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--- |
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license: creativeml-openrail-m |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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- endpoints-template |
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inference: true |
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--- |
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# Fork of [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) |
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> Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. |
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> 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). |
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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). |
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### License (CreativeML OpenRAIL-M) |
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The full license can be found here: https://huggingface.co/spaces/CompVis/stable-diffusion-license |
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--- |
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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). |
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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` |
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### expected Request payload |
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```json |
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{ |
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"inputs": "A prompt used for image generation" |
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} |
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``` |
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below is an example on how to run a request using Python and `requests`. |
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## Run Request |
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```python |
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import json |
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from typing import List |
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import requests as r |
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import base64 |
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from PIL import Image |
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from io import BytesIO |
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ENDPOINT_URL = "" |
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HF_TOKEN = "" |
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# helper decoder |
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def decode_base64_image(image_string): |
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base64_image = base64.b64decode(image_string) |
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buffer = BytesIO(base64_image) |
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return Image.open(buffer) |
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def predict(prompt:str=None): |
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payload = {"inputs": code_snippet,"parameters": parameters} |
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response = r.post( |
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ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json={"inputs": prompt} |
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) |
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resp = response.json() |
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return decode_base64_image(resp["image"]) |
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prediction = predict( |
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prompt="the first animal on the mars" |
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) |
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``` |
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expected output |
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