From 9b808b6ca102cfec0c273626a0bcadf897b7c942 Mon Sep 17 00:00:00 2001 From: Volpeon Date: Mon, 19 Dec 2022 21:10:58 +0100 Subject: Improved dataset prompt handling, fixed --- .gitignore | 5 +- data/csv.py | 41 +- dreambooth.py | 1133 -------------------------------------------------- textual_inversion.py | 1034 --------------------------------------------- train_dreambooth.py | 1133 ++++++++++++++++++++++++++++++++++++++++++++++++++ train_ti.py | 1032 +++++++++++++++++++++++++++++++++++++++++++++ 6 files changed, 2190 insertions(+), 2188 deletions(-) delete mode 100644 dreambooth.py delete mode 100644 textual_inversion.py create mode 100644 train_dreambooth.py create mode 100644 train_ti.py diff --git a/.gitignore b/.gitignore index d84b4dd..fba4926 100644 --- a/.gitignore +++ b/.gitignore @@ -160,8 +160,7 @@ cython_debug/ #.idea/ output/ -conf/ -embeddings_ti/ -embeddings_ag/ +conf*/ +embeddings*/ v1-inference.yaml* *.old diff --git a/data/csv.py b/data/csv.py index 053457b..6525e45 100644 --- a/data/csv.py +++ b/data/csv.py @@ -16,26 +16,29 @@ def prepare_prompt(prompt: Union[str, Dict[str, str]]): return {"content": prompt} if isinstance(prompt, str) else prompt -def shuffle_prompt(prompt: str, dropout: float = 0): - def handle_block(block: str): - words = block.split(", ") - words = [w for w in words if w != ""] - if dropout != 0: - words = [w for w in words if np.random.random() > dropout] - np.random.shuffle(words) - return ", ".join(words) - - prompt = prompt.split(". ") - prompt = [handle_block(b) for b in prompt if b != ""] +def keywords_to_prompt(prompt: list[str], dropout: float = 0) -> str: + if dropout != 0: + prompt = [keyword for keyword in prompt if np.random.random() > dropout] np.random.shuffle(prompt) - prompt = ". ".join(prompt) - return prompt + return ", ".join(prompt) + + +def prompt_to_keywords(prompt: str, expansions: dict[str, str]) -> list[str]: + def expand_keyword(keyword: str) -> list[str]: + return [keyword] + expansions[keyword].split(", ") if keyword in expansions else [keyword] + + return [ + kw + for keyword in prompt.split(", ") + for kw in expand_keyword(keyword) + if keyword != "" + ] class CSVDataItem(NamedTuple): instance_image_path: Path class_image_path: Path - prompt: str + prompt: list[str] nprompt: str @@ -91,7 +94,7 @@ class CSVDataModule(pl.LightningDataModule): self.num_workers = num_workers self.batch_size = batch_size - def prepare_items(self, template, data) -> list[CSVDataItem]: + def prepare_items(self, template, expansions, data) -> list[CSVDataItem]: image = template["image"] if "image" in template else "{}" prompt = template["prompt"] if "prompt" in template else "{content}" nprompt = template["nprompt"] if "nprompt" in template else "{content}" @@ -100,7 +103,8 @@ class CSVDataModule(pl.LightningDataModule): CSVDataItem( self.data_root.joinpath(image.format(item["image"])), None, - prompt.format(**prepare_prompt(item["prompt"] if "prompt" in item else "")), + prompt_to_keywords(prompt.format( + **prepare_prompt(item["prompt"] if "prompt" in item else "")), expansions), nprompt.format(**prepare_prompt(item["nprompt"] if "nprompt" in item else "")), ) for item in data @@ -130,6 +134,7 @@ class CSVDataModule(pl.LightningDataModule): with open(self.data_file, 'rt') as f: metadata = json.load(f) template = metadata[self.template_key] if self.template_key in metadata else {} + expansions = metadata["expansions"] if "expansions" in metadata else {} items = metadata["items"] if "items" in metadata else [] if self.mode is not None: @@ -138,7 +143,7 @@ class CSVDataModule(pl.LightningDataModule): for item in items if "mode" in item and self.mode in item["mode"] ] - items = self.prepare_items(template, items) + items = self.prepare_items(template, expansions, items) items = self.filter_items(items) num_images = len(items) @@ -255,7 +260,7 @@ class CSVDataset(Dataset): example = {} - example["prompts"] = shuffle_prompt(unprocessed_example["prompts"]) + example["prompts"] = keywords_to_prompt(unprocessed_example["prompts"], self.dropout) example["nprompts"] = unprocessed_example["nprompts"] example["instance_images"] = self.image_transforms(unprocessed_example["instance_images"]) diff --git a/dreambooth.py b/dreambooth.py deleted file mode 100644 index 3eecf9c..0000000 --- a/dreambooth.py +++ /dev/null @@ -1,1133 +0,0 @@ -import argparse -import itertools -import math -import datetime -import logging -import json -from pathlib import Path - -import torch -import torch.nn.functional as F -import torch.utils.checkpoint - -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import LoggerType, set_seed -from diffusers import AutoencoderKL, DDPMScheduler, DPMSolverMultistepScheduler, UNet2DConditionModel -from diffusers.optimization import get_scheduler, get_cosine_with_hard_restarts_schedule_with_warmup -from diffusers.training_utils import EMAModel -from PIL import Image -from tqdm.auto import tqdm -from transformers import CLIPTextModel, CLIPTokenizer -from slugify import slugify - -from common import load_text_embeddings -from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion -from pipelines.util import set_use_memory_efficient_attention_xformers -from data.csv import CSVDataModule -from training.optimization import get_one_cycle_schedule -from models.clip.prompt import PromptProcessor - -logger = get_logger(__name__) - - -torch.backends.cuda.matmul.allow_tf32 = True -torch.backends.cudnn.benchmark = True - - -def parse_args(): - parser = argparse.ArgumentParser( - description="Simple example of a training script." - ) - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default=None, - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--tokenizer_name", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--train_data_file", - type=str, - default=None, - help="A folder containing the training data." - ) - parser.add_argument( - "--train_data_template", - type=str, - default="template", - ) - parser.add_argument( - "--instance_identifier", - type=str, - default=None, - help="A token to use as a placeholder for the concept.", - ) - parser.add_argument( - "--class_identifier", - type=str, - default=None, - help="A token to use as a placeholder for the concept.", - ) - parser.add_argument( - "--placeholder_token", - type=str, - nargs='*', - default=[], - help="A token to use as a placeholder for the concept.", - ) - parser.add_argument( - "--initializer_token", - type=str, - nargs='*', - default=[], - help="A token to use as initializer word." - ) - parser.add_argument( - "--train_text_encoder", - action="store_true", - default=True, - help="Whether to train the whole text encoder." - ) - parser.add_argument( - "--train_text_encoder_epochs", - default=999999, - help="Number of epochs the text encoder will be trained." - ) - parser.add_argument( - "--tag_dropout", - type=float, - default=0.1, - help="Tag dropout probability.", - ) - parser.add_argument( - "--num_class_images", - type=int, - default=400, - help="How many class images to generate." - ) - parser.add_argument( - "--repeats", - type=int, - default=1, - help="How many times to repeat the training data." - ) - parser.add_argument( - "--output_dir", - type=str, - default="output/dreambooth", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument( - "--embeddings_dir", - type=str, - default=None, - help="The embeddings directory where Textual Inversion embeddings are stored.", - ) - parser.add_argument( - "--mode", - type=str, - default=None, - help="A mode to filter the dataset.", - ) - parser.add_argument( - "--seed", - type=int, - default=None, - help="A