Squashed commit of the following:
commit 106b43466a15f9eca6241eedf77fd4a414f5a148
Author: toshiaki1729 <116595002+toshiaki1729@users.noreply.github.com>
Date: Sun Dec 11 18:15:12 2022 +0900
add method to score manually
commit 1b1d94f7879c73fce259ee8ad3c7bd34023b19ac
Author: toshiaki1729 <116595002+toshiaki1729@users.noreply.github.com>
Date: Sun Dec 11 18:06:11 2022 +0900
save tag scores on loading
commit 2abade6c4925c0797bad21fdfaa6b9bb4a1f1486
Author: toshiaki1729 <116595002+toshiaki1729@users.noreply.github.com>
Date: Sun Dec 11 17:57:16 2022 +0900
fix wrong params, wrong clip
commit 4ca3da6a47d951979e70079eefcc032512813cc5
Author: toshiaki1729 <116595002+toshiaki1729@users.noreply.github.com>
Date: Sun Dec 11 17:46:17 2022 +0900
allow using wdv1.4 tagger when loading
#21
commit 3df922552e503612826300e44ff25da4df260b0a
Author: toshiaki1729 <116595002+toshiaki1729@users.noreply.github.com>
Date: Sun Dec 11 16:32:28 2022 +0900
implement wdv1.4 tagger (on the way)
wdv1.4 tagger now available only in selected image
commit e4062bb79f31543bbe675e2e119e346b26efcb0c
Author: toshiaki1729 <116595002+toshiaki1729@users.noreply.github.com>
Date: Sun Dec 11 16:07:18 2022 +0900
implement interrogator to test wdv1.4 tagger
commit 07067ea79569b5869e94e5fa42eadee30cfd33b4
Author: toshiaki1729 <116595002+toshiaki1729@users.noreply.github.com>
Date: Sun Dec 11 15:35:39 2022 +0900
just rearrange importing
commit ee3710237eb4beeb6332c660293a30b7913d8e0d
Author: toshiaki1729 <116595002+toshiaki1729@users.noreply.github.com>
Date: Sun Dec 11 15:33:09 2022 +0900
implement tag scorer (not tested)
pull/41/head
toshiaki1729
parent
7a81dffeb2
commit
f7d29328b5
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@ -0,0 +1,9 @@
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import launch
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from modules.shared import cmd_opts
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if 'all' in cmd_opts.use_cpu or 'interrogate' in cmd_opts.use_cpu:
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if not launch.is_installed("onnxruntime"):
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launch.run_pip("install onnxruntime", "requirements for using SmilingWolf/wd-v1-4-vit-tagger on CPU device")
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else:
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if not launch.is_installed("onnxruntime-gpu"):
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launch.run_pip("install onnxruntime-gpu", "requirements for using SmilingWolf/wd-v1-4-vit-tagger on GPU device")
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@ -8,6 +8,7 @@ from PIL import Image
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from enum import Enum
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import modules.deepbooru as deepbooru
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from scripts.dataset_tag_editor.dataset import Dataset, Data
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from . import tag_scorer
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re_tags = re.compile(r'^(.+) \[\d+\]$')
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@ -38,6 +39,17 @@ def interrogate_image_booru(path):
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return deepbooru.model.tag(img)
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def interrogate_image_waifu(path):
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try:
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img = Image.open(path).convert('RGB')
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except:
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return ''
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else:
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with tag_scorer.WaifuDiffusion() as scorer:
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res = scorer.predict(img, threshold=shared.opts.interrogate_deepbooru_score_threshold)
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return ', '.join(tag_scorer.get_arranged_tags(res))
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def get_filepath_set(dir: str, recursive: bool):
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if recursive:
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dirs_to_see = [dir]
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@ -65,6 +77,8 @@ class DatasetTagEditor:
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self.dataset = Dataset()
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self.tag_counts = {}
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self.dataset_dir = ''
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self.booru_tag_scores = None
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self.waifu_tag_scores = None
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def get_tag_list(self):
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if len(self.tag_counts) == 0:
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@ -342,7 +356,25 @@ class DatasetTagEditor:
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print(e)
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def load_dataset(self, img_dir: str, recursive: bool = False, load_caption_from_filename: bool = True, interrogate_method: InterrogateMethod = InterrogateMethod.NONE, use_booru: bool = True, use_clip: bool = False):
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def score_dataset_booru(self):
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with tag_scorer.DeepDanbooru() as scorer:
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self.booru_tag_scores = dict()
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for img_path in self.dataset.datas.keys():
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img = Image.open(img_path)
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probs = scorer.predict(img)
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self.booru_tag_scores[img_path] = probs
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def score_dataset_waifu(self):
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with tag_scorer.DeepDanbooru() as scorer:
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self.waifu_tag_scores = dict()
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for img_path in self.dataset.datas.keys():
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img = Image.open(img_path)
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probs = scorer.predict(img)
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self.waifu_tag_scores[img_path] = probs
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def load_dataset(self, img_dir: str, recursive: bool = False, load_caption_from_filename: bool = True, interrogate_method: InterrogateMethod = InterrogateMethod.NONE, use_booru: bool = True, use_clip: bool = False, use_waifu: bool = False):
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self.clear()
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print(f'Loading dataset from {img_dir}')
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if recursive:
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@ -359,7 +391,9 @@ class DatasetTagEditor:
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print(f'Total {len(filepath_set)} files under the directory including not image files.')
