diff --git a/examples/image2image.py b/examples/image2image.py
new file mode 100644
index 00000000..c889ebbd
--- /dev/null
+++ b/examples/image2image.py
@@ -0,0 +1,57 @@
+import requests
+from PIL import Image
+from io import BytesIO
+from datetime import datetime
+from PIL import Image
+from diffusers import StableDiffusionImg2ImgPipeline
+import logging
+
+logging.basicConfig(level=logging.INFO)
+class Image2Image:
+    def __init__(self, model_ref, max_height=512):
+        """
+        :param model_ref: str, the reference to load the mdoel from Huggingface
+        :param max_height: int, the image will be resized up to this value in height and proportionally for width. With the default value of 512, image generation typically finishes within 1 min.
+        """
+        self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_ref).to("mps")
+        self.max_height = max_height
+
+        # Recommended if your computer has < 64 GB of RAM
+        self.pipe.enable_attention_slicing()
+
+    def promptWithImage(self, prompt, image_path):
+        """
+        Create an image based on prompt text and the base image.
+        :param prompt: str
+        :image_path: str, path to load the image, load from web if starting with "http://" or "https://". Otherwise, load from the local file system.
+        :return Image
+        """
+        if image_path.startswith("http://") or image_path.startswith("https://"):
+            image_content = requests.get(image_path).content
+            init_image = Image.open(BytesIO(image_content)).convert("RGB")
+        else:
+            init_image = Image.open(image_path).convert("RGB")
+        
+        # resize to max_height and for width with same scale.
+        width,height = init_image.size
+        new_width,new_height = int(width * (self.max_height) / height),self.max_height
+        init_image = init_image.resize((new_width, new_height))
+        logging.info(f"resize image: ({width,height}) -> ({new_width,new_height})")
+
+        images = self.pipe(prompt=prompt, image=init_image).images
+        return images[0]
+    
+def main():
+    model_ref = "runwayml/stable-diffusion-v1-5"
+
+    prompt = "A fantasy landscape, trending on artstation"
+    image_path = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+
+    image = Image2Image(model_ref).promptWithImage(prompt, image_path)
+
+    time_str = datetime.now().strftime("%Y%m%d-%H%M%S")
+    file_path = f"{prompt.replace(' ','_')}_{time_str}.png"
+    image.save(file_path)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/examples/text2image.py b/examples/text2image.py
new file mode 100644
index 00000000..11935d11
--- /dev/null
+++ b/examples/text2image.py
@@ -0,0 +1,37 @@
+from diffusers import DiffusionPipeline
+from datetime import datetime
+
+class Text2Image:
+    def __init__(self, model_ref) -> None:
+        """
+        :param model_ref: str, the reference to load the mdoel from Huggingface
+        """
+        self.pipe = DiffusionPipeline.from_pretrained(model_ref).to("mps")
+
+        # Recommended if your computer has < 64 GB of RAM
+        self.pipe.enable_attention_slicing()
+
+    def imagine(self, prompt):
+        """
+        Create an image based on prompt text.
+        :param prompt: str, prompt text
+        :return Image
+        """
+        # First-time "warmup" pass if PyTorch version is 1.13 (see explanation above)
+        _ = self.pipe(prompt, num_inference_steps=1)
+
+        # Results match those from the CPU device after the warmup pass.
+        return self.pipe(prompt).images[0]
+
+def main():
+    model_ref = "runwayml/stable-diffusion-v1-5"
+    prompt = "a photo of an astronaut riding a horse on mars"
+
+    image = Text2Image(model_ref).imagine(prompt)
+
+    time_str = datetime.now().strftime("%Y%m%d-%H%M%S")
+    file_path = f"{prompt.replace(' ','_')}_{time_str}.png"
+    image.save(file_path)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file