# -*- encoding: utf-8 -*- ''' Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. @File : train.py @Time : 2022/11/09 22:27 @Author : Gan Ruyi @Version : 1.0 @Contact : ganruyi@idea.edu.cn @License : (C)Copyright 2022-2023, CCNL-IDEA ''' import hashlib import itertools import os from pathlib import Path from tqdm.auto import tqdm import torch import argparse from pytorch_lightning import ( LightningModule, Trainer, ) from pytorch_lightning.callbacks import ( LearningRateMonitor, ) from transformers import BertTokenizer, BertModel, CLIPTokenizer, CLIPTextModel from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel from torch.nn import functional as F from fengshen.data.dreambooth_datasets.dreambooth_datasets import PromptDataset, DreamBoothDataset from fengshen.data.universal_datamodule import UniversalDataModule from fengshen.models.model_utils import ( add_module_args, configure_optimizers, get_total_steps, ) from fengshen.utils.universal_checkpoint import UniversalCheckpoint from fengshen.data.dreambooth_datasets.dreambooth_datasets import add_data_args class StableDiffusionDreamBooth(LightningModule): @staticmethod def add_module_specific_args(parent_parser): parser = parent_parser.add_argument_group('Taiyi Stable Diffusion Module') parser.add_argument('--train_text_encoder', action='store_true', default=False) # dreambooth train unet only default parser.add_argument('--train_unet', action='store_true', default=True) return parent_parser def __init__(self, args): super().__init__() if 'Taiyi-Stable-Diffusion-1B-Chinese-v0.1' in args.model_path: self.tokenizer = BertTokenizer.from_pretrained( args.model_path, subfolder="tokenizer") self.text_encoder = BertModel.from_pretrained( args.model_path, subfolder="text_encoder") # load from taiyi_finetune-v0 else: self.tokenizer = CLIPTokenizer.from_pretrained( args.model_path, subfolder="tokenizer") self.text_encoder = CLIPTextModel.from_pretrained( args.model_path, subfolder="text_encoder") self.vae = AutoencoderKL.from_pretrained( args.model_path, subfolder="vae") self.unet = UNet2DConditionModel.from_pretrained( args.model_path, subfolder="unet") self.noise_scheduler = DDPMScheduler.from_config( args.model_path, subfolder="scheduler") # set model self.vae.requires_grad_(False) if not args.train_text_encoder: self.requires_grad_(False) if not args.train_unet: self.requires_grad_(False) self.save_hyperparameters(args) def generate_extra_data(self): global_rank = self.global_rank device = self.trainer.device_ids[global_rank] print('generate on device {} of global_rank {}'.format(device, global_rank)) class_images_dir = Path(self.hparams.class_data_dir) if not class_images_dir.exists(): class_images_dir.mkdir(parents=True) cur_class_images = len(list(class_images_dir.iterdir())) if cur_class_images < self.hparams.num_class_images: pipeline = StableDiffusionPipeline.from_pretrained( self.hparams.model_path, safety_checker=None, ) pipeline.set_progress_bar_config(disable=True) num_new_images = self.hparams.num_class_images - cur_class_images print(f"Number of class images to sample: {num_new_images}.") sample_dataset = PromptDataset(self.hparams.class_prompt, num_new_images) sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=self.hparams.sample_batch_size) pipeline.to(device) for example in tqdm( sample_dataloader, desc="Generating class images", disable=global_rank != 0 ): images = pipeline(example["prompt"]).images for i, image in enumerate(images): hash_image = hashlib.sha1(image.tobytes()).hexdigest() image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" image.save(image_filename) del pipeline # if torch.cuda.is_available(): # torch.cuda.empty_cache() def setup(self, stage) -> None: if self.hparams.with_prior_preservation: self.generate_extra_data() if stage == 'fit': self.total_steps = get_total_steps(self.trainer, self.hparams) print('Total steps: {}' .format(self.total_steps)) def configure_optimizers(self): model_params = [] if self.hparams.train_unet and self.hparams.train_text_encoder: model_params = itertools.chain(self.unet.parameters(), self.text_encoder.parameters()) elif self.hparams.train_unet: model_params = self.unet.parameters() elif self.hparams.train_text_encoder: model_params = self.text_encoder.parameters() return configure_optimizers(self, model_params=model_params) def training_step(self, batch, batch_idx): if self.hparams.train_text_encoder: self.text_encoder.train() if