File size: 2,505 Bytes
e4d55ce f3b3a2f e4d55ce e6af97e e4d55ce 9b2c38b e4d55ce f806422 e4d55ce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
from typing import Dict, List, Any
import torch
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DDIMScheduler
# set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type != 'cuda':
raise ValueError("need to run on GPU")
model_id = "stabilityai/stable-diffusion-2-1-base"
class EndpointHandler():
def __init__(self, path=""):
# load the optimized model
self.textPipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
self.textPipe.scheduler = DDIMScheduler.from_config(self.textPipe.scheduler.config)
self.textPipe = self.textPipe.to(device)
# create an img2img model
self.imgPipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
self.imgPipe.scheduler = DDIMScheduler.from_config(self.imgPipe.scheduler.config)
self.imgPipe = self.imgPipe.to(device)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
raise NotImplementedError()
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`dict`:. base64 encoded image
"""
image_data = data.pop("inputs", data).decode('base64')
init_image = Image.open(image_data).convert("RGB")
init_image.thumbnail((512, 512))
params = data.pop("parameters", data)
# hyperparamters
num_inference_steps = params.pop("num_inference_steps", 25)
guidance_scale = params.pop("guidance_scale", 7.5)
negative_prompt = params.pop("negative_prompt", None)
prompt = params.pop("prompt", None)
height = params.pop("height", None)
width = params.pop("width", None)
manual_seed = params.pop("manual_seed", -1)
out = None
generator = torch.Generator(device='cuda')
generator.manual_seed(manual_seed)
# run img2img pipeline
out = self.imgPipe(prompt,
image=init_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 generated PIL image
return out.images[0]
|