Hude rework to move the batch to a DTO to make it far more modular to the future ui

extensions_built_in/sd_trainer/SDTrainer.py

@@ -35,6 +35,7 @@ class SDTrainer(BaseSDTrainProcess):
     def hook_train_loop(self, batch):
         dtype = get_torch_dtype(self.train_config.dtype)
         noisy_latents, noise, timesteps, conditioned_prompts, imgs = self.process_general_training_batch(batch)
+        network_weight_list = batch.get_network_weight_list()
 
         self.optimizer.zero_grad()
         flush()
@@ -53,6 +54,9 @@ class SDTrainer(BaseSDTrainProcess):
         else:
             network = BlankNetwork()
 
+        # set the weights
+        network.multiplier = network_weight_list
+
         # activate network if it exits
         with network:
             with torch.set_grad_enabled(grad_on_text_encoder):
@@ -114,5 +118,7 @@ class SDTrainer(BaseSDTrainProcess):
         loss_dict = OrderedDict(
             {'loss': loss.item()}
         )
+        # reset network multiplier
+        network.multiplier = 1.0
 
         return loss_dict

extensions_built_in/sd_trainer/config/train.example.yaml

@@ -19,7 +19,7 @@ config:
         max_step_saves_to_keep: 5 # only affects step counts
       datasets:
         - folder_path: "/path/to/dataset"
-          caption_type: "txt"
+          caption_ext: "txt"
           default_caption: "[trigger]"
           buckets: true
           resolution: 512

jobs/process/BaseSDTrainProcess.py

@@ -6,6 +6,7 @@ from typing import Union
 from torch.utils.data import DataLoader
 
 from toolkit.data_loader import get_dataloader_from_datasets
+from toolkit.data_transfer_object.data_loader import FileItemDTO, DataLoaderBatchDTO
 from toolkit.embedding import Embedding
 from toolkit.lora_special import LoRASpecialNetwork
 from toolkit.optimizer import get_optimizer
@@ -23,7 +24,7 @@ import torch
 from tqdm import tqdm
 
 from toolkit.config_modules import SaveConfig, LogingConfig, SampleConfig, NetworkConfig, TrainConfig, ModelConfig, \
-    GenerateImageConfig, EmbeddingConfig, DatasetConfig
+    GenerateImageConfig, EmbeddingConfig, DatasetConfig, preprocess_dataset_raw_config
 
 
 def flush():
@@ -67,6 +68,8 @@ class BaseSDTrainProcess(BaseTrainProcess):
         self.trigger_word = self.get_conf('trigger_word', None)
 
         raw_datasets = self.get_conf('datasets', None)
+        if raw_datasets is not None and len(raw_datasets) > 0:
+            raw_datasets = preprocess_dataset_raw_config(raw_datasets)
         self.datasets = None
         self.datasets_reg = None
         if raw_datasets is not None and len(raw_datasets) > 0:
@@ -94,6 +97,12 @@ class BaseSDTrainProcess(BaseTrainProcess):
             if latest_save_path is not None:
                 print(f"#### IMPORTANT RESUMING FROM {latest_save_path} ####")
                 self.model_config.name_or_path = latest_save_path
+                meta = load_metadata_from_safetensors(latest_save_path)
+                # if 'training_info' in Orderdict keys
+                if 'training_info' in meta and 'step' in meta['training_info']:
+                    self.step_num = meta['training_info']['step']
+                    self.start_step = self.step_num
+                    print(f"Found step {self.step_num} in metadata, starting from there")
 
         self.sd = StableDiffusion(
             device=self.device,
@@ -307,16 +316,9 @@ class BaseSDTrainProcess(BaseTrainProcess):
 
     def process_general_training_batch(self, batch):
         with torch.no_grad():
-            imgs, prompts, dataset_config = batch
-
-            # convert the 0 or 1 for is reg to a bool list
-            if isinstance(dataset_config, list):
-                is_reg_list = [x.get('is_reg', 0) for x in dataset_config]
-            else:
-                is_reg_list = dataset_config.get('is_reg', [0 for _ in range(imgs.shape[0])])
-            if isinstance(is_reg_list, torch.Tensor):
-                is_reg_list = is_reg_list.numpy().tolist()
-            is_reg_list = [bool(x) for x in is_reg_list]
+            imgs = batch.tensor
+            prompts = batch.get_caption_list()
+            is_reg_list = batch.get_is_reg_list()
 
             conditioned_prompts = []
 
