import copy import glob import os import shutil import time from collections import OrderedDict from PIL import Image from PIL.ImageOps import exif_transpose from safetensors.torch import save_file, load_file from torch.utils.data import DataLoader, ConcatDataset import torch from torch import nn from torchvision.transforms import transforms from jobs.process import BaseTrainProcess from toolkit.image_utils import show_tensors from toolkit.kohya_model_util import load_vae, convert_diffusers_back_to_ldm from toolkit.data_loader import ImageDataset from toolkit.losses import ComparativeTotalVariation, get_gradient_penalty, PatternLoss from toolkit.metadata import get_meta_for_safetensors from toolkit.optimizer import get_optimizer from toolkit.style import get_style_model_and_losses from toolkit.train_tools import get_torch_dtype from diffusers import AutoencoderKL from tqdm import tqdm import time import numpy as np from .models.vgg19_critic import Critic from torchvision.transforms import Resize import lpips IMAGE_TRANSFORMS = transforms.Compose( [ transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) def unnormalize(tensor): return (tensor / 2 + 0.5).clamp(0, 1) class TrainVAEProcess(BaseTrainProcess): def __init__(self, process_id: int, job, config: OrderedDict): super().__init__(process_id, job, config) self.data_loader = None self.vae = None self.device = self.get_conf('device', self.job.device) self.vae_path = self.get_conf('vae_path', required=True) self.datasets_objects = self.get_conf('datasets', required=True) self.batch_size = self.get_conf('batch_size', 1, as_type=int) self.resolution = self.get_conf('resolution', 256, as_type=int) self.learning_rate = self.get_conf('learning_rate', 1e-6, as_type=float) self.sample_every = self.get_conf('sample_every', None) self.optimizer_type = self.get_conf('optimizer', 'adam') self.epochs = self.get_conf('epochs', None, as_type=int) self.max_steps = self.get_conf('max_steps', None, as_type=int) self.save_every = self.get_conf('save_every', None) self.dtype = self.get_conf('dtype', 'float32') self.sample_sources = self.get_conf('sample_sources', None) self.log_every = self.get_conf('log_every', 100, as_type=int) self.style_weight = self.get_conf('style_weight', 0, as_type=float) self.content_weight = self.get_conf('content_weight', 0, as_type=float) self.kld_weight = self.get_conf('kld_weight', 0, as_type=float) self.mse_weight = self.get_conf('mse_weight', 1e0, as_type=float) self.tv_weight = self.get_conf('tv_weight', 1e0, as_type=float) self.lpips_weight = self.get_conf('lpips_weight', 1e0, as_type=float) self.critic_weight = self.get_conf('critic_weight', 1, as_type=float) self.pattern_weight = self.get_conf('pattern_weight', 1, as_type=float) self.optimizer_params = self.get_conf('optimizer_params', {}) self.blocks_to_train = self.get_conf('blocks_to_train', ['all']) self.torch_dtype = get_torch_dtype(self.dtype) self.vgg_19 = None self.style_weight_scalers = [] self.content_weight_scalers = [] self.lpips_loss:lpips.LPIPS = None self.vae_scale_factor = 8 self.step_num = 0 self.epoch_num = 0 self.use_critic = self.get_conf('use_critic', False, as_type=bool) self.critic = None if self.use_critic: self.critic = Critic( device=self.device, dtype=self.dtype, process=self, **self.get_conf('critic', {}) # pass any other params ) if self.sample_every is not None and self.sample_sources is None: raise ValueError('sample_every is specified but sample_sources is not') if self.epochs is None and self.max_steps is None: raise ValueError('epochs or max_steps must be specified') self.data_loaders = [] # check datasets assert isinstance(self.datasets_objects, list) for dataset in self.datasets_objects: if 'path' not in dataset: raise ValueError('dataset must have a path') # check if is dir if not os.path.isdir(dataset['path']): raise