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from typing import Dict, List, Any
import torch
from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
from PIL import Image
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.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
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)
encoded_image = data.pop("image", None)
params = data.pop("parameters", data)
# hyperparamters
num_inference_steps = params.pop("num_inference_steps", 20)
guidance_scale = params.pop("guidance_scale", 7.5)
negative_prompt = params.pop("negative_prompt", None)
height = params.pop("height", None)
width = params.pop("width", None)
manual_seed = params.pop("manual_seed", -1)
generator = torch.Generator(device).manual_seed(manual_seed)
if encoded_image is not None:
image = self.decode_base64_image(encoded_image)
# run inference pipeline
out = self.pipe(inputs,
image=image,
generator=generator,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=1,
negative_prompt=negative_prompt,
height=height,
width=width
)
# return first generate PIL image
return out.images[0]
# helper to decode input image
def decode_base64_image(self, image_string):
base64_image = base64.b64decode(image_string)
buffer = BytesIO(base64_image)
image = Image.open(buffer)
return image
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