Spaces:
Running
Running
File size: 4,906 Bytes
f357c72 ba9e1f1 9432c1a f357c72 ba9e1f1 f357c72 23c73d4 f357c72 23c73d4 f357c72 ba9e1f1 f357c72 ba9e1f1 f357c72 d34aeeb f357c72 d34aeeb |
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 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
from PIL import Image
import torch
from transformers import (
AutoImageProcessor,
AutoModelForImageClassification,
)
import gradio as gr
import spaces # ZERO GPU
MODEL_NAMES = ["p1atdev/wd-swinv2-tagger-v3-hf"]
MODEL_NAME = MODEL_NAMES[0]
model = AutoModelForImageClassification.from_pretrained(
MODEL_NAME,
)
model.to("cuda" if torch.cuda.is_available() else "cpu")
processor = AutoImageProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)
def _people_tag(noun: str, minimum: int = 1, maximum: int = 5):
return (
[f"1{noun}"]
+ [f"{num}{noun}s" for num in range(minimum + 1, maximum + 1)]
+ [f"{maximum+1}+{noun}s"]
)
PEOPLE_TAGS = (
_people_tag("girl") + _people_tag("boy") + _people_tag("other") + ["no humans"]
)
RATING_MAP = {
"general": "safe",
"sensitive": "sensitive",
"questionable": "nsfw",
"explicit": "explicit, nsfw",
}
DESCRIPTION_MD = """
# WD Tagger with 🤗 transformers
Currently supports the following model(s):
- [p1atdev/wd-swinv2-tagger-v3-hf](https://huggingface.co/p1atdev/wd-swinv2-tagger-v3-hf)
""".strip()
def postprocess_results(
results: dict[str, float], general_threshold: float, character_threshold: float
):
results = {
k: v for k, v in sorted(results.items(), key=lambda item: item[1], reverse=True)
}
rating = {}
character = {}
general = {}
for k, v in results.items():
if k.startswith("rating:"):
rating[k.replace("rating:", "")] = v
continue
elif k.startswith("character:"):
character[k.replace("character:", "")] = v
continue
general[k] = v
character = {k: v for k, v in character.items() if v >= character_threshold}
general = {k: v for k, v in general.items() if v >= general_threshold}
return rating, character, general
def animagine_prompt(rating: list[str], character: list[str], general: list[str]):
people_tags: list[str] = []
other_tags: list[str] = []
rating_tag = RATING_MAP[rating[0]]
for tag in general:
if tag in PEOPLE_TAGS:
people_tags.append(tag)
else:
other_tags.append(tag)
all_tags = people_tags + character + other_tags + [rating_tag]
return ", ".join(all_tags)
@spaces.GPU(enable_queue=True)
def predict_tags(
image: Image.Image, general_threshold: float = 0.3, character_threshold: float = 0.8
):
inputs = processor.preprocess(image, return_tensors="pt")
outputs = model(**inputs.to(model.device, model.dtype))
logits = torch.sigmoid(outputs.logits[0]) # take the first logits
# get probabilities
results = {
model.config.id2label[i]: float(logit.float()) for i, logit in enumerate(logits)
}
# rating, character, general
rating, character, general = postprocess_results(
results, general_threshold, character_threshold
)
prompt = animagine_prompt(
list(rating.keys()), list(character.keys()), list(general.keys())
)
return rating, character, general, prompt
def demo():
with gr.Blocks() as ui:
gr.Markdown(DESCRIPTION_MD)
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input image", type="pil")
with gr.Group():
general_threshold = gr.Slider(
label="Threshold",
minimum=0.0,
maximum=1.0,
value=0.3,
step=0.01,
interactive=True,
)
character_threshold = gr.Slider(
label="Character threshold",
minimum=0.0,
maximum=1.0,
value=0.8,
step=0.01,
interactive=True,
)
_model_radio = gr.Dropdown(
choices=MODEL_NAMES,
label="Model",
value=MODEL_NAMES[0],
interactive=True,
)
start_btn = gr.Button(value="Start", variant="primary")
with gr.Column():
prompt_text = gr.Text(label="Prompt")
rating_tags_label = gr.Label(label="Rating tags")
character_tags_label = gr.Label(label="Character tags")
general_tags_label = gr.Label(label="General tags")
start_btn.click(
predict_tags,
inputs=[input_image, general_threshold, character_threshold],
outputs=[
rating_tags_label,
character_tags_label,
general_tags_label,
prompt_text,
],
)
return ui
if __name__ == "__main__":
demo().queue().launch()
|