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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 TrainingStrategy, TrainingCallbacks, save_samples
def textual_inversion_strategy_callbacks(
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]],
gradient_checkpointing: bool = False,
use_emb_decay: bool = False,
emb_decay_target: float = 0.4,
emb_decay: float = 1e-2,
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,
tokenizer=tokenizer,
vae=vae,
sample_scheduler=sample_scheduler,
train_dataloader=train_dataloader,
val_dataloader=val_dataloader,
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,
)
ema_embeddings.to(accelerator.device)
else:
ema_embeddings = None
def ema_context():
if ema_embeddings is not None:
return ema_embeddings.apply_temporary(
text_encoder.text_model.embeddings.temp_token_embedding.parameters()
)
else:
return nullcontext()
def on_accum_model():
return text_encoder.text_model.embeddings.temp_token_embedding
@contextmanager
def on_train(epoch: int):
tokenizer.train()
yield
@contextmanager
def on_eval():
tokenizer.eval()
with ema_context():
yield
@torch.no_grad()
def on_before_optimize(lr: float, epoch: int):
if use_emb_decay:
w = text_encoder.text_model.embeddings.temp_token_embedding.weight
return torch.all(w.grad == 0, dim=1)
@torch.no_grad()
def on_after_optimize(zero_ids, lr: float):
if ema_embeddings is not None:
ema_embeddings.step(text_encoder.text_model.embeddings.temp_token_embedding.parameters())
if use_emb_decay:
lambda_ = emb_decay * lr
if lambda_ != 0:
w = text_encoder.text_model.embeddings.temp_token_embedding.weight
mask = torch.ones(w.shape[0], dtype=torch.bool)
mask[zero_ids] = False
norm = w[mask, :].norm(dim=-1, keepdim=True)
w[mask].add_((w[mask] / norm.clamp_min(1e-12)) * lambda_ * (emb_decay_target - norm))
def on_log():
if ema_embeddings is not None:
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 / f"{slugify(token)}_{step}_{postfix}.bin"
)
@torch.no_grad()
def on_sample(step):
with ema_context():
unet_ = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
orig_unet_dtype = unet_.dtype
orig_text_encoder_dtype = text_encoder_.dtype
unet_.to(dtype=weight_dtype)
text_encoder_.to(dtype=weight_dtype)
save_samples_(step=step, unet=unet_, text_encoder=text_encoder_)
unet_.to(dtype=orig_unet_dtype)
text_encoder_.to(dtype=orig_text_encoder_dtype)
del unet_
del text_encoder_
if torch.cuda.is_available():
torch.cuda.empty_cache()
return TrainingCallbacks(
on_accum_model=on_accum_model,
on_train=on_train,
on_eval=on_eval,
on_before_optimize=on_before_optimize,
on_after_optimize=on_after_optimize,
on_log=on_log,
on_checkpoint=on_checkpoint,
on_sample=on_sample,
)
def textual_inversion_prepare(
accelerator: Accelerator,
text_encoder: CLIPTextModel,
unet: UNet2DConditionModel,
optimizer: torch.optim.Optimizer,
train_dataloader: DataLoader,
val_dataloader: Optional[DataLoader],
lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
gradient_checkpointing: bool = False,
**kwargs
):
weight_dtype = torch.float32
if accelerator.state.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.state.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler)
unet.to(accelerator.device, dtype=weight_dtype)
unet.requires_grad_(False)
unet.eval()
if gradient_checkpointing:
unet.train()
text_encoder.text_model.encoder.requires_grad_(False)
text_encoder.text_model.final_layer_norm.requires_grad_(False)
text_encoder.eval()
return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {}
textual_inversion_strategy = TrainingStrategy(
callbacks=textual_inversion_strategy_callbacks,
prepare=textual_inversion_prepare,
)
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