ValueError(f"dataset path does is not a directory: {dataset['path']}") # make training folder if not os.path.exists(self.save_root): os.makedirs(self.save_root, exist_ok=True) self._pattern_loss = None def update_training_metadata(self): self.add_meta(OrderedDict({"training_info": self.get_training_info()})) def get_training_info(self): info = OrderedDict({ 'step': self.step_num, 'epoch': self.epoch_num, }) return info def load_datasets(self): if self.data_loader is None: print(f"Loading datasets") datasets = [] for dataset in self.datasets_objects: print(f" - Dataset: {dataset['path']}") ds = copy.copy(dataset) ds['resolution'] = self.resolution image_dataset = ImageDataset(ds) datasets.append(image_dataset) concatenated_dataset = ConcatDataset(datasets) self.data_loader = DataLoader( concatenated_dataset, batch_size=self.batch_size, shuffle=True, num_workers=6 ) def remove_oldest_checkpoint(self): max_to_keep = 4 folders = glob.glob(os.path.join(self.save_root, f"{self.job.name}*_diffusers")) if len(folders) > max_to_keep: folders.sort(key=os.path.getmtime) for folder in folders[:-max_to_keep]: print(f"Removing {folder}") shutil.rmtree(folder) def setup_vgg19(self): if self.vgg_19 is None: self.vgg_19, self.style_losses, self.content_losses, self.vgg19_pool_4 = get_style_model_and_losses( single_target=True, device=self.device, output_layer_name='pool_4', dtype=self.torch_dtype ) self.vgg_19.to(self.device, dtype=self.torch_dtype) self.vgg_19.requires_grad_(False) # we run random noise through first to get layer scalers to normalize the loss per layer # bs of 2 because we run pred and target through stacked noise = torch.randn((2, 3, self.resolution, self.resolution), device=self.device, dtype=self.torch_dtype) self.vgg_19(noise) for style_loss in self.style_losses: # get a scaler to normalize to 1 scaler = 1 / torch.mean(style_loss.loss).item() self.style_weight_scalers.append(scaler) for content_loss in self.content_losses: # get a scaler to normalize to 1 scaler = 1 / torch.mean(content_loss.loss).item() self.content_weight_scalers.append(scaler) self.print(f"Style weight scalers: {self.style_weight_scalers}") self.print(f"Content weight scalers: {self.content_weight_scalers}") def get_style_loss(self): if self.style_weight > 0: # scale all losses with loss scalers loss = torch.sum( torch.stack([loss.loss * scaler for loss, scaler in zip(self.style_losses, self.style_weight_scalers)])) return loss else: return torch.tensor(0.0, device=self.device) def get_content_loss(self): if self.content_weight > 0: # scale all losses with loss scalers loss = torch.sum(torch.stack( [loss.loss * scaler for loss, scaler in zip(self.content_losses, self.content_weight_scalers)])) return loss else: return torch.tensor(0.0, device=self.device) def get_mse_loss(self, pred, target): if self.mse_weight > 0: loss_fn = nn.MSELoss() loss = loss_fn(pred, target) return loss else: return torch.tensor(0.0, device=self.device) def get_kld_loss(self, mu, log_var): if self.kld_weight > 0: # Kullback-Leibler divergence # added here for full training (not implemented). Not needed for only decoder # as we are not changing the distribution of the latent space # normally it would help keep a normal distribution for latents KLD = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp()) # KL divergence return KLD else: return torch.tensor(0.0, device=self.device) def get_tv_loss(self, pred, target): if self.tv_weight > 0: get_tv_loss = ComparativeTotalVariation() loss = get_tv_loss(pred, target) return loss else: return torch.tensor(0.0, device=self.device) def get_pattern_loss(self, pred, target): if self._pattern_loss is None: self._pattern_loss = PatternLoss(pattern_size=16, dtype=self.torch_dtype).to(self.device, dtype=self.torch_dtype) loss = torch.mean(self._pattern_loss(pred, target)) return loss def