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Diffstat (limited to 'modules/deepbooru.py')
| -rw-r--r-- | modules/deepbooru.py | 173 |
1 files changed, 173 insertions, 0 deletions
diff --git a/modules/deepbooru.py b/modules/deepbooru.py new file mode 100644 index 00000000..4ad334a1 --- /dev/null +++ b/modules/deepbooru.py @@ -0,0 +1,173 @@ +import os.path +from concurrent.futures import ProcessPoolExecutor +import multiprocessing +import time +import re + +re_special = re.compile(r'([\\()])') + +def get_deepbooru_tags(pil_image): + """ + This method is for running only one image at a time for simple use. Used to the img2img interrogate. + """ + from modules import shared # prevents circular reference + + try: + create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts()) + return get_tags_from_process(pil_image) + finally: + release_process() + + +OPT_INCLUDE_RANKS = "include_ranks" +def create_deepbooru_opts(): + from modules import shared + + return { + "use_spaces": shared.opts.deepbooru_use_spaces, + "use_escape": shared.opts.deepbooru_escape, + "alpha_sort": shared.opts.deepbooru_sort_alpha, + OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks, + } + + +def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts): + model, tags = get_deepbooru_tags_model() + while True: # while process is running, keep monitoring queue for new image + pil_image = queue.get() + if pil_image == "QUIT": + break + else: + deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts) + + +def create_deepbooru_process(threshold, deepbooru_opts): + """ + Creates deepbooru process. A queue is created to send images into the process. This enables multiple images + to be processed in a row without reloading the model or creating a new process. To return the data, a shared + dictionary is created to hold the tags created. To wait for tags to be returned, a value of -1 is assigned + to the dictionary and the method adding the image to the queue should wait for this value to be updated with + the tags. + """ + from modules import shared # prevents circular reference + shared.deepbooru_process_manager = multiprocessing.Manager() + shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue() + shared.deepbooru_process_return = shared.deepbooru_process_manager.dict() + shared.deepbooru_process_return["value"] = -1 + shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts)) + shared.deepbooru_process.start() + + +def get_tags_from_process(image): + from modules import shared + + shared.deepbooru_process_return["value"] = -1 + shared.deepbooru_process_queue.put(image) + while shared.deepbooru_process_return["value"] == -1: + time.sleep(0.2) + caption = shared.deepbooru_process_return["value"] + shared.deepbooru_process_return["value"] = -1 + + return caption + + +def release_process(): + """ + Stops the deepbooru process to return used memory + """ + from modules import shared # prevents circular reference + shared.deepbooru_process_queue.put("QUIT") + shared.deepbooru_process.join() + shared.deepbooru_process_queue = None + shared.deepbooru_process = None + shared.deepbooru_process_return = None + shared.deepbooru_process_manager = None + +def get_deepbooru_tags_model(): + import deepdanbooru as dd + import tensorflow as tf + import numpy as np + this_folder = os.path.dirname(__file__) + model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru')) + if not os.path.exists(os.path.join(model_path, 'project.json')): + # there is no point importing these every time + import zipfile + from basicsr.utils.download_util import load_file_from_url + load_file_from_url( + r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip", + model_path) + with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref: + zip_ref.extractall(model_path) + os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip")) + + tags = dd.project.load_tags_from_project(model_path) + model = dd.project.load_model_from_project( + model_path, compile_model=False + ) + return model, tags + + +def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts): + import deepdanbooru as dd + import tensorflow as tf + import numpy as np + + alpha_sort = deepbooru_opts['alpha_sort'] + use_spaces = deepbooru_opts['use_spaces'] + use_escape = deepbooru_opts['use_escape'] + include_ranks = deepbooru_opts['include_ranks'] + + width = model.input_shape[2] + height = model.input_shape[1] + image = np.array(pil_image) + image = tf.image.resize( + image, + size=(height, width), + method=tf.image.ResizeMethod.AREA, + preserve_aspect_ratio=True, + ) + image = image.numpy() # EagerTensor to np.array + image = dd.image.transform_and_pad_image(image, width, height) + image = image / 255.0 + image_shape = image.shape + image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2])) + + y = model.predict(image)[0] + + result_dict = {} + + for i, tag in enumerate(tags): + result_dict[tag] = y[i] + + unsorted_tags_in_theshold = [] + result_tags_print = [] + for tag in tags: + if result_dict[tag] >= threshold: + if tag.startswith("rating:"): + continue + unsorted_tags_in_theshold.append((result_dict[tag], tag)) + result_tags_print.append(f'{result_dict[tag]} {tag}') + + # sort tags + result_tags_out = [] + sort_ndx = 0 + if alpha_sort: + sort_ndx = 1 + + # sort by reverse by likelihood and normal for alpha, and format tag text as requested + unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort)) + for weight, tag in unsorted_tags_in_theshold: + # note: tag_outformat will still have a colon if include_ranks is True + tag_outformat = tag.replace(':', ' ') + 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}:{weight:.3f})" + + result_tags_out.append(tag_outformat) + + print('\n'.join(sorted(result_tags_print, reverse=True))) + + return ', '.join(result_tags_out) |