seed for reproducible training." - ) - parser.add_argument( - "--resolution", - type=int, - default=768, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--center_crop", - action="store_true", - help="Whether to center crop images before resizing to resolution" - ) - parser.add_argument( - "--dataloader_num_workers", - type=int, - default=0, - help=( - "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main" - " process." - ), - ) - parser.add_argument( - "--num_train_epochs", - type=int, - default=100 - ) - parser.add_argument( - "--max_train_steps", - type=int, - default=None, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--gradient_checkpointing", - action="store_true", - help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", - ) - parser.add_argument( - "--learning_rate_unet", - type=float, - default=2e-6, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--learning_rate_text", - type=float, - default=2e-6, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--scale_lr", - action="store_true", - default=True, - help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", - ) - parser.add_argument( - "--lr_scheduler", - type=str, - default="one_cycle", - help=( - 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup", "one_cycle"]' - ), - ) - parser.add_argument( - "--lr_warmup_epochs", - type=int, - default=10, - help="Number of steps for the warmup in the lr scheduler." - ) - parser.add_argument( - "--lr_cycles", - type=int, - default=None, - help="Number of restart cycles in the lr scheduler (if supported)." - ) - parser.add_argument( - "--use_ema", - action="store_true", - default=True, - help="Whether to use EMA model." - ) - parser.add_argument( - "--ema_inv_gamma", - type=float, - default=1.0 - ) - parser.add_argument( - "--ema_power", - type=float, - default=6/7 - ) - parser.add_argument( - "--ema_max_decay", - type=float, - default=0.9999 - ) - parser.add_argument( - "--use_8bit_adam", - action="store_true", - default=True, - help="Whether or not to use 8-bit Adam from bitsandbytes." - ) - parser.add_argument( - "--adam_beta1", - type=float, - default=0.9, - help="The beta1 parameter for the Adam optimizer." - ) - parser.add_argument( - "--adam_beta2", - type=float, - default=0.999, - help="The beta2 parameter for the Adam optimizer." - ) - parser.add_argument( - "--adam_weight_decay", - type=float, - default=1e-2, - help="Weight decay to use." - ) - parser.add_argument( - "--adam_epsilon", - type=float, - default=1e-08, - help="Epsilon value for the Adam optimizer" - ) - parser.add_argument( - "--mixed_precision", - type=str, - default="no", - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose" - "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." - "and an Nvidia Ampere GPU." - ), - ) - parser.add_argument( - "--sample_frequency", - type=int, - default=1, - help="How often to save a checkpoint and sample image", - ) - parser.add_argument( - "--sample_image_size", - type=int, - default=768, - help="Size of sample images", - ) - parser.add_argument( - "--sample_batches", - type=int, - default=1, - help="Number of sample batches to generate per checkpoint", - ) - parser.add_argument( - "--sample_batch_size", - type=int, - default=1, - help="Number of samples to generate per batch", - ) - parser.add_argument( - "--valid_set_size", - type=int, - default=None, - help="Number of images in the validation dataset." - ) - parser.add_argument( - "--train_batch_size", - type=int, - default=1, - help="Batch size (per device) for the training dataloader." - ) - parser.add_argument( - "--sample_steps", - type=int, - default=15, - help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.", - ) - parser.add_argument( - "--prior_loss_weight", - type=float, - default=1.0, - help="The weight of prior preservation loss." - ) - parser.add_argument( - "--max_grad_norm", - default=1.0, - type=float, - help="Max gradient norm." - ) - parser.add_argument( - "--noise_timesteps", - type=int, - default=1000, - ) - parser.add_argument( - "--config", - type=str, - default=None, - help="Path to a JSON configuration file containing arguments for invoking this script." - ) - - args = parser.parse_args() - if args.config is not None: - with open(args.config, 'rt') as f: - args = parser.parse_args( - namespace=argparse.Namespace(**json.load(f)["args"])) - - if args.train_data_file is None: - raise ValueError("You must specify --train_data_file") - - if args.pretrained_model_name_or_path is None: - raise ValueError("You must specify --pretrained_model_name_or_path") - - if args.instance_identifier is None: - raise ValueError("You must specify --instance_identifier") - - if isinstance(args.initializer_token, str): - args.initializer_token = [args.initializer_token] - - if isinstance(args.placeholder_token, str): - args.placeholder_token = [args.placeholder_token] - - if len(args.placeholder_token) == 0: - args.placeholder_token = [f"<*{i}>" for i in range(len(args.initializer_token))] - - if len(args.placeholder_token) != len(args.initializer_token): - raise ValueError("Number of items in --placeholder_token and --initializer_token must match") - - if args.output_dir is None: - raise ValueError("You must specify --output_dir") - - return args - - -def save_args(basepath: Path, args, extra={}): - info = {"args": vars(args)} - info["args"].update(extra) - with open(basepath.joinpath("args.json"), "w") as f: - json.dump(info, f, indent=4) - - -def freeze_params(params): - for param in params: - param.requires_grad = False - - -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 - - -class AverageMeter: - def __init__(self, name=None): - self.name = name - self.reset() - - def reset(self): - self.sum = self.count = self.avg = 0 - - def update(self, val, n=1): - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - -class Checkpointer: - def __init__( - self, - datamodule, - accelerator, - vae, - unet, - ema_unet, - tokenizer, - text_encoder, - scheduler, - output_dir: Path, - instance_identifier, - placeholder_token, - placeholder_token_id, - sample_image_size, - sample_batches, - sample_batch_size, - seed - ): - self.datamodule = datamodule - self.accelerator = accelerator - self.vae = vae - self.unet = unet - self.ema_unet = ema_unet - self.tokenizer = tokenizer - self.text_encoder = text_encoder - self.scheduler = scheduler - self.output_dir = output_dir - self.instance_identifier = instance_identifier - self.placeholder_token = placeholder_token - self.placeholder_token_id = placeholder_token_id - self.sample_image_size = sample_image_size - self.seed = seed or torch.random.seed() - self.sample_batches = sample_batches - self.sample_batch_size = sample_batch_size - - @torch.no_grad() - def save_model(self): - print("Saving model...") - - unet = self.ema_unet.averaged_model if self.ema_unet is not None else self.accelerator.unwrap_model(self.unet) - text_encoder = self.accelerator.unwrap_model(self.text_encoder) - - pipeline = VlpnStableDiffusion( - text_encoder=text_encoder, - vae=self.vae, - unet=unet, - tokenizer=self.tokenizer, - scheduler=self.scheduler, - ) - pipeline.save_pretrained(self.output_dir.joinpath("model")) - - del unet - del text_encoder - del pipeline - - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - @torch.no_grad() - def save_samples(self, step, num_inference_steps, guidance_scale=7.5, eta=0.0): - samples_path = Path(self.output_dir).joinpath("samples") - - unet = self.ema_unet.averaged_model if self.ema_unet is not None else self.accelerator.unwrap_model(self.unet) - text_encoder = self.accelerator.unwrap_model(self.text_encoder) - - pipeline = VlpnStableDiffusion( - text_encoder=text_encoder, - vae=self.vae, - unet=unet, - tokenizer=self.tokenizer, - scheduler=self.scheduler, - ).to(self.accelerator.device) - pipeline.set_progress_bar_config(dynamic_ncols=True) - - train_data = self.datamodule.train_dataloader() - val_data = self.datamodule.val_dataloader() - - generator = torch.Generator(device=pipeline.device).manual_seed(self.seed) - stable_latents = torch.randn( - (self.sample_batch_size, pipeline.unet.in_channels, self.sample_image_size // 8, self.sample_image_size // 8), - device=pipeline.device, - generator=generator, - ) - - with torch.autocast("cuda"), torch.inference_mode(): - for pool, data, latents in [("stable", val_data, stable_latents), ("val", val_data, None), ("train", train_data, None)]: - all_samples = [] - file_path = samples_path.joinpath(pool, f"step_{step}.jpg") - file_path.parent.mkdir(parents=True, exist_ok=True) - - data_enum = enumerate(data) - - batches = [ - batch - for j, batch in data_enum - if j * data.batch_size < self.sample_batch_size * self.sample_batches - ] - prompts = [ - prompt.format(identifier=self.instance_identifier) - for batch in batches - for prompt in batch["prompts"] - ] - nprompts = [ - prompt - for batch in batches - for prompt in batch["nprompts"] - ] - - for i in range(self.sample_batches): - prompt = prompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] - nprompt = nprompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] - - samples = pipeline( - prompt=prompt, - negative_prompt=nprompt, - height=self.sample_image_size, - width=self.sample_image_size, - image=latents[:len(prompt)] if latents is not None else None, - generator=generator if latents is not None else None, - guidance_scale=guidance_scale, - eta=eta, - num_inference_steps=num_inference_steps, - output_type='pil' - ).images - - all_samples += samples - - del samples - - image_grid = make_grid(all_samples, self.sample_batches, self.sample_batch_size) - image_grid.save(file_path, quality=85) - - del all_samples - del image_grid - - del unet - del text_encoder - del pipeline - del generator - del stable_latents - - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - -def main(): - args = parse_args() - - if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1: - raise ValueError( - "Gradient accumulation is not supported when training the text encoder in distributed training. " - "Please set gradient_accumulation_steps to 1. This feature will be supported in the future." - ) - - instance_identifier = args.instance_identifier - - if len(args.placeholder_token) != 0: - instance_identifier = instance_identifier.format(args.placeholder_token[0]) - - now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") - basepath = Path(args.output_dir).joinpath(slugify(instance_identifier), now) - basepath.mkdir(parents=True, exist_ok=True) - - accelerator = Accelerator( - log_with=LoggerType.TENSORBOARD, - logging_dir=f"{basepath}", - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision - ) - - logging.basicConfig(filename=basepath.joinpath("log.txt"), level=logging.DEBUG) - - args.seed = args.seed or (torch.random.seed() >> 32) - set_seed(args.seed) - - save_args(basepath, args) - - # Load the tokenizer and add the placeholder token as a additional special token - if args.tokenizer_name: - tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) - elif args.pretrained_model_name_or_path: - tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer') - - # Load models and create wrapper for stable diffusion - text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder') - vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae') - unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet') - noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder='scheduler') - checkpoint_scheduler = DPMSolverMultistepScheduler.from_pretrained( - args.pretrained_model_name_or_path, subfolder='scheduler') - - vae.enable_slicing() - set_use_memory_efficient_attention_xformers(unet, True) - set_use_memory_efficient_attention_xformers(vae, True) - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - text_encoder.gradient_checkpointing_enable() - - ema_unet = None - if args.use_ema: - ema_unet = EMAModel( - unet, - inv_gamma=args.ema_inv_gamma, - power=args.ema_power, - max_value=args.ema_max_decay, - device=accelerator.device - ) - - # Freeze text_encoder and vae - vae.requires_grad_(False) - - if args.embeddings_dir is not None: - embeddings_dir = Path(args.embeddings_dir) - if not embeddings_dir.exists() or not embeddings_dir.is_dir(): - raise ValueError("--embeddings_dir must point to an existing directory") - added_tokens = load_text_embeddings(tokenizer, text_encoder, embeddings_dir) - print(f"Added {len(added_tokens)} tokens from embeddings dir: {added_tokens}") - - if len(args.placeholder_token) != 0: - # Convert the initializer_token, placeholder_token to ids - initializer_token_ids = torch.stack([ - torch.tensor(tokenizer.encode(token, add_special_tokens=False)[:1]) - for token in args.initializer_token - ]) - - num_added_tokens = tokenizer.add_tokens(args.placeholder_token) - print(f"Added {num_added_tokens} new tokens.") - - placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) - - # Resize the token embeddings as we are adding new special tokens to the tokenizer - text_encoder.resize_token_embeddings(len(tokenizer)) - - token_embeds = text_encoder.get_input_embeddings().weight.data - original_token_embeds = token_embeds.clone().to(accelerator.device) - initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids) - - for (token_id, embeddings) in zip(placeholder_token_id, initializer_token_embeddings): - token_embeds[token_id] = embeddings - else: - placeholder_token_id = [] - - if args.train_text_encoder: - print(f"Training entire text encoder.") - else: - print(f"Training added text embeddings") - - freeze_params(itertools.chain( - text_encoder.text_model.encoder.parameters(), - text_encoder.text_model.final_layer_norm.parameters(), - text_encoder.text_model.embeddings.position_embedding.parameters(), - )) - - index_fixed_tokens = torch.arange(len(tokenizer)) - index_fixed_tokens = index_fixed_tokens[~torch.isin(index_fixed_tokens, torch.tensor(placeholder_token_id))] - - prompt_processor = PromptProcessor(tokenizer, text_encoder) - - if args.scale_lr: - args.learning_rate_unet = ( - args.learning_rate_unet * args.gradient_accumulation_steps * - args.train_batch_size * accelerator.num_processes - ) - args.learning_rate_text = ( - args.learning_rate_text * args.gradient_accumulation_steps * - args.train_batch_size * accelerator.num_processes - ) - - # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs - if args.use_8bit_adam: - try: - import bitsandbytes as bnb - except ImportError: - raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.") - - optimizer_class = bnb.optim.AdamW8bit - else: - optimizer_class = torch.optim.AdamW - - if args.train_text_encoder: - text_encoder_params_to_optimize = text_encoder.parameters() - else: - text_encoder_params_to_optimize = text_encoder.get_input_embeddings().parameters() - - # Initialize the optimizer - optimizer = optimizer_class( - [ - { - 'params': unet.parameters(), - 'lr': args.learning_rate_unet, - }, - { - 'params': text_encoder_params_to_optimize, - 'lr': args.learning_rate_text, - } - ], - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - ) - - weight_dtype = torch.float32 - if args.mixed_precision == "fp16": - weight_dtype = torch.float16 - elif