Update README

README.md

@@ -1,3 +1,52 @@
 ---
-license: openrail
+license: creativeml-openrail-m
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- endpoints-template
+inference: false
+duplicated_from: philschmid/stable-diffusion-v1-4-endpoints
 ---
+# 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"
+)
+```