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Merge branch 'deepdanbooru_pre_process' into master

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JC-Array 2022-10-10 18:10:49 -05:00 committed by GitHub
parent aca1553bde 76ef3d75f6
commit 47f5e216da
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4 changed files with 111 additions and 22 deletions
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100
modules/deepbooru.py
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@ -1,21 +1,75 @@
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.deepbooru_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",
model_path)
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"))
@ -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 +106,28 @@ 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
print(alpha_sort)
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

6
modules/shared.py
View File

@ -265,6 +265,12 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
}))
if cmd_opts.deepdanbooru:
options_templates.update(options_section(('deepbooru-params', "DeepBooru parameters"), {
"deepbooru_sort_alpha": OptionInfo(True, "Sort Alphabetical", gr.Checkbox),
'deepbooru_threshold': OptionInfo(0.5, "Threshold", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
}))
class Options:
data = None

22
modules/textual_inversion/preprocess.py
View File

@ -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()
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]
@ -80,6 +94,10 @@ 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"):

5
modules/ui.py
View File

@ -1036,6 +1036,10 @@ 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):
@ -1102,6 +1106,7 @@ def create_ui(wrap_gradio_gpu_call):
process_flip,
process_split,
process_caption,
process_caption_deepbooru
],
outputs=[
ti_output,