diff options
Diffstat (limited to 'modules/textual_inversion/preprocess.py')
| -rw-r--r-- | modules/textual_inversion/preprocess.py | 232 |
1 files changed, 0 insertions, 232 deletions
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py deleted file mode 100644 index dbd856bd..00000000 --- a/modules/textual_inversion/preprocess.py +++ /dev/null @@ -1,232 +0,0 @@ -import os
-from PIL import Image, ImageOps
-import math
-import tqdm
-
-from modules import paths, shared, images, deepbooru
-from modules.textual_inversion import autocrop
-
-
-def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, 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=0.15, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
- 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_keep_original_size, 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, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
-
- 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 caption:
- 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 = f"{existing_caption} {caption}"
- elif params.preprocess_txt_action == 'append' and existing_caption:
- caption = f"{caption} {existing_caption}"
- elif params.preprocess_txt_action == 'copy' and existing_caption:
- caption = existing_caption
-
- caption = caption.strip()
-
- if caption:
- 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
-
-# not using torchvision.transforms.CenterCrop because it doesn't allow float regions
-def center_crop(image: Image, w: int, h: int):
- iw, ih = image.size
- if ih / h < iw / w:
- sw = w * ih / h
- box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih
- else:
- sh = h * iw / w
- box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2
- return image.resize((w, h), Image.Resampling.LANCZOS, box)
-
-
-def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold):
- iw, ih = image.size
- err = lambda w, h: 1-(lambda x: x if x < 1 else 1/x)(iw/ih/(w/h))
- wh = max(((w, h) for w in range(mindim, maxdim+1, 64) for h in range(mindim, maxdim+1, 64)
- if minarea <= w * h <= maxarea and err(w, h) <= threshold),
- key= lambda wh: (wh[0]*wh[1], -err(*wh))[::1 if objective=='Maximize area' else -1],
- default=None
- )
- return wh and center_crop(image, *wh)
-
-
-def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, 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, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
- 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
-
- pbar = tqdm.tqdm(files)
- for index, imagefile in enumerate(pbar):
- params.subindex = 0
- filename = os.path.join(src, imagefile)
- try:
- img = Image.open(filename)
- img = ImageOps.exif_transpose(img)
- img = img.convert("RGB")
- except Exception:
- continue
-
- description = f"Preprocessing [Image {index}/{len(files)}]"
- pbar.set_description(description)
- shared.state.textinfo = description
-
- params.src = filename
-
- existing_caption = None
- existing_caption_filename = f"{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(paths.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_multicrop:
- cropped = multicrop_pic(img, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
- if cropped is not None:
- save_pic(cropped, index, params, existing_caption=existing_caption)
- else:
- print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)")
- process_default_resize = False
-
- if process_keep_original_size:
- save_pic(img, 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()
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