deepbooru: added option to use spaces or underscores

deepbooru: added option to quote (\) in tags
deepbooru/BLIP: write caption to file instead of image filename
deepbooru/BLIP: now possible to use both for captions
deepbooru: process is stopped even if an exception occurs
This commit is contained in:
AUTOMATIC 2022-10-12 21:55:43 +03:00
parent c3c8eef9fd
commit 698d303b04
4 changed files with 96 additions and 72 deletions

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@ -2,33 +2,44 @@ 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
create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, shared.opts.deepbooru_sort_alpha)
shared.deepbooru_process_return["value"] = -1
shared.deepbooru_process_queue.put(pil_image)
while shared.deepbooru_process_return["value"] == -1:
time.sleep(0.2)
tags = shared.deepbooru_process_return["value"]
release_process()
return tags
try:
create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts())
return get_tags_from_process(pil_image)
finally:
release_process()
def deepbooru_process(queue, deepbooru_process_return, threshold, alpha_sort):
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,
}
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, alpha_sort)
deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts)
def create_deepbooru_process(threshold, alpha_sort):
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
@ -41,10 +52,23 @@ def create_deepbooru_process(threshold, alpha_sort):
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, alpha_sort))
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
@ -81,10 +105,15 @@ def get_deepbooru_tags_model():
return model, tags
def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort):
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']
width = model.input_shape[2]
height = model.input_shape[1]
image = np.array(pil_image)
@ -129,4 +158,12 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort)
print('\n'.join(sorted(result_tags_print, reverse=True)))
return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
tags_text = ', '.join(result_tags_out)
if use_spaces:
tags_text = tags_text.replace('_', ' ')
if use_escape:
tags_text = re.sub(re_special, r'\\\1', tags_text)
return tags_text.replace(':', ' ')

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@ -260,6 +260,8 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
"deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
"deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
"deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
}))
options_templates.update(options_section(('ui', "User interface"), {

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@ -10,7 +10,28 @@ from modules.shared import opts, cmd_opts
if cmd_opts.deepdanbooru:
import modules.deepbooru as deepbooru
def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
try:
if process_caption:
shared.interrogator.load()
if process_caption_deepbooru:
deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts())
preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
finally:
if process_caption:
shared.interrogator.send_blip_to_ram()
if process_caption_deepbooru:
deepbooru.release_process()
def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
width = process_width
height = process_height
src = os.path.abspath(process_src)
@ -25,30 +46,28 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
shared.state.textinfo = "Preprocessing..."
shared.state.job_count = len(files)
if process_caption:
shared.interrogator.load()
if process_caption_deepbooru:
deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, opts.deepbooru_sort_alpha)
def save_pic_with_caption(image, index):
if process_caption:
caption = "-" + shared.interrogator.generate_caption(image)
caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
elif process_caption_deepbooru:
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"]
caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
shared.deepbooru_process_return["value"] = -1
else:
caption = filename
caption = os.path.splitext(caption)[0]
caption = os.path.basename(caption)
caption = ""
if process_caption:
caption += shared.interrogator.generate_caption(image)
if process_caption_deepbooru:
if len(caption) > 0:
caption += ", "
caption += deepbooru.get_tags_from_process(image)
filename_part = filename
filename_part = os.path.splitext(filename_part)[0]
filename_part = os.path.basename(filename_part)
basename = f"{index:05}-{subindex[0]}-{filename_part}"
image.save(os.path.join(dst, f"{basename}.png"))
if len(caption) > 0:
with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:
file.write(caption)
image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
subindex[0] += 1
def save_pic(image, index):
@ -93,34 +112,3 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
save_pic(img, index)
shared.state.nextjob()
if process_caption:
shared.interrogator.send_blip_to_ram()
if process_caption_deepbooru:
deepbooru.release_process()
def sanitize_caption(base_path, original_caption, suffix):
operating_system = platform.system().lower()
if (operating_system == "windows"):
invalid_path_characters = "\\/:*?\"<>|"
max_path_length = 259
else:
invalid_path_characters = "/" #linux/macos
max_path_length = 1023
caption = original_caption
for invalid_character in invalid_path_characters:
caption = caption.replace(invalid_character, "")
fixed_path_length = len(base_path) + len(suffix)
if fixed_path_length + len(caption) <= max_path_length:
return caption
caption_tokens = caption.split()
new_caption = ""
for token in caption_tokens:
last_caption = new_caption
new_caption = new_caption + token + " "
if (len(new_caption) + fixed_path_length - 1 > max_path_length):
break
print(f"\nPath will be too long. Truncated caption: {original_caption}\nto: {last_caption}", file=sys.stderr)
return last_caption.strip()

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@ -1074,11 +1074,8 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Row():
process_flip = gr.Checkbox(label='Create flipped copies')
process_split = gr.Checkbox(label='Split oversized images into two')
process_caption = gr.Checkbox(label='Use BLIP caption as filename')
if cmd_opts.deepdanbooru:
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename')
else:
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename', visible=False)
process_caption = gr.Checkbox(label='Use BLIP for caption')
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
with gr.Row():
with gr.Column(scale=3):