Merge branch 'master' into xygrid_infotext_improvements

1 files changed, 196 insertions, 0 deletions

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
new file mode 100644
index 00000000..feb876c6
--- /dev/null
+++ b/modules/textual_inversion/preprocess.py
@@ -0,0 +1,196 @@
+import os

+from PIL import Image, ImageOps

+import math

+import platform

+import sys

+import tqdm

+import time

+

+from modules import shared, images, deepbooru

+from modules.paths import models_path

+from modules.shared import opts, cmd_opts

+from modules.textual_inversion import autocrop

+

+

+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_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):

+    try:

+        if process_caption:

+            shared.interrogator.load()

+

+        if process_caption_deepbooru:

+            deepbooru.model.start()

+

+        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_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug)

+

+    finally:

+

+        if process_caption:

+            shared.interrogator.send_blip_to_ram()

+

+        if process_caption_deepbooru:

+            deepbooru.model.stop()

+

+

+def listfiles(dirname):

+    return os.listdir(dirname)

+

+

+class PreprocessParams:

+    src = None

+    dstdir = None

+    subindex = 0

+    flip = False

+    process_caption = False

+    process_caption_deepbooru = False

+    preprocess_txt_action = None

+

+

+def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None):

+    caption = ""

+

+    if params.process_caption:

+        caption += shared.interrogator.generate_caption(image)

+

+    if params.process_caption_deepbooru:

+        if len(caption) > 0:

+            caption += ", "

+        caption += deepbooru.model.tag_multi(image)

+

+    filename_part = params.src

+    filename_part = os.path.splitext(filename_part)[0]

+    filename_part = os.path.basename(filename_part)

+

+    basename = f"{index:05}-{params.subindex}-{filename_part}"

+    image.save(os.path.join(params.dstdir, f"{basename}.png"))

+

+    if params.preprocess_txt_action == 'prepend' and existing_caption:

+        caption = existing_caption + ' ' + caption

+    elif params.preprocess_txt_action == 'append' and existing_caption:

+        caption = caption + ' ' + existing_caption

+    elif params.preprocess_txt_action == 'copy' and existing_caption:

+        caption = existing_caption

+

+    caption = caption.strip()

+

+    if len(caption) > 0:

+        with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:

+            file.write(caption)

+

+    params.subindex += 1

+

+

+def save_pic(image, index, params, existing_caption=None):

+    save_pic_with_caption(image, index, params, existing_caption=existing_caption)

+

+    if params.flip:

+        save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption)

+

+

+def split_pic(image, inverse_xy, width, height, overlap_ratio):

+    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

+

+

+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_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=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 = listfiles(src)

+

+    shared.state.job = "preprocess"

+    shared.state.textinfo = "Preprocessing..."

+    shared.state.job_count = len(files)

+

+    params = PreprocessParams()

+    params.dstdir = dst

+    params.flip = process_flip

+    params.process_caption = process_caption

+    params.process_caption_deepbooru = process_caption_deepbooru

+    params.preprocess_txt_action = preprocess_txt_action

+

+    for index, imagefile in enumerate(tqdm.tqdm(files)):

+        params.subindex = 0

+        filename = os.path.join(src, imagefile)

+        try:

+            img = Image.open(filename).convert("RGB")

+        except Exception:

+            continue

+

+        params.src = filename

+

+        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

+

+        process_default_resize = True

+

+        if process_split and ratio < 1.0 and ratio <= split_threshold:

+            for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio):

+                save_pic(splitted, index, params, existing_caption=existing_caption)

+            process_default_resize = False

+

+        if process_focal_crop and img.height != img.width:

+

+            dnn_model_path = None

+            try:

+                dnn_model_path = autocrop.download_and_cache_models(os.path.join(models_path, "opencv"))

+            except Exception as e:

+                print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)

+

+            autocrop_settings = autocrop.Settings(

+                crop_width = width,

+                crop_height = height,

+                face_points_weight = process_focal_crop_face_weight,

+                entropy_points_weight = process_focal_crop_entropy_weight,

+                corner_points_weight = process_focal_crop_edges_weight,

+                annotate_image = process_focal_crop_debug,

+                dnn_model_path = dnn_model_path,

+            )

+            for focal in autocrop.crop_image(img, autocrop_settings):

+                save_pic(focal, index, params, existing_caption=existing_caption)

+            process_default_resize = False

+

+        if process_default_resize:

+            img = images.resize_image(1, img, width, height)

+            save_pic(img, index, params, existing_caption=existing_caption)

+

+        shared.state.nextjob()