@@ -473,6 +475,7 @@ class BaseSDTrainProcess(BaseTrainProcess):
 
             # resume state from embedding
             self.step_num = self.embedding.step
+            self.start_step = self.step_num
 
             # set trainable params
             params = self.embedding.get_trainable_params()
@@ -556,13 +559,18 @@ class BaseSDTrainProcess(BaseTrainProcess):
             with torch.no_grad():
                 # if is even step and we have a reg dataset, use that
                 # todo improve this logic to send one of each through if we can buckets and batch size might be an issue
-                if step % 2 == 0 and dataloader_reg is not None:
+                is_reg_step = False
+                is_save_step = self.save_config.save_every and self.step_num % self.save_config.save_every == 0
+                is_sample_step = self.sample_config.sample_every and self.step_num % self.sample_config.sample_every == 0
+                # don't do a reg step on sample or save steps as we dont want to normalize on those
+                if step % 2 == 0 and dataloader_reg is not None and not is_save_step and not is_sample_step:
                     try:
                         batch = next(dataloader_iterator_reg)
                     except StopIteration:
                         # hit the end of an epoch, reset
                         dataloader_iterator_reg = iter(dataloader_reg)
                         batch = next(dataloader_iterator_reg)
+                    is_reg_step = True
                 elif dataloader is not None:
                     try:
                         batch = next(dataloader_iterator)
@@ -601,11 +609,11 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 if self.step_num != self.start_step:
                     # pause progress bar
                     self.progress_bar.unpause()  # makes it so doesn't track time
-                    if self.sample_config.sample_every and self.step_num % self.sample_config.sample_every == 0:
+                    if is_sample_step:
                         # print above the progress bar
                         self.sample(self.step_num)
 
-                    if self.save_config.save_every and self.step_num % self.save_config.save_every == 0:
+                    if is_save_step:
                         # print above the progress bar
                         self.print(f"Saving at step {self.step_num}")
                         self.save(self.step_num)
@@ -623,10 +631,14 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 # end of step
                 self.step_num = step
 
-                # apply network normalizer if we are using it
-                if self.network is not None and self.network.is_normalizing:
+                # apply network normalizer if we are using it, not on regularization steps
+                if self.network is not None and self.network.is_normalizing and not is_reg_step:
                     self.network.apply_stored_normalizer()
 
+                # if the batch is a DataLoaderBatchDTO, then we need to clean it up
+                if isinstance(batch, DataLoaderBatchDTO):
+                    batch.cleanup()
+
         self.sample(self.step_num + 1)
         print("")
         self.save()