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def load_images(filepath_set: Set[str]):
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from . import tag_scorer
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def load_images(filepath_set: Set[str], scorers: List[tag_scorer.TagScorer]):
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for img_path in filepath_set:
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img_dir = os.path.dirname(img_path)
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img_filename, img_ext = os.path.splitext(os.path.basename(img_path))
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@ -387,46 +421,63 @@ class DatasetTagEditor:
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tokens = self.re_word.findall(caption_text)
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caption_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
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if interrogate_method != InterrogateMethod.NONE and ((interrogate_method != InterrogateMethod.PREFILL) or (interrogate_method == InterrogateMethod.PREFILL and not caption_text)):
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interrogate_tags = []
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caption_tags = [t.strip() for t in caption_text.split(',')]
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if interrogate_method != InterrogateMethod.NONE and ((interrogate_method != InterrogateMethod.PREFILL) or (interrogate_method == InterrogateMethod.PREFILL and not caption_tags)):
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try:
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img = Image.open(img_path).convert('RGB')
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except Exception as e:
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print(e)
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print(f'Cannot interrogate file: {img_path}')
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else:
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interrogate_text = ''
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if use_clip:
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interrogate_text += shared.interrogator.generate_caption(img)
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tmp = [t.strip() for t in shared.interrogator.generate_caption(img).split(',')]
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interrogate_tags += [t for t in tmp if t]
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if use_booru:
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tmp = deepbooru.model.tag_multi(img)
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interrogate_text += (', ' if interrogate_text and tmp else '') + tmp
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for scorer in scorers:
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probs = scorer.predict(img)
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interrogate_tags += [t for t, p in probs.items() if p > shared.opts.interrogate_deepbooru_score_threshold]
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if isinstance(scorer, tag_scorer.DeepDanbooru):
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if not self.booru_tag_scores:
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self.booru_tag_scores = dict()
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self.booru_tag_scores[img_path] = probs
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elif isinstance(scorer, tag_scorer.WaifuDiffusion):
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if not self.waifu_tag_scores:
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self.waifu_tag_scores = dict()
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self.waifu_tag_scores[img_path] = probs
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if interrogate_method == InterrogateMethod.OVERWRITE:
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caption_text = interrogate_text
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elif interrogate_method == InterrogateMethod.PREPEND:
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caption_text = interrogate_text + (', ' if interrogate_text and caption_text else '') + caption_text
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else:
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caption_text += (', ' if interrogate_text and caption_text else '') + interrogate_text
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img.close()
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self.set_tags_by_image_path(img_path, [t.strip() for t in caption_text.split(',')])
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if interrogate_method == InterrogateMethod.OVERWRITE:
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tags = interrogate_tags
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elif interrogate_method == InterrogateMethod.PREPEND:
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tags = interrogate_tags + caption_tags
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else:
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tags = caption_tags + interrogate_tags
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self.set_tags_by_image_path(img_path, tags)
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try:
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scorers = []
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if interrogate_method != InterrogateMethod.NONE:
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if use_clip:
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shared.interrogator.load()
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if use_booru:
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deepbooru.model.start()
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scorer = tag_scorer.DeepDanbooru()
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scorer.start()
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scorers.append(scorer)
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if use_waifu:
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scorer = tag_scorer.WaifuDiffusion()
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scorer.start()
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scorers.append(scorer)
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load_images(filepath_set = filepath_set)
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load_images(filepath_set = filepath_set, scorers=scorers)
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finally:
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if interrogate_method != InterrogateMethod.NONE:
