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from typing import Dict, List, Any
import torch
import random
from torch import autocast
from diffusers import DiffusionPipeline
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 = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V4.0")
self.pipe = self.pipe.to(device)
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
"""
inputs = data.pop("inputs", data)
random_generators= [torch.Generator().manual_seed(i) for i in range(inputs["num_images_per_prompt"])]
print(random_generators)
# run inference pipeline
with autocast(device.type):
images = self.pipe(prompt=inputs["prompt"], negative_prompt=inputs["negative_prompt"], num_images_per_prompt=inputs["num_images_per_prompt"], guidance_scale=7.5, generator=random_generators).images
# encode image as base 64
buffered = BytesIO()
base64_images = []
for image in images:
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
base64_images.append(img_str.decode())
# postprocess the prediction
return {"images": base64_images} |