jobs/process/TrainLoRAHack.py

@@ -1,76 +0,0 @@
-# ref:
-# - https://github.com/p1atdev/LECO/blob/main/train_lora.py
-import time
-from collections import OrderedDict
-import os
-
-from toolkit.config_modules import SliderConfig
-from toolkit.paths import REPOS_ROOT
-import sys
-
-sys.path.append(REPOS_ROOT)
-sys.path.append(os.path.join(REPOS_ROOT, 'leco'))
-from toolkit.train_tools import get_torch_dtype, apply_noise_offset
-import gc
-
-import torch
-from leco import train_util, model_util
-from leco.prompt_util import PromptEmbedsCache
-from .BaseSDTrainProcess import BaseSDTrainProcess, StableDiffusion
-
-
-def flush():
-    torch.cuda.empty_cache()
-    gc.collect()
-
-
-class LoRAHack:
-    def __init__(self, **kwargs):
-        self.type = kwargs.get('type', 'suppression')
-
-
-class TrainLoRAHack(BaseSDTrainProcess):
-    def __init__(self, process_id: int, job, config: OrderedDict):
-        super().__init__(process_id, job, config)
-        self.hack_config = LoRAHack(**self.get_conf('hack', {}))
-
-    def hook_before_train_loop(self):
-        # we don't need text encoder so move it to cpu
-        self.sd.text_encoder.to("cpu")
-        flush()
-        # end hook_before_train_loop
-
-        if self.hack_config.type == 'suppression':
-            # set all params to self.current_suppression
-            params = self.network.parameters()
-            for param in params:
-                # get random noise for each param
-                noise = torch.randn_like(param) - 0.5
-                # apply noise to param
-                param.data = noise * 0.001
-
-
-    def supress_loop(self):
-        dtype = get_torch_dtype(self.train_config.dtype)
-
-
-        loss_dict = OrderedDict(
-            {'sup': 0.0}
-        )
-        # increase noise
-        for param in self.network.parameters():
-            # get random noise for each param
-            noise = torch.randn_like(param) - 0.5
-            # apply noise to param
-            param.data = param.data + noise * 0.001
-
-
-
-        return loss_dict
-
-    def hook_train_loop(self, batch):
-        if self.hack_config.type == 'suppression':
-            return self.supress_loop()
-        else:
-            raise NotImplementedError(f'unknown hack type: {self.hack_config.type}')
-        # end hook_train_loop

jobs/process/__init__.py

@@ -7,7 +7,6 @@ from .TrainVAEProcess import TrainVAEProcess
 from .BaseMergeProcess import BaseMergeProcess
 from .TrainSliderProcess import TrainSliderProcess
 from .TrainSliderProcessOld import TrainSliderProcessOld
-from .TrainLoRAHack import TrainLoRAHack
 from .TrainSDRescaleProcess import TrainSDRescaleProcess
 from .ModRescaleLoraProcess import ModRescaleLoraProcess
 from .GenerateProcess import GenerateProcess

testing/test_bucket_dataloader.py

@@ -22,7 +22,7 @@ batch_size = 4
 dataset_config = DatasetConfig(
     folder_path=dataset_folder,
     resolution=resolution,
-    caption_type='txt',
+    caption_ext='txt',
     default_caption='default',
     buckets=True,
     bucket_tolerance=bucket_tolerance,

toolkit/config_modules.py

@@ -5,6 +5,7 @@ import random
 
 ImgExt = Literal['jpg', 'png', 'webp']
 
+
 class SaveConfig:
     def __init__(self, **kwargs):
         self.save_every: int = kwargs.get('save_every', 1000)
@@ -167,9 +168,13 @@ class DatasetConfig:
 
     def __init__(self, **kwargs):
         self.type = kwargs.get('type', 'image')  # sd, slider, reference
+        # will be legacy
         self.folder_path: str = kwargs.get('folder_path', None)
+        # can be json or folder path
+        self.dataset_path: str = kwargs.get('dataset_path', None)
+
         self.default_caption: str = kwargs.get('default_caption', None)
-        self.caption_type: str = kwargs.get('caption_type', None)
+        self.caption_ext: str = kwargs.get('caption_ext', None)
         self.random_scale: bool = kwargs.get('random_scale', False)
         self.random_crop: bool = kwargs.get('random_crop', False)
         self.resolution: int = kwargs.get('resolution', 512)
@@ -182,6 +187,33 @@ class DatasetConfig:
         self.shuffle_tokens: bool = kwargs.get('shuffle_tokens', False)
         self.caption_dropout_rate: float = float(kwargs.get('caption_dropout_rate', 0.0))
 
+        # legacy compatability
+        legacy_caption_type = kwargs.get('caption_type', None)
+        if legacy_caption_type:
+            self.caption_ext = legacy_caption_type
+        self.caption_type = self.caption_ext
+
+
+def preprocess_dataset_raw_config(raw_config: List[dict]) -> List[dict]:
+    """
+    This just splits up the datasets by resolutions so you dont have to do it manually
+    :param raw_config:
+    :return:
+    """
+    # split up datasets by resolutions
+    new_config = []
+    for dataset in raw_config:
+        resolution = dataset.get('resolution', 512)
+        if isinstance(resolution, list):
+            resolution_list = resolution
+        else:
+            resolution_list = [resolution]
+        for res in resolution_list:
+            dataset_copy = dataset.copy()
+            dataset_copy['resolution'] = res
+            new_config.append(dataset_copy)
+    return new_config
+
 