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if use_clip:
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shared.interrogator.send_blip_to_ram()
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if use_booru:
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deepbooru.model.stop()
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for scorer in scorers:
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scorer.stop()
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self.construct_tag_counts()
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print(f'Loading Completed: {len(self.dataset)} images found')
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@ -481,6 +532,8 @@ class DatasetTagEditor:
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self.dataset.clear()
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self.tag_counts.clear()
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self.dataset_dir = ''
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self.booru_tag_scores = None
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self.waifu_tag_scores = None
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def construct_tag_counts(self):
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@ -1,5 +1,5 @@
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from scripts.dataset_tag_editor.dataset import Dataset
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from typing import Set, List
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from typing import Set, Dict
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from enum import Enum
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class TagFilter(Dataset.Filter):
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@ -92,4 +92,28 @@ class PathFilter(Dataset.Filter):
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return dataset
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class TagScoreFilter(Dataset.Filter):
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class Mode(Enum):
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NONE = 0
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LESS_THAN = 1
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GREATER_THAN = 2
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def __init__(self, scores: Dict[str, Dict[str, float]], tag: str, threshold: float, mode: Mode = Mode.NONE):
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self.scores = scores
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self.mode = mode
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self.tag = tag
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self.threshold = threshold
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def apply(self, dataset: Dataset):
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if self.mode == TagScoreFilter.Mode.NONE:
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return dataset
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paths_remove = {path for path, scores in self.scores.items() if (scores.get(self.tag) or 0) > self.threshold}
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if self.mode == TagScoreFilter.Mode.GREATER_THAN:
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paths_remove = {path for path in dataset.datas.keys()} - paths_remove
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for path in paths_remove:
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dataset.remove_by_path(path)
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return dataset
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@ -0,0 +1,101 @@
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from PIL import Image
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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 typing import Optional, Dict
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from modules import devices, shared
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from modules import deepbooru as db
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from . import waifu_diffusion_tagger
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class TagScorer:
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def __enter__(self):
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self.start()
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return self
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def __exit__(self, exception_type, exception_value, traceback):
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self.stop()
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pass
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def start(self):
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pass
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def stop(self):
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pass
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def predict(self,image: Image.Image, threshold: Optional[float]):
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raise NotImplementedError
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def name(self):
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raise NotImplementedError
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def get_replaced_tag(tag: str):
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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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if use_spaces:
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tag = tag.replace('_', ' ')
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if use_escape:
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tag = re.sub(db.re_special, r'\\\1', tag)
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return tag
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def get_arranged_tags(probs: Dict[str, float]):
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alpha_sort = shared.opts.deepbooru_sort_alpha
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if alpha_sort:
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return sorted(probs)
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else:
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return [tag for tag, _ in sorted(probs.items(), key=lambda x: -x[1])]
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class DeepDanbooru(TagScorer):
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def start(self):
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db.model.start()
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def stop(self):
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db.model.stop()
