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from typing import Dict, List, Any
from PIL import Image
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
import base64
from io import BytesIO
# set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type != 'cuda':
raise ValueError("need to run on GPU")
class EndpointHandler():
def __init__(self, path=""):
# load the optimized model
self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
self.pipe = self.pipe.to(device)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Args:
images (:obj:`string`)
Return:
A :obj:`dict`:. base64 encoded image
"""
inputs = data.pop("inputs", data)
print("Printing inputs")
print(inputs)
print("")
print("Printing image")
print(inputs['image'])
print("")
# decode base64 image to PIL
#image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
#print("Printing loaded image into library")
#print(image)
#print("")
# run inference pipeline
#upscaled_image = self.pipe(prompt="", image = image).images[0]
# encode image as base 64
#buffered = BytesIO()
#upscaled_image.save(buffered, format="JPEG")
#img_str = base64.b64encode(buffered.getvalue())
# postprocess the prediction
return {"image": inputs['image']}
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