Merge pull request #2143 from JC-Array/deepdanbooru_pre_process
deepbooru tags for textual inversion preproccessing
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AUTOMATIC1111
GitHub
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2e2d45b281
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@ -1,21 +1,75 @@
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import os.path
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from concurrent.futures import ProcessPoolExecutor
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from multiprocessing import get_context
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import multiprocessing
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import time
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def get_deepbooru_tags(pil_image):
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"""
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This method is for running only one image at a time for simple use. Used to the img2img interrogate.
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"""
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from modules import shared # prevents circular reference
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create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, shared.opts.deepbooru_sort_alpha)
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shared.deepbooru_process_return["value"] = -1
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shared.deepbooru_process_queue.put(pil_image)
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while shared.deepbooru_process_return["value"] == -1:
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time.sleep(0.2)
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tags = shared.deepbooru_process_return["value"]
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release_process()
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return tags
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def _load_tf_and_return_tags(pil_image, threshold):
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def deepbooru_process(queue, deepbooru_process_return, threshold, alpha_sort):
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model, tags = get_deepbooru_tags_model()
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while True: # while process is running, keep monitoring queue for new image
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pil_image = queue.get()
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if pil_image == "QUIT":
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break
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else:
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deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort)
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def create_deepbooru_process(threshold, alpha_sort):
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"""
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Creates deepbooru process. A queue is created to send images into the process. This enables multiple images
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to be processed in a row without reloading the model or creating a new process. To return the data, a shared
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dictionary is created to hold the tags created. To wait for tags to be returned, a value of -1 is assigned
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to the dictionary and the method adding the image to the queue should wait for this value to be updated with
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the tags.
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"""
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from modules import shared # prevents circular reference
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shared.deepbooru_process_manager = multiprocessing.Manager()
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shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
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shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
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shared.deepbooru_process_return["value"] = -1
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shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, alpha_sort))
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shared.deepbooru_process.start()
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def release_process():
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"""
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Stops the deepbooru process to return used memory
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"""
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from modules import shared # prevents circular reference
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shared.deepbooru_process_queue.put("QUIT")
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shared.deepbooru_process.join()
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shared.deepbooru_process_queue = None
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shared.deepbooru_process = None
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shared.deepbooru_process_return = None
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shared.deepbooru_process_manager = None
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def get_deepbooru_tags_model():
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import deepdanbooru as dd
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import tensorflow as tf
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import numpy as np
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this_folder = os.path.dirname(__file__)
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model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
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if not os.path.exists(os.path.join(model_path, 'project.json')):
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# there is no point importing these every time
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import zipfile
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from basicsr.utils.download_util import load_file_from_url
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load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
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model_path)
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load_file_from_url(
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r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
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model_path)
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with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
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zip_ref.extractall(model_path)
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os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
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@ -24,7 +78,13 @@ def _load_tf_and_return_tags(pil_image, threshold):
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model = dd.project.load_model_from_project(
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model_path, compile_model=True
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)
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return model, tags
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def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort):
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import deepdanbooru as dd
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import tensorflow as tf
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import numpy as np
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width = model.input_shape[2]
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height = model.input_shape[1]
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image = np.array(pil_image)
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@ -46,28 +106,27 @@ def _load_tf_and_return_tags(pil_image, threshold):
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for i, tag in enumerate(tags):
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result_dict[tag] = y[i]
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result_tags_out = []
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unsorted_tags_in_theshold = []
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result_tags_print = []
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for tag in tags:
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if result_dict[tag] >= threshold:
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if tag.startswith("rating:"):
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continue
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result_tags_out.append(tag)
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unsorted_tags_in_theshold.append((result_dict[tag], tag))
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result_tags_print.append(f'{result_dict[tag]} {tag}')
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# sort tags
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result_tags_out = []
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sort_ndx = 0
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if alpha_sort:
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sort_ndx = 1
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# sort by reverse by likelihood and normal for alpha
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unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
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for weight, tag in unsorted_tags_in_theshold:
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result_tags_out.append(tag)
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print('\n'.join(sorted(result_tags_print, reverse=True)))
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return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
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def subprocess_init_no_cuda():
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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def get_deepbooru_tags(pil_image, threshold=0.5):
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context = get_context('spawn')
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with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
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f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
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ret = f.result() # will rethrow any exceptions
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return ret
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@ -249,15 +249,20 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
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}))
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options_templates.update(options_section(('interrogate', "Interrogate Options"), {
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interrogate_option_dictionary = {
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"interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
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"interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
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"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
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"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
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"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
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"interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
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"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
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}))
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"interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)")
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}
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if cmd_opts.deepdanbooru:
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interrogate_option_dictionary["interrogate_deepbooru_score_threshold"] = OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01})
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interrogate_option_dictionary["deepbooru_sort_alpha"] = OptionInfo(True, "Interrogate: deepbooru sort alphabetically", gr.Checkbox)
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options_templates.update(options_section(('interrogate', "Interrogate Options"), interrogate_option_dictionary))
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options_templates.update(options_section(('ui', "User interface"), {
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"show_progressbar": OptionInfo(True, "Show progressbar"),
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@ -3,11 +3,14 @@ from PIL import Image, ImageOps
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import platform
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import sys
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import tqdm
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import time
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from modules import shared, images
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from modules.shared import opts, cmd_opts
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if cmd_opts.deepdanbooru:
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import modules.deepbooru as deepbooru
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def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption):
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def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
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width = process_width
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height = process_height
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src = os.path.abspath(process_src)
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@ -25,10 +28,21 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
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if process_caption:
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shared.interrogator.load()
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if process_caption_deepbooru:
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deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, opts.deepbooru_sort_alpha)
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def save_pic_with_caption(image, index):
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if process_caption:
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caption = "-" + shared.interrogator.generate_caption(image)
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caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
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elif process_caption_deepbooru:
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shared.deepbooru_process_return["value"] = -1
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shared.deepbooru_process_queue.put(image)
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while shared.deepbooru_process_return["value"] == -1:
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time.sleep(0.2)
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caption = "-" + shared.deepbooru_process_return["value"]
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caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
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shared.deepbooru_process_return["value"] = -1
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else:
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caption = filename
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caption = os.path.splitext(caption)[0]
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@ -83,6 +97,10 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
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if process_caption:
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shared.interrogator.send_blip_to_ram()
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if process_caption_deepbooru:
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deepbooru.release_process()
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def sanitize_caption(base_path, original_caption, suffix):
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operating_system = platform.system().lower()
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if (operating_system == "windows"):
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@ -324,7 +324,7 @@ def interrogate(image):
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def interrogate_deepbooru(image):
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prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
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prompt = get_deepbooru_tags(image)
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return gr_show(True) if prompt is None else prompt
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@ -1065,6 +1065,10 @@ def create_ui(wrap_gradio_gpu_call):
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process_flip = gr.Checkbox(label='Create flipped copies')
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process_split = gr.Checkbox(label='Split oversized images into two')
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process_caption = gr.Checkbox(label='Use BLIP caption as filename')
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if cmd_opts.deepdanbooru:
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process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename')
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else:
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process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename', visible=False)
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with gr.Row():
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with gr.Column(scale=3):
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@ -1142,6 +1146,7 @@ def create_ui(wrap_gradio_gpu_call):
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process_flip,
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process_split,
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process_caption,
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process_caption_deepbooru
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],
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outputs=[
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ti_output,
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