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from typing import Dict, List, Any
import torch
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DDIMScheduler
from transformers.utils import logging
import base64
import requests
from io import BytesIO
from PIL import Image
logging.set_verbosity_info()
logger = logging.get_logger("transformers")
def load_image(image_url):
if image_url.startswith('data:'):
# Decode base64 data_uri
image_data = base64.b64decode(image_url.split(',')[1])
image = Image.open(BytesIO(image_data))
else:
# Load standard image url
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))
return image
# 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]]]:
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`dict`:. base64 encoded image
"""
prompt = data.pop("inputs", data)
url = data.pop("url", data)
init_image = load_image(url).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)
height = params.pop("height", None)
strength = params.pop("strength", 0.8)
width = params.pop("width", None)
manual_seed = params.pop("manual_seed", -1)
logger.info(f"strength: {strength}, manual_seed: {manual_seed}, inference_steps: {num_inference_steps}, guidance_scale: {guidance_scale}")
out = None
generator = torch.Generator(device='cuda')
generator.manual_seed(manual_seed)
# run img2img pipeline
out = self.imgPipe(prompt,
image=init_image,
strength=strength,
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]
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