 class GenerateImageConfig:
     def __init__(

toolkit/data_loader.py

@@ -1,3 +1,4 @@
+import json
 import os
 import random
 from typing import List
@@ -13,10 +14,9 @@ from tqdm import tqdm
 import albumentations as A
 
 from toolkit import image_utils
-from toolkit.config_modules import DatasetConfig
+from toolkit.config_modules import DatasetConfig, preprocess_dataset_raw_config
 from toolkit.dataloader_mixins import CaptionMixin, BucketsMixin
-from toolkit.data_transfer_object.data_loader import FileItemDTO
-
+from toolkit.data_transfer_object.data_loader import FileItemDTO, DataLoaderBatchDTO
 
 
 class ImageDataset(Dataset, CaptionMixin):
@@ -29,7 +29,7 @@ class ImageDataset(Dataset, CaptionMixin):
         self.include_prompt = self.get_config('include_prompt', False)
         self.default_prompt = self.get_config('default_prompt', '')
         if self.include_prompt:
-            self.caption_type = self.get_config('caption_type', 'txt')
+            self.caption_type = self.get_config('caption_ext', 'txt')
         else:
             self.caption_type = None
         # we always random crop if random scale is enabled
@@ -288,24 +288,17 @@ class PairedImageDataset(Dataset):
         return img, prompt, (self.neg_weight, self.pos_weight)
 
 
-printed_messages = []
-
-
-def print_once(msg):
-    global printed_messages
-    if msg not in printed_messages:
-        print(msg)
-        printed_messages.append(msg)
-
-
-
 class AiToolkitDataset(Dataset, CaptionMixin, BucketsMixin):
 
     def __init__(self, dataset_config: 'DatasetConfig', batch_size=1):
         super().__init__()
         self.dataset_config = dataset_config
-        self.folder_path = dataset_config.folder_path
-        self.caption_type = dataset_config.caption_type
+        folder_path = dataset_config.folder_path
+        self.dataset_path = dataset_config.dataset_path
+        if self.dataset_path is None:
+            self.dataset_path = folder_path
+
+        self.caption_type = dataset_config.caption_ext
         self.default_caption = dataset_config.default_caption
         self.random_scale = dataset_config.random_scale
         self.scale = dataset_config.scale
@@ -313,147 +306,96 @@ class AiToolkitDataset(Dataset, CaptionMixin, BucketsMixin):
         # we always random crop if random scale is enabled
         self.random_crop = self.random_scale if self.random_scale else dataset_config.random_crop
         self.resolution = dataset_config.resolution
+        self.caption_dict = None
         self.file_list: List['FileItemDTO'] = []
 
-        # get the file list
-        file_list = [
-            os.path.join(self.folder_path, file) for file in os.listdir(self.folder_path) if
-            file.lower().endswith(('.jpg', '.jpeg', '.png', '.webp'))
-        ]
+        # check if dataset_path is a folder or json
+        if os.path.isdir(self.dataset_path):
+            file_list = [
+                os.path.join(self.dataset_path, file) for file in os.listdir(self.dataset_path) if
+                file.lower().endswith(('.jpg', '.jpeg', '.png', '.webp'))
+            ]
+        else:
+            # assume json
+            with open(self.dataset_path, 'r') as f:
+                self.caption_dict = json.load(f)
+                # keys are file paths
+                file_list = list(self.caption_dict.keys())
 
