270 lines
9.4 KiB
Python
270 lines
9.4 KiB
Python
import os
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import logging
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import yaml
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import json
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from functools import total_ordering
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from attrs import define, field, Factory
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from data.image_train_item import ImageCaption, ImageTrainItem
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from utils.fs_helpers import *
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from typing import Iterable
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from tqdm import tqdm
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DEFAULT_MAX_CAPTION_LENGTH = 2048
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def overlay(overlay, base):
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return overlay if overlay is not None else base
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def safe_set(val):
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if isinstance(val, str):
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return dict.fromkeys([val]) if val else dict()
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if isinstance(val, Iterable):
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return dict.fromkeys((i for i in val if i is not None))
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return val or dict()
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@define(frozen=True)
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class Tag:
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value: str
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weight: float = field(default=1.0, converter=lambda x: x if x is not None else 1.0)
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@classmethod
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def parse(cls, data):
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if isinstance(data, str):
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return Tag(data)
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if isinstance(data, dict):
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value = str(data.get("tag"))
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weight = data.get("weight")
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if value:
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return Tag(value, weight)
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return None
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@define
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class ImageConfig:
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# Captions
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main_prompts: dict[str, None] = field(factory=dict, converter=safe_set)
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rating: float = None
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max_caption_length: int = None
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tags: dict[Tag, None] = field(factory=dict, converter=safe_set)
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# Options
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multiply: float = None
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cond_dropout: float = None
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flip_p: float = None
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shuffle_tags: bool = False
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def merge(self, other):
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if other is None:
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return self
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return ImageConfig(
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main_prompts=other.main_prompts | self.main_prompts,
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rating=overlay(other.rating, self.rating),
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max_caption_length=overlay(other.max_caption_length, self.max_caption_length),
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tags= other.tags | self.tags,
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multiply=overlay(other.multiply, self.multiply),
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cond_dropout=overlay(other.cond_dropout, self.cond_dropout),
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flip_p=overlay(other.flip_p, self.flip_p),
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shuffle_tags=overlay(other.shuffle_tags, self.shuffle_tags),
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)
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@classmethod
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def from_dict(cls, data: dict):
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# Parse standard yaml tag file (with options)
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parsed_cfg = ImageConfig(
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main_prompts=safe_set(data.get("main_prompt")),
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rating=data.get("rating"),
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max_caption_length=data.get("max_caption_length"),
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tags=safe_set(map(Tag.parse, data.get("tags", []))),
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multiply=data.get("multiply"),
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cond_dropout=data.get("cond_dropout"),
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flip_p=data.get("flip_p"),
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shuffle_tags=data.get("shuffle_tags"),
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)
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# Alternatively parse from dedicated `caption` attribute
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if cap_attr := data.get('caption'):
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parsed_cfg = parsed_cfg.merge(ImageConfig.parse(cap_attr))
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return parsed_cfg
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@classmethod
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def fold(cls, configs):
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acc = ImageConfig()
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for cfg in configs:
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acc = acc.merge(cfg)
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acc.shuffle_tags = any(cfg.shuffle_tags for cfg in configs)
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#print(f"accum shuffle:{acc.shuffle_tags}")
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return acc
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def ensure_caption(self):
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return self
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@classmethod
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def from_caption_text(cls, text: str):
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if not text:
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return ImageConfig()
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if os.path.isfile(text):
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return ImageConfig.from_file(text)
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split_caption = list(map(str.strip, text.split(",")))
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return ImageConfig(
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main_prompts=split_caption[0],
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tags=map(Tag.parse, split_caption[1:])
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)
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@classmethod
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def from_file(cls, file: str):
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match ext(file):
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case '.jpg' | '.jpeg' | '.png' | '.bmp' | '.webp' | '.jfif':
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return ImageConfig(image=file)
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case ".json":
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return ImageConfig.from_dict(json.load(read_text(file)))
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case ".yaml" | ".yml":
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return ImageConfig.from_dict(yaml.safe_load(read_text(file)))
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case ".txt" | ".caption":
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return ImageConfig.from_caption_text(read_text(file))
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case _:
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return logging.warning(" *** Unrecognized config extension {ext}")
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@classmethod
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def parse(cls, input):
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if isinstance(input, str):
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if os.path.isfile(input):
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return ImageConfig.from_file(input)
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else:
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return ImageConfig.from_caption_text(input)
