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) probits = torch.nn.functional.sigmoid(logits[0]) values, indices = probits.topk(250) 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=512, 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()