         # this might take a while
         print(f"  -  Preprocessing image dimensions")
         bad_count = 0
         for file in tqdm(file_list):
-            try:
-                w, h = image_utils.get_image_size(file)
-            except image_utils.UnknownImageFormat:
-                print_once(f'Warning: Some images in the dataset cannot be fast read. ' + \
-                           f'This process is faster for png, jpeg')
-                img = Image.open(file)
-                h, w = img.size
-            # TODO allow smaller images
-            if int(min(h, w) * self.scale) >= self.resolution:
-                self.file_list.append(
-                    FileItemDTO(
-                        path=file,
-                        width=w,
-                        height=h,
-                        scale_to_width=int(w * self.scale),
-                        scale_to_height=int(h * self.scale),
-                        dataset_config=dataset_config
-                    )
-                )
-            else:
+            file_item = FileItemDTO(
+                path=file,
+                dataset_config=dataset_config
+            )
+            if file_item.scale_to_width < self.resolution or file_item.scale_to_height < self.resolution:
                 bad_count += 1
+            else:
+                self.file_list.append(file_item)
 
         print(f"  -  Found {len(self.file_list)} images")
         print(f"  -  Found {bad_count} images that are too small")
-        assert len(self.file_list) > 0, f"no images found in {self.folder_path}"
+        assert len(self.file_list) > 0, f"no images found in {self.dataset_path}"
 
-        if self.dataset_config.buckets:
-            # setup buckets
-            self.setup_buckets()
+        self.setup_epoch()
 
         self.transform = transforms.Compose([
             transforms.ToTensor(),
             transforms.Normalize([0.5], [0.5]),  # normalize to [-1, 1]
         ])
 
+    def setup_epoch(self):
+        # TODO: set this up to redo cropping and everything else
+        # do not call for now
+        if self.dataset_config.buckets:
+            # setup buckets
+            self.setup_buckets()
+
     def __len__(self):
         if self.dataset_config.buckets:
             return len(self.batch_indices)
         return len(self.file_list)
 
-    def _get_single_item(self, index):
+    def _get_single_item(self, index) -> 'FileItemDTO':
         file_item = self.file_list[index]
-        # todo make sure this matches
-        img = exif_transpose(Image.open(file_item.path)).convert('RGB')
-        w, h = img.size
-        if w > h and file_item.scale_to_width < file_item.scale_to_height:
-            # throw error, they should match
-            raise ValueError(
-                f"unexpected values: w={w}, h={h}, file_item.scale_to_width={file_item.scale_to_width}, file_item.scale_to_height={file_item.scale_to_height}, file_item.path={file_item.path}")
-        elif h > w and file_item.scale_to_height < file_item.scale_to_width:
-            # throw error, they should match
-            raise ValueError(
-                f"unexpected values: w={w}, h={h}, file_item.scale_to_width={file_item.scale_to_width}, file_item.scale_to_height={file_item.scale_to_height}, file_item.path={file_item.path}")
-
-        # Downscale the source image first
-        img = img.resize((int(img.size[0] * self.scale), int(img.size[1] * self.scale)), Image.BICUBIC)
-        min_img_size = min(img.size)
-
-        if self.dataset_config.buckets:
-            # todo allow scaling and cropping, will be hard to add
-            # scale and crop based on file item
-            img = img.resize((file_item.scale_to_width, file_item.scale_to_height), Image.BICUBIC)
-            img = transforms.CenterCrop((file_item.crop_height, file_item.crop_width))(img)
-        else:
-            if self.random_crop:
-                if self.random_scale and min_img_size > self.resolution:
-                    if min_img_size < self.resolution:
-                        print(
-                            f"Unexpected values: min_img_size={min_img_size}, self.resolution={self.resolution}, image file={file_item.path}")
-                        scale_size = self.resolution
-                    else:
-                        scale_size = random.randint(self.resolution, int(min_img_size))
-                    img = img.resize((scale_size, scale_size), Image.BICUBIC)
-                img = transforms.RandomCrop(self.resolution)(img)
-            else:
-                img = transforms.CenterCrop(min_img_size)(img)
-                img = img.resize((self.resolution, self.resolution), Image.BICUBIC)
-
-        img = self.transform(img)
-
-        # todo convert it all
-        dataset_config_dict = {
-            "is_reg": 1 if self.dataset_config.is_reg else 0,
-        }
-
-        if self.caption_type is not None:
-            prompt = self.get_caption_item(index)
-            return img, prompt, dataset_config_dict
-        else:
-            return img, dataset_config_dict
+        file_item.load_and_process_image(self.transform)
+        file_item.load_caption(self.caption_dict)
+        return file_item
 