save(self, step=None): if not os.path.exists(self.save_root): os.makedirs(self.save_root, exist_ok=True) step_num = '' if step is not None: # zeropad 9 digits step_num = f"_{str(step).zfill(9)}" self.update_training_metadata() filename = f'{self.job.name}{step_num}_diffusers' self.vae = self.vae.to("cpu", dtype=torch.float16) self.vae.save_pretrained( save_directory=os.path.join(self.save_root, filename) ) self.vae = self.vae.to(self.device, dtype=self.torch_dtype) self.print(f"Saved to {os.path.join(self.save_root, filename)}") if self.use_critic: self.critic.save(step) self.remove_oldest_checkpoint() def sample(self, step=None): sample_folder = os.path.join(self.save_root, 'samples') if not os.path.exists(sample_folder): os.makedirs(sample_folder, exist_ok=True) with torch.no_grad(): for i, img_url in enumerate(self.sample_sources): img = exif_transpose(Image.open(img_url)) img = img.convert('RGB') # crop if not square if img.width != img.height: min_dim = min(img.width, img.height) img = img.crop((0, 0, min_dim, min_dim)) # resize img = img.resize((self.resolution, self.resolution)) input_img = img img = IMAGE_TRANSFORMS(img).unsqueeze(0).to(self.device, dtype=self.torch_dtype) img = img decoded = self.vae(img).sample decoded = (decoded / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 decoded = decoded.cpu().permute(0, 2, 3, 1).squeeze(0).float().numpy() # convert to pillow image decoded = Image.fromarray((decoded * 255).astype(np.uint8)) # stack input image and decoded image input_img = input_img.resize((self.resolution, self.resolution)) decoded = decoded.resize((self.resolution, self.resolution)) output_img = Image.new('RGB', (self.resolution * 2, self.resolution)) output_img.paste(input_img, (0, 0)) output_img.paste(decoded, (self.resolution, 0)) scale_up = 2 if output_img.height <= 300: scale_up = 4 # scale up using nearest neighbor output_img = output_img.resize((output_img.width * scale_up, output_img.height * scale_up), Image.NEAREST) step_num = '' if step is not None: # zero-pad 9 digits step_num = f"_{str(step).zfill(9)}" seconds_since_epoch = int(time.time()) # zero-pad 2 digits i_str = str(i).zfill(2) filename = f"{seconds_since_epoch}{step_num}_{i_str}.png" output_img.save(os.path.join(sample_folder, filename)) def load_vae(self): path_to_load = self.vae_path # see if we have a checkpoint in out output to resume from self.print(f"Looking for latest checkpoint in {self.save_root}") files = glob.glob(os.path.join(self.save_root, f"{self.job.name}*_diffusers")) if files and len(files) > 0: latest_file = max(files, key=os.path.getmtime) print(f" - Latest checkpoint is: {latest_file}") path_to_load = latest_file # todo update step and epoch count else: self.print(f" - No checkpoint found, starting from scratch") # load vae self.print(f"Loading VAE") self.print(f" - Loading VAE: {path_to_load}") if self.vae is None: self.vae = AutoencoderKL.from_pretrained(path_to_load) # set decoder to train self.vae.to(self.device, dtype=self.torch_dtype) self.vae.requires_grad_(False) self.vae.eval() self.vae.decoder.train() self.vae_scale_factor = 2 ** (len(self.vae.config['block_out_channels']) - 1) def run(self): super().run() self.load_datasets() max_step_epochs = self.max_steps // len(self.data_loader) num_epochs = self.epochs if num_epochs is None or num_epochs > max_step_epochs: num_epochs = max_step_epochs max_epoch_steps = len(self.data_loader) * num_epochs num_steps = self.max_steps if num_steps is None or num_steps > max_epoch_steps: num_steps = max_epoch_steps self.max_steps = num_steps self.epochs = num_epochs start_step = self.step_num self.first_step = start_step self.print(f"Training VAE") self.print(f" - Training folder: {self.training_folder}") self.print(f" - Batch size: {self.batch_size}") self.print(f" - Learning rate: {self.learning_rate}") self.print(f" - Epochs: {num_epochs}") self.print(f" - Max steps: {self.max_steps}") # load vae self.load_vae() params = [] # only set last 2 layers to trainable for param in self.vae.decoder.parameters(): param.requires_grad = False train_all = 'all' in self.blocks_to_train if train_all: params = list(self.vae.decoder.parameters()) self.vae.decoder.requires_grad_(True) else: # mid_block if train_all or 'mid_block' in self.blocks_to_train: params += list(self.vae.decoder.mid_block.parameters()) self.vae.decoder.mid_block.requires_grad_(True) # up_blocks if train_all or 'up_blocks' in self.blocks_to_train: params += list(self.vae.decoder.up_blocks.parameters()) self.vae.decoder.up_blocks.requires_grad_(True) # conv_out (single conv layer output) if train_all or 'conv_out' in self.blocks_to_train: params += list(self.vae.decoder.conv_out.parameters()) self.vae.decoder.conv_out.requires_grad_(True) if self.style_weight > 0 or self.content_weight > 0 or self.use_critic: self.setup_vgg19() self.vgg_19.requires_grad_(False) self.vgg_19.eval() if self.use_critic: self.critic.setup() if self.lpips_weight > 0 and self.lpips_loss is None: # self.lpips_loss = lpips.LPIPS(net='vgg') self.lpips_loss = lpips.LPIPS(net='vgg').to(self.device, dtype=self.torch_dtype) optimizer = get_optimizer(params, self.optimizer_type, self.learning_rate, optimizer_params=self.optimizer_params) # setup scheduler # todo allow other schedulers scheduler = torch.optim.lr_scheduler.ConstantLR( optimizer, total_iters=num_steps, factor=1, verbose=False ) # setup tqdm progress bar self.progress_bar = tqdm( total=num_steps, desc='Training VAE', leave=True ) # sample first self.sample() blank_losses = OrderedDict({ "total": [], "lpips": [], "style": [], "content": [], "mse": [], "kl": [], "tv": [], "ptn": [], "crD": [], "crG": [], }) epoch_losses = copy.deepcopy(blank_losses) log_losses = copy.deepcopy(blank_losses) # range start at self.epoch_num go to self.epochs for epoch in range(self.epoch_num, self.epochs, 1): if self.step_num >= self.max_steps: break for batch in self.data_loader: if self.step_num >= self.max_steps: break with torch.no_grad(): batch = batch.to(self.device, dtype=self.torch_dtype) # resize so it matches size of vae evenly if batch.shape[2] % self.vae_scale_factor != 0 or batch.shape[3] % self.vae_scale_factor != 0: batch = Resize((batch.shape[2] // self.vae_scale_factor * self.vae_scale_factor, batch.shape[3] // self.vae_scale_factor * self.vae_scale_factor))(batch) # forward pass dgd = self.vae.encode(batch).latent_dist mu, logvar = dgd.mean, dgd.logvar latents = dgd.sample() latents.detach().requires_grad_(True) pred = self.vae.decode(latents).sample with torch.no_grad(): show_tensors( pred.clamp(-1, 1).clone(), "combined tensor" ) # Run through VGG19 if self.style_weight > 0 or self.content_weight > 0 or self.use_critic: stacked = torch.cat([pred, batch], dim=0) stacked = (stacked / 2 + 0.5).clamp(0, 1) self.vgg_19(stacked) if self.use_critic: critic_d_loss = self.critic.step(self.vgg19_pool_4.tensor.detach()) else: critic_d_loss = 0.0 style_loss = self.get_style_loss() * self.style_weight content_loss = self.get_content_loss() * self.content_weight kld_loss = self.get_kld_loss(mu, logvar) * self.kld_weight mse_loss = self.get_mse_loss(pred, batch) * self.mse_weight if self.lpips_weight > 0: lpips_loss = self.lpips_loss( pred.clamp(-1, 1), batch.clamp(-1, 1) ).mean() * self.lpips_weight else: lpips_loss = torch.tensor(0.0, device=self.device, dtype=self.torch_dtype) tv_loss = self.get_tv_loss(pred, batch) * self.tv_weight pattern_loss = self.get_pattern_loss(pred, batch) * self.pattern_weight if self.use_critic: critic_gen_loss = self.critic.get_critic_loss(self.vgg19_pool_4.tensor) * self.critic_weight # do not let abs critic gen loss be higher than abs lpips * 0.1 if using it if self.lpips_weight > 