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# brought from webUI modules/deepbooru.py and modified
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def predict(self, image: Image.Image, threshold: Optional[float] = None):
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from modules import images
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pic = images.resize_image(2, 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 = db.model.model(x)[0].detach().cpu().numpy()
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probability_dict = dict()
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for tag, probability in zip(db.model.model.tags, y):
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if threshold and 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[get_replaced_tag(tag)] = probability
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return probability_dict
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def name(self):
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return 'DeepDanbooru'
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class WaifuDiffusion(TagScorer):
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def start(self):
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waifu_diffusion_tagger.instance.load()
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return self
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def stop(self):
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waifu_diffusion_tagger.instance.unload()
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# brought from https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags/blob/main/app.py and modified
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def predict(self, image: Image.Image, threshold: Optional[float] = None):
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# may not use ratings
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# rating = dict(labels[:4])
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labels = waifu_diffusion_tagger.instance.apply(image)
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if threshold:
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probability_dict = dict([(get_replaced_tag(x[0]), x[1]) for x in labels[4:] if x[1] > threshold])
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else:
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probability_dict = dict([(get_replaced_tag(x[0]), x[1]) for x in labels[4:]])
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return probability_dict
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def name(self):
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return 'wd-v1-4-tags'
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@ -0,0 +1,65 @@
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from PIL import Image
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import numpy as np
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from typing import Optional, List, Tuple
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import onnxruntime as ort
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from modules import shared
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class WaifuDiffusionTagger():
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# brought from https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags/blob/main/app.py and modified
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MODEL_REPO = "SmilingWolf/wd-v1-4-vit-tagger"
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MODEL_FILENAME = "model.onnx"
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LABEL_FILENAME = "selected_tags.csv"
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def __init__(self):
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self.model: ort.InferenceSession = None
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self.labels = []
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def load(self):
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import huggingface_hub
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if not self.model:
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path_model = huggingface_hub.hf_hub_download(
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self.MODEL_REPO, self.MODEL_FILENAME
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)
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if 'all' in shared.cmd_opts.use_cpu or 'interrogate' in shared.cmd_opts.use_cpu:
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providers = ['CPUExecutionProvider']
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else:
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
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self.model = ort.InferenceSession(path_model, providers=providers)
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path_label = huggingface_hub.hf_hub_download(
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self.MODEL_REPO, self.LABEL_FILENAME
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)
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import pandas as pd
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self.labels = pd.read_csv(path_label)["name"].tolist()
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def unload(self):
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if not shared.opts.interrogate_keep_models_in_memory:
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self.model = None
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# brought from https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags/blob/main/app.py and modified
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def apply(self, image: Image.Image):
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if not self.model:
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return dict()
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from modules import images
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_, height, width, _ = self.model.get_inputs()[0].shape
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# the way to fill empty pixels is quite different from original one;
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# original: fill by white pixels
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# this: repeat the pixels on the edge
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image = images.resize_image(2, image.convert("RGB"), width, height)
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image_np = np.array(image, dtype=np.float32)
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# PIL RGB to OpenCV BGR