     def __getitem__(self, item):
         if self.dataset_config.buckets:
+            # for buckets we collate ourselves for now
+            # todo allow a scheduler to dynamically make buckets
             # we collate ourselves
             idx_list = self.batch_indices[item]
-            tensor_list = []
-            prompt_list = []
-            dataset_config_dict_list = []
-            for idx in idx_list:
-                if self.caption_type is not None:
-                    img, prompt, dataset_config_dict = self._get_single_item(idx)
-                    prompt_list.append(prompt)
-                    dataset_config_dict_list.append(dataset_config_dict)
-                else:
-                    img, dataset_config_dict = self._get_single_item(idx)
-                    dataset_config_dict_list.append(dataset_config_dict)
-                tensor_list.append(img.unsqueeze(0))
-
-            if self.caption_type is not None:
-                return torch.cat(tensor_list, dim=0), prompt_list, dataset_config_dict_list
-            else:
-                return torch.cat(tensor_list, dim=0), dataset_config_dict_list
+            return [self._get_single_item(idx) for idx in idx_list]
         else:
             # Dataloader is batching
             return self._get_single_item(item)
 
 
 def get_dataloader_from_datasets(dataset_options, batch_size=1):
-    # TODO do bucketing
     if dataset_options is None or len(dataset_options) == 0:
         return None
 
     datasets = []
     has_buckets = False
+
+    dataset_config_list = []
+    # preprocess them all
     for dataset_option in dataset_options:
         if isinstance(dataset_option, DatasetConfig):
-            config = dataset_option
+            dataset_config_list.append(dataset_option)
         else:
-            config = DatasetConfig(**dataset_option)
+            # preprocess raw data
+            split_configs = preprocess_dataset_raw_config([dataset_option])
+            for x in split_configs:
+                dataset_config_list.append(DatasetConfig(**x))
+
+    for config in dataset_config_list:
+
         if config.type == 'image':
             dataset = AiToolkitDataset(config, batch_size=batch_size)
             datasets.append(dataset)
@@ -463,21 +405,28 @@ def get_dataloader_from_datasets(dataset_options, batch_size=1):
             raise ValueError(f"invalid dataset type: {config.type}")
 
     concatenated_dataset = ConcatDataset(datasets)
+
+    # todo build scheduler that can get buckets from all datasets that match
+    # todo and evenly distribute reg images
+
+    def dto_collation(batch: List['FileItemDTO']):
+        # create DTO batch
+        batch = DataLoaderBatchDTO(
+            file_items=batch
+        )
+        return batch
+
     if has_buckets:
         # make sure they all have buckets
         for dataset in datasets:
             assert dataset.dataset_config.buckets, f"buckets not found on dataset {dataset.dataset_config.folder_path}, you either need all buckets or none"
 
-        def custom_collate_fn(batch):
-            # just return as is
-            return batch
-
         data_loader = DataLoader(
             concatenated_dataset,
-            batch_size=None,  # we batch in the dataloader
+            batch_size=None,  # we batch in the datasets for now
             drop_last=False,
             shuffle=True,
-            collate_fn=custom_collate_fn,  # Use the custom collate function
+            collate_fn=dto_collation,  # Use the custom collate function
             num_workers=2
         )
     else:
@@ -485,6 +434,7 @@ def get_dataloader_from_datasets(dataset_options, batch_size=1):
             concatenated_dataset,
             batch_size=batch_size,
             shuffle=True,
-            num_workers=2
+            num_workers=2,
+            collate_fn=dto_collation
         )
     return data_loader

toolkit/data_transfer_object/data_loader.py

@@ -1,36 +1,84 @@
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, List, Union
 import torch
 import random
 
-from toolkit.dataloader_mixins import CaptionProcessingDTOMixin
+from PIL import Image
+from PIL.ImageOps import exif_transpose
+
+from toolkit import image_utils
+from toolkit.dataloader_mixins import CaptionProcessingDTOMixin, ImageProcessingDTOMixin
 
 if TYPE_CHECKING:
     from toolkit.config_modules import DatasetConfig
 
+printed_messages = []
 
