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refactored the deepbooru module to improve speed on running multiple interogations in a row. Added the option to generate deepbooru tags for textual inversion preproccessing.

This commit is contained in:
JC_Array 2022-10-09 23:58:18 -05:00
parent 45fbd1c5fe
commit 1f92336be7
3 changed files with 122 additions and 36 deletions
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80
modules/deepbooru.py
View File

@ -1,20 +1,73 @@
import os.path
from concurrent.futures import ProcessPoolExecutor
from multiprocessing import get_context
import multiprocessing
def _load_tf_and_return_tags(pil_image, threshold):
def get_deepbooru_tags(pil_image, threshold=0.5):
"""
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(threshold)
shared.deepbooru_process_return["value"] = -1
shared.deepbooru_process_queue.put(pil_image)
while shared.deepbooru_process_return["value"] == -1:
time.sleep(0.2)
release_process()
return ret
def deepbooru_process(queue, deepbooru_process_return, threshold):
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)
def create_deepbooru_process(threshold=0.5):
"""
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))
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 +77,13 @@ 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=0.5):
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)
@ -58,16 +117,3 @@ def _load_tf_and_return_tags(pil_image, threshold):
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

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_flip, process_split, process_caption):
def preprocess(process_src, process_dst, process_flip, process_split, process_caption, process_caption_deepbooru=False):
size = 512
src = os.path.abspath(process_src)
dst = os.path.abspath(process_dst)
@ -24,10 +27,21 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
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]
@ -79,6 +93,10 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
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"):

22
modules/ui.py
View File

@ -1034,6 +1034,9 @@ 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')
with gr.Row():
with gr.Column(scale=3):
@ -1086,6 +1089,25 @@ def create_ui(wrap_gradio_gpu_call):
]
)
if cmd_opts.deepdanbooru:
# if process_caption_deepbooru is None, it will cause an error, as a result only include it if it is enabled
run_preprocess.click(
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
process_src,
process_dst,
process_flip,
process_split,
process_caption,
process_caption_deepbooru,
],
outputs=[
ti_output,
ti_outcome,
],
)
else:
run_preprocess.click(
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",