0: max_target = lpips_loss.abs() * 0.1 with torch.no_grad(): crit_g_scaler = 1.0 if critic_gen_loss.abs() > max_target: crit_g_scaler = max_target / critic_gen_loss.abs() critic_gen_loss *= crit_g_scaler else: critic_gen_loss = torch.tensor(0.0, device=self.device, dtype=self.torch_dtype) loss = style_loss + content_loss + kld_loss + mse_loss + tv_loss + critic_gen_loss + pattern_loss + lpips_loss # Backward pass and optimization optimizer.zero_grad() loss.backward() optimizer.step() scheduler.step() # update progress bar loss_value = loss.item() # get exponent like 3.54e-4 loss_string = f"loss: {loss_value:.2e}" if self.lpips_weight > 0: loss_string += f" lpips: {lpips_loss.item():.2e}" if self.content_weight > 0: loss_string += f" cnt: {content_loss.item():.2e}" if self.style_weight > 0: loss_string += f" sty: {style_loss.item():.2e}" if self.kld_weight > 0: loss_string += f" kld: {kld_loss.item():.2e}" if self.mse_weight > 0: loss_string += f" mse: {mse_loss.item():.2e}" if self.tv_weight > 0: loss_string += f" tv: {tv_loss.item():.2e}" if self.pattern_weight > 0: loss_string += f" ptn: {pattern_loss.item():.2e}" if self.use_critic and self.critic_weight > 0: loss_string += f" crG: {critic_gen_loss.item():.2e}" if self.use_critic: loss_string += f" crD: {critic_d_loss:.2e}" if self.optimizer_type.startswith('dadaptation') or \ self.optimizer_type.lower().startswith('prodigy'): learning_rate = ( optimizer.param_groups[0]["d"] * optimizer.param_groups[0]["lr"] ) else: learning_rate = optimizer.param_groups[0]['lr'] lr_critic_string = '' if self.use_critic: lr_critic = self.critic.get_lr() lr_critic_string = f" lrC: {lr_critic:.1e}" self.progress_bar.set_postfix_str(f"lr: {learning_rate:.1e}{lr_critic_string} {loss_string}") self.progress_bar.set_description(f"E: {epoch}") self.progress_bar.update(1) epoch_losses["total"].append(loss_value) epoch_losses["lpips"].append(lpips_loss.item()) epoch_losses["style"].append(style_loss.item()) epoch_losses["content"].append(content_loss.item()) epoch_losses["mse"].append(mse_loss.item()) epoch_losses["kl"].append(kld_loss.item()) epoch_losses["tv"].append(tv_loss.item()) epoch_losses["ptn"].append(pattern_loss.item()) epoch_losses["crG"].append(critic_gen_loss.item()) epoch_losses["crD"].append(critic_d_loss) log_losses["total"].append(loss_value) log_losses["lpips"].append(lpips_loss.item()) log_losses["style"].append(style_loss.item()) log_losses["content"].append(content_loss.item()) log_losses["mse"].append(mse_loss.item()) log_losses["kl"].append(kld_loss.item()) log_losses["tv"].append(tv_loss.item()) log_losses["ptn"].append(pattern_loss.item()) log_losses["crG"].append(critic_gen_loss.item()) log_losses["crD"].append(critic_d_loss) # don't do on first step if self.step_num != start_step: if self.sample_every and self.step_num % self.sample_every == 0: # print above the progress bar self.print(f"Sampling at step {self.step_num}") self.sample(self.step_num) if self.save_every and self.step_num % self.save_every == 0: # print above the progress bar self.print(f"Saving at step {self.step_num}") self.save(self.step_num) if self.log_every and self.step_num % self.log_every == 0: # log to tensorboard if self.writer is not None: # get avg loss for key in log_losses: log_losses[key] = sum(log_losses[key]) / (len(log_losses[key]) + 1e-6) # if log_losses[key] > 0: self.writer.add_scalar(f"loss/{key}", log_losses[key], self.step_num) # reset log losses log_losses = copy.deepcopy(blank_losses) self.step_num += 1 # end epoch if self.writer is not None: eps = 1e-6 # get avg loss for key in epoch_losses: epoch_losses[key] = sum(log_losses[key]) / (len(log_losses[key]) + eps) if epoch_losses[key] > 0: self.writer.add_scalar(f"epoch loss/{key}", epoch_losses[key], epoch) # reset epoch losses epoch_losses = copy.deepcopy(blank_losses) self.save()