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from typing import Dict, List, Any
from PIL import Image
from io import BytesIO
import torch
import base64
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class EndpointHandler():
def __init__(self, path=""):
model_id = "timbrooks/instruct-pix2pix"
self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, safety_checker=None)
self.pipe.to(device)
self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj:`string`)
parameters (:obj:)
Return:
A :obj:`string`:. image string
"""
image_data = data.pop('inputs', data)
# decode base64 image to PIL
image = Image.open(BytesIO(base64.b64decode(image_data)))
parameters = data.pop('parameters', [])
prompt = parameters.pop('prompt', None)
negative_prompt = parameters.pop('negative_prompt', None)
num_inference_steps = parameters.pop('num_inference_steps', 10)
image_guidance_scale = parameters.pop('image_guidance_scale', 1.5)
guidance_scale = parameters.pop('guidance_scale', 7.5)
images = self.pipe(
prompt,
image = image,
negative_prompt = negative_prompt,
num_inference_steps = num_inference_steps,
image_guidance_scale = image_guidance_scale,
guidance_scale = guidance_scale
).images
return images[0] |