from dataclasses import dataclass import math from contextlib import _GeneratorContextManager, nullcontext from typing import Callable, Any, Tuple, Union, Optional, Type from functools import partial from pathlib import Path import itertools import torch import torch.nn.functional as F from torch.utils.data import DataLoader from accelerate import Accelerator from transformers import CLIPTextModel from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel, DPMSolverMultistepScheduler from tqdm.auto import tqdm from PIL import Image from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion from models.clip.embeddings import ManagedCLIPTextEmbeddings, patch_managed_embeddings from models.clip.util import get_extended_embeddings from models.clip.tokenizer import MultiCLIPTokenizer from training.util import AverageMeter def const(result=None): def fn(*args, **kwargs): return result return fn @dataclass class TrainingCallbacks(): on_prepare: Callable[[], None] = const() on_model: Callable[[], torch.nn.Module] = const(None) on_log: Callable[[], dict[str, Any]] = const({}) on_train: Callable[[int], _GeneratorContextManager] = const(nullcontext()) on_before_optimize: Callable[[int], None] = const() on_after_optimize: Callable[[float], None] = const() on_eval: Callable[[], _GeneratorContextManager] = const(nullcontext()) on_sample: Callable[[int], None] = const() on_checkpoint: Callable[[int, str], None] = const() def make_grid(images, rows, cols): w, h = images[0].size grid = Image.new('RGB', size=(cols*w, rows*h)) for i, image in enumerate(images): grid.paste(image, box=(i % cols*w, i//cols*h)) return grid def get_models(pretrained_model_name_or_path: str): tokenizer = MultiCLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder='tokenizer') text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder='text_encoder') vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder='vae') unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder='unet') noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder='scheduler') sample_scheduler = DPMSolverMultistepScheduler.from_pretrained( pretrained_model_name_or_path, subfolder='scheduler') embeddings = patch_managed_embeddings(text_encoder) return tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings def save_samples( accelerator: Accelerator, unet: UNet2DConditionModel, text_encoder: CLIPTextModel, tokenizer: MultiCLIPTokenizer, vae: AutoencoderKL, sample_scheduler: DPMSolverMultistepScheduler, train_dataloader: DataLoader, val_dataloader: DataLoader, dtype: torch.dtype, output_dir: Path, seed: int, step: int, batch_size: int = 1, num_batches: int = 1, num_steps: int = 20, guidance_scale: float = 7.5, image_size: Optional[int] = None, ): print(f"Saving samples for step {step}...") samples_path = output_dir.joinpath("samples") grid_cols = min(batch_size, 4) grid_rows = (num_batches * batch_size) // grid_cols unet = accelerator.unwrap_model(unet) text_encoder = accelerator.unwrap_model(text_encoder) orig_unet_dtype = unet.dtype orig_text_encoder_dtype = text_encoder.dtype unet.to(dtype=dtype) text_encoder.to(dtype=dtype) pipeline = VlpnStableDiffusion( text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer, scheduler=sample_scheduler, ).to(accelerator.device) pipeline.set_progress_bar_config(dynamic_ncols=True) generator = torch.Generator(device=accelerator.device).manual_seed(seed) for pool, data, gen in [ ("stable", val_dataloader, generator), ("val", val_dataloader, None), ("train", train_dataloader, None) ]: all_samples = [] file_path = samples_path.joinpath(pool, f"step_{step}.jpg") file_path.parent.mkdir(parents=True, exist_ok=True) batches = list(itertools.islice(itertools.cycle(data), batch_size * num_batches)) prompt_ids = [ prompt for batch in batches for prompt in batch["prompt_ids"] ] nprompt_ids = [ prompt for batch in batches for prompt in batch["nprompt_ids"] ] for i in range(num_batches): start = i * batch_size end = (i + 1) * batch_size prompt = prompt_ids[start:end] nprompt = nprompt_ids[start:end] samples = pipeline( prompt=prompt, negative_prompt=nprompt, height=image_size, width=image_size, generator=gen, guidance_scale=guidance_scale, num_inference_steps=num_steps, output_type='pil' ).images all_samples += samples image_grid = make_grid(all_samples, grid_rows, grid_cols) image_grid.save(file_path, quality=85) unet.to(dtype=orig_unet_dtype) text_encoder.to(dtype=orig_text_encoder_dtype) del unet del text_encoder del generator del pipeline if torch.cuda.is_available(): torch.cuda.empty_cache() def generate_class_images( accelerator: Accelerator, text_encoder: CLIPTextModel, vae: AutoencoderKL, unet: UNet2DConditionModel, tokenizer: MultiCLIPTokenizer, sample_scheduler: