readme: ---
  license: openrail++
  tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-guided-to-image-inpainting
  - endpoints-template
  thumbnail: "https://huggingface.co/philschmid/stable-diffusion-2-inpainting-endpoint/resolve/main/Stable%20Diffusion%20Inference%20endpoints%20-%20inpainting.png"
  inference: true
  ---
  
  
  # Fork of [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting)
  
  > Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
  > For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion).
  
  For more information about the model, license and limitations check the original model card at [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting).
  
  ---
  
  This repository implements a custom `handler` task for `text-guided-to-image-inpainting` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [handler.py](https://huggingface.co/philschmid/stable-diffusion-2-inpainting-endpoint/blob/main/handler.py).
  
  There is also a [notebook](https://huggingface.co/philschmid/stable-diffusion-2-inpainting-endpoint/blob/main/create_handler.ipynb) included, on how to create the `handler.py`
  
  ![thubmnail](Stable%20Diffusion%20Inference%20endpoints%20-%20inpainting.png)
  
  
  ### expected Request payload
  
  ```json
  {
      "inputs": "A prompt used for image generation",
      "image" : "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAABGdBTUEAALGPC",
      "mask_image": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAABGdBTUEAALGPC",
  }
  ```
  
  below is an example on how to run a request using Python and `requests`.
  
  ## Run Request 
  ```python
  import json
  from typing import List
  import requests as r
  import base64
  from PIL import Image
  from io import BytesIO
  
  ENDPOINT_URL = ""
  HF_TOKEN = ""
  
  # helper image utils
  def encode_image(image_path):
    with open(image_path, "rb") as i:
      b64 = base64.b64encode(i.read())
    return b64.decode("utf-8")
  
  
  def predict(prompt, image, mask_image):
      image = encode_image(image)
      mask_image = encode_image(mask_image)
  
      # prepare sample payload
      request = {"inputs": prompt, "image": image, "mask_image": mask_image}
      # headers
      headers = {
          "Authorization": f"Bearer {HF_TOKEN}",
          "Content-Type": "application/json",
          "Accept": "image/png" # important to get an image back
      }
  
      response = r.post(ENDPOINT_URL, headers=headers, json=payload)
      img = Image.open(BytesIO(response.content))
      return img
  
  prediction = predict(
      prompt="Face of a bengal cat, high resolution, sitting on a park bench",
      image="dog.png",
      mask_image="mask_dog.png"
  )
  ```
  expected output
  
  ![sample](result.png)
  