50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
from multiprocessing import context
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from httplib2 import Response
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import torch
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import uuid
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import os
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from diffusers import StableDiffusionPipeline
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from dotenv import load_dotenv
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from os import getenv
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from fastapi import FastAPI, Response
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from pydantic import BaseModel
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import io
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load_dotenv()
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pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=getenv("TOKEN"))
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pipe.to("cuda")
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class Text(BaseModel):
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text: str
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app = FastAPI()
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@app.get("/",
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responses = {
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200: {
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"content": {"image/png": {}}
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}
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},
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response_class=Response
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)
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async def root(text: Text):
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# get your token at https://huggingface.co/settings/tokens
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prompt = text.text
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print(prompt)
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image = pipe(prompt).images[0]
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# print(image)
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# image = Image.new('RGB', (1000, 1000), (100,200,10))
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imgByteArr = io.BytesIO()
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# image.save expects a file as a argument, passing a bytes io ins
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image.save(imgByteArr, format="PNG")
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# Turn the BytesIO object back into a bytes object
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imgByteArr = imgByteArr.getvalue()
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# media_type here sets the media type of the actual response sent to the client.
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return Response(content=imgByteArr, media_type="image/png")
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