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elif isinstance(input, dict):
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return ImageConfig.from_dict(input)
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@define()
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class Dataset:
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image_configs: dict[str, ImageConfig]
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def __global_cfg(fileset):
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cfgs = []
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for cfgfile in ['global.yaml', 'global.yml']:
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if cfgfile in fileset:
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cfgs.append(ImageConfig.from_file(fileset[cfgfile]))
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return ImageConfig.fold(cfgs)
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def __local_cfg(fileset):
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cfgs = []
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if 'multiply.txt' in fileset:
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cfgs.append(ImageConfig(multiply=read_float(fileset['multiply.txt'])))
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if 'cond_dropout.txt' in fileset:
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cfgs.append(ImageConfig(cond_dropout=read_float(fileset['cond_dropout.txt'])))
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if 'flip_p.txt' in fileset:
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cfgs.append(ImageConfig(flip_p=read_float(fileset['flip_p.txt'])))
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if 'local.yaml' in fileset:
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cfgs.append(ImageConfig.from_file(fileset['local.yaml']))
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if 'local.yml' in fileset:
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cfgs.append(ImageConfig.from_file(fileset['local.yml']))
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result = ImageConfig.fold(cfgs)
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if 'shuffle_tags.txt' in fileset:
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result.shuffle_tags = True
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return result
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def __sidecar_cfg(imagepath, fileset):
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cfgs = []
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for cfgext in ['.txt', '.caption', '.yml', '.yaml']:
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cfgfile = barename(imagepath) + cfgext
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if cfgfile in fileset:
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cfgs.append(ImageConfig.from_file(fileset[cfgfile]))
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return ImageConfig.fold(cfgs)
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# Use file name for caption only as a last resort
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@classmethod
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def __ensure_caption(cls, cfg: ImageConfig, file: str):
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if cfg.main_prompts:
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return cfg
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cap_cfg = ImageConfig.from_caption_text(barename(file).split("_")[0])
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return cfg.merge(cap_cfg)
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@classmethod
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def from_path(cls, data_root):
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# Create a visitor that maintains global config stack
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# and accumulates image configs as it traverses dataset
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image_configs = {}
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def process_dir(files, parent_globals):
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fileset = {os.path.basename(f): f for f in files}
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global_cfg = parent_globals.merge(Dataset.__global_cfg(fileset))
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local_cfg = Dataset.__local_cfg(fileset)
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for img in filter(is_image, files):
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img_cfg = Dataset.__sidecar_cfg(img, fileset)
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resolved_cfg = ImageConfig.fold([global_cfg, local_cfg, img_cfg])
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image_configs[img] = Dataset.__ensure_caption(resolved_cfg, img)
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return global_cfg
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walk_and_visit(data_root, process_dir, ImageConfig())
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return Dataset(image_configs)
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@classmethod
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def from_json(cls, json_path):
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"""
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Import a dataset definition from a JSON file
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"""
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image_configs = {}
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with open(json_path, encoding='utf-8', mode='r') as stream:
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for data in json.load(stream):
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img = data.get("image")
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cfg = Dataset.__ensure_caption(ImageConfig.parse(data), img)
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if not img:
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logging.warning(f" *** Error parsing json image entry in {json_path}: {data}")
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continue
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image_configs[img] = cfg
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return Dataset(image_configs)
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def image_train_items(self, aspects):
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items = []
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for image in tqdm(self.image_configs, desc="preloading", dynamic_ncols=True):
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config = self.image_configs[image]
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#print(f" ********* shuffle: {config.shuffle_tags}")
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if len(config.main_prompts) > 1:
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logging.warning(f" *** Found multiple multiple main_prompts for image {image}, but only one will be applied: {config.main_prompts}")
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if len(config.main_prompts) < 1:
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logging.warning(f" *** No main_prompts for image {image}")
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tags = []
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tag_weights = []
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for tag in sorted(config.tags, key=lambda x: x.weight or 1.0, reverse=True):
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tags.append(tag.value)
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tag_weights.append(tag.weight)
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use_weights = len(set(tag_weights)) > 1
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try:
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caption = ImageCaption(
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main_prompt=next(iter(config.main_prompts)),
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rating=config.rating or 1.0,
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tags=tags,
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tag_weights=tag_weights,
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max_target_length=config.max_caption_length or DEFAULT_MAX_CAPTION_LENGTH,
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use_weights=use_weights)
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item = ImageTrainItem(
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image=None,
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caption=caption,
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aspects=aspects,
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pathname=os.path.abspath(image),
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flip_p=config.flip_p or 0.0,
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multiplier=config.multiply or 1.0,
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cond_dropout=config.cond_dropout,
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shuffle_tags=config.shuffle_tags,
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)
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items.append(item)
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except Exception as e:
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logging.error(f" *** Error preloading image or caption for: {image}, error: {e}")
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raise e
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return items |