self.hparams.train_unet: self.unet.train() latents = self.vae.encode(batch["pixel_values"]).latent_dist.sample() latents = latents * 0.18215 # Sample noise that we'll add to the latents noise = torch.randn(latents.shape).to(latents.device) noise = noise.to(dtype=self.unet.dtype) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint( 0, self.noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) timesteps = timesteps.long() # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps) noisy_latents = noisy_latents.to(dtype=self.unet.dtype) # Get the text embedding for conditioning # with torch.no_grad(): encoder_hidden_states = self.text_encoder(batch["input_ids"])[0] # Predict the noise residual noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states).sample if self.hparams.with_prior_preservation: # Chunk the noise and noise_pred into two parts and compute the loss on each part separately. noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0) noise, noise_prior = torch.chunk(noise, 2, dim=0) # Compute instance loss loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() # Compute prior loss prior_loss = F.mse_loss(noise_pred_prior, noise_prior, reduction="mean") # Add the prior loss to the instance loss. loss = loss + args.prior_loss_weight * prior_loss else: loss = F.mse_loss(noise_pred, noise, reduction="mean") self.log("train_loss", loss.item(), on_epoch=False, prog_bar=True, logger=True) if self.trainer.global_rank == 0: if (self.global_step+1) % 5000 == 0: print('saving model...') pipeline = StableDiffusionPipeline.from_pretrained( args.model_path, unet=self.unet, text_encoder=self.text_encoder, tokenizer=self.tokenizer, ) pipeline.save_pretrained(os.path.join( args.default_root_dir, f'hf_out_{self.trainer.current_epoch}')) return {"loss": loss} def on_train_end(self) -> None: if self.trainer.global_rank == 0: print('saving model...') pipeline = StableDiffusionPipeline.from_pretrained( args.model_path, unet=self.unet, text_encoder=self.text_encoder, tokenizer=self.tokenizer, ) pipeline.save_pretrained(os.path.join( args.default_root_dir, f'hf_out_{self.trainer.current_epoch}')) def on_load_checkpoint(self, checkpoint) -> None: # 兼容低版本lightning，低版本lightning从ckpt起来时steps数会被重置为0 global_step_offset = checkpoint["global_step"] if 'global_samples' in checkpoint: self.consumed_samples = checkpoint['global_samples'] self.trainer.fit_loop.epoch_loop._batches_that_stepped = global_step_offset if __name__ == '__main__': args_parser = argparse.ArgumentParser() args_parser = add_module_args(args_parser) args_parser = add_data_args(args_parser) args_parser = UniversalDataModule.add_data_specific_args(args_parser) args_parser = Trainer.add_argparse_args(args_parser) args_parser = StableDiffusionDreamBooth.add_module_specific_args(args_parser) args_parser = UniversalCheckpoint.add_argparse_args(args_parser) args = args_parser.parse_args() model = StableDiffusionDreamBooth(args) tokenizer = model.tokenizer datasets = DreamBoothDataset( instance_data_dir=args.instance_data_dir, instance_prompt=args.instance_prompt, tokenizer=tokenizer, class_data_dir=args.class_data_dir, class_prompt=args.class_prompt, size=512, center_crop=args.center_crop, ) # construct the datasets to a dict for universal_datamodule datasets = {'train': datasets} def collate_fn(examples): # print(examples) input_ids = [example["instance_prompt_ids"] for example in examples] pixel_values = [example["instance_images"] for example in examples] # Concat class and instance examples for prior preservation. # We do this to avoid doing two forward passes. if args.with_prior_preservation: input_ids += [example["class_prompt_ids"] for example in examples] pixel_values += [example["class_images"] for example in examples] pixel_values = torch.stack(pixel_values) pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() input_ids = tokenizer.pad( {"input_ids": input_ids}, padding="max_length", max_length=tokenizer.model_max_length, return_tensors="pt", ).input_ids batch = { "input_ids": input_ids, "pixel_values": pixel_values, } return batch datamodule = UniversalDataModule( tokenizer=tokenizer, collate_fn=collate_fn, args=args, datasets=datasets) lr_monitor = LearningRateMonitor(logging_interval='step') checkpoint_callback = UniversalCheckpoint(args) trainer = Trainer.from_argparse_args(args, callbacks=[ lr_monitor, checkpoint_callback]) trainer.fit(model, datamodule, ckpt_path=args.load_ckpt_path)