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---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- endpoints-template
inference: false
---
# 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
|