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# dataset_and_utils.py file taken from https://github.com/replicate/cog-sdxl/blob/main/dataset_and_utils.py
import os
from typing import Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
import PIL
import torch
import torch.utils.checkpoint
from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
from PIL import Image
from safetensors import safe_open
from safetensors.torch import save_file
from torch.utils.data import Dataset
from transformers import AutoTokenizer, PretrainedConfig
def prepare_image(
pil_image: PIL.Image.Image, w: int = 512, h: int = 512
) -> torch.Tensor:
pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
arr = np.array(pil_image.convert("RGB"))
arr = arr.astype(np.float32) / 127.5 - 1
arr = np.transpose(arr, [2, 0, 1])
image = torch.from_numpy(arr).unsqueeze(0)
return image
def prepare_mask(
pil_image: PIL.Image.Image, w: int = 512, h: int = 512
) -> torch.Tensor:
pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
arr = np.array(pil_image.convert("L"))
arr = arr.astype(np.float32) / 255.0
arr = np.expand_dims(arr, 0)
image = torch.from_numpy(arr).unsqueeze(0)
return image
class PreprocessedDataset(Dataset):
def __init__(
self,
csv_path: str,
tokenizer_1,
tokenizer_2,
vae_encoder,
text_encoder_1=None,
text_encoder_2=None,
do_cache: bool = False,
size: int = 512,
text_dropout: float = 0.0,
scale_vae_latents: bool = True,
substitute_caption_map: Dict[str, str] = {},
):
super().__init__()
self.data = pd.read_csv(csv_path)
self.csv_path = csv_path
self.caption = self.data["caption"]
# make it lowercase
self.caption = self.caption.str.lower()
for key, value in substitute_caption_map.items():
self.caption = self.caption.str.replace(key.lower(), value)
self.image_path = self.data["image_path"]
if "mask_path" not in self.data.columns:
self.mask_path = None
else:
self.mask_path = self.data["mask_path"]
if text_encoder_1 is None:
self.return_text_embeddings = False
else:
self.text_encoder_1 = text_encoder_1
self.text_encoder_2 = text_encoder_2
self.return_text_embeddings = True
assert (
NotImplementedError
), "Preprocessing Text Encoder is not implemented yet"
self.tokenizer_1 = tokenizer_1
self.tokenizer_2 = tokenizer_2
self.vae_encoder = vae_encoder
self.scale_vae_latents = scale_vae_latents
self.text_dropout = text_dropout
self.size = size
if do_cache:
self.vae_latents = []
self.tokens_tuple = []
self.masks = []
self.do_cache = True
print("Captions to train on: ")
for idx in range(len(self.data)):
token, vae_latent, mask = self._process(idx)
self.vae_latents.append(vae_latent)
self.tokens_tuple.append(token)
self.masks.append(mask)
del self.vae_encoder
else:
self.do_cache = False
@torch.no_grad()
def _process(
self, idx: int
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
image_path = self.image_path[idx]
image_path = os.path.join(os.path.dirname(self.csv_path), image_path)
image = PIL.Image.open(image_path).convert("RGB")
image = prepare_image(image, self.size, self.size).to(
dtype=self.vae_encoder.dtype, device=self.vae_encoder.device
)
caption = self.caption[idx]
print(caption)
# tokenizer_1
ti1 = self.tokenizer_1(
caption,
padding="max_length",
max_length=77,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
).input_ids
ti2 = self.tokenizer_2(
caption,
padding="max_length",
max_length=77,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
).input_ids
vae_latent = self.vae_encoder.encode(image).latent_dist.sample()
if self.scale_vae_latents:
vae_latent = vae_latent * self.vae_encoder.config.scaling_factor
if self.mask_path is None:
mask = torch.ones_like(
vae_latent, dtype=self.vae_encoder.dtype, device=self.vae_encoder.device
)
else:
mask_path = self.mask_path[idx]
mask_path = os.path.join(os.path.dirname(self.csv_path), mask_path)
mask = PIL.Image.open(mask_path)
