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from typing import Optional
from functools import partial
from contextlib import contextmanager, nullcontext
from pathlib import Path
import itertools
import torch
from torch.utils.data import DataLoader
from accelerate import Accelerator
from transformers import CLIPTextModel
from diffusers import AutoencoderKL, UNet2DConditionModel, DPMSolverMultistepScheduler
from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
from models.clip.tokenizer import MultiCLIPTokenizer
from training.util import EMAModel
from training.functional import TrainingStrategy, TrainingCallbacks, save_samples
def dreambooth_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,
train_text_encoder_epochs: int,
max_grad_norm: float = 1.0,
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,
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_unet = EMAModel(
unet.parameters(),
inv_gamma=ema_inv_gamma,
power=ema_power,
max_value=ema_max_decay,
)
ema_unet.to(accelerator.device)
else:
ema_unet = None
def ema_context():
if ema_unet is not None:
return ema_unet.apply_temporary(unet.parameters())
else:
return nullcontext()
def on_accum_model():
return unet
@contextmanager
def on_train(epoch: int):
tokenizer.train()
if epoch < train_text_encoder_epochs:
text_encoder.train()
elif epoch == train_text_encoder_epochs:
text_encoder.requires_grad_(False)
text_encoder.eval()
yield
@contextmanager
def on_eval():
tokenizer.eval()
text_encoder.eval()
with ema_context():
yield
def on_before_optimize(lr: float, epoch: int):
params_to_clip = [unet.parameters()]
if epoch < train_text_encoder_epochs:
params_to_clip.append(text_encoder.parameters())
accelerator.clip_grad_norm_(itertools.chain(*params_to_clip), max_grad_norm)
@torch.no_grad()
def on_after_optimize(_, lr: float):
if ema_unet is not None:
ema_unet.step(unet.parameters())
def on_log():
if ema_unet is not None:
return {"ema_decay": ema_unet.decay}
return {}
@torch.no_grad()
def on_checkpoint(step, postfix):
if postfix != "end":
return
print("Saving model...")
unet_ = accelerator.unwrap_model(unet, keep_fp32_wrapper=False)
text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=False)
with ema_context():
pipeline = VlpnStableDiffusion(
text_encoder=text_encoder_,
vae=vae,
unet=unet_,
tokenizer=tokenizer,
scheduler=sample_scheduler,
)
pipeline.save_pretrained(checkpoint_output_dir)
del unet_
del text_encoder_
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
@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 dreambooth_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,
**kwargs
):
text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler)
text_encoder.text_model.embeddings.requires_grad_(False)
return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {}
dreambooth_strategy = TrainingStrategy(
callbacks=dreambooth_strategy_callbacks,
prepare=dreambooth_prepare
)
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