-class FileItemDTO(CaptionProcessingDTOMixin):
+
+def print_once(msg):
+    global printed_messages
+    if msg not in printed_messages:
+        print(msg)
+        printed_messages.append(msg)
+
+
+class FileItemDTO(CaptionProcessingDTOMixin, ImageProcessingDTOMixin):
     def __init__(self, **kwargs):
         self.path = kwargs.get('path', None)
-        self.caption_path: str = kwargs.get('caption_path', None)
+        self.dataset_config: 'DatasetConfig' = kwargs.get('dataset_config', None)
+        # process width and height
+        try:
+            w, h = image_utils.get_image_size(self.path)
+        except image_utils.UnknownImageFormat:
+            print_once(f'Warning: Some images in the dataset cannot be fast read. ' + \
+                       f'This process is faster for png, jpeg')
+            img = exif_transpose(Image.open(self.path))
+            h, w = img.size
+        self.width: int = w
+        self.height: int = h
+
+        # self.caption_path: str = kwargs.get('caption_path', None)
         self.raw_caption: str = kwargs.get('raw_caption', None)
-        self.width: int = kwargs.get('width', None)
-        self.height: int = kwargs.get('height', None)
         # we scale first, then crop
-        self.scale_to_width: int = kwargs.get('scale_to_width', self.width)
-        self.scale_to_height: int = kwargs.get('scale_to_height', self.height)
+        self.scale_to_width: int = kwargs.get('scale_to_width', int(self.width * self.dataset_config.scale))
+        self.scale_to_height: int = kwargs.get('scale_to_height', int(self.height * self.dataset_config.scale))
         # crop values are from scaled size
         self.crop_x: int = kwargs.get('crop_x', 0)
         self.crop_y: int = kwargs.get('crop_y', 0)
         self.crop_width: int = kwargs.get('crop_width', self.scale_to_width)
         self.crop_height: int = kwargs.get('crop_height', self.scale_to_height)
 
-        # process config
-        self.dataset_config: 'DatasetConfig' = kwargs.get('dataset_config', None)
+        self.network_weight: float = self.dataset_config.network_weight
+        self.is_reg = self.dataset_config.is_reg
+        self.tensor: Union[torch.Tensor, None] = None
 
-        self.network_network_weight: float = self.dataset_config.network_weight
+    def cleanup(self):
+        self.tensor = None
 
 
 class DataLoaderBatchDTO:
     def __init__(self, **kwargs):
-        self.file_item: 'FileItemDTO' = kwargs.get('file_item', None)
-        self.dataset_config: 'DatasetConfig' = kwargs.get('dataset_config', None)
+        self.file_items: List['FileItemDTO'] = kwargs.get('file_items', None)
+        self.tensor: torch.Tensor = torch.cat([x.tensor.unsqueeze(0) for x in self.file_items])
+
+    def get_is_reg_list(self):
+        return [x.is_reg for x in self.file_items]
+
+    def get_network_weight_list(self):
+        return [x.network_weight for x in self.file_items]
+
+    def get_caption_list(
+            self,
+            trigger=None,
+            to_replace_list=None,
+            add_if_not_present=True
+    ):
+        return [x.get_caption(
+            trigger=trigger,
+            to_replace_list=to_replace_list,
+            add_if_not_present=add_if_not_present
+        ) for x in self.file_items]
+
+    def cleanup(self):
+        self.tensor = None
+        for file_item in self.file_items:
+            file_item.cleanup()

toolkit/dataloader_mixins.py

@@ -1,8 +1,11 @@
 import os
 import random
-from typing import TYPE_CHECKING, List, Dict
+from typing import TYPE_CHECKING, List, Dict, Union
 
 from toolkit.prompt_utils import inject_trigger_into_prompt
+from torchvision import transforms
+from PIL import Image
+from PIL.ImageOps import exif_transpose
 
 if TYPE_CHECKING:
     from toolkit.data_loader import AiToolkitDataset
@@ -159,6 +162,38 @@ class BucketsMixin:
 
