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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 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 TrainingCallbacks, save_samples
def dreambooth_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,
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,
):
if accelerator.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
raise ValueError(
"Gradient accumulation is not supported when training the text encoder in distributed training. "
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
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_unet = EMAModel(
unet.parameters(),
inv_gamma=ema_inv_gamma,
power=ema_power,
max_value=ema_max_decay,
)
else:
ema_unet = None
def ema_context():
if use_ema:
return ema_unet.apply_temporary(unet.parameters())
else:
return nullcontext()
def on_model():
return unet
def on_prepare():
unet.requires_grad_(True)
text_encoder.requires_grad_(True)
text_encoder.text_model.embeddings.persist()
text_encoder.text_model.embeddings.temp_token_embedding.requires_grad_(False)
if use_ema:
ema_unet.to(accelerator.device)
@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(epoch: int):
if accelerator.sync_gradients:
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 use_ema:
ema_unet.step(unet.parameters())
def on_log():
if use_ema:
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)
text_encoder_ = accelerator.unwrap_model(text_encoder)
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():
save_samples_(step=step)
return TrainingCallbacks(
on_prepare=on_prepare,
on_model=on_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,
)
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