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from os import getenv
from pathlib import Path
from typing import Optional
import gradio as gr
import numpy as np
import onnxruntime as rt
from PIL import Image
from tagger.common import LabelData, load_labels, preprocess_image
from tagger.model import create_session
HF_TOKEN = getenv("HF_TOKEN", None)
WORK_DIR = Path.cwd().resolve()
MODEL_VARIANTS: dict[str, str] = {
"MOAT": "SmilingWolf/wd-v1-4-moat-tagger-v2",
"SwinV2": "SmilingWolf/wd-v1-4-swinv2-tagger-v2",
"ConvNeXT": "SmilingWolf/wd-v1-4-convnext-tagger-v2",
"ConvNeXTv2": "SmilingWolf/wd-v1-4-convnextv2-tagger-v2",
"ViT": "SmilingWolf/wd-v1-4-vit-tagger-v2",
}
# allowed extensions
IMAGE_EXTENSIONS = [".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp", ".tiff", ".tif"]
# model input shape
IMAGE_SIZE = 448
example_images = sorted(
[
str(x.relative_to(WORK_DIR))
for x in WORK_DIR.joinpath("examples").iterdir()
if x.is_file() and x.suffix.lower() in IMAGE_EXTENSIONS
]
)
loaded_models: dict[str, Optional[rt.InferenceSession]] = {k: None for k, _ in MODEL_VARIANTS.items()}
def load_model(variant: str) -> rt.InferenceSession:
global loaded_models
# resolve the repo name
model_repo = MODEL_VARIANTS.get(variant, None)
if model_repo is None:
raise ValueError(f"Unknown model variant: {variant}")
if loaded_models.get(variant, None) is None:
# save model to cache
loaded_models[variant] = create_session(model_repo, token=HF_TOKEN)
return loaded_models[variant]
def predict(
image: Image.Image,
variant: str,
general_threshold: float = 0.35,
character_threshold: float = 0.85,
):
# Load model
model: rt.InferenceSession = load_model(variant)
# load labels
labels: LabelData = load_labels()
# get input size and name
_, h, w, _ = model.get_inputs()[0].shape
input_name = model.get_inputs()[0].name
output_name = model.get_outputs()[0].name
# preprocess image
image = preprocess_image(image, (h, w))
# turn into BGR24 numpy array of N,H,W,C since thats what these want
inputs = image.convert("RGB").convert("BGR;24")
inputs = np.array(inputs).astype(np.float32)
inputs = np.expand_dims(inputs, axis=0)
# Run the ONNX model
probs = model.run([output_name], {input_name: inputs})
# Convert indices+probs to labels
probs = list(zip(labels.names, probs[0][0].astype(float)))
# First 4 labels are actually ratings
rating_labels = dict([probs[i] for i in labels.rating])
# General labels, pick any where prediction confidence > threshold
gen_labels = [probs[i] for i in labels.general]
gen_labels = dict([x for x in gen_labels if x[1] > general_threshold])
gen_labels = dict(sorted(gen_labels.items(), key=lambda item: item[1], reverse=True))
# Character labels, pick any where prediction confidence > threshold
char_labels = [probs[i] for i in labels.character]
char_labels = dict([x for x in char_labels if x[1] > character_threshold])
char_labels = dict(sorted(char_labels.items(), key=lambda item: item[1], reverse=True))
# Combine general and character labels, sort by confidence
combined_names = [x for x in gen_labels]
combined_names.extend([x for x in char_labels])
# Convert to a string suitable for use as a training caption
caption = ", ".join(combined_names)
booru = caption.replace("_", " ").replace("(", "\(").replace(")", "\)")
return image, caption, booru, rating_labels, char_labels, gen_labels
with gr.Blocks(title="pi-chan's tagger") as demo:
with gr.Row(equal_height=False):
with gr.Column():
img_input = gr.Image(
label="Input",
type="pil",
image_mode="RGB",
sources=["upload", "clipboard"],
)
variant = gr.Radio(choices=list(MODEL_VARIANTS.keys()), label="Model Variant", value="MOAT")
gen_thresh = gr.Slider(0.0, 1.0, value=0.35, label="General Tag Threshold")
char_thresh = gr.Slider(0.0, 1.0, value=0.85, label="Character Tag Threshold")
show_processed = gr.Checkbox(label="Show Preprocessed", value=False)
with gr.Row():
submit = gr.Button(value="Submit", variant="primary", size="lg")
clear = gr.ClearButton(
components=[],
variant="secondary",
size="lg",
)
with gr.Row():
examples = gr.Examples(
examples=[
[imgpath, var, 0.35, 0.85]
for imgpath in example_images
for var in ["MOAT", "ConvNeXTv2"]
],
inputs=[img_input, variant, gen_thresh, char_thresh],
)
with gr.Column():
img_output = gr.Image(label="Preprocessed", type="pil", image_mode="RGB", scale=1, visible=False)
with gr.Group():
tags_string = gr.Textbox(
label="Caption", placeholder="Caption will appear here", show_copy_button=True
)
tags_booru = gr.Textbox(
label="Tags", placeholder="Tag string will appear here", show_copy_button=True
)
rating = gr.Label(label="Rating")
character = gr.Label(label="Character")
general = gr.Label(label="General")
# tell clear button which components to clear
clear.add([img_input, img_output, tags_string, rating, character, general])
# show/hide processed image
def on_select_show_processed(evt: gr.SelectData):
return gr.update(visible=evt.selected)
show_processed.select(on_select_show_processed, inputs=[], outputs=[img_output])
submit.click(
predict,
inputs=[img_input, variant, gen_thresh, char_thresh],
outputs=[img_output, tags_string, tags_booru, rating, character, general],
api_name="predict",
)
if __name__ == "__main__":
demo.queue(max_size=10)
demo.launch(server_name="0.0.0.0", server_port=7871)
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