mask = prepare_mask(mask, self.size, self.size).to(
dtype=self.vae_encoder.dtype, device=self.vae_encoder.device
)
mask = torch.nn.functional.interpolate(
mask, size=(vae_latent.shape[-2], vae_latent.shape[-1]), mode="nearest"
)
mask = mask.repeat(1, vae_latent.shape[1], 1, 1)
assert len(mask.shape) == 4 and len(vae_latent.shape) == 4
return (ti1.squeeze(), ti2.squeeze()), vae_latent.squeeze(), mask.squeeze()
def __len__(self) -> int:
return len(self.data)
def atidx(
self, idx: int
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
if self.do_cache:
return self.tokens_tuple[idx], self.vae_latents[idx], self.masks[idx]
else:
return self._process(idx)
def __getitem__(
self, idx: int
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
token, vae_latent, mask = self.atidx(idx)
return token, vae_latent, mask
def import_model_class_from_model_name_or_path(
pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path, subfolder=subfolder, revision=revision
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "CLIPTextModelWithProjection":
from transformers import CLIPTextModelWithProjection
return CLIPTextModelWithProjection
else:
raise ValueError(f"{model_class} is not supported.")
def load_models(pretrained_model_name_or_path, revision, device, weight_dtype):
tokenizer_one = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path,
subfolder="tokenizer",
revision=revision,
use_fast=False,
)
tokenizer_two = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path,
subfolder="tokenizer_2",
revision=revision,
use_fast=False,
)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(
pretrained_model_name_or_path, subfolder="scheduler"
)
# import correct text encoder classes
text_encoder_cls_one = import_model_class_from_model_name_or_path(
pretrained_model_name_or_path, revision
)
text_encoder_cls_two = import_model_class_from_model_name_or_path(
pretrained_model_name_or_path, revision, subfolder="text_encoder_2"
)
text_encoder_one = text_encoder_cls_one.from_pretrained(
pretrained_model_name_or_path, subfolder="text_encoder", revision=revision
)
text_encoder_two = text_encoder_cls_two.from_pretrained(
pretrained_model_name_or_path, subfolder="text_encoder_2", revision=revision
)
vae = AutoencoderKL.from_pretrained(
pretrained_model_name_or_path, subfolder="vae", revision=revision
)
unet = UNet2DConditionModel.from_pretrained(
pretrained_model_name_or_path, subfolder="unet", revision=revision
)
vae.requires_grad_(False)
text_encoder_one.requires_grad_(False)
text_encoder_two.requires_grad_(False)
unet.to(device, dtype=weight_dtype)
vae.to(device, dtype=torch.float32)
text_encoder_one.to(device, dtype=weight_dtype)
text_encoder_two.to(device, dtype=weight_dtype)
return (
tokenizer_one,
tokenizer_two,
noise_scheduler,
text_encoder_one,
text_encoder_two,
vae,
unet,
)
def unet_attn_processors_state_dict(unet) -> Dict[str, torch.tensor]:
"""
Returns:
a state dict containing just the attention processor parameters.
"""
attn_processors = unet.attn_processors
attn_processors_state_dict = {}
for attn_processor_key, attn_processor in attn_processors.items():
for parameter_key, parameter in attn_processor.state_dict().items():
attn_processors_state_dict[
f"{attn_processor_key}.{parameter_key}"
] = parameter
return attn_processors_state_dict
class TokenEmbeddingsHandler:
def __init__(self, text_encoders, tokenizers):
self.text_encoders = text_encoders
self.tokenizers = tokenizers
self.train_ids: Optional[torch.Tensor] = None
self.inserting_toks: Optional[List[str]] = None
self.embeddings_settings = {}
def initialize_new_tokens(self, inserting_toks: List[str]):
idx = 0
for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):
assert isinstance(
inserting_toks, list
), "inserting_toks should be a list of strings."
assert all(
isinstance(tok, str) for tok in inserting_toks
), "All elements in inserting_toks should be strings."
