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from typing import TYPE_CHECKING, List, Union
import cv2
import torch
from PIL import Image
from PIL.ImageOps import exif_transpose
import av
from toolkit import image_utils
from toolkit.basic import get_quick_signature_string
from toolkit.dataloader_mixins import (
CaptionProcessingDTOMixin,
ImageProcessingDTOMixin,
LatentCachingFileItemDTOMixin,
ControlFileItemDTOMixin,
ArgBreakMixin,
PoiFileItemDTOMixin,
MaskFileItemDTOMixin,
AugmentationFileItemDTOMixin,
UnconditionalFileItemDTOMixin,
ClipImageFileItemDTOMixin,
InpaintControlFileItemDTOMixin,
TextEmbeddingFileItemDTOMixin,
AudioProcessingDTOMixin,
)
from toolkit.prompt_utils import PromptEmbeds, concat_prompt_embeds
if TYPE_CHECKING:
from toolkit.config_modules import DatasetConfig
printed_messages = []
def print_once(msg):
global printed_messages
if msg not in printed_messages:
print(msg)
printed_messages.append(msg)
class FileItemDTO(
LatentCachingFileItemDTOMixin,
TextEmbeddingFileItemDTOMixin,
CaptionProcessingDTOMixin,
ImageProcessingDTOMixin,
AudioProcessingDTOMixin,
ControlFileItemDTOMixin,
InpaintControlFileItemDTOMixin,
ClipImageFileItemDTOMixin,
MaskFileItemDTOMixin,
AugmentationFileItemDTOMixin,
UnconditionalFileItemDTOMixin,
PoiFileItemDTOMixin,
ArgBreakMixin,
):
def __init__(self, *args, **kwargs):
self.path = kwargs.get("path", "")
self.dataset_config: "DatasetConfig" = kwargs.get("dataset_config", None)
self.is_video = self.dataset_config.num_frames > 1 or self.dataset_config.auto_frame_count
self.is_audio_model = kwargs.get("is_audio_model", False)
self.sample_rate = kwargs.get("sample_rate", 48000)
self.num_frames = self.dataset_config.num_frames
self.temporal_compression = kwargs.get("temporal_compression", 8)
size_database = kwargs.get("size_database", {})
dataset_root = kwargs.get("dataset_root", None)
self.encode_control_in_text_embeddings = kwargs.get(
"encode_control_in_text_embeddings", False
)
self.te_padding_side = kwargs.get("te_padding_side", "right")
self.latent_space_version = kwargs.get("latent_space_version", "sd1")
self.text_embedding_space_version = kwargs.get("text_embedding_space_version", "sd1")
if dataset_root is not None:
# remove dataset root from path
file_key = self.path.replace(dataset_root, "")
else:
file_key = os.path.basename(self.path)
file_signature = get_quick_signature_string(self.path)
if file_signature is None:
raise Exception("Error: Could not get file signature for {self.path}")
use_db_entry = False
if file_key in size_database:
db_entry = size_database[file_key]
if (
db_entry is not None
and len(db_entry) >= 3
and db_entry[2] == file_signature
):
use_db_entry = True
if self.is_audio_model:
# get the length of the audio file in ms
with av.open(self.path) as c:
if c.duration is not None:
w = int(c.duration / 1_000)
else:
s = c.streams.audio[0]
w = int(float(s.duration * s.time_base) * 1_000)
h = 1
elif use_db_entry:
w, h, _ = size_database[file_key]
elif self.is_video:
# Open the video file
video = cv2.VideoCapture(self.path)
# Check if video opened successfully
if not video.isOpened():
raise Exception(f"Error: Could not open video file {self.path}")
# Get width and height
width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
w, h = width, height
# Release the video capture object immediately
video.release()
size_database[file_key] = (width, height, file_signature)
else:
if self.dataset_config.fast_image_size:
