
+                        output_tile = self.model(input_tile)
+                except Exception as error:
+                    print('Error', error)
+                print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
+
+                # output tile area on total image
+                output_start_x = input_start_x * self.scale
+                output_end_x = input_end_x * self.scale
+                output_start_y = input_start_y * self.scale
+                output_end_y = input_end_y * self.scale
+
+                # output tile area without padding
+                output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
+                output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
+                output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
+                output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
+
+                # put tile into output image
+                self.output[:, :, output_start_y:output_end_y,
+                            output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
+                                                                       output_start_x_tile:output_end_x_tile]
+
+    def post_process(self):
+        # remove extra pad
+        if self.mod_scale is not None:
+            _, _, h, w = self.output.size()
+            self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
+        # remove prepad
+        if self.pre_pad != 0:
+            _, _, h, w = self.output.size()
+            self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
+        return self.output
+
+    def enhance(self, img, tile=False, alpha_upsampler='realesrgan'):
+        # img: numpy
+        img = img.astype(np.float32)
+        if np.max(img) > 255:  # 16-bit image
+            max_range = 65535
+            print('\tInput is a 16-bit image')
+        else:
+            max_range = 255
+        img = img / max_range
+        if len(img.shape) == 2:  # gray image
+            img_mode = 'L'
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+        elif img.shape[2] == 4:  # RGBA image with alpha channel
+            img_mode = 'RGBA'
+            alpha = img[:, :, 3]
+            img = img[:, :, 0:3]
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            if alpha_upsampler == 'realesrgan':
+                alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
+        else:
+            img_mode = 'RGB'
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+        # ------------------- process image (without the alpha channel) ------------------- #
+        self.pre_process(img)
+        if self.tile_size > 0:
+            self.tile_process()
+        else:
+            self.process()
+        output_img = self.post_process()
+        output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+        output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
+        if img_mode == 'L':
+            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
+
+        # ------------------- process the alpha channel if necessary ------------------- #
+        if img_mode == 'RGBA':
+            if alpha_upsampler == 'realesrgan':
+                self.pre_process(alpha)
+                if self.tile_size > 0:
+                    self.tile_process()
+                else:
+                    self.process()
+                output_alpha = self.post_process()
+                output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+                output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
+                output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
+            else:
+                h, w = alpha.shape[0:2]
+                output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR)
+
+            # merge the alpha channel
+            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
+            output_img[:, :, 3] = output_alpha
+
+        # ------------------------------ return ------------------------------ #
+        if max_range == 65535:  # 16-bit image
+            output = (output_img * 65535.0).round().astype(np.uint16)
+        else:
+            output = (output_img * 255.0).round().astype(np.uint8)
+        return output, img_mode
+
+
+def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
+    """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
+    """
+    if model_dir is None:
+        hub_dir = get_dir()
+        model_dir = os.path.join(hub_dir, 'checkpoints')
+
+    os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True)
+
+    parts = urlparse(url)
+    filename = os.path.basename(parts.path)
+    if file_name is not None:
+        filename = file_name
+    cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename))
+    if not os.path.exists(cached_file):
+        print(f'Downloading: "{url}" to {cached_file}\n')
+        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
+    return cached_file