import os import re import torch import numpy as np from modules import modelloader, paths, deepbooru_model, devices, images, shared re_special = re.compile(r'([\\()])') class DeepDanbooru: def __init__(self): self.model = None def load(self): if self.model is not None: return files = modelloader.load_models( model_path=os.path.join(paths.models_path, "torch_deepdanbooru"), model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt', ext_filter=[".pt"], download_name='model-resnet_custom_v3.pt', ) self.model = deepbooru_model.DeepDanbooruModel() self.model.load_state_dict(torch.load(files[0], map_location="cpu")) self.model.eval() self.model.to(devices.cpu, devices.dtype) def start(self): self.load() self.model.to(devices.device) def stop(self): if not shared.opts.interrogate_keep_models_in_memory: self.model.to(devices.cpu) devices.torch_gc() def tag(self, pil_image): self.start() res = self.tag_multi(pil_image) self.stop() return res def tag_multi(self, pil_image, force_disable_ranks=False): threshold = shared.opts.interrogate_deepbooru_score_threshold use_spaces = shared.opts.deepbooru_use_spaces use_escape = shared.opts.deepbooru_escape alpha_sort = shared.opts.deepbooru_sort_alpha include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512) a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255 with torch.no_grad(), devices.autocast(): x = torch.from_numpy(a).to(devices.device) y = self.model(x)[0].detach().cpu().numpy() probability_dict = {} for tag, probability in zip(self.model.tags, y): if probability < threshold: continue if tag.startswith("rating:"): continue probability_dict[tag] = probability if alpha_sort: tags = sorted(probability_dict) else: tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])] res = [] filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")} for tag in [x for x in tags if x not in filtertags]: probability = probability_dict[tag] tag_outformat = tag if use_spaces: tag_outformat = tag_outformat.replace('_', ' ') if use_escape: tag_outformat = re.sub(re_special, r'\\\1', tag_outformat) if include_ranks: tag_outformat = f"({tag_outformat}:{probability:.3f})" res.append(tag_outformat) return ", ".join(res) model = DeepDanbooru()