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import json
import gradio as gr
from PIL import Image
import safetensors.torch
import spaces
import timm
from timm.models import VisionTransformer
import torch
from torchvision.transforms import transforms
from torchvision.transforms import InterpolationMode
import torchvision.transforms.functional as TF
torch.set_grad_enabled(False)
class Fit(torch.nn.Module):
def __init__(
self,
bounds: tuple[int, int] | int,
interpolation = InterpolationMode.LANCZOS,
grow: bool = True,
pad: float | None = None
):
super().__init__()
self.bounds = (bounds, bounds) if isinstance(bounds, int) else bounds
self.interpolation = interpolation
self.grow = grow
self.pad = pad
def forward(self, img: Image) -> Image:
wimg, himg = img.size
hbound, wbound = self.bounds
hscale = hbound / himg
wscale = wbound / wimg
if not self.grow:
hscale = min(hscale, 1.0)
wscale = min(wscale, 1.0)
scale = min(hscale, wscale)
if scale == 1.0:
return img
hnew = min(round(himg * scale), hbound)
wnew = min(round(wimg * scale), wbound)
img = TF.resize(img, (hnew, wnew), self.interpolation)
if self.pad is None:
return img
hpad = hbound - hnew
wpad = wbound - wnew
tpad = hpad // 2
bpad = hpad - tpad
lpad = wpad // 2
rpad = wpad - lpad
return TF.pad(img, (lpad, tpad, rpad, bpad), self.pad)
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}(" +
f"bounds={self.bounds}, " +
f"interpolation={self.interpolation.value}, " +
f"grow={self.grow}, " +
f"pad={self.pad})"
)
class CompositeAlpha(torch.nn.Module):
def __init__(
self,
background: tuple[float, float, float] | float,
):
super().__init__()
self.background = (background, background, background) if isinstance(background, float) else background
self.background = torch.tensor(self.background).unsqueeze(1).unsqueeze(2)
def forward(self, img: torch.Tensor) -> torch.Tensor:
if img.shape[-3] == 3:
return img
alpha = img[..., 3, None, :, :]
img[..., :3, :, :] *= alpha
background = self.background.expand(-1, img.shape[-2], img.shape[-1])
if background.ndim == 1:
background = background[:, None, None]
elif background.ndim == 2:
background = background[None, :, :]
img[..., :3, :, :] += (1.0 - alpha) * background
return img[..., :3, :, :]
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}(" +
f"background={self.background})"
)
transform = transforms.Compose([
Fit((384, 384)),
transforms.ToTensor(),
CompositeAlpha(0.5),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
transforms.CenterCrop((384, 384)),
])
model = timm.create_model(
"vit_so400m_patch14_siglip_384.webli",
pretrained=False,
num_classes=9083,
) # type: VisionTransformer
safetensors.torch.load_model(model, "JTP_PILOT-e4-vit_so400m_patch14_siglip_384.safetensors")
model.eval()
with open("tagger_tags.json", "r") as file:
tags = json.load(file) # type: dict
allowed_tags = list(tags.keys())
for idx, tag in enumerate(allowed_tags):
allowed_tags[idx] = tag.replace("_", " ")
sorted_tag_score = {}
@spaces.GPU(duration=5)
def run_classifier(image, threshold):
global sorted_tag_score
img = image.convert('RGB')
tensor = transform(img).unsqueeze(0)
with torch.no_grad():
logits = model(tensor)
probabilities = torch.nn.functional.sigmoid(logits[0])
indices = torch.topk(probabilities, 250).indices
values = probabilities[indices]
tag_score = dict()
for i in range(indices.size(0)):
tag_score[allowed_tags[indices[i]]] = values[i].item()
sorted_tag_score = dict(sorted(tag_score.items(), key=lambda item: item[1], reverse=True))
return create_tags(threshold)
def create_tags(threshold):
global sorted_tag_score
filtered_tag_score = {key: value for key, value in sorted_tag_score.items() if value > threshold}
text_no_impl = ", ".join(filtered_tag_score.keys())
return text_no_impl, filtered_tag_score
with gr.Blocks(css=".output-class { display: none; }") as demo:
gr.Markdown("""
## Joint Tagger Project: PILOT Demo
This tagger is designed for use on furry images (though may very well work on out-of-distribution images, potentially with funny results). A threshold of 0.2 is recommended. Lower thresholds often turn up more valid tags, but can also result in some amount of hallucinated tags.
This tagger is the result of joint efforts between members of the RedRocket team. Special thanks to Minotoro at frosting.ai for providing the compute power for this project.
""")
with gr.Row():
with gr.Column():
image_input = gr.Image(label="Source", sources=['upload'], type='pil', height="60vh", show_label=False)
threshold_slider = gr.Slider(minimum=0.00, maximum=1.00, step=0.01, value=0.20, label="Threshold")
with gr.Column():
tag_string = gr.Textbox(label="Tag String")
label_box = gr.Label(label="Tag Predictions", num_top_classes=250, show_label=False)
image_input.upload(
fn=run_classifier,
inputs=[image_input, threshold_slider],
outputs=[tag_string, label_box]
)
threshold_slider.input(
fn=create_tags,
inputs=[threshold_slider],
outputs=[tag_string, label_box]
)
if __name__ == "__main__":
demo.launch() |