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from typing import Dict, List, Any
import torch
import os
import PIL
from PIL import Image
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,low_cpu_mem_usage=False)
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
"""
postive_prompt = data.pop("postive_prompt", data)
negative_prompt = data.pop("negative_prompt", None)
height = data.pop("height", 512)
width = data.pop("width", 512)
guidance_scale = data.pop("guidance_scale", 7.5)
# run inference pipeline
with autocast(device.type):
if negative_prompt is None:
image = self.pipe(inputs,prompt = postive_prompt ,height = height ,width = width ,guidance_scale=float(guidance_scale))["sample"][0]
else:
image = self.pipe(inputs,prompt = postive_prompt ,negative_prompt = negative_prompt,height = height ,width = width ,guidance_scale=float(guidance_scale))["sample"][0]
# encode image as base 64
buffered = BytesIO()
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
# postprocess the prediction
return {"image": img_str.decode()} |