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-stable-diffusion-v1-5/ filter=lfs diff=lfs merge=lfs -text
-stable-diffusion-v1-5/README.md filter=lfs diff=lfs merge=lfs -text
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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("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16,
-            revision="fp16", use_auth_token="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")
-        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()}

stable-diffusion-v1-5

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-Subproject commit 63534535d4730d5976c5c647a7f2adaea1102f5b

stable-diffusion-v1-5/v1-5-pruned.ckpt

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