diff --git a/examples/apps/hugging_face_integration_app/Dockerfile b/examples/apps/hugging_face_integration_app/Dockerfile
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+++ b/examples/apps/hugging_face_integration_app/Dockerfile
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+FROM nvcr.io/nvidia/clara-holoscan/holoscan:v2.1.0-dgpu
+RUN pip install --upgrade pip
+RUN pip install monai-deploy-app-sdk
+RUN pip install transformers
+RUN pip install hub
+RUN pip install diffusers
+RUN pip install torch
+RUN pip install pydicom
+RUN pip install accelerate
diff --git a/examples/apps/hugging_face_integration_app/debug.sh b/examples/apps/hugging_face_integration_app/debug.sh
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+++ b/examples/apps/hugging_face_integration_app/debug.sh
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+IMAGE=vikash112/monai-hugging:0.1.0
+docker build -t $IMAGE .
+
+monai_dir=/raid/Vikash/Tools/HUGGINGFACE/med_image_generation
+
+NV_GPU=1 nvidia-docker run -it --rm --shm-size=4g --ulimit memlock=-1 --ulimit stack=67108864 -v $monai_dir:/workspace/app/test $IMAGE /bin/bash
diff --git a/examples/apps/hugging_face_integration_app/med_image_generation/app.py b/examples/apps/hugging_face_integration_app/med_image_generation/app.py
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index 00000000..8751d03c
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+++ b/examples/apps/hugging_face_integration_app/med_image_generation/app.py
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+import logging 
+from pathlib import Path
+import torch 
+from diffusers import StableDiffusionPipeline
+from monai.deploy.core import AppContext, Application
+from PIL import Image
+import numpy as np
+import argparse
+
+
+
+class App(Application):
+	name = "Diffusion Image App"
+	description = "Simple application showing diffusion to generate Images"
+	def compose(self):
+		model_id = "Nihirc/Prompt2MedImage"
+		device = "cuda"
+		parser = argparse.ArgumentParser()
+		parser.add_argument("--input_prompt", type=str, default="Generate a X-ray")
+		parser.add_argument("--output", type=str, default="./out.jpg")
+		args = parser.parse_args()
+
+		input_prompt = args.input_prompt
+		output_path = args.output
+		print("Input Prompt: ", input_prompt)
+		pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+		pipe = pipe.to(device)
+		#prompt = "Show me an X ray of ankle with a fractured toe"
+		prompt = "Show me an X ray pevic fracture"
+		image = pipe(prompt).images[0]
+		image.save(output_path)
+
+
+if __name__ == "__main__":
+	logging.info(f"Begin {__name__}")
+	App().run()
+	logging.info(f"End {__name__}")
+
+	
+
diff --git a/examples/apps/hugging_face_integration_app/med_image_generation/out.jpg b/examples/apps/hugging_face_integration_app/med_image_generation/out.jpg
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index 00000000..2431118b
Binary files /dev/null and b/examples/apps/hugging_face_integration_app/med_image_generation/out.jpg differ
diff --git a/examples/apps/hugging_face_integration_app/med_image_generation/run.sh b/examples/apps/hugging_face_integration_app/med_image_generation/run.sh
new file mode 100644
index 00000000..02472ee7
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+++ b/examples/apps/hugging_face_integration_app/med_image_generation/run.sh
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+
+python app.py --input_prompt "The patient had residual paralysis of the hand after poliomyelitis. It was necessary to stabilize the thumb with reference to the index finger. This was accomplished by placing a graft from the bone bank between the first and second metacarpals. The roentgenogram shows the complete healing of the graft one year later." --output out.jpg