args.mixed_precision == "bf16": - weight_dtype = torch.bfloat16 - - def collate_fn(examples): - prompts = [example["prompts"] for example in examples] - nprompts = [example["nprompts"] for example in 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 - if args.num_class_images != 0 and "class_prompt_ids" in examples[0]: - 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(dtype=weight_dtype, memory_format=torch.contiguous_format) - - inputs = prompt_processor.unify_input_ids(input_ids) - - batch = { - "prompts": prompts, - "nprompts": nprompts, - "input_ids": inputs.input_ids, - "pixel_values": pixel_values, - "attention_mask": inputs.attention_mask, - } - return batch - - datamodule = CSVDataModule( - data_file=args.train_data_file, - batch_size=args.train_batch_size, - prompt_processor=prompt_processor, - instance_identifier=instance_identifier, - class_identifier=args.class_identifier, - class_subdir="cls", - num_class_images=args.num_class_images, - size=args.resolution, - repeats=args.repeats, - mode=args.mode, - dropout=args.tag_dropout, - center_crop=args.center_crop, - template_key=args.train_data_template, - valid_set_size=args.valid_set_size, - num_workers=args.dataloader_num_workers, - collate_fn=collate_fn - ) - - datamodule.prepare_data() - datamodule.setup() - - if args.num_class_images != 0: - missing_data = [item for item in datamodule.data_train if not item.class_image_path.exists()] - - if len(missing_data) != 0: - batched_data = [ - missing_data[i:i+args.sample_batch_size] - for i in range(0, len(missing_data), args.sample_batch_size) - ] - - pipeline = VlpnStableDiffusion( - text_encoder=text_encoder, - vae=vae, - unet=unet, - tokenizer=tokenizer, - scheduler=checkpoint_scheduler, - ).to(accelerator.device) - pipeline.set_progress_bar_config(dynamic_ncols=True) - - with torch.autocast("cuda"), torch.inference_mode(): - for batch in batched_data: - image_name = [item.class_image_path for item in batch] - prompt = [item.prompt.format(identifier=args.class_identifier) for item in batch] - nprompt = [item.nprompt for item in batch] - - images = pipeline( - prompt=prompt, - negative_prompt=nprompt, - num_inference_steps=args.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() - - train_dataloader = datamodule.train_dataloader() - val_dataloader = datamodule.val_dataloader() - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if args.max_train_steps is None: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - overrode_max_train_steps = True - num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - - warmup_steps = args.lr_warmup_epochs * num_update_steps_per_epoch * args.gradient_accumulation_steps - - if args.lr_scheduler == "one_cycle": - lr_scheduler = get_one_cycle_schedule( - optimizer=optimizer, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - ) - elif args.lr_scheduler == "cosine_with_restarts": - lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup( - optimizer=optimizer, - num_warmup_steps=warmup_steps, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - num_cycles=args.lr_cycles or math.ceil(math.sqrt( - ((args.max_train_steps - warmup_steps) / num_update_steps_per_epoch))), - ) - else: - lr_scheduler = get_scheduler( - args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=warmup_steps, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - ) - - unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare( - unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler - ) - - # Move text_encoder and vae to device - vae.to(accelerator.device, dtype=weight_dtype) - - # Keep text_encoder and vae in eval mode as we don't train these - vae.eval() - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - - num_val_steps_per_epoch = len(val_dataloader) - num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - val_steps = num_val_steps_per_epoch * num_epochs - - # We need to initialize the trackers we use, and also store our configuration. - # The trackers initializes automatically on the main process. - if accelerator.is_main_process: - config = vars(args).copy() - config["initializer_token"] = " ".join(config["initializer_token"]) - config["placeholder_token"] = " ".join(config["placeholder_token"]) - accelerator.init_trackers("dreambooth", config=config) - - # Train! - total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps - - logger.info("***** Running training *****") - logger.info(f" Num Epochs = {num_epochs}") - logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") - logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") - logger.info(f" Total optimization steps = {args.max_train_steps}") - # Only show the progress bar once on each machine. - - global_step = 0 - - avg_loss = AverageMeter() - avg_acc = AverageMeter() - - avg_loss_val = AverageMeter() - avg_acc_val = AverageMeter() - - max_acc_val = 0.0 - - checkpointer = Checkpointer( - datamodule=datamodule, - accelerator=accelerator, - vae=vae, - unet=unet, - ema_unet=ema_unet, - tokenizer=tokenizer, - text_encoder=text_encoder, - scheduler=checkpoint_scheduler, - output_dir=basepath, - instance_identifier=instance_identifier, - placeholder_token=args.placeholder_token, - placeholder_token_id=placeholder_token_id, - sample_image_size=args.sample_image_size, - sample_batch_size=args.sample_batch_size, - sample_batches=args.sample_batches, - seed=args.seed - ) - - if accelerator.is_main_process: - checkpointer.save_samples(0, args.sample_steps) - - local_progress_bar = tqdm( - range(num_update_steps_per_epoch + num_val_steps_per_epoch), - disable=not accelerator.is_local_main_process, - dynamic_ncols=True - ) - local_progress_bar.set_description("Epoch X / Y") - - global_progress_bar = tqdm( - range(args.max_train_steps + val_steps), - disable=not accelerator.is_local_main_process, - dynamic_ncols=True - ) - global_progress_bar.set_description("Total progress") - - try: - for epoch in range(num_epochs): - local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}") - local_progress_bar.reset() - - unet.train() - - if epoch < args.train_text_encoder_epochs: - text_encoder.train() - elif epoch == args.train_text_encoder_epochs: - freeze_params(text_encoder.parameters()) - - sample_checkpoint = False - - for step, batch in enumerate(train_dataloader): - with accelerator.accumulate(unet): - # Convert images to latent space - latents = vae.encode(batch["pixel_values"]).latent_dist.sample() - latents = latents * 0.18215 - - # Sample noise that we'll add to the latents - noise = torch.randn_like(latents) - bsz = latents.shape[0] - # Sample a random timestep for each image - timesteps = torch.randint(0, 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 = noise_scheduler.add_noise(latents, noise, timesteps) - - # Get the text embedding for conditioning - encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"]) - - # 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 args.num_class_images != 0: - # 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="none").mean([1, 2, 3]).