diffsynth/diffusion/runner.py

import os, torch
from tqdm import tqdm
from accelerate import Accelerator
from .training_module import DiffusionTrainingModule
from .logger import ModelLogger


def launch_training_task(
    accelerator: Accelerator,
    dataset: torch.utils.data.Dataset,
    model: DiffusionTrainingModule,
    model_logger: ModelLogger,
    learning_rate: float = 1e-5,
    weight_decay: float = 1e-2,
    num_workers: int = 1,
    save_steps: int = None,
    num_epochs: int = 1,
    args = None,
):
    if args is not None:
        learning_rate = args.learning_rate
        weight_decay = args.weight_decay
        num_workers = args.dataset_num_workers
        save_steps = args.save_steps
        num_epochs = args.num_epochs
    
    optimizer = torch.optim.AdamW(model.trainable_modules(), lr=learning_rate, weight_decay=weight_decay)
    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers)
    
    model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
    
    for epoch_id in range(num_epochs):
        progress_bar = tqdm(
            dataloader, 
            desc=f"Epoch {epoch_id + 1}/{num_epochs}", 
            disable=not accelerator.is_main_process,
            leave=True
        )
        for data in progress_bar:
            # Skip samples dropped by the dataset (e.g. corrupted videos).
            if data is None:
                continue

            if data.get("video") is None:
                continue
            with accelerator.accumulate(model):
                optimizer.zero_grad()
                if dataset.load_from_cache:
                    loss = model({}, inputs=data)
                else:
                    loss = model(data)
                accelerator.backward(loss)
                optimizer.step()
                model_logger.on_step_end(accelerator, model, save_steps)
                scheduler.step()
                if accelerator.is_main_process:
                    current_lr = 0.0
                    if hasattr(optimizer, "param_groups"):
                        current_lr = optimizer.param_groups[0]['lr']

                    progress_bar.set_postfix(
                        loss=f"{loss.item():.4f}",
                        lr=f"{current_lr:.1e}"
                    )
        if save_steps is None:
            model_logger.on_epoch_end(accelerator, model, epoch_id)
    model_logger.on_training_end(accelerator, model, save_steps)


def launch_data_process_task(
    accelerator: Accelerator,
    dataset: torch.utils.data.Dataset,
    model: DiffusionTrainingModule,
    model_logger: ModelLogger,
    num_workers: int = 8,
    args = None,
):
    if args is not None:
        num_workers = args.dataset_num_workers
        
    dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], num_workers=num_workers)
    model, dataloader = accelerator.prepare(model, dataloader)
    
    for data_id, data in enumerate(tqdm(dataloader)):
        with accelerator.accumulate(model):
            with torch.no_grad():
                folder = os.path.join(model_logger.output_path, str(accelerator.process_index))
                os.makedirs(folder, exist_ok=True)
                save_path = os.path.join(model_logger.output_path, str(accelerator.process_index), f"{data_id}.pth")
                data = model(data)
                torch.save(data, save_path)