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from contextlib import nullcontext
from typing import Optional
from functools import partial
from contextlib import contextmanager, nullcontext
from pathlib import Path
import torch
from torch.utils.data import DataLoader
from accelerate import Accelerator
from transformers import CLIPTextModel
from diffusers import AutoencoderKL, UNet2DConditionModel, DPMSolverMultistepScheduler
from slugify import slugify
from models.clip.tokenizer import MultiCLIPTokenizer
from training.util import EMAModel
from training.functional import TrainingCallbacks, save_samples
def textual_inversion_strategy(
accelerator: Accelerator,
unet: UNet2DConditionModel,
text_encoder: CLIPTextModel,
tokenizer: MultiCLIPTokenizer,
vae: AutoencoderKL,
sample_scheduler: DPMSolverMultistepScheduler,
train_dataloader: DataLoader,
val_dataloader: Optional[DataLoader],
sample_output_dir: Path,
checkpoint_output_dir: Path,
seed: int,
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 = 0,
use_ema: bool = False,
ema_inv_gamma: float = 1.0,
ema_power: int = 1,
ema_max_decay: float = 0.9999,
sample_batch_size: int = 1,
sample_num_batches: int = 1,
sample_num_steps: int = 20,
sample_guidance_scale: float = 7.5,
sample_image_size: Optional[int] = None,
):
sample_output_dir.mkdir(parents=True, exist_ok=True)
checkpoint_output_dir.mkdir(parents=True, exist_ok=True)
weight_dtype = torch.float32
if accelerator.state.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.state.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
save_samples_ = partial(
save_samples,
accelerator=accelerator,
unet=unet,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae,
sample_scheduler=sample_scheduler,
train_dataloader=train_dataloader,
val_dataloader=val_dataloader,
dtype=weight_dtype,
output_dir=sample_output_dir,
seed=seed,
batch_size=sample_batch_size,
num_batches=sample_num_batches,
num_steps=sample_num_steps,
guidance_scale=sample_guidance_scale,
image_size=sample_image_size,
)
if use_ema:
ema_embeddings = EMAModel(
text_encoder.text_model.embeddings.temp_token_embedding.parameters(),
inv_gamma=ema_inv_gamma,
power=ema_power,
max_value=ema_max_decay,
)
else:
ema_embeddings = None
def ema_context():
if use_ema:
return ema_embeddings.apply_temporary(
text_encoder.text_model.embeddings.temp_token_embedding.parameters()
)
else:
return nullcontext()
def on_model():
return text_encoder
def on_prepare():
text_encoder.text_model.embeddings.temp_token_embedding.requires_grad_(True)
if use_ema:
ema_embeddings.to(accelerator.device)
if gradient_checkpointing:
unet.train()
@contextmanager
def on_train(epoch: int):
tokenizer.train()
yield
@contextmanager
def on_eval():
tokenizer.eval()
with ema_context():
yield
@torch.no_grad()
def on_after_optimize(lr: float):
if use_emb_decay:
text_encoder.text_model.embeddings.normalize(
emb_decay_target,
min(1.0, max(0.0, emb_decay_factor * ((lr - emb_decay_start) / (learning_rate - emb_decay_start))))
)
if use_ema:
ema_embeddings.step(text_encoder.text_model.embeddings.temp_token_embedding.parameters())
def on_log():
if use_ema:
return {"ema_decay": ema_embeddings.decay}
return {}
@torch.no_grad()
def on_checkpoint(step, postfix):
print(f"Saving checkpoint for step {step}...")
with ema_context():
for (token, ids) in zip(placeholder_tokens, placeholder_token_ids):
text_encoder.text_model.embeddings.save_embed(
ids,
checkpoint_output_dir.joinpath(f"{slugify(token)}_{step}_{postfix}.bin")
)
@torch.no_grad()
def on_sample(step):
with ema_context():
save_samples_(step=step)
return TrainingCallbacks(
on_prepare=on_prepare,
on_model=on_model,
on_train=on_train,
on_eval=on_eval,
on_after_optimize=on_after_optimize,
on_log=on_log,
on_checkpoint=on_checkpoint,
on_sample=on_sample,
)
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