self.inserting_toks = inserting_toks
special_tokens_dict = {"additional_special_tokens": self.inserting_toks}
tokenizer.add_special_tokens(special_tokens_dict)
text_encoder.resize_token_embeddings(len(tokenizer))
self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks)
# random initialization of new tokens
std_token_embedding = (
text_encoder.text_model.embeddings.token_embedding.weight.data.std()
)
print(f"{idx} text encodedr's std_token_embedding: {std_token_embedding}")
text_encoder.text_model.embeddings.token_embedding.weight.data[
self.train_ids
] = (
torch.randn(
len(self.train_ids), text_encoder.text_model.config.hidden_size
)
.to(device=self.device)
.to(dtype=self.dtype)
* std_token_embedding
)
self.embeddings_settings[
f"original_embeddings_{idx}"
] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
self.embeddings_settings[f"std_token_embedding_{idx}"] = std_token_embedding
inu = torch.ones((len(tokenizer),), dtype=torch.bool)
inu[self.train_ids] = False
self.embeddings_settings[f"index_no_updates_{idx}"] = inu
print(self.embeddings_settings[f"index_no_updates_{idx}"].shape)
idx += 1
def save_embeddings(self, file_path: str):
assert (
self.train_ids is not None
), "Initialize new tokens before saving embeddings."
tensors = {}
for idx, text_encoder in enumerate(self.text_encoders):
assert text_encoder.text_model.embeddings.token_embedding.weight.data.shape[
0
] == len(self.tokenizers[0]), "Tokenizers should be the same."
new_token_embeddings = (
text_encoder.text_model.embeddings.token_embedding.weight.data[
self.train_ids
]
)
tensors[f"text_encoders_{idx}"] = new_token_embeddings
save_file(tensors, file_path)
@property
def dtype(self):
return self.text_encoders[0].dtype
@property
def device(self):
return self.text_encoders[0].device
def _load_embeddings(self, loaded_embeddings, tokenizer, text_encoder):
# Assuming new tokens are of the format <s_i>
self.inserting_toks = [f"<s{i}>" for i in range(loaded_embeddings.shape[0])]
special_tokens_dict = {"additional_special_tokens": self.inserting_toks}
tokenizer.add_special_tokens(special_tokens_dict)
text_encoder.resize_token_embeddings(len(tokenizer))
self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks)
assert self.train_ids is not None, "New tokens could not be converted to IDs."
text_encoder.text_model.embeddings.token_embedding.weight.data[
self.train_ids
] = loaded_embeddings.to(device=self.device).to(dtype=self.dtype)
@torch.no_grad()
def retract_embeddings(self):
for idx, text_encoder in enumerate(self.text_encoders):
index_no_updates = self.embeddings_settings[f"index_no_updates_{idx}"]
text_encoder.text_model.embeddings.token_embedding.weight.data[
index_no_updates
] = (
self.embeddings_settings[f"original_embeddings_{idx}"][index_no_updates]
.to(device=text_encoder.device)
.to(dtype=text_encoder.dtype)
)
# for the parts that were updated, we need to normalize them
# to have the same std as before
std_token_embedding = self.embeddings_settings[f"std_token_embedding_{idx}"]
index_updates = ~index_no_updates
new_embeddings = (
text_encoder.text_model.embeddings.token_embedding.weight.data[
index_updates
]
)
off_ratio = std_token_embedding / new_embeddings.std()
new_embeddings = new_embeddings * (off_ratio**0.1)
text_encoder.text_model.embeddings.token_embedding.weight.data[
index_updates
] = new_embeddings
def load_embeddings(self, file_path: str):
with safe_open(file_path, framework="pt", device=self.device.type) as f:
for idx in range(len(self.text_encoders)):
text_encoder = self.text_encoders[idx]
tokenizer = self.tokenizers[idx]
loaded_embeddings = f.get_tensor(f"text_encoders_{idx}")
self._load_embeddings(loaded_embeddings, tokenizer, text_encoder) |