diff --git a/colorize.py b/colorize.py
new file mode 100644
index 0000000..b620524
--- /dev/null
+++ b/colorize.py
@@ -0,0 +1,116 @@
+import torch
+
+import os
+import colorgram.colorgram as cgm
+
+import sys
+
+from PIL import Image
+from torchvision import transforms
+from preprocess import re_scale, save_image, make_colorgram_tensor, scale
+
+from models import DeepUNetPaintGenerator
+from utils import load_checkpoints
+
+topk = 4
+
+
+def get_rgb(colorgram_result):
+    """
+    from colorgram_result, result rgb value as tuple of (r,g,b)
+    """
+    color = colorgram_result.rgb
+    return (color.r, color.g, color.b)
+
+
+def crop_region(image):
+    """
+    from image, crop 4 region and return
+    """
+    width, height = image.size
+    h1 = height // 4
+    h2 = h1 + h1
+    h3 = h2 + h1
+    h4 = h3 + h1
+    image1 = image.crop((0, 0, width, h1))
+    image2 = image.crop((0, h1, width, h2))
+    image3 = image.crop((0, h2, width, h3))
+    image4 = image.crop((0, h3, width, h4))
+
+    return (image1, image2, image3, image4)
+
+
+def get_topk(color_info, k):
+    colors = list(color_info.values())
+    return list(map(lambda x: x[k], colors))
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+out_root = './data/colorize_result'
+if not os.path.exists(out_root):
+    os.mkdir(out_root)
+generator = 'deepunetG_030.pth.tar'
+
+model = DeepUNetPaintGenerator()
+model = model.to(device)
+load_checkpoints(generator, model, device_type=device.type)
+for param in model.parameters():
+    param.requires_grad = False
+
+
+def main():
+    if len(sys.argv) < 3:
+        raise RuntimeError(
+            'Command Line Argument Must be (sketch file, style file)')
+
+    style_f = './data/styles/%s' % sys.argv[2]
+    test_f = './data/test/%s' % sys.argv[1]
+
+    filename = sys.argv[1][:-4] + sys.argv[2][:-4] + '.png'
+
+    style = Image.open(style_f)
+    style_pil = style
+    test = Image.open(test_f)
+    test_pil = test
+
+    transform = transforms.Compose(
+        [transforms.CenterCrop(512),
+         transforms.ToTensor()])
+
+    test = transform(test)
+    test = scale(test)
+    test = test.unsqueeze(0).to(device)
+
+    to_pil = transforms.ToPILImage()
+
+    try:
+        images = list(crop_region(style))
+        result = {}
+        for i, img in enumerate(images, 1):
+            colors = cgm.extract(img, topk + 1)
+            result[str(i)] = {
+                '%d' % i: get_rgb(colors[i])
+                for i in range(1, topk + 1)
+            }
+
+        color_tensor = make_colorgram_tensor(result)
+        color_tensor = color_tensor.unsqueeze(0).to(device)
+
+        fakeB, _ = model(test, color_tensor)
+        fakeB = fakeB.squeeze(0)
+        fakeB = re_scale(fakeB.detach().cpu())
+        fakeB = to_pil(fakeB)
+
+        result_image = Image.new('RGB', (512 * 3, 512))
+        result_image.paste(test_pil, (512 * 0, 0, 512 * 1, 512))
+        result_image.paste(style_pil, (512 * 1, 0, 512 * 2, 512))
+        result_image.paste(fakeB, (512 * 2, 0, 512 * 3, 512))
+        save_image(result_image, os.path.join(out_root, filename))
+
+    except IndexError:
+        exit(1)
+
+
+if __name__ == "__main__":
+    main()
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