Merge pull request #2143 from JC-Array/deepdanbooru_pre_process
deepbooru tags for textual inversion preproccessing
This commit is contained in:
AUTOMATIC1111
GitHub
commit
2e2d45b281
|
|
@ -1,20 +1,74 @@
|
|||
import os.path
|
||||
from concurrent.futures import ProcessPoolExecutor
|
||||
from multiprocessing import get_context
|
||||
import multiprocessing
|
||||
import time
|
||||
|
||||
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
|
||||
|
||||
|
||||
def _load_tf_and_return_tags(pil_image, threshold):
|
||||
def deepbooru_process(queue, deepbooru_process_return, threshold, alpha_sort):
|
||||
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)
|
||||
|
||||
|
||||
def create_deepbooru_process(threshold, alpha_sort):
|
||||
"""
|
||||
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, alpha_sort))
|
||||
shared.deepbooru_process.start()
|
||||
|
||||
|
||||
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",
|
||||
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)
|
||||
|
|
@ -24,7 +78,13 @@ def _load_tf_and_return_tags(pil_image, threshold):
|
|||
model = dd.project.load_model_from_project(
|
||||
model_path, compile_model=True
|
||||
)
|
||||
return model, tags
|
||||
|
||||
|
||||
def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort):
|
||||
import deepdanbooru as dd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
width = model.input_shape[2]
|
||||
height = model.input_shape[1]
|
||||
image = np.array(pil_image)
|
||||
|
|
@ -46,28 +106,27 @@ def _load_tf_and_return_tags(pil_image, threshold):
|
|||
|
||||
for i, tag in enumerate(tags):
|
||||
result_dict[tag] = y[i]
|
||||
result_tags_out = []
|
||||
|
||||
unsorted_tags_in_theshold = []
|
||||
result_tags_print = []
|
||||
for tag in tags:
|
||||
if result_dict[tag] >= threshold:
|
||||
if tag.startswith("rating:"):
|
||||
continue
|
||||
result_tags_out.append(tag)
|
||||
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
|
||||
unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
|
||||
for weight, tag in unsorted_tags_in_theshold:
|
||||
result_tags_out.append(tag)
|
||||
|
||||
print('\n'.join(sorted(result_tags_print, reverse=True)))
|
||||
|
||||
return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
|
||||
|
||||
|
||||
def subprocess_init_no_cuda():
|
||||
import os
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
|
||||
|
||||
|
||||
def get_deepbooru_tags(pil_image, threshold=0.5):
|
||||
context = get_context('spawn')
|
||||
with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
|
||||
f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
|
||||
ret = f.result() # will rethrow any exceptions
|
||||
return ret
|
||||
|
|
@ -249,15 +249,20 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('interrogate', "Interrogate Options"), {
|
||||
interrogate_option_dictionary = {
|
||||
"interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
|
||||
"interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
|
||||
"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
|
||||
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
||||
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
||||
"interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
|
||||
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
}))
|
||||
"interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)")
|
||||
}
|
||||
|
||||
if cmd_opts.deepdanbooru:
|
||||
interrogate_option_dictionary["interrogate_deepbooru_score_threshold"] = OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01})
|
||||
interrogate_option_dictionary["deepbooru_sort_alpha"] = OptionInfo(True, "Interrogate: deepbooru sort alphabetically", gr.Checkbox)
|
||||
|
||||
options_templates.update(options_section(('interrogate', "Interrogate Options"), interrogate_option_dictionary))
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
||||
|
|
|
|||
|
|
@ -3,11 +3,14 @@ from PIL import Image, ImageOps
|
|||
import platform
|
||||
import sys
|
||||
import tqdm
|
||||
import time
|
||||
|
||||
from modules import shared, images
|
||||
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):
|
||||
def preprocess(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)
|
||||
|
|
@ -25,10 +28,21 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
|
|||
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]
|
||||
|
|
@ -83,6 +97,10 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
|
|||
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"):
|
||||
|
|
|
|||
|
|
@ -324,7 +324,7 @@ def interrogate(image):
|
|||
|
||||
|
||||
def interrogate_deepbooru(image):
|
||||
prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
|
||||
prompt = get_deepbooru_tags(image)
|
||||
return gr_show(True) if prompt is None else prompt
|
||||
|
||||
|
||||
|
|
@ -1065,6 +1065,10 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
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)
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
|
|
@ -1142,6 +1146,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
process_flip,
|
||||
process_split,
|
||||
process_caption,
|
||||
process_caption_deepbooru
|
||||
],
|
||||
outputs=[
|
||||
ti_output,
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user