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import torch
import json
import base64
import io
from PIL import Image
from diffusers import DPMSolverMultistepScheduler, StableDiffusionXLInpaintPipeline
# 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="mrcuddle/URPM-Inpaint-SDXL"):
"""Load the SDXL Inpainting model."""
self.pipeline = StableDiffusionXLInpaintPipeline.from_pretrained(
path, torch_dtype=torch.float16
)
self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(self.pipeline.scheduler.config)
self.pipeline = self.pipeline.to(device)
def __call__(self, data: dict):
"""Custom call function for Hugging Face Inference Endpoints."""
try:
inputs = data.pop("inputs", data)
encoded_image = data.pop("image", None)
encoded_mask_image = data.pop("mask_image", None)
num_inference_steps = data.pop("num_inference_steps", 25)
guidance_scale = data.pop("guidance_scale", 7.5)
negative_prompt = data.pop("negative_prompt", None)
height = data.pop("height", None)
width = data.pop("width", None)
# Process images
if encoded_image and encoded_mask_image:
image = self.decode_base64_image(encoded_image)
mask_image = self.decode_base64_image(encoded_mask_image)
else:
raise ValueError("Both image and mask_image are required")
# Run inference
output_image = self.pipeline(
prompt=inputs,
image=image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=1,
negative_prompt=negative_prompt,
height=height,
width=width
).images[0]
return json.dumps({"output": self.encode_base64_image(output_image)})
except Exception as e:
return json.dumps({"error": str(e)})
def decode_base64_image(self, image_string):
"""Decode base64 encoded image."""
base64_image = base64.b64decode(image_string)
buffer = io.BytesIO(base64_image)
return Image.open(buffer).convert("RGB")
def encode_base64_image(self, image):
"""Encode PIL image to base64."""
buffered = io.BytesIO()
image.save(buffered, format="PNG")
return base64.b64encode(buffered.getvalue()).decode("utf-8")
# Create an instance of EndpointHandler
handler = EndpointHandler()
def handle(data: dict):
return handler(data) |