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# https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags
# https://github.com/pythongosssss/ComfyUI-WD14-Tagger/blob/main/wd14tagger.py
# {
# "wd-v1-4-moat-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2",
# "wd-v1-4-convnextv2-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnextv2-tagger-v2",
# "wd-v1-4-convnext-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2",
# "wd-v1-4-convnext-tagger": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger",
# "wd-v1-4-vit-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger-v2"
# }
import numpy as np
import csv
import onnxruntime as ort
from PIL import Image
from onnxruntime import InferenceSession
from modules.config import path_clip_vision
from modules.model_loader import load_file_from_url
global_model = None
global_csv = None
def default_interrogator(image_rgb, threshold=0.35, character_threshold=0.85, exclude_tags=""):
global global_model, global_csv
model_name = "wd-v1-4-moat-tagger-v2"
model_onnx_filename = load_file_from_url(
url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.onnx',
model_dir=path_clip_vision,
file_name=f'{model_name}.onnx',
)
model_csv_filename = load_file_from_url(
url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.csv',
model_dir=path_clip_vision,
file_name=f'{model_name}.csv',
)
if global_model is not None:
model = global_model
else:
model = InferenceSession(model_onnx_filename, providers=ort.get_available_providers())
global_model = model
input = model.get_inputs()[0]
height = input.shape[1]
image = Image.fromarray(image_rgb) # RGB
ratio = float(height)/max(image.size)
new_size = tuple([int(x*ratio) for x in image.size])
image = image.resize(new_size, Image.LANCZOS)
square = Image.new("RGB", (height, height), (255, 255, 255))
square.paste(image, ((height-new_size[0])//2, (height-new_size[1])//2))
image = np.array(square).astype(np.float32)
image = image[:, :, ::-1] # RGB -> BGR
image = np.expand_dims(image, 0)
if global_csv is not None:
csv_lines = global_csv
else:
csv_lines = []
with open(model_csv_filename) as f:
reader = csv.reader(f)
next(reader)
for row in reader:
csv_lines.append(row)
global_csv = csv_lines
tags = []
general_index = None
character_index = None
for line_num, row in enumerate(csv_lines):
if general_index is None and row[2] == "0":
general_index = line_num
elif character_index is None and row[2] == "4":
character_index = line_num
tags.append(row[1])
label_name = model.get_outputs()[0].name
probs = model.run([label_name], {input.name: image})[0]
result = list(zip(tags, probs[0]))
general = [item for item in result[general_index:character_index] if item[1] > threshold]
character = [item for item in result[character_index:] if item[1] > character_threshold]
all = character + general
remove = [s.strip() for s in exclude_tags.lower().split(",")]
all = [tag for tag in all if tag[0] not in remove]
res = ", ".join((item[0].replace("(", "\\(").replace(")", "\\)") for item in all)).replace('_', ' ')
return res
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