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 + args.prior_loss_weight * prior_loss - else: - loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - - acc = (model_pred == latents).float().mean() - - accelerator.backward(loss) - - if accelerator.sync_gradients: - params_to_clip = ( - itertools.chain(unet.parameters(), text_encoder.parameters()) - if args.train_text_encoder and epoch < args.train_text_encoder_epochs - else unet.parameters() - ) - accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) - - optimizer.step() - if not accelerator.optimizer_step_was_skipped: - lr_scheduler.step() - if args.use_ema: - ema_unet.step(unet) - optimizer.zero_grad(set_to_none=True) - - if not args.train_text_encoder: - # Let's make sure we don't update any embedding weights besides the newly added token - with torch.no_grad(): - text_encoder.get_input_embeddings( - ).weight[index_fixed_tokens] = original_token_embeds[index_fixed_tokens] - - 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: - 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/unet": lr_scheduler.get_last_lr()[0], - "lr/text": lr_scheduler.get_last_lr()[1] - } - if args.use_ema: - logs["ema_decay"] = 1 - ema_unet.decay - - accelerator.log(logs, step=global_step) - - local_progress_bar.set_postfix(**logs) - - if global_step >= args.max_train_steps: - break - - accelerator.wait_for_everyone() - - unet.eval() - text_encoder.eval() - - with torch.inference_mode(): - for step, batch in enumerate(val_dataloader): - latents = vae.encode(batch["pixel_values"]).latent_dist.sample() - latents = latents * 0.18215 - - noise = torch.randn_like(latents) - bsz = latents.shape[0] - timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, - (bsz,), device=latents.device) - timesteps = timesteps.long() - - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - - encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"]) - - 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}") - - loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - - acc = (model_pred == latents).float().mean() - - avg_loss_val.update(loss.detach_(), bsz) - avg_acc_val.update(acc.detach_(), bsz) - - if accelerator.sync_gradients: - 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) - - accelerator.log({ - "val/loss": avg_loss_val.avg.item(), - "val/acc": avg_acc_val.avg.item(), - }, step=global_step) - - local_progress_bar.clear() - global_progress_bar.clear() - - 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}") - max_acc_val = avg_acc_val.avg.item() - - if accelerator.is_main_process: - if (epoch + 1) % args.sample_frequency == 0: - checkpointer.save_samples(global_step, args.sample_steps) - - # Create the pipeline using using the trained modules and save it. - if accelerator.is_main_process: - print("Finished! Saving final checkpoint and resume state.") - checkpointer.save_model() - - accelerator.end_training() - - except KeyboardInterrupt: - if accelerator.is_main_process: - print("Interrupted, saving checkpoint and resume state...") - checkpointer.save_model() - accelerator.end_training() - quit() - - -if __name__ == "__main__": - main() diff --git a/textual_inversion.py b/textual_inversion.py deleted file mode 100644 index e281c73..0000000 --- a/textual_inversion.py +++ /dev/null @@ -1,1034 +0,0 @@ -import argparse -import itertools -import math -import os -import datetime -import logging -import json -from pathlib import Path - -import numpy as np -import torch -import torch.nn.functional as F -import torch.utils.checkpoint - -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import LoggerType, set_seed -from diffusers import AutoencoderKL, DDPMScheduler, DPMSolverMultistepScheduler, UNet2DConditionModel -from diffusers.optimization import get_scheduler, get_cosine_with_hard_restarts_schedule_with_warmup -from PIL import Image -from tqdm.auto import tqdm -from transformers import CLIPTextModel, CLIPTokenizer -from slugify import slugify - -from common import load_text_embeddings, load_text_embedding -from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion -from pipelines.util import set_use_memory_efficient_attention_xformers -from data.csv import CSVDataModule, CSVDataItem -from training.optimization import get_one_cycle_schedule -from models.clip.prompt import PromptProcessor - -logger = get_logger(__name__) - - -torch.backends.cuda.matmul.allow_tf32 = True -torch.backends.cudnn.benchmark = True - - -def parse_args(): - parser = argparse.ArgumentParser( - description="Simple example of a training script." - ) - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default=None, - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--tokenizer_name", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--train_data_file", - type=str, - default=None, - help="A CSV file containing the training data." - ) - parser.add_argument( - "--train_data_template", - type=str, - default="template", - ) - parser.add_argument( - "--instance_identifier", - type=str, - default=None, - help="A token to use as a placeholder for the concept.", - ) - parser.add_argument( - "--class_identifier", - type=str, - default=None, - help="A token to use as a placeholder for the concept.", - ) - parser.add_argument( - "--placeholder_token", - type=str, - nargs='*', - help="A token to use as a placeholder for the concept.", - ) - parser.add_argument( - "--initializer_token", - type=str, - nargs='*', - help="A token to use as initializer word." - ) - parser.add_argument( - "--num_class_images", - type=int, - default=400, - help="How many class images to generate." - ) - parser.add_argument( - "--repeats", - type=int, - default=1, - help="How many times to repeat the training data." - ) - parser.add_argument( - "--output_dir", - type=str, - default="output/text-inversion", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument( - "--embeddings_dir", - type=str, - default=None, - help="The embeddings directory where Textual Inversion embeddings are stored.", - ) - parser.add_argument( - "--mode", - type=str, - default=None, - help="A mode to filter the dataset.", - ) - parser.add_argument( - "--seed", - type=int, - default=None, - help="A seed for reproducible training.") - parser.add_argument( - "--resolution", - type=int, - default=768, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--center_crop", - action="store_true", - help="Whether to center crop images before resizing to resolution" - ) - parser.add_argument( - "--tag_dropout", - type=float, - default=0, - help="Tag dropout probability.", - ) - parser.add_argument( - "--dataloader_num_workers", - type=int, - default=0, - help=( - "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main" - " process." - ), - ) - parser.add_argument( - "--num_train_epochs", - type=int, - default=100 - ) - parser.add_argument( - "--max_train_steps", - type=int, - default=None, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--gradient_checkpointing", - action="store_true", - help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", - ) - parser.add_argument( - "--learning_rate", - type=float, - default=1e-4, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--scale_lr", - action="store_true", - default=True, - help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", - ) - parser.add_argument( - "--lr_scheduler", - type=str, - default="one_cycle", - help=( - 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup", "one_cycle"]' - ), - ) - parser.add_argument( - "--lr_warmup_epochs", - type=int, - default=10, - help="Number of steps for the warmup in the lr scheduler." - ) - parser.add_argument( - "--lr_cycles", - type=int, - default=None, - help="Number of restart cycles in the lr scheduler." - ) - parser.add_argument( - "--use_8bit_adam", - action="store_true", - help="Whether or not to use 8-bit Adam from bitsandbytes." - ) - parser.add_argument( - "--adam_beta1", - type=float, - default=0.9, - help="The beta1 parameter for the Adam optimizer." - ) - parser.add_argument( - "--adam_beta2", - type=float, - default=0.999, - help="The beta2 parameter for the Adam optimizer." - ) - parser.add_argument( - "--adam_weight_decay", - type=float, - default=1e-2, - help="Weight