DPMSolverMultistepScheduler, data_train, sample_batch_size: int, sample_image_size: int, sample_steps: int ): missing_data = [item for item in data_train if not item.class_image_path.exists()] if len(missing_data) == 0: return batched_data = [ missing_data[i:i+sample_batch_size] for i in range(0, len(missing_data), sample_batch_size) ] pipeline = VlpnStableDiffusion( text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer, scheduler=sample_scheduler, ).to(accelerator.device) pipeline.set_progress_bar_config(dynamic_ncols=True) with torch.inference_mode(): for batch in batched_data: image_name = [item.class_image_path for item in batch] prompt = [item.cprompt for item in batch] nprompt = [item.nprompt for item in batch] images = pipeline( prompt=prompt, negative_prompt=nprompt, height=sample_image_size, width=sample_image_size, num_inference_steps=sample_steps ).images for i, image in enumerate(images): image.save(image_name[i]) del pipeline if torch.cuda.is_available(): torch.cuda.empty_cache() def add_placeholder_tokens( tokenizer: MultiCLIPTokenizer, embeddings: ManagedCLIPTextEmbeddings, placeholder_tokens: list[str], initializer_tokens: list[str], num_vectors: Union[list[int], int] ): initializer_token_ids = [ tokenizer.encode(token, add_special_tokens=False) for token in initializer_tokens ] placeholder_token_ids = tokenizer.add_multi_tokens(placeholder_tokens, num_vectors) embeddings.resize(len(tokenizer)) for (placeholder_token_id, initializer_token_id) in zip(placeholder_token_ids, initializer_token_ids): embeddings.add_embed(placeholder_token_id, initializer_token_id) return placeholder_token_ids, initializer_token_ids def loss_step( vae: AutoencoderKL, noise_scheduler: DDPMScheduler, unet: UNet2DConditionModel, text_encoder: CLIPTextModel, with_prior_preservation: bool, prior_loss_weight: float, seed: int, step: int, batch: dict[str, Any], eval: bool = False ): # Convert images to latent space latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach() latents = latents * 0.18215 generator = torch.Generator(device=latents.device).manual_seed(seed + step) if eval else None # Sample noise that we'll add to the latents noise = torch.randn( latents.shape, dtype=latents.dtype, layout=latents.layout, device=latents.device, generator=generator ) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint( 0, noise_scheduler.config.num_train_timesteps, (bsz,), generator=generator, 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 = noise_scheduler.add_noise(latents, noise, timesteps) noisy_latents = noisy_latents.to(dtype=unet.dtype) # Get the text embedding for conditioning encoder_hidden_states = get_extended_embeddings( text_encoder, batch["input_ids"], batch["attention_mask"] ) encoder_hidden_states = encoder_hidden_states.to(dtype=unet.dtype) # Predict the noise residual model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") if with_prior_preservation: # Chunk the noise and model_pred into two parts and compute the loss on each part separately. model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) target, target_prior = torch.chunk(target, 2, dim=0) # Compute instance loss loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") # Compute prior loss prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") # Add the prior loss to the instance loss. loss = loss + prior_loss_weight * prior_loss else: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") acc = (model_pred == target).float().mean() return loss, acc, bsz def train_loop( accelerator: Accelerator, optimizer: torch.optim.Optimizer, lr_scheduler: torch.optim.lr_scheduler._LRScheduler, train_dataloader: DataLoader, val_dataloader: DataLoader, loss_step: Union[Callable[[int, Any], Tuple[Any, Any, int]], Callable[[int, Any, bool], Tuple[Any, Any, int]]], sample_frequency: int = 10, checkpoint_frequency: int = 50, global_step_offset: int = 0, num_epochs: int = 100, callbacks: TrainingCallbacks = TrainingCallbacks(), ): num_training_steps_per_epoch = math.ceil(len(train_dataloader) / accelerator.gradient_accumulation_steps) num_val_steps_per_epoch = len(val_dataloader) num_training_steps = num_training_steps_per_epoch * num_epochs num_val_steps = num_val_steps_per_epoch * num_epochs global_step = 0 avg_loss = AverageMeter() avg_acc = AverageMeter() avg_loss_val = AverageMeter() avg_acc_val = AverageMeter() max_acc_val = 0.0 local_progress_bar = tqdm( range(num_training_steps_per_epoch + num_val_steps_per_epoch), disable=not accelerator.is_local_main_process, dynamic_ncols=True ) local_progress_bar.set_description(f"Epoch 1 / {num_epochs}") global_progress_bar = tqdm( range(num_training_steps + num_val_steps), disable=not