File size: 3,466 Bytes
7235d74 074f00a 7235d74 66a2908 7235d74 66a2908 7235d74 66a2908 7235d74 66a2908 7235d74 074f00a 7235d74 |
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 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
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-Hyper-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:
# Extract inputs from JSON payload
inputs = data.get("inputs", "")
encoded_image = data.get("image", None)
encoded_mask_image = data.get("mask_image", None)
# Extract optional parameters with default values
num_inference_steps = data.get("num_inference_steps", 25)
guidance_scale = data.get("guidance_scale", 7.5)
negative_prompt = data.get("negative_prompt", None)
height = data.get("height", None)
width = data.get("width", None)
# Ensure both images are provided
if not encoded_image or not encoded_mask_image:
raise ValueError("Both 'image' and 'mask_image' are required in base64 format.")
# Decode base64 images
image = self.decode_base64_image(encoded_image)
mask_image = self.decode_base64_image(encoded_mask_image)
print("\n--- Running Inference ---")
print(f"Prompt: {inputs}")
print(f"Steps: {num_inference_steps}, Guidance Scale: {guidance_scale}")
print(f"Negative Prompt: {negative_prompt}")
print(f"Image Size: {image.size}, Mask Size: {mask_image.size}")
# 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 base64-encoded image
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 to a PIL Image."""
try:
base64_image = base64.b64decode(image_string)
buffer = io.BytesIO(base64_image)
return Image.open(buffer).convert("RGB")
except Exception as e:
raise ValueError(f"Failed to decode base64 image: {e}")
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) |