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from typing import Dict, List, Any
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline
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 StableDiffusionInpaintPipeline pipeline
self.pipe = StableDiffusionInpaintPipeline.from_pretrained(path, torch_dtype=torch.float16)
# use DPMSolverMultistepScheduler
self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
# move to device
self.pipe = self.pipe.to(device)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
"""
:param data: A dictionary contains `inputs` and optional `image` field.
:return: A dictionary with `image` field contains image in base64.
"""
inputs = data.pop("inputs", data)
encoded_image = data.pop("image", None)
encoded_mask_image = data.pop("mask_image", None)
# hyperparamters
num_inference_steps = data.pop("num_inference_steps", 25)
guidance_scale = data.pop("guidance_scale", 7.5)
negative_prompt = data.pop("negative_prompt", None)
height = data.pop("height", None)
width = data.pop("width", None)
# process image
if encoded_image is not None and encoded_mask_image is not None:
image = self.decode_base64_image(encoded_image)
mask_image = self.decode_base64_image(encoded_mask_image)
else:
image = None
mask_image = None
# run inference pipeline
out = self.pipe(inputs,
image=image,
mask_image=mask_image,
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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