accelerator.is_local_main_process, dynamic_ncols=True ) global_progress_bar.set_description("Total progress") model = callbacks.on_model() on_log = callbacks.on_log on_train = callbacks.on_train on_before_optimize = callbacks.on_before_optimize on_after_optimize = callbacks.on_after_optimize on_eval = callbacks.on_eval on_sample = callbacks.on_sample on_checkpoint = callbacks.on_checkpoint try: for epoch in range(num_epochs): if accelerator.is_main_process: if epoch % sample_frequency == 0: on_sample(global_step + global_step_offset) if epoch % checkpoint_frequency == 0 and epoch != 0: on_checkpoint(global_step + global_step_offset, "training") local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}") local_progress_bar.reset() model.train() with on_train(epoch): for step, batch in enumerate(train_dataloader): with accelerator.accumulate(model): loss, acc, bsz = loss_step(step, batch) accelerator.backward(loss) on_before_optimize(epoch) optimizer.step() lr_scheduler.step() optimizer.zero_grad(set_to_none=True) avg_loss.update(loss.detach_(), bsz) avg_acc.update(acc.detach_(), bsz) # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: on_after_optimize(lr_scheduler.get_last_lr()[0]) local_progress_bar.update(1) global_progress_bar.update(1) global_step += 1 logs = { "train/loss": avg_loss.avg.item(), "train/acc": avg_acc.avg.item(), "train/cur_loss": loss.item(), "train/cur_acc": acc.item(), "lr": lr_scheduler.get_last_lr()[0], } logs.update(on_log()) accelerator.log(logs, step=global_step) local_progress_bar.set_postfix(**logs) if global_step >= num_training_steps: break accelerator.wait_for_everyone() model.eval() cur_loss_val = AverageMeter() cur_acc_val = AverageMeter() with torch.inference_mode(), on_eval(): for step, batch in enumerate(val_dataloader): loss, acc, bsz = loss_step(step, batch, True) loss = loss.detach_() acc = acc.detach_() cur_loss_val.update(loss, bsz) cur_acc_val.update(acc, bsz) avg_loss_val.update(loss, bsz) avg_acc_val.update(acc, bsz) local_progress_bar.update(1) global_progress_bar.update(1) logs = { "val/loss": avg_loss_val.avg.item(), "val/acc": avg_acc_val.avg.item(), "val/cur_loss": loss.item(), "val/cur_acc": acc.item(), } local_progress_bar.set_postfix(**logs) logs["val/cur_loss"] = cur_loss_val.avg.item() logs["val/cur_acc"] = cur_acc_val.avg.item() accelerator.log(logs, step=global_step) local_progress_bar.clear() global_progress_bar.clear() if accelerator.is_main_process: if avg_acc_val.avg.item() > max_acc_val: accelerator.print( f"Global step {global_step}: Validation accuracy reached new maximum: {max_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}") on_checkpoint(global_step + global_step_offset, "milestone") max_acc_val = avg_acc_val.avg.item() # Create the pipeline using using the trained modules and save it. if accelerator.is_main_process: print("Finished!") on_checkpoint(global_step + global_step_offset, "end") on_sample(global_step + global_step_offset) except KeyboardInterrupt: if accelerator.is_main_process: print("Interrupted") on_checkpoint(global_step + global_step_offset, "end") def train( accelerator: Accelerator, unet: UNet2DConditionModel, text_encoder: CLIPTextModel, vae: AutoencoderKL, noise_scheduler: DDPMScheduler, train_dataloader: DataLoader, val_dataloader: DataLoader, dtype: torch.dtype, seed: int, optimizer: torch.optim.Optimizer, lr_scheduler: torch.optim.lr_scheduler._LRScheduler, callbacks_fn: Callable[..., TrainingCallbacks], num_train_epochs: int = 100, sample_frequency: int = 20, checkpoint_frequency: int = 50, global_step_offset: int = 0, with_prior_preservation: bool = False, prior_loss_weight: float = 1.0, **kwargs, ): unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare( unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler ) vae.to(accelerator.device, dtype=dtype) for model in (unet, text_encoder, vae): model.requires_grad_(False) model.eval() callbacks = callbacks_fn( accelerator=accelerator, unet=unet, text_encoder=text_encoder, vae=vae, train_dataloader=train_dataloader, val_dataloader=val_dataloader, seed=seed, **kwargs, ) callbacks.on_prepare() loss_step_ = partial( loss_step, vae, noise_scheduler, unet, text_encoder, with_prior_preservation, prior_loss_weight, seed, ) if accelerator.is_main_process: accelerator.init_trackers("textual_inversion") train_loop( accelerator=accelerator, optimizer=optimizer, lr_scheduler=lr_scheduler, train_dataloader=train_dataloader, val_dataloader=val_dataloader, loss_step=loss_step_, sample_frequency=sample_frequency, checkpoint_frequency=checkpoint_frequency, global_step_offset=global_step_offset, num_epochs=num_train_epochs, callbacks=callbacks, ) accelerator.end_training() accelerator.free_memory()