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image_np = image_np[:, :, ::-1]
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image_np = np.expand_dims(image_np, 0)
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input_name = self.model.get_inputs()[0].name
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label_name = self.model.get_outputs()[0].name
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probs = self.model.run([label_name], {input_name: image_np})[0]
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labels: List[Tuple[str, float]] = list(zip(self.labels, probs[0].astype(float)))
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return labels
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instance = WaifuDiffusionTagger()
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@ -2,7 +2,7 @@ from typing import List, Set
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from modules import shared, script_callbacks
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from modules.shared import opts, cmd_opts
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import gradio as gr
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from scripts.dataset_tag_editor.dataset_tag_editor import DatasetTagEditor, interrogate_image_clip, interrogate_image_booru, InterrogateMethod
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from scripts.dataset_tag_editor.dataset_tag_editor import DatasetTagEditor, InterrogateMethod
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from scripts.dataset_tag_editor.filters import TagFilter, PathFilter
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from scripts.dataset_tag_editor.ui import TagFilterUI
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@ -52,7 +52,7 @@ def get_current_move_or_delete_target_num(target_data: str, idx: int):
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return f'Target dataset num: 0'
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def load_files_from_dir(dir: str, recursive: bool, load_caption_from_filename: bool, use_interrogator: str, use_clip: bool, use_booru: bool):
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def load_files_from_dir(dir: str, recursive: bool, load_caption_from_filename: bool, use_interrogator: str, use_clip: bool, use_booru: bool, use_waifu: bool):
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global total_image_num, displayed_image_num, tmp_selection_img_path_set, gallery_selected_image_path, selection_selected_image_path, path_filter
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|
||||
interrogate_method = InterrogateMethod.NONE
|
||||
|
|
@ -65,7 +65,7 @@ def load_files_from_dir(dir: str, recursive: bool, load_caption_from_filename: b
|
|||
elif use_interrogator == 'Append':
|
||||
interrogate_method = InterrogateMethod.APPEND
|
||||
|
||||
dataset_tag_editor.load_dataset(img_dir=dir, recursive=recursive, load_caption_from_filename=load_caption_from_filename, interrogate_method=interrogate_method, use_clip=use_clip, use_booru=use_booru)
|
||||
dataset_tag_editor.load_dataset(dir, recursive, load_caption_from_filename, interrogate_method, use_booru, use_clip, use_waifu)
|
||||
img_paths = dataset_tag_editor.get_filtered_imgpaths(filters=get_filters())
|
||||
path_filter = PathFilter()
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total_image_num = displayed_image_num = len(dataset_tag_editor.get_img_path_set())
|
||||
|
|
@ -237,14 +237,22 @@ def change_selected_image_caption(tags_text: str, idx: int):
|
|||
|
||||
def interrogate_selected_image_clip():
|
||||
global gallery_selected_image_path
|
||||
from scripts.dataset_tag_editor.dataset_tag_editor import interrogate_image_clip
|
||||
return interrogate_image_clip(gallery_selected_image_path)
|
||||
|
||||
|
||||
def interrogate_selected_image_booru():
|
||||
global gallery_selected_image_path
|
||||
from scripts.dataset_tag_editor.dataset_tag_editor import interrogate_image_booru
|
||||
return interrogate_image_booru(gallery_selected_image_path)
|
||||
|
||||
|
||||
def interrogate_selected_image_waifu():
|
||||
global gallery_selected_image_path
|
||||
from scripts.dataset_tag_editor.dataset_tag_editor import interrogate_image_waifu
|
||||
return interrogate_image_waifu(gallery_selected_image_path)
|
||||
|
||||
|
||||
# ================================================================
|
||||
# Callbacks for "Batch Edit Captions" tab
|
||||
# ================================================================
|
||||
|
|
@ -365,6 +373,7 @@ def on_ui_tabs():
|
|||
with gr.Row():
|
||||
cb_use_clip_to_prefill = gr.Checkbox(value=False, label='Use BLIP')
|
||||
cb_use_booru_to_prefill = gr.Checkbox(value=False, label='Use DeepDanbooru')
|
||||
cb_use_waifu_to_prefill = gr.Checkbox(value=False, label='Use WDv1.4 Tagger')
|
||||
|
||||
gl_dataset_images = gr.Gallery(label='Dataset Images', elem_id="dataset_tag_editor_dataset_gallery").style(grid=opts.dataset_editor_image_columns)
|
||||
txt_gallery = gr.HTML(value=get_current_gallery_txt())
|
||||
|
|
@ -452,6 +461,7 @@ def on_ui_tabs():
|
|||
with gr.Row():
|
||||
btn_interrogate_clip = gr.Button(value='Interrogate with BLIP')
|
||||
btn_interrogate_booru = gr.Button(value='Interrogate with DeepDanbooru')
|
||||
btn_interrogate_waifu = gr.Button(value='Interrogate with WDv1.4 tagger')
|
||||
tb_interrogate_selected_image = gr.Textbox(label='Interrogate Result', interactive=True, lines=6)
|
||||
with gr.Row():
|
||||
btn_copy_interrogate = gr.Button(value='Copy and Overwrite')
|
||||
|
|
@ -500,7 +510,7 @@ def on_ui_tabs():
|
|||
|
||||
btn_load_datasets.click(
|
||||
fn=load_files_from_dir,
|
||||
inputs=[tb_img_directory, cb_load_recursive, cb_load_caption_from_filename, rb_use_interrogator, cb_use_clip_to_prefill, cb_use_booru_to_prefill],
|
||||
inputs=[tb_img_directory, cb_load_recursive, cb_load_caption_from_filename, rb_use_interrogator, cb_use_clip_to_prefill, cb_use_booru_to_prefill, cb_use_waifu_to_prefill],
|
||||
outputs=[gl_dataset_images, gl_selected_images, txt_gallery, txt_selection] + [tag_filter_ui.cbg_tags, tag_filter_ui_neg.cbg_tags, gl_dataset_images, nb_hidden_image_index, txt_gallery] + [tb_common_tags, tb_edit_tags]
|
||||
)
|
||||
btn_load_datasets.click(
|
||||
|
|
@ -629,6 +639,11 @@ def on_ui_tabs():
|
|||
outputs=[tb_interrogate_selected_image]
|
||||
)
|
||||
|
||||
btn_interrogate_waifu.click(
|
||||
fn=interrogate_selected_image_waifu,
|
||||
outputs=[tb_interrogate_selected_image]
|
||||
)
|
||||
|
||||
btn_copy_interrogate.click(
|
||||
fn=lambda a:a,
|
||||
inputs=[tb_interrogate_selected_image],
|
||||
|
|
|
|||
Loading…
Reference in New Issue