decay to use." - ) - parser.add_argument( - "--adam_epsilon", - type=float, - default=1e-08, - help="Epsilon value for the Adam optimizer" - ) - parser.add_argument( - "--mixed_precision", - type=str, - default="no", - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose" - "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." - "and an Nvidia Ampere GPU." - ), - ) - parser.add_argument( - "--checkpoint_frequency", - type=int, - default=5, - help="How often to save a checkpoint and sample image (in epochs)", - ) - parser.add_argument( - "--sample_frequency", - type=int, - default=1, - help="How often to save a checkpoint and sample image (in epochs)", - ) - parser.add_argument( - "--sample_image_size", - type=int, - default=768, - help="Size of sample images", - ) - parser.add_argument( - "--sample_batches", - type=int, - default=1, - help="Number of sample batches to generate per checkpoint", - ) - parser.add_argument( - "--sample_batch_size", - type=int, - default=1, - help="Number of samples to generate per batch", - ) - parser.add_argument( - "--valid_set_size", - type=int, - default=None, - help="Number of images in the validation dataset." - ) - parser.add_argument( - "--train_batch_size", - type=int, - default=1, - help="Batch size (per device) for the training dataloader." - ) - parser.add_argument( - "--sample_steps", - type=int, - default=15, - help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.", - ) - parser.add_argument( - "--prior_loss_weight", - type=float, - default=1.0, - help="The weight of prior preservation loss." - ) - parser.add_argument( - "--noise_timesteps", - type=int, - default=1000, - ) - parser.add_argument( - "--resume_from", - type=str, - default=None, - help="Path to a directory to resume training from (ie, logs/token_name/2022-09-22T23-36-27)" - ) - parser.add_argument( - "--global_step", - type=int, - default=0, - ) - parser.add_argument( - "--config", - type=str, - default=None, - help="Path to a JSON configuration file containing arguments for invoking this script." - ) - - args = parser.parse_args() - if args.config is not None: - with open(args.config, 'rt') as f: - args = parser.parse_args( - namespace=argparse.Namespace(**json.load(f)["args"])) - - if args.train_data_file is None: - raise ValueError("You must specify --train_data_file") - - if args.pretrained_model_name_or_path is None: - raise ValueError("You must specify --pretrained_model_name_or_path") - - if isinstance(args.initializer_token, str): - args.initializer_token = [args.initializer_token] - - if len(args.initializer_token) == 0: - raise ValueError("You must specify --initializer_token") - - if isinstance(args.placeholder_token, str): - args.placeholder_token = [args.placeholder_token] - - if len(args.placeholder_token) == 0: - args.placeholder_token = [f"<*{i}>" for i in range(args.initializer_token)] - - if len(args.placeholder_token) != len(args.initializer_token): - raise ValueError("You must specify --placeholder_token") - - if args.output_dir is None: - raise ValueError("You must specify --output_dir") - - return args - - -def freeze_params(params): - for param in params: - param.requires_grad = False - - -def save_args(basepath: Path, args, extra={}): - info = {"args": vars(args)} - info["args"].update(extra) - with open(basepath.joinpath("args.json"), "w") as f: - json.dump(info, f, indent=4) - - -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 - - -class Checkpointer: - def __init__( - self, - datamodule, - accelerator, - vae, - unet, - tokenizer, - text_encoder, - scheduler, - instance_identifier, - placeholder_token, - placeholder_token_id, - output_dir: Path, - sample_image_size, - sample_batches, - sample_batch_size, - seed - ): - self.datamodule = datamodule - self.accelerator = accelerator - self.vae = vae - self.unet = unet - self.tokenizer = tokenizer - self.text_encoder = text_encoder - self.scheduler = scheduler - self.instance_identifier = instance_identifier - self.placeholder_token = placeholder_token - self.placeholder_token_id = placeholder_token_id - self.output_dir = output_dir - self.sample_image_size = sample_image_size - self.seed = seed or torch.random.seed() - self.sample_batches = sample_batches - self.sample_batch_size = sample_batch_size - - @torch.no_grad() - def checkpoint(self, step, postfix): - print("Saving checkpoint for step %d..." % step) - - checkpoints_path = self.output_dir.joinpath("checkpoints") - checkpoints_path.mkdir(parents=True, exist_ok=True) - - text_encoder = self.accelerator.unwrap_model(self.text_encoder) - - for (placeholder_token, placeholder_token_id) in zip(self.placeholder_token, self.placeholder_token_id): - # Save a checkpoint - learned_embeds = text_encoder.get_input_embeddings().weight[placeholder_token_id] - learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()} - - filename = f"%s_%d_%s.bin" % (slugify(placeholder_token), step, postfix) - torch.save(learned_embeds_dict, checkpoints_path.joinpath(filename)) - - del text_encoder - del learned_embeds - - @torch.no_grad() - def save_samples(self, step, height, width, guidance_scale, eta, num_inference_steps): - samples_path = Path(self.output_dir).joinpath("samples") - - text_encoder = self.accelerator.unwrap_model(self.text_encoder) - - # Save a sample image - pipeline = VlpnStableDiffusion( - text_encoder=text_encoder, - vae=self.vae, - unet=self.unet, - tokenizer=self.tokenizer, - scheduler=self.scheduler, - ).to(self.accelerator.device) - pipeline.set_progress_bar_config(dynamic_ncols=True) - - train_data = self.datamodule.train_dataloader() - val_data = self.datamodule.val_dataloader() - - generator = torch.Generator(device=pipeline.device).manual_seed(self.seed) - stable_latents = torch.randn( - (self.sample_batch_size, pipeline.unet.in_channels, height // 8, width // 8), - device=pipeline.device, - generator=generator, - ) - - with torch.autocast("cuda"), torch.inference_mode(): - for pool, data, latents in [("stable", val_data, stable_latents), ("val", val_data, None), ("train", train_data, None)]: - all_samples = [] - file_path = samples_path.joinpath(pool, f"step_{step}.jpg") - file_path.parent.mkdir(parents=True, exist_ok=True) - - data_enum = enumerate(data) - - batches = [ - batch - for j, batch in data_enum - if j * data.batch_size < self.sample_batch_size * self.sample_batches - ] - prompts = [ - prompt.format(identifier=self.instance_identifier) - for batch in batches - for prompt in batch["prompts"] - ] - nprompts = [ - prompt - for batch in batches - for prompt in batch["nprompts"] - ] - - for i in range(self.sample_batches): - prompt = prompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] - nprompt = nprompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] - - samples = pipeline( - prompt=prompt, - negative_prompt=nprompt, - height=self.sample_image_size, - width=self.sample_image_size, - image=latents[:len(prompt)] if latents is not None else None, - generator=generator if latents is not None else None, - guidance_scale=guidance_scale, - eta=eta, - num_inference_steps=num_inference_steps, - output_type='pil' - ).images - - all_samples += samples - - del samples - - image_grid = make_grid(all_samples, self.sample_batches, self.sample_batch_size) - image_grid.save(file_path, quality=85) - - del all_samples - del image_grid - - del text_encoder - del pipeline - del generator - del stable_latents - - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - -def main(): - args = parse_args() - - instance_identifier = args.instance_identifier - - if len(args.placeholder_token) != 0: - instance_identifier = instance_identifier.format(args.placeholder_token[0]) - - global_step_offset = args.global_step - now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") - basepath = Path(args.output_dir).joinpath(slugify(instance_identifier), now) - basepath.mkdir(parents=True, exist_ok=True) - - accelerator = Accelerator( - log_with=LoggerType.TENSORBOARD, - logging_dir=f"{basepath}", - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision - ) - - logging.basicConfig(filename=basepath.joinpath("log.txt"), level=logging.DEBUG) - - args.seed = args.seed or (torch.random.seed() >> 32) - set_seed(args.seed) - - # Load the tokenizer and add the placeholder token as a additional special token - if args.tokenizer_name: - tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) - elif args.pretrained_model_name_or_path: - tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer') - - # Load models and create wrapper for stable diffusion - text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder') - vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae') - unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet') - noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder='scheduler') - checkpoint_scheduler = DPMSolverMultistepScheduler.from_pretrained( - args.pretrained_model_name_or_path, subfolder='scheduler') - - vae.enable_slicing() - set_use_memory_efficient_attention_xformers(unet, True) - set_use_memory_efficient_attention_xformers(vae, True) - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - text_encoder.gradient_checkpointing_enable() - - if args.embeddings_dir is not None: - embeddings_dir = Path(args.embeddings_dir) - if not embeddings_dir.exists() or not embeddings_dir.is_dir(): - raise ValueError("--embeddings_dir must point to an existing directory") - added_tokens_from_dir = load_text_embeddings(tokenizer, text_encoder, embeddings_dir) - print(f"Added {len(added_tokens_from_dir)} tokens from embeddings dir: {added_tokens_from_dir}") - - # Convert the initializer_token, placeholder_token to ids - initializer_token_ids = torch.stack([ - torch.tensor(tokenizer.encode(token, add_special_tokens=False)[:1]) - for token in args.initializer_token - ]) - - num_added_tokens = tokenizer.add_tokens(args.placeholder_token) - print(f"Added {num_added_tokens} new tokens.") - - placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) - - # Resize the token embeddings as we are adding new special tokens to the tokenizer - text_encoder.resize_token_embeddings(len(tokenizer)) - - # Initialise the newly added placeholder token with the embeddings of the initializer token - token_embeds = text_encoder.get_input_embeddings().weight.data - - if args.resume_from is not None: - resumepath = Path(args.resume_from).joinpath("checkpoints") - - for (token_id, token) in zip(placeholder_token_id, args.placeholder_token): - load_text_embedding(token_embeds, token_id, resumepath.joinpath(f"{token}_{args.global_step}_end.bin")) - - original_token_embeds = token_embeds.clone().to(accelerator.device) - - initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids) - for (token_id, embeddings) in zip(placeholder_token_id, initializer_token_embeddings): - token_embeds[token_id] = embeddings - - index_fixed_tokens = torch.arange(len(tokenizer)) - index_fixed_tokens = index_fixed_tokens[~torch.isin(index_fixed_tokens, torch.tensor(placeholder_token_id))] - - # Freeze vae and unet - freeze_params(vae.parameters()) - freeze_params(unet.parameters()) - # Freeze all parameters except for the token embeddings in text encoder - freeze_params(itertools.chain( - text_encoder.text_model.encoder.parameters(), - text_encoder.text_model.final_layer_norm.parameters(), - text_encoder.text_model.embeddings.position_embedding.parameters(), - )) - - prompt_processor = PromptProcessor(tokenizer, text_encoder) - - if args.scale_lr: - args.learning_rate = ( - args.learning_rate * args.gradient_accumulation_steps * - args.train_batch_size * accelerator.num_processes - ) - - # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs - if args.use_8bit_adam: - try: - import bitsandbytes as bnb - except ImportError: - raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.") - - optimizer_class = bnb.optim.AdamW8bit - else: - optimizer_class = torch.optim.AdamW - - # Initialize the optimizer - optimizer = optimizer_class( - text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - ) - - weight_dtype = torch.float32 - if args.mixed_precision == "fp16": - weight_dtype = torch.float16 - elif args.mixed_precision == "bf16": - weight_dtype = torch.bfloat16 - - def keyword_filter(item: CSVDataItem): - return any(keyword in item.prompt for keyword in args.placeholder_token) - - def collate_fn(examples): - prompts = [example["prompts"] for example in examples] - nprompts = [example["nprompts"] for example in 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 - if args.num_class_images != 0 and "class_prompt_ids" in examples[0]: - 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(dtype=weight_dtype, memory_format=torch.contiguous_format) - - inputs = prompt_processor.unify_input_ids(input_ids) - - batch = { - "prompts": prompts, - "nprompts": nprompts, - "input_ids": inputs.input_ids, - "pixel_values": pixel_values, - "attention_mask": inputs.attention_mask, - } - return batch - - datamodule = CSVDataModule( - data_file=args.train_data_file, - batch_size=args.train_batch_size, - prompt_processor=prompt_processor, - instance_identifier=args.instance_identifier, - class_identifier=args.class_identifier, - class_subdir="cls", - num_class_images=args.num_class_images, - size=args.resolution, - repeats=args.repeats, - mode=args.mode, - dropout=args.tag_dropout, - center_crop=args.center_crop, - template_key=args.train_data_template, - valid_set_size=args.valid_set_size, - num_workers=args.dataloader_num_workers, - filter=keyword_filter, - collate_fn=collate_fn - ) - - datamodule.prepare_data() - datamodule.setup() - - if args.num_class_images != 0: - missing_data = [item for item in datamodule.data_train if not item.class_image_path.exists()] - - if len(missing_data) != 0: - batched_data = [ - missing_data[i:i+args.sample_batch_size] - for i in range(0, len(missing_data), args.sample_batch_size) - ] - - pipeline = VlpnStableDiffusion( - text_encoder=text_encoder, - vae=vae, - unet=unet, - tokenizer=tokenizer, - scheduler=checkpoint_scheduler, - ).to(accelerator.device) - pipeline.set_progress_bar_config(dynamic_ncols=True) - - with torch.autocast("cuda"), torch.inference_mode(): - for batch in batched_data: - image_name = [item.class_image_path for item in batch] - prompt = [item.prompt.format(identifier=args.class_identifier) for item in batch] - nprompt = [item.nprompt for item in batch] - - images = pipeline( - prompt=prompt, - negative_prompt=nprompt, - num_inference_steps=args.