add handler, README

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README.md

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+---
+license: creativeml-openrail-m
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- endpoints-template
+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"
+)
+```

handler.py

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+import base64
+from io import BytesIO
+from typing import Dict, List, Any
+
+import torch
+from PIL import Image
+from diffusers import StableDiffusionPipeline
+
+
+REPO_ID = "runwayml/stable-diffusion-v1-5"
+
+
+# helper decoder
+def decode_base64_image(image_string):
+    base64_image = base64.b64decode(image_string)
+    buffer = BytesIO(base64_image)
+    return Image.open(buffer)
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
+        # self.pipe = self.pipe.to("cuda")
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`dict`:. base64 encoded image
+        """
+        prompts = data.pop("inputs", None)
+        encoded_image = data.pop("image", None)
+        init_image = None
+        if encoded_image:
+            init_image = decode_base64_image(encoded_image)
+            init_image.thumbnail((768, 768))
+        image = self.pipe(prompts, init_image=init_image).images[0]
+
+        # encode image as base 64
+        buffered = BytesIO()
+        image.save(buffered, format="png")
+
+        # post process the prediction
+        return {"image": buffered.getvalue()}