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