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from typing import Dict, List, Any
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline, AutoPipelineForInpainting, AutoPipelineForImage2Image
from PIL import Image
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 StableDiffusionInpaintPipeline pipeline
self.pipe = AutoPipelineForInpainting.from_pretrained(
"runwayml/stable-diffusion-inpainting",
revision="fp16",
torch_dtype=torch.float16,
)
# use DPMSolverMultistepScheduler
self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
# move to device
self.pipe = self.pipe.to(device)
self.pipe2 = AutoPipelineForInpainting.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
self.pipe2.to("cuda")
self.pipe3 = AutoPipelineForImage2Image.from_pipe(self.pipe2)
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.
"""
encoded_image = data.pop("image", None)
encoded_mask_image = data.pop("mask_image", None)
prompt = data.pop("prompt", "")
# process image
if encoded_image is not None and encoded_mask_image is not None:
image = self.decode_base64_image(encoded_image)
mask_image = self.decode_base64_image(encoded_mask_image)
else:
image = None
mask_image = None
self.pipe.enable_xformers_memory_efficient_attention()
# run inference pipeline
out = self.pipe(prompt=prompt, image=image, mask_image=mask_image)
image = out.images[0].resize((1024, 1024))
self.pipe2.enable_xformers_memory_efficient_attention()
image = self.pipe2(
prompt=prompt,
image=image,
mask_image=mask_image,
guidance_scale=8.0,
num_inference_steps=100,
strength=0.2,
output_type="latent", # let's keep in latent to save some VRAM
).images[0]
self.pipe3.enable_xformers_memory_efficient_attention()
image = self.pipe3(
prompt=prompt,
image=image,
guidance_scale=8.0,
num_inference_steps=100,
strength=0.2,
).images[0]
# return first generate PIL image
return image
# helper to decode input image
def decode_base64_image(self, image_string):
base64_image = base64.b64decode(image_string)
buffer = BytesIO(base64_image)
image = Image.open(buffer)
return image
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