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from typing import Dict, List, Any
import base64
from PIL import Image
from io import BytesIO
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from diffusers import StableDiffusionPipeline
import torch
# # set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type != 'cuda':
raise ValueError("need to run on GPU")
# set mixed precision dtype
dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
class EndpointHandler():
def __init__(self, path=""):
self.stable_diffusion_id = "Lykon/dreamshaper-8"
self.pipe = StableDiffusionPipeline.from_pretrained(self.stable_diffusion_id,torch_dtype=dtype,safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=dtype)).to(device.type)
self.generator = torch.Generator(device=device.type).manual_seed(3)
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.
# """
prompt = data.pop("inputs", None)
num_inference_steps = data.pop("num_inference_steps", 30)
guidance_scale = data.pop("guidance_scale", 7.4)
negative_prompt = data.pop("negative_prompt", None)
height = data.pop("height", None)
width = data.pop("width", None)
# run inference pipeline
out = self.pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=1,
height=height,
width=width,
generator=self.generator
)
# return first generate PIL image
return out.images[0] |