diff --git a/scripts/gen_sliding_images.py b/scripts/gen_sliding_images.py
new file mode 100644
index 000000000..5010812e1
--- /dev/null
+++ b/scripts/gen_sliding_images.py
@@ -0,0 +1,156 @@
+import sys
+import os
+import glob
+
+sys.path.append("../")
+from models import networks
+from options.train_options import TrainOptions
+import cv2
+import torch
+from torchvision import transforms
+from torchvision.utils import save_image
+import numpy as np
+import argparse
+
+
+def sliding_window(image, stepSize, windowSize):
+    # slide a window across the image
+    for y in range(0, image.shape[0], stepSize):
+        for x in range(0, image.shape[1], stepSize):
+            # yield the current window
+            yield (x, y, image[y : y + windowSize[1], x : x + windowSize[0]])
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--model-in-file", help="file path to generator model (.pth file)", required=True
+)
+parser.add_argument(
+    "--model-type",
+    default="mobile_resnet_9blocks",
+    help="model type, e.g. mobile_resnet_9blocks",
+)
+parser.add_argument(
+    "--model-config",
+    help="optional model configuration, e.g /path/to/segformer_config_b0.py",
+)
+parser.add_argument(
+    "--padding-type",
+    type=str,
+    help="whether to use padding, zeros or reflect",
+    default="reflect",
+)
+parser.add_argument("--img-size", default=256, type=int, help="square image size")
+parser.add_argument(
+    "--img-in", help="image or image folder to transform", required=True
+)
+parser.add_argument(
+    "--stepsize",
+    type=int,
+    default=128,
+    help="sliding window stepsize, to be set to image input size",
+)
+parser.add_argument("--windowsize", type=int, default=256, help="window input size")
+parser.add_argument(
+    "--output-dir", help="full size pictures output directory", required=True
+)
+parser.add_argument("--bw", action="store_true", help="whether input/output is bw")
+parser.add_argument("--cpu", action="store_true", help="whether to use CPU")
+args = parser.parse_args()
+
+if args.bw:
+    input_nc = output_nc = 1
+else:
+    input_nc = output_nc = 3
+
+# loading model
+opt = TrainOptions().parse_json({})
+opt.data_crop_size = args.img_size
+opt.data_load_size = args.img_size
+opt.G_attn_nb_mask_attn = 10
+opt.G_attn_nb_mask_input = 1
+opt.G_netG = args.model_type
+opt.G_padding_type = args.padding_type
+opt.model_input_nc = input_nc
+opt.model_output_nc = output_nc
+if "segformer" in args.model_type:
+    opt.G_config_segformer = (
+        args.model_config
+    )  # e.g. '/path/to/models/configs/segformer/segformer_config_b0.py'
+opt.jg_dir = os.path.join("/".join(__file__.split("/")[:-2]))
+model = networks.define_G(**vars(opt))
+
+model.eval()
+model.load_state_dict(torch.load(args.model_in_file))
+
+if not args.cpu:
+    model = model.cuda()
+
+if os.path.isfile(args.img_in):
+    images = [args.img_in]
+else:
+    images = glob.glob(args.img_in + "*.*")
+
+# preprocessing transforms
+tranlist = [
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+]
+tran = transforms.Compose(tranlist)
+
+for image in images:
+
+    # reading image
+    img = cv2.imread(args.img_in)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    print(image, " / shape=", img.shape)
+
+    outputmap = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)
+
+    # - walk through sliding windows
+    i = 0
+    for (x, y, window) in sliding_window(
+        img, stepSize=args.stepsize, windowSize=(args.windowsize, args.windowsize)
+    ):
+
+        # - if window is smaller than input sizes, fill it up correctly
+        windowtmp = window.copy()
+        resized = False
+        if window.shape[0] != args.stepsize or window.shape[1] != args.stepsize:
+            resized = True
+            windowfull = np.zeros((args.windowsize, args.windowsize, 3), np.uint8)
+            windowfull[0 : window.shape[0], 0 : window.shape[1]] = window.copy()
+            window = windowfull
+
+        # - get the local image window
+        # windowpath = '/tmp/img'+str(i)+'.png'
+        # cv2.imwrite(windowpath, window)
+
+        # preprocessing
+        img_tensor = tran(window)
+        if not args.cpu:
+            img_tensor = img_tensor.cuda()
+
+        # run through model
+        out_tensor = model(img_tensor.unsqueeze(0))[0].detach()
+
+        # post-processing
+        out_img = out_tensor.data.cpu().float().numpy()
+        print(out_img.shape)
+        out_img = (np.transpose(out_img, (1, 2, 0)) + 1) / 2.0 * 255.0
+        # print(out_img)
+        out_img = cv2.cvtColor(out_img, cv2.COLOR_RGB2BGR)
+
+        # - combine the output images
+        if resized:
+            out_img = out_img[0 : windowtmp.shape[0], 0 : windowtmp.shape[1]]
+        outputmap[y : y + window.shape[1], x : x + window.shape[0]] = out_img
+
+    i += 1
+
+    # save the full size output image
+    imgoutpath = (
+        args.output_dir + "/" + os.path.basename(image).replace(".jpg", "") + "_gan.jpg"
+    )
+    cv2.imwrite(imgoutpath, outputmap)
+    print("Successfully generated image ", imgoutpath)