 
 class CaptionProcessingDTOMixin:
+
+    # todo allow for loading from sd-scripts style dict
+    def load_caption(self: 'FileItemDTO', caption_dict: Union[dict, None]):
+        if self.raw_caption is not None:
+            # we already loaded it
+            pass
+        elif caption_dict is not None and self.path in caption_dict and "caption" in caption_dict[self.path]:
+            self.raw_caption = caption_dict[self.path]["caption"]
+        else:
+            # see if prompt file exists
+            path_no_ext = os.path.splitext(self.path)[0]
+            prompt_ext = self.dataset_config.caption_ext
+            prompt_path = f"{path_no_ext}.{prompt_ext}"
+
+            if os.path.exists(prompt_path):
+                with open(prompt_path, 'r', encoding='utf-8') as f:
+                    prompt = f.read()
+                    # remove any newlines
+                    prompt = prompt.replace('\n', ', ')
+                    # remove new lines for all operating systems
+                    prompt = prompt.replace('\r', ', ')
+                    prompt_split = prompt.split(',')
+                    # remove empty strings
+                    prompt_split = [p.strip() for p in prompt_split if p.strip()]
+                    # join back together
+                    prompt = ', '.join(prompt_split)
+            else:
+                prompt = ''
+                if self.dataset_config.default_caption is not None:
+                    prompt = self.dataset_config.default_caption
+            self.raw_caption = prompt
+
     def get_caption(
             self: 'FileItemDTO',
             trigger=None,
@@ -201,3 +236,51 @@ class CaptionProcessingDTOMixin:
         caption = ', '.join(token_list)
         caption = inject_trigger_into_prompt(caption, trigger, to_replace_list, add_if_not_present)
         return caption
+
+
+class ImageProcessingDTOMixin:
+    def load_and_process_image(
+            self: 'FileItemDTO',
+            transform: Union[None, transforms.Compose]
+    ):
+        # todo make sure this matches
+        img = exif_transpose(Image.open(self.path)).convert('RGB')
+        w, h = img.size
+        if w > h and self.scale_to_width < self.scale_to_height:
+            # throw error, they should match
+            raise ValueError(
+                f"unexpected values: w={w}, h={h}, file_item.scale_to_width={self.scale_to_width}, file_item.scale_to_height={self.scale_to_height}, file_item.path={self.path}")
+        elif h > w and self.scale_to_height < self.scale_to_width:
+            # throw error, they should match
+            raise ValueError(
+                f"unexpected values: w={w}, h={h}, file_item.scale_to_width={self.scale_to_width}, file_item.scale_to_height={self.scale_to_height}, file_item.path={self.path}")
+
+        if self.dataset_config.buckets:
+            # todo allow scaling and cropping, will be hard to add
+            # scale and crop based on file item
+            img = img.resize((self.scale_to_width, self.scale_to_height), Image.BICUBIC)
+            img = transforms.CenterCrop((self.crop_height, self.crop_width))(img)
+        else:
+            # Downscale the source image first
+            img = img.resize(
+                (int(img.size[0] * self.dataset_config.scale), int(img.size[1] * self.dataset_config.scale)),
+                Image.BICUBIC)
+            min_img_size = min(img.size)
+            if self.dataset_config.random_crop:
+                if self.dataset_config.random_scale and min_img_size > self.dataset_config.resolution:
+                    if min_img_size < self.dataset_config.resolution:
+                        print(
+                            f"Unexpected values: min_img_size={min_img_size}, self.resolution={self.dataset_config.resolution}, image file={self.path}")
+                        scale_size = self.dataset_config.resolution
+                    else:
+                        scale_size = random.randint(self.dataset_config.resolution, int(min_img_size))
+                    img = img.resize((scale_size, scale_size), Image.BICUBIC)
+                img = transforms.RandomCrop(self.dataset_config.resolution)(img)
+            else:
+                img = transforms.CenterCrop(min_img_size)(img)
+                img = img.resize((self.dataset_config.resolution, self.dataset_config.resolution), Image.BICUBIC)
+
+        if transform:
+            img = transform(img)
+
+        self.tensor = img