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supernode.py
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supernode.py
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import hashlib
import json
import os
import uuid
from enum import Enum
import folder_paths
import node_helpers
import numpy as np
import torch
from PIL import Image, ImageOps, ImageSequence
from bizyair.common.env_var import BIZYAIR_SERVER_ADDRESS
from bizyair.image_utils import (
decode_base64_to_np,
decode_data,
encode_data,
encode_image_to_base64,
)
from nodes import LoadImage
from .utils import (
decode_and_deserialize,
get_api_key,
send_post_request,
serialize_and_encode,
)
class RemoveBackground:
API_URL = f"{BIZYAIR_SERVER_ADDRESS}/supernode/removebg"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
}
}
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "remove_background"
CATEGORY = "☁️BizyAir"
def remove_background(self, image):
API_KEY = get_api_key()
device = image.device
_, h, w, _ = image.shape
assert (
w <= 1536 and h <= 1536
), f"width and height must be less than 1536, but got {w} and {h}"
payload = {
"is_compress": True,
"image": None,
}
auth = f"Bearer {API_KEY}"
headers = {
"accept": "application/json",
"content-type": "application/json",
"authorization": auth,
}
input_image, compress = serialize_and_encode(image, compress=True)
payload["image"] = input_image
payload["is_compress"] = compress
response: str = send_post_request(
self.API_URL, payload=payload, headers=headers
)
tensors = decode_and_deserialize(response)
t_images = tensors["images"].to(device)
t_mask = tensors["mask"].to(device)
return (t_images, t_mask)
class GenerateLightningImage:
API_URL = f"{BIZYAIR_SERVER_ADDRESS}/supernode/realvis4lightning"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"prompt": (
"STRING",
{"multiline": True, "dynamicPrompts": True, "default": "a dog"},
),
"seed": ("INT", {"default": 1, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
"width": ("INT", {"default": 1024, "min": 16, "max": 1024, "step": 8}),
"height": ("INT", {"default": 1024, "min": 16, "max": 1024, "step": 8}),
"cfg": (
"FLOAT",
{
"default": 1.5,
"min": 0.0,
"max": 10.0,
"step": 0.1,
"round": 0.01,
},
),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "generate_image"
CATEGORY = "☁️BizyAir"
def generate_image(self, prompt, seed, width, height, cfg, batch_size):
API_KEY = get_api_key()
assert (
width <= 1024 and height <= 1024
), f"width and height must be less than 1024, but got {width} and {height}"
payload = {
"batch_size": batch_size,
"width": width,
"height": height,
"prompt": prompt,
"cfg": cfg,
"seed": seed,
}
auth = f"Bearer {API_KEY}"
headers = {
"accept": "application/json",
"content-type": "application/json",
"authorization": auth,
}
response: str = send_post_request(
self.API_URL, payload=payload, headers=headers
)
tensors_np = decode_and_deserialize(response)
tensors = torch.from_numpy(tensors_np)
return (tensors,)
NODE_CLASS_MAPPINGS = {
"BizyAirRemoveBackground": RemoveBackground,
"BizyAirGenerateLightningImage": GenerateLightningImage,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"BizyAirRemoveBackground": "☁️BizyAir Remove Image Background",
"BizyAirGenerateLightningImage": "☁️BizyAir Generate Photorealistic Images",
}