app.py

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

class GatedHead(torch.nn.Module):
    def __init__(self,
        num_features: int,
        num_classes: int
    ):
        super().__init__()
        self.num_classes = num_classes
        self.linear = torch.nn.Linear(num_features, num_classes * 2)

        self.act = torch.nn.Sigmoid()
        self.gate = torch.nn.Sigmoid()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.linear(x)
        x = self.act(x[:, :self.num_classes]) * self.gate(x[:, self.num_classes:])
        return x

model.head = GatedHead(min(model.head.weight.shape), 9083)

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("_", " ")

@spaces.GPU(duration=5)
def create_tags(image, threshold):
    img = image.convert('RGB')
    tensor = transform(img).unsqueeze(0)

    with torch.no_grad():
        probits = model(tensor)
        indices = torch.where(probits > threshold)[0]
        values = probits[indices]

    temp = []
    tag_score = dict()
    for i in range(indices.size(0)):
        temp.append([allowed_tags[indices[i]], values[i].item()])
        tag_score[allowed_tags[indices[i]]] = values[i].item()
    temp = [t[0] for t in temp]
    text_no_impl = ", ".join(temp)
    return text_no_impl, tag_score

with gr.Blocks() as demo:
    gr.Markdown("""
    ## Joint Tagger Project: JTP-PILOT² Demo **BETA**
    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, with distinctions given to Thessalo for creating the foundation for this project with his efforts, RedHotTensors for redesigning the process into a second-order method that models information expectation, and drhead for dataset prep, creation of training code and supervision of training runs.

    Special thanks to Minotoro at frosting.ai for providing the compute power for this project.
    """)
    gr.Interface(
        create_tags,
        inputs=[gr.Image(label="Source", sources=['upload', 'webcam'], type='pil'), gr.Slider(minimum=0.00, maximum=1.00, step=0.01, value=0.20, label="Threshold")],
        outputs=[
            gr.Textbox(label="Tag String"),
            gr.Label(label="Tag Predictions", num_top_classes=200),
        ],
        allow_flagging="never",
    )

if __name__ == "__main__":
    demo.launch()