stable-diffusion-webui/modules/textual_inversion/preprocess.py

166 lines
6.2 KiB
Python

import os
from PIL import Image, ImageOps
import math
import platform
import sys
import tqdm
import time
from modules import shared, images
from modules.shared import opts, cmd_opts
from modules.textual_inversion import autocrop
if cmd_opts.deepdanbooru:
import modules.deepbooru as deepbooru
def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_entropy_focus=False):
try:
if process_caption:
shared.interrogator.load()
if process_caption_deepbooru:
db_opts = deepbooru.create_deepbooru_opts()
db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_entropy_focus)
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, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_entropy_focus=False):
width = process_width
height = process_height
src = os.path.abspath(process_src)
dst = os.path.abspath(process_dst)
split_threshold = max(0.0, min(1.0, split_threshold))
overlap_ratio = max(0.0, min(0.9, overlap_ratio))
assert src != dst, 'same directory specified as source and destination'
os.makedirs(dst, exist_ok=True)
files = os.listdir(src)
shared.state.textinfo = "Preprocessing..."
shared.state.job_count = len(files)
def save_pic_with_caption(image, index, existing_caption=None):
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 preprocess_txt_action == 'prepend' and existing_caption:
caption = existing_caption + ' ' + caption
elif preprocess_txt_action == 'append' and existing_caption:
caption = caption + ' ' + existing_caption
elif preprocess_txt_action == 'copy' and existing_caption:
caption = existing_caption
caption = caption.strip()
if len(caption) > 0:
with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:
file.write(caption)
subindex[0] += 1
def save_pic(image, index, existing_caption=None):
save_pic_with_caption(image, index, existing_caption=existing_caption)
if process_flip:
save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)
def split_pic(image, inverse_xy):
if inverse_xy:
from_w, from_h = image.height, image.width
to_w, to_h = height, width
else:
from_w, from_h = image.width, image.height
to_w, to_h = width, height
h = from_h * to_w // from_w
if inverse_xy:
image = image.resize((h, to_w))
else:
image = image.resize((to_w, h))
split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
y_step = (h - to_h) / (split_count - 1)
for i in range(split_count):
y = int(y_step * i)
if inverse_xy:
splitted = image.crop((y, 0, y + to_h, to_w))
else:
splitted = image.crop((0, y, to_w, y + to_h))
yield splitted
for index, imagefile in enumerate(tqdm.tqdm(files)):
subindex = [0]
filename = os.path.join(src, imagefile)
try:
img = Image.open(filename).convert("RGB")
except Exception:
continue
existing_caption = None
existing_caption_filename = os.path.splitext(filename)[0] + '.txt'
if os.path.exists(existing_caption_filename):
with open(existing_caption_filename, 'r', encoding="utf8") as file:
existing_caption = file.read()
if shared.state.interrupted:
break
if img.height > img.width:
ratio = (img.width * height) / (img.height * width)
inverse_xy = False
else:
ratio = (img.height * width) / (img.width * height)
inverse_xy = True
processing_option_ran = False
if process_split and ratio < 1.0 and ratio <= split_threshold:
for splitted in split_pic(img, inverse_xy):
save_pic(splitted, index, existing_caption=existing_caption)
processing_option_ran = True
if process_entropy_focus and img.height != img.width:
autocrop_settings = autocrop.Settings(
crop_width = width,
crop_height = height,
face_points_weight = 0.9,
entropy_points_weight = 0.7,
corner_points_weight = 0.5,
annotate_image = False
)
focal = autocrop.crop_image(img, autocrop_settings)
save_pic(focal, index, existing_caption=existing_caption)
processing_option_ran = True
if not processing_option_ran:
img = images.resize_image(1, img, width, height)
save_pic(img, index, existing_caption=existing_caption)
shared.state.nextjob()