# original method is significantly faster, but some images are read sideways. Not sure why. Do slow method by default.
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))
w, h = img.size
else:
img = exif_transpose(Image.open(self.path))
w, h = img.size
size_database[file_key] = (w, h, file_signature)
self.width: int = w
self.height: int = h
self.dataloader_transforms = kwargs.get("dataloader_transforms", None)
super().__init__(*args, **kwargs)
# self.caption_path: str = kwargs.get('caption_path', None)
self.raw_caption: str = kwargs.get("raw_caption", None)
# we scale first, then crop
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)
self.flip_x: bool = kwargs.get("flip_x", False)
self.flip_y: bool = kwargs.get("flip_x", False)
self.augments: List[str] = self.dataset_config.augments
self.loss_multiplier: float = self.dataset_config.loss_multiplier
self.network_weight: float = self.dataset_config.network_weight
self.is_reg = self.dataset_config.is_reg
self.prior_reg = self.dataset_config.prior_reg
self.tensor: Union[torch.Tensor, None] = None
self.audio_data = None
self.audio_tensor = None
def cleanup(self):
self.tensor = None
self.audio_data = None
self.audio_tensor = None
self.cleanup_latent()
self.cleanup_text_embedding()
self.cleanup_control()
self.cleanup_inpaint()
self.cleanup_clip_image()
self.cleanup_mask()
self.cleanup_unconditional()
class DataLoaderBatchDTO:
def __init__(self, **kwargs):
try:
self.file_items: List["FileItemDTO"] = kwargs.get("file_items", None)
is_latents_cached = self.file_items[0].is_latent_cached
self.tensor: Union[torch.Tensor, None] = None
self.latents: Union[torch.Tensor, None] = None
self.control_tensor: Union[torch.Tensor, None] = None
self.control_tensor_list: Union[List[List[torch.Tensor]], None] = None
self.clip_image_tensor: Union[torch.Tensor, None] = None
self.mask_tensor: Union[torch.Tensor, None] = None
self.unaugmented_tensor: Union[torch.Tensor, None] = None
self.unconditional_tensor: Union[torch.Tensor, None] = None
self.unconditional_latents: Union[torch.Tensor, None] = None
self.clip_image_embeds: Union[List[dict], None] = None
self.clip_image_embeds_unconditional: Union[List[dict], None] = None
self.sigmas: Union[torch.Tensor, None] = (
None # can be added elseware and passed along training code
)
self.extra_values: Union[torch.Tensor, None] = (
torch.tensor([x.extra_values for x in self.file_items])
if len(self.file_items[0].extra_values) > 0
else None
)
self.audio_data: Union[List, None] = (
[x.audio_data for x in self.file_items]
if self.file_items[0].audio_data is not None
else None
)
self.audio_tensor: Union[torch.Tensor, None] = None
self.first_frame_latents: Union[torch.Tensor, None] = None
self.audio_latents: Union[torch.Tensor, None] = None
# just for holding noise and preds during training
self.audio_target: Union[torch.Tensor, None] = None
self.audio_pred: Union[torch.Tensor, None] = None
self.num_frames: int = self.file_items[0].num_frames
if not is_latents_cached:
# only return a tensor if latents are not cached
self.tensor: torch.Tensor = torch.cat(
[x.tensor.unsqueeze(0) for x in self.file_items]
)
# if we have encoded latents, we concatenate them
self.latents: Union[torch.Tensor, None] = None
if is_latents_cached:
# this get_latent call with trigger loading all cached items from the disk
self.latents = torch.cat(
[x.get_latent().unsqueeze(0) for x in self.file_items]
)
if any(
[x._cached_first_frame_latent is not None for x in self.file_items]
):
self.first_frame_latents = torch.cat(
[
x._cached_first_frame_latent.unsqueeze(0)
if x._cached_first_frame_latent is not None
else torch.zeros_like(
self.file_items[0]._cached_first_frame_latent
).unsqueeze(0)
for x in self.file_items
]
)
if any([x._cached_audio_latent is not None for x in self.file_items]):
self.audio_latents = torch.cat(
[
x._cached_audio_latent.unsqueeze(0)
if x._cached_audio_latent is not None
else torch.zeros_like(
self.file_items[0]._cached_audio_latent
).unsqueeze(0)
for x in self.file_items
]
)
self.prompt_embeds: Union[PromptEmbeds, None] = None
# if self.file_items[0].control_tensor is not None:
# if any have a control tensor, we concatenate them
if any([x.control_tensor is not None for x in self.file_items]):
# find one to use as a base
base_control_tensor = None
for x in self.file_items:
if x.control_tensor is not None:
base_control_tensor = x.control_tensor
break
control_tensors = []
for x in self.file_items:
if x.control_tensor is None:
control_tensors.append(torch.zeros_like(base_control_tensor))
else:
control_tensors.append(x.control_tensor)
self.control_tensor = torch.cat(
[x.unsqueeze(0) for x in control_tensors]
)
# handle control tensor list
if any([x.control_tensor_list is not None for x in self.file_items]):
self.control_tensor_list = []
for x in self.file_items:
if x.control_tensor_list is not None:
self.control_tensor_list.append(x.control_tensor_list)
else:
raise Exception(
f"Could not find control tensors for all file items, missing for {x.path}"
)
self.inpaint_tensor: Union[torch.Tensor, None] = None
if any([x.inpaint_tensor is not None for x in self.file_items]):
# find one to use as a base
base_inpaint_tensor = None
