readme: ---
  license: creativeml-openrail-m
  tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - endpoints-template
  inference: true
  ---
  
  # Fork of [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)
  
  > 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 [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4).
  
  ### License (CreativeML OpenRAIL-M)
  
  The full license can be found here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
  
  ---
  
  This repository implements a custom `handler` task for `text-to-image` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/stable-diffusion-v1-4-endpoints/blob/main/handler.py).
  
  There is also a [notebook](https://huggingface.co/philschmid/stable-diffusion-v1-4-endpoints/blob/main/create_handler.ipynb) included, on how to create the `handler.py`
  
  ### expected Request payload
  ```json
  {
      "inputs": "A prompt used for image generation"
  }
  ```
  
  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 decoder
  def decode_base64_image(image_string):
    base64_image = base64.b64decode(image_string)
    buffer = BytesIO(base64_image)
    return  Image.open(buffer)
  
  
  def predict(prompt:str=None):
      payload = {"inputs": code_snippet,"parameters": parameters}
      response = r.post(
          ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json={"inputs": prompt}
      )
      resp = response.json()
      return decode_base64_image(resp["image"])
  
  prediction = predict(
      prompt="the first animal on the mars"
  )
  ```
  expected output
  
  ![sample](sample.jpg)
  