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from contextlib import contextmanager, nullcontext
import torch
from slugify import slugify
from diffusers import UNet2DConditionModel
from transformers import CLIPTextModel
from trainer.base import TrainingStrategy, Checkpointer
from training.util import EMAModel
class TextualInversionCheckpointer(Checkpointer):
def __init__(
self,
ema_embeddings: EMAModel,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
self.ema_embeddings = ema_embeddings
@torch.no_grad()
def checkpoint(self, step, postfix):
print(f"Saving checkpoint for step {step}...")
checkpoints_path = self.output_dir.joinpath("checkpoints")
checkpoints_path.mkdir(parents=True, exist_ok=True)
text_encoder = self.accelerator.unwrap_model(self.text_encoder)
ema_context = self.ema_embeddings.apply_temporary(
text_encoder.text_model.embeddings.temp_token_embedding.parameters()
) if self.ema_embeddings is not None else nullcontext()
with ema_context:
for (token, ids) in zip(self.placeholder_tokens, self.placeholder_token_ids):
text_encoder.text_model.embeddings.save_embed(
ids,
checkpoints_path.joinpath(f"{slugify(token)}_{step}_{postfix}.bin")
)
@torch.inference_mode()
def save_samples(self, step):
ema_context = self.ema_embeddings.apply_temporary(
self.text_encoder.text_model.embeddings.temp_token_embedding.parameters()
) if self.ema_embeddings is not None else nullcontext()
with ema_context:
super().save_samples(step)
class TextualInversionTrainingStrategy(TrainingStrategy):
def __init__(
self,
unet: UNet2DConditionModel,
text_encoder: CLIPTextModel,
placeholder_tokens: list[str],
placeholder_token_ids: list[list[int]],
learning_rate: float,
gradient_checkpointing: bool = False,
use_emb_decay: bool = False,
emb_decay_target: float = 0.4,
emb_decay_factor: float = 1,
emb_decay_start: float = 1e-4,
use_ema: bool = False,
ema_inv_gamma: float = 1.0,
ema_power: int = 1,
ema_max_decay: float = 0.9999,
*args,
**kwargs,
):
super().__init__(
unet=unet,
text_encoder=text_encoder,
*args,
**kwargs
)
self.text_encoder = text_encoder
self.unet = unet
self.placeholder_tokens = placeholder_tokens
self.placeholder_token_ids = placeholder_token_ids
self.gradient_checkpointing = gradient_checkpointing
self.learning_rate = learning_rate
self.use_emb_decay = use_emb_decay
self.emb_decay_target = emb_decay_target
self.emb_decay_factor = emb_decay_factor
self.emb_decay_start = emb_decay_start
self.text_encoder.text_model.embeddings.temp_token_embedding.requires_grad_(True)
self.ema_embeddings = None
if use_ema:
self.ema_embeddings = EMAModel(
self.text_encoder.text_model.embeddings.temp_token_embedding.parameters(),
inv_gamma=ema_inv_gamma,
power=ema_power,
max_value=ema_max_decay,
)
self.checkpointer = TextualInversionCheckpointer(
unet=unet,
text_encoder=text_encoder,
ema_embeddings=self.ema_embeddings,
*args,
**kwargs
)
@property
def main_model(self):
return self.text_encoder
@contextmanager
def on_train(self, epoch: int):
try:
if self.gradient_checkpointing:
self.unet.train()
with super().on_eval():
yield
finally:
pass
@contextmanager
def on_eval(self):
try:
if self.gradient_checkpointing:
self.unet.eval()
ema_context = self.ema_embeddings.apply_temporary(
self.text_encoder.text_model.embeddings.temp_token_embedding.parameters()
) if self.ema_embeddings is not None else nullcontext()
with ema_context, super().on_eval():
yield
finally:
pass
@torch.no_grad()
def on_after_optimize(self, lr: float):
if self.use_emb_decay:
self.text_encoder.text_model.embeddings.normalize(
self.emb_decay_target,
min(1.0, max(0.0, self.emb_decay_factor * ((lr - self.emb_decay_start) / (self.learning_rate - self.emb_decay_start))))
)
if self.ema_embeddings is not None:
self.ema_embeddings.step(self.text_encoder.text_model.embeddings.temp_token_embedding.parameters())
def on_log(self):
log = super().on_log()
added = {}
if self.ema_embeddings is not None:
added = {"ema_decay": self.ema_embeddings.decay}
return log.update(added)
|
