mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-19 14:58:12 +00:00
72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
import os, torch
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from tqdm import tqdm
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from accelerate import Accelerator
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from .training_module import DiffusionTrainingModule
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from .logger import ModelLogger
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def launch_training_task(
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accelerator: Accelerator,
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dataset: torch.utils.data.Dataset,
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model: DiffusionTrainingModule,
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model_logger: ModelLogger,
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learning_rate: float = 1e-5,
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weight_decay: float = 1e-2,
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num_workers: int = 1,
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save_steps: int = None,
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num_epochs: int = 1,
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args = None,
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):
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if args is not None:
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learning_rate = args.learning_rate
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weight_decay = args.weight_decay
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num_workers = args.dataset_num_workers
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save_steps = args.save_steps
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num_epochs = args.num_epochs
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optimizer = torch.optim.AdamW(model.trainable_modules(), lr=learning_rate, weight_decay=weight_decay)
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scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
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dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers)
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model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
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for epoch_id in range(num_epochs):
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for data in tqdm(dataloader):
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with accelerator.accumulate(model):
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optimizer.zero_grad()
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if dataset.load_from_cache:
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loss = model({}, inputs=data)
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else:
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loss = model(data)
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accelerator.backward(loss)
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optimizer.step()
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model_logger.on_step_end(accelerator, model, save_steps)
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scheduler.step()
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if save_steps is None:
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model_logger.on_epoch_end(accelerator, model, epoch_id)
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model_logger.on_training_end(accelerator, model, save_steps)
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def launch_data_process_task(
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accelerator: Accelerator,
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dataset: torch.utils.data.Dataset,
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model: DiffusionTrainingModule,
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model_logger: ModelLogger,
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num_workers: int = 8,
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args = None,
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):
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if args is not None:
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num_workers = args.dataset_num_workers
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dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], num_workers=num_workers)
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model, dataloader = accelerator.prepare(model, dataloader)
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for data_id, data in enumerate(tqdm(dataloader)):
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with accelerator.accumulate(model):
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with torch.no_grad():
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folder = os.path.join(model_logger.output_path, str(accelerator.process_index))
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os.makedirs(folder, exist_ok=True)
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save_path = os.path.join(model_logger.output_path, str(accelerator.process_index), f"{data_id}.pth")
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data = model(data)
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torch.save(data, save_path)
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