real-esrgan

serving real-esrgan

Intro

This repo containerizes Real-ESRGAN into a serving container using fastapi.

The model license can be found here.

Setup

1. Clone repo if you haven't. Navigate to the real-esrgan folder.

2. Build container. Don't forget to change the project_id to yours.

   docker build . -t gcr.io/{project_id}/real-esrgan:latest

3. Run container. You need NVIDIA docker and a GPU.

   docker run -p 80:8080 --gpus all -e AIP_HEALTH_ROUTE=/health -e AIP_HTTP_PORT=8080 -e AIP_PREDICT_ROUTE=/predict gcr.io/{project_id}/real-esrgan:latest -d

4. Make a prediction

   python generate_request.py
   curl -X POST -d @request.json -H "Content-Type: application/json; charset=utf-8" localhost/predict > response.json

Deploy in Vertex AI.

You'll need to enable Vertex AI and have authenticated with a service account that has the Vertex AI admin or editor role.

1. Push the image

   gcloud auth configure-docker
   docker build . -t gcr.io/{project_id}/real-esrgan:latest
   docker push gcr.io/{project_id}/real-esrgan:latest

2. Deploy in Vertex AI Endpoints.

   python ../gcp_deploy.py --image-uri gcr.io/<project_id>/real-esrgan:latest --model-name real-esrgan --endpoint-name real-esrgan-endpoint --endpoint-deployed-name real-esrgan-deployed-name

3. Test the endpoint.

   from google.cloud import aiplatform
   
   from google.protobuf import json_format
   from google.protobuf.struct_pb2 import Value
   
   from PIL import Image
   
   # Format is projects/<project_id>/locations/us-central1/endpoints/<endpoint_id>
   ENDPOINT_NAME = ''
   
   def im_2_b64(image, format='PNG'):
       buff = BytesIO()
       image.save(buff, format=format)
       img_str = base64.b64encode(buff.getvalue())
       return img_str
   
   image = Image.open("image.png")
   
   base64_image = im_2_b64(image).decode('utf-8')
   
   instances_list = [{'image' : base64_image}]
   instances = [json_format.ParseDict(s, Value()) for s in instances_list]
   
   parameters = {
       'face_enhance' : True,
       'tile' : 0, 
       'tile_pad' : 10, 
       'prepad' : 0,
       'fp32' : true,
       'outscale' : 4
   }
   parameters = json_format.ParseDict(parameters,Value())
   endpoint = aiplatform.Endpoint(ENDPOINT_NAME)
   results = endpoint.predict(instances=instances,parameters=parameters)
       ```