for x in self.file_items:
if x.inpaint_tensor is not None:
base_inpaint_tensor = x.inpaint_tensor
break
inpaint_tensors = []
for x in self.file_items:
if x.inpaint_tensor is None:
inpaint_tensors.append(torch.zeros_like(base_inpaint_tensor))
else:
inpaint_tensors.append(x.inpaint_tensor)
self.inpaint_tensor = torch.cat(
[x.unsqueeze(0) for x in inpaint_tensors]
)
self.loss_multiplier_list: List[float] = [
x.loss_multiplier for x in self.file_items
]
if any([x.clip_image_tensor is not None for x in self.file_items]):
# find one to use as a base
base_clip_image_tensor = None
for x in self.file_items:
if x.clip_image_tensor is not None:
base_clip_image_tensor = x.clip_image_tensor
break
clip_image_tensors = []
for x in self.file_items:
if x.clip_image_tensor is None:
clip_image_tensors.append(
torch.zeros_like(base_clip_image_tensor)
)
else:
clip_image_tensors.append(x.clip_image_tensor)
self.clip_image_tensor = torch.cat(
[x.unsqueeze(0) for x in clip_image_tensors]
)
if any([x.mask_tensor is not None for x in self.file_items]):
# find one to use as a base
base_mask_tensor = None
for x in self.file_items:
if x.mask_tensor is not None:
base_mask_tensor = x.mask_tensor
break
mask_tensors = []
for x in self.file_items:
if x.mask_tensor is None:
mask_tensors.append(torch.zeros_like(base_mask_tensor))
else:
mask_tensors.append(x.mask_tensor)
self.mask_tensor = torch.cat([x.unsqueeze(0) for x in mask_tensors])
# add unaugmented tensors for ones with augments
if any([x.unaugmented_tensor is not None for x in self.file_items]):
# find one to use as a base
base_unaugmented_tensor = None
for x in self.file_items:
if x.unaugmented_tensor is not None:
base_unaugmented_tensor = x.unaugmented_tensor
break
unaugmented_tensor = []
for x in self.file_items:
if x.unaugmented_tensor is None:
unaugmented_tensor.append(
torch.zeros_like(base_unaugmented_tensor)
)
else:
unaugmented_tensor.append(x.unaugmented_tensor)
self.unaugmented_tensor = torch.cat(
[x.unsqueeze(0) for x in unaugmented_tensor]
)
# add unconditional tensors
if any([x.unconditional_tensor is not None for x in self.file_items]):
# find one to use as a base
base_unconditional_tensor = None
for x in self.file_items:
if x.unaugmented_tensor is not None:
base_unconditional_tensor = x.unconditional_tensor
break
unconditional_tensor = []
for x in self.file_items:
if x.unconditional_tensor is None:
unconditional_tensor.append(
torch.zeros_like(base_unconditional_tensor)
)
else:
unconditional_tensor.append(x.unconditional_tensor)
self.unconditional_tensor = torch.cat(
[x.unsqueeze(0) for x in unconditional_tensor]
)
if any([x.clip_image_embeds is not None for x in self.file_items]):
self.clip_image_embeds = []
for x in self.file_items:
if x.clip_image_embeds is not None:
self.clip_image_embeds.append(x.clip_image_embeds)
else:
raise Exception("clip_image_embeds is None for some file items")
if any(
[x.clip_image_embeds_unconditional is not None for x in self.file_items]
):
self.clip_image_embeds_unconditional = []
for x in self.file_items:
if x.clip_image_embeds_unconditional is not None:
self.clip_image_embeds_unconditional.append(
x.clip_image_embeds_unconditional
)
else:
raise Exception(
"clip_image_embeds_unconditional is None for some file items"
)
if any([x.prompt_embeds is not None for x in self.file_items]):
# find one to use as a base
base_prompt_embeds = None
for x in self.file_items:
if x.prompt_embeds is not None:
base_prompt_embeds = x.prompt_embeds
break
prompt_embeds_list = []
for x in self.file_items:
if x.prompt_embeds is None:
y = base_prompt_embeds
else:
y = x.prompt_embeds
if x.text_embedding_space_version == "zimage":
# z image needs to be a list if it is not already
if not isinstance(y.text_embeds, list):
y.text_embeds = [y.text_embeds]
prompt_embeds_list.append(y)
padding_side = self.file_items[0].te_padding_side
self.prompt_embeds = concat_prompt_embeds(prompt_embeds_list, padding_side=padding_side)
if any([x.audio_tensor is not None for x in self.file_items]):
# find one to use as a base
base_audio_tensor = None
for x in self.file_items:
if x.audio_tensor is not None:
base_audio_tensor = x.audio_tensor
break
audio_tensors = []
for x in self.file_items:
if x.audio_tensor is None:
audio_tensors.append(torch.zeros_like(base_audio_tensor))
else:
audio_tensors.append(x.audio_tensor)
self.audio_tensor = torch.cat([x.unsqueeze(0) for x in audio_tensors])
except Exception as e:
print(e)
raise e
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.caption for x in self.file_items]
def get_caption_short_list(
self, trigger=None, to_replace_list=None, add_if_not_present=True
):
return [x.caption_short for x in self.file_items]
def cleanup(self):
del self.latents
del self.tensor
del self.control_tensor
del self.audio_tensor
del self.audio_data
del self.audio_target
del self.audio_pred
del self.first_frame_latents
del self.audio_latents
for file_item in self.file_items:
file_item.cleanup()
@property
def dataset_config(self) -> "DatasetConfig":
if len(self.file_items) > 0:
return self.file_items[0].dataset_config
else:
return None
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