diff --git a/demo.py b/demo.py
index 8d04ade..6f00333 100644
--- a/demo.py
+++ b/demo.py
@@ -10,7 +10,10 @@
 # utils
 import cv2
 import numpy as np
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
 import PIL.Image, PIL.PngImagePlugin
+import datetime
 import piexif
 
 
@@ -94,13 +97,20 @@ def main(args):
         guidance_scale = args.guidance_scale,
         eta = args.eta
     )
-
+    color_coverted = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    pil_image=Image.fromarray(color_coverted)
+    metadata = PngInfo()
+    metadata.add_text("prompt", args.prompt)
+    metadata.add_text("timestamp", datetime.datetime.now().isoformat())
+    metadata.add_text("tool", "stable diffusion openvino 1.4")
+    metadata.add_text("dimension", "512x512")
+    metadata.add_text("seed", str(args.seed))
+    metadata.add_text("num-inference-steps", str(args.num_inference_steps))
+    metadata.add_text("strength", str(args.strength))
+    metadata.add_text("guidancescale", str(args.guidance_scale))
+    pil_image.save(args.output, "PNG", pnginfo=metadata) 
     pil_image = PIL.Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-
-
-    pil_image.save(args.output,
-                   **build_image_metadata(args))
-
+    pil_image.save(args.output, **build_image_metadata(args))
 
 
 if __name__ == "__main__":
diff --git a/demo_batch.py b/demo_batch.py
new file mode 100644
index 0000000..7d409dc
--- /dev/null
+++ b/demo_batch.py
@@ -0,0 +1,102 @@
+# -- coding: utf-8 --`
+import argparse
+import os
+# engine
+from stable_diffusion_engine import StableDiffusionEngine
+# scheduler
+from diffusers import LMSDiscreteScheduler, PNDMScheduler
+# utils
+import cv2
+import numpy as np
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+import datetime
+
+
+def main(args):
+    if args.seed is not None:
+        np.random.seed(args.seed)
+    if args.init_image is None:
+        scheduler = LMSDiscreteScheduler(
+            beta_start=args.beta_start,
+            beta_end=args.beta_end,
+            beta_schedule=args.beta_schedule,
+            tensor_format="np"
+        )
+    else:
+        scheduler = PNDMScheduler(
+            beta_start=args.beta_start,
+            beta_end=args.beta_end,
+            beta_schedule=args.beta_schedule,
+            skip_prk_steps = True,
+            tensor_format="np"
+        )
+    engine = StableDiffusionEngine(
+        model = args.model,
+        scheduler = scheduler,
+        tokenizer = args.tokenizer
+    )
+    image = engine(
+        prompt = args.prompt,
+        init_image = None if args.init_image is None else cv2.imread(args.init_image),
+        mask = None if args.mask is None else cv2.imread(args.mask, 0),
+        strength = args.strength,
+        num_inference_steps = args.num_inference_steps,
+        guidance_scale = args.guidance_scale,
+        eta = args.eta
+    )
+    #cv2.imwrite(args.output, image)
+    # convert from openCV2 to PIL. Notice the COLOR_BGR2RGB which means that 
+    # # the color is converted from BGR to RGB
+    color_coverted = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    pil_image=Image.fromarray(color_coverted)
+    metadata = PngInfo()
+    metadata.add_text("prompt", args.prompt)
+    metadata.add_text("timestamp", datetime.datetime.now().isoformat())
+    metadata.add_text("tool", "stable diffusion openvino 1.4")
+    metadata.add_text("dimension", "512x512")
+    metadata.add_text("seed", str(args.seed))
+    metadata.add_text("num-inference-steps", str(args.num_inference_steps))
+    metadata.add_text("strength", str(args.strength))
+    metadata.add_text("guidancescale", str(args.guidance_scale))
+    pil_image.save(args.output, "PNG", pnginfo=metadata) 
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # pipeline configure
+    parser.add_argument("--model", type=str, default="bes-dev/stable-diffusion-v1-4-openvino", help="model name")
+    # randomizer params
+    parser.add_argument("--seed", type=int, default=None, help="random seed for generating consistent images per prompt")
+    # scheduler params
+    parser.add_argument("--beta-start", type=float, default=0.00085, help="LMSDiscreteScheduler::beta_start")
+    parser.add_argument("--beta-end", type=float, default=0.012, help="LMSDiscreteScheduler::beta_end")
+    parser.add_argument("--beta-schedule", type=str, default="scaled_linear", help="LMSDiscreteScheduler::beta_schedule")
+    # diffusion params
+    parser.add_argument("--num-inference-steps", type=int, default=32, help="num inference steps")
+    parser.add_argument("--guidance-scale", type=float, default=7.5, help="guidance scale")
+    parser.add_argument("--eta", type=float, default=0.0, help="eta")
+    # tokenizer
+    parser.add_argument("--tokenizer", type=str, default="openai/clip-vit-large-patch14", help="tokenizer")
+    # prompt
+    parser.add_argument("--prompt", type=str, default="Street-art painting of Emilia Clarke in style of Banksy, photorealism", help="prompt")
+    # img2img params
+    parser.add_argument("--init-image", type=str, default=None, help="path to initial image")
+    parser.add_argument("--strength", type=float, default=0.5, help="how strong the initial image should be noised [0.0, 1.0]")
+    # inpainting
+    parser.add_argument("--mask", type=str, default=None, help="mask of the region to inpaint on the initial image")
+    # output name
+    parser.add_argument("--output", type=str, default="output.png", help="output image name")
+        # diffusion params
+    parser.add_argument("--batch", type=int, default=1, help="run prompts in batch N times")
+    args = parser.parse_args()
+    output = args.output
+    if args.batch > 1:
+        for i in range(1,args.batch+1,1):
+            args.output = output.replace(".png", "_") +  str(i) + ".png"
+            print(args.output)
+            main(args)
+    else:
+        print(args.output)
+        main(args)
+
diff --git a/image_metadata.py b/image_metadata.py
new file mode 100644
index 0000000..4b38fb7
--- /dev/null
+++ b/image_metadata.py
@@ -0,0 +1,33 @@
+from PIL import Image
+from PIL.ExifTags import TAGS
+
+import sys
+if len (sys.argv) != 2 :
+    print("Usage: python image_metadata.py imagefile.png ")
+    sys.exit (1)
+
+image_file = sys.argv[1]
+# open the image file
+image = Image.open(image_file)
+lowercase_filename = image_file.lower()
+
+if lowercase_filename.endswith('.png'):
+    #print(image.text) 
+    for k, v in image.text.items():
+        print(k,": ", v)
+elif lowercase_filename.endswith('.jpg'):
+# extracting the exif metadata
+    exifdata = image.getexif() 
+    # looping through all the tags present in exifdata
+    for tagid in exifdata:
+        # getting the tag name instead of tag id
+        tagname = TAGS.get(tagid, tagid)
+        # passing the tagid to get its respective value
+        value = exifdata.get(tagid)
+        # printing the final result
+        print(f"{tagname:25}: {value}")
+else:
+    print("file type not supported")
+
+
+