File size: 1,095 Bytes
3d0c874 8ccead5 3d0c874 5726ea9 3d0c874 5726ea9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | from typing import Dict, List, Any
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: Any) -> "PIL.Image":
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`dict`:. base64 encoded image
"""
inputs = data.pop("inputs", data)
# run inference pipeline
with autocast(device.type):
image = self.pipe(inputs, guidance_scale=7.5)["sample"][0]
# encoding image as base 64 is done by the default toolkit
return image
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