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from typing import Optional
from functools import partial
from contextlib import contextmanager
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 diffusers.loaders import AttnProcsLayers
from slugify import slugify
from models.clip.tokenizer import MultiCLIPTokenizer
from training.util import EMAModel
from training.functional import TrainingStrategy, TrainingCallbacks, save_samples
def lora_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,
lora_layers: AttnProcsLayers,
max_grad_norm: float = 1.0,
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,
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,
)
def on_prepare():
lora_layers.requires_grad_(True)
def on_accum_model():
return unet
@contextmanager
def on_train(epoch: int):
tokenizer.train()
yield
@contextmanager
def on_eval():
tokenizer.eval()
yield
def on_before_optimize(lr: float, epoch: int):
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(lora_layers.parameters(), max_grad_norm)
@torch.no_grad()
def on_checkpoint(step, postfix):
print(f"Saving checkpoint for step {step}...")
unet_ = accelerator.unwrap_model(unet, False)
unet_.save_attn_procs(checkpoint_output_dir / f"{step}_{postfix}")
del unet_
@torch.no_grad()
def on_sample(step):
save_samples_(step=step)
return TrainingCallbacks(
on_prepare=on_prepare,
on_accum_model=on_accum_model,
on_train=on_train,
on_eval=on_eval,
on_before_optimize=on_before_optimize,
on_checkpoint=on_checkpoint,
on_sample=on_sample,
)
def lora_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,
lora_layers: AttnProcsLayers,
**kwargs
):
lora_layers, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
lora_layers, optimizer, train_dataloader, val_dataloader, lr_scheduler)
return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {"lora_layers": lora_layers}
lora_strategy = TrainingStrategy(
callbacks=lora_strategy_callbacks,
prepare=lora_prepare,
)
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