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() - - train_dataloader = datamodule.train_dataloader() - val_dataloader = datamodule.val_dataloader() - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if args.max_train_steps is None: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - overrode_max_train_steps = True - num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - - warmup_steps = args.lr_warmup_epochs * num_update_steps_per_epoch * args.gradient_accumulation_steps - - if args.lr_scheduler == "one_cycle": - lr_scheduler = get_one_cycle_schedule( - optimizer=optimizer, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - ) - elif args.lr_scheduler == "cosine_with_restarts": - lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup( - optimizer=optimizer, - num_warmup_steps=warmup_steps, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - num_cycles=args.lr_cycles or math.ceil(math.sqrt( - ((args.max_train_steps - warmup_steps) / num_update_steps_per_epoch))), - ) - else: - lr_scheduler = get_scheduler( - args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=warmup_steps, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - ) - - text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare( - text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler - ) - - # Move vae and unet to device - vae.to(accelerator.device, dtype=weight_dtype) - unet.to(accelerator.device, dtype=weight_dtype) - - # Keep vae and unet in eval mode as we don't train these - vae.eval() - unet.eval() - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - - num_val_steps_per_epoch = len(val_dataloader) - num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - val_steps = num_val_steps_per_epoch * num_epochs - - # We need to initialize the trackers we use, and also store our configuration. - # The trackers initializes automatically on the main process. - if accelerator.is_main_process: - config = vars(args).copy() - config["initializer_token"] = " ".join(config["initializer_token"]) - config["placeholder_token"] = " ".join(config["placeholder_token"]) - accelerator.init_trackers("textual_inversion", config=config) - - # Train! - total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps - - logger.info("***** Running training *****") - logger.info(f" Num Epochs = {num_epochs}") - logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") - logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") - logger.info(f" Total optimization steps = {args.max_train_steps}") - # Only show the progress bar once on each machine. - - global_step = 0 - min_val_loss = np.inf - - checkpointer = Checkpointer( - datamodule=datamodule, - accelerator=accelerator, - vae=vae, - unet=unet, - tokenizer=tokenizer, - text_encoder=text_encoder, - scheduler=checkpoint_scheduler, - instance_identifier=args.instance_identifier, - placeholder_token=args.placeholder_token, - placeholder_token_id=placeholder_token_id, - output_dir=basepath, - sample_image_size=args.sample_image_size, - sample_batch_size=args.sample_batch_size, - sample_batches=args.sample_batches, - seed=args.seed - ) - - if accelerator.is_main_process: - checkpointer.save_samples( - 0, - args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) - - local_progress_bar = tqdm( - range(num_update_steps_per_epoch + num_val_steps_per_epoch), - disable=not accelerator.is_local_main_process, - dynamic_ncols=True - ) - local_progress_bar.set_description("Epoch X / Y") - - global_progress_bar = tqdm( - range(args.max_train_steps + val_steps), - disable=not accelerator.is_local_main_process, - dynamic_ncols=True - ) - global_progress_bar.set_description("Total progress") - - try: - for epoch in range(num_epochs): - local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}") - local_progress_bar.reset() - - text_encoder.train() - train_loss = 0.0 - - sample_checkpoint = False - - for step, batch in enumerate(train_dataloader): - with accelerator.accumulate(text_encoder): - # Convert images to latent space - latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach() - latents = latents * 0.18215 - - # Sample noise that we'll add to the latents - noise = torch.randn_like(latents) - bsz = latents.shape[0] - # Sample a random timestep for each image - timesteps = torch.randint(0, 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 = noise_scheduler.add_noise(latents, noise, timesteps) - - # Get the text embedding for conditioning - encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"]) - encoder_hidden_states = encoder_hidden_states.to(dtype=weight_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 args.num_class_images != 0: - # 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="none").mean([1, 2, 3]).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 + args.prior_loss_weight * prior_loss - else: - loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - - accelerator.backward(loss) - - optimizer.step() - if not accelerator.optimizer_step_was_skipped: - lr_scheduler.step() - optimizer.zero_grad(set_to_none=True) - - # Let's make sure we don't update any embedding weights besides the newly added token - with torch.no_grad(): - text_encoder.get_input_embeddings( - ).weight[index_fixed_tokens] = original_token_embeds[index_fixed_tokens] - - loss = loss.detach().item() - train_loss += loss - - # Checks if the accelerator has performed an optimization step behind the scenes - if accelerator.sync_gradients: - local_progress_bar.update(1) - global_progress_bar.update(1) - - global_step += 1 - - logs = {"train/loss": loss, "lr": lr_scheduler.get_last_lr()[0]} - - accelerator.log(logs, step=global_step) - - local_progress_bar.set_postfix(**logs) - - if global_step >= args.max_train_steps: - break - - train_loss /= len(train_dataloader) - - accelerator.wait_for_everyone() - - text_encoder.eval() - val_loss = 0.0 - - with torch.inference_mode(): - for step, batch in enumerate(val_dataloader): - latents = vae.encode(batch["pixel_values"]).latent_dist.sample() - latents = latents * 0.18215 - - noise = torch.randn_like(latents) - bsz = latents.shape[0] - timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, - (bsz,), device=latents.device) - timesteps = timesteps.long() - - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - - encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"]) - encoder_hidden_states = encoder_hidden_states.to(dtype=weight_dtype) - - 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}") - - loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - - loss = loss.detach().item() - val_loss += loss - - if accelerator.sync_gradients: - local_progress_bar.update(1) - global_progress_bar.update(1) - - logs = {"val/loss": loss} - local_progress_bar.set_postfix(**logs) - - val_loss /= len(val_dataloader) - - accelerator.log({"val/loss": val_loss}, step=global_step) - - local_progress_bar.clear() - global_progress_bar.clear() - - if accelerator.is_main_process: - if min_val_loss > val_loss: - accelerator.print( - f"Global step {global_step}: Validation loss reached new minimum: {min_val_loss:.2e} -> {val_loss:.2e}") - checkpointer.checkpoint(global_step + global_step_offset, "milestone") - min_val_loss = val_loss - - if (epoch + 1) % args.checkpoint_frequency == 0: - checkpointer.checkpoint(global_step + global_step_offset, "training") - save_args(basepath, args, { - "global_step": global_step + global_step_offset - }) - - if (epoch + 1) % args.sample_frequency == 0: - checkpointer.save_samples( - global_step + global_step_offset, - args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) - - # Create the pipeline using using the trained modules and save it. - if accelerator.is_main_process: - print("Finished! Saving final checkpoint and resume state.") - checkpointer.checkpoint(global_step + global_step_offset, "end") - save_args(basepath, args, { - "global_step": global_step + global_step_offset - }) - accelerator.end_training() - - except KeyboardInterrupt: - if accelerator.is_main_process: - print("Interrupted, saving checkpoint and resume state...") - checkpointer.checkpoint(global_step + global_step_offset, "end") - save_args(basepath, args, { - "global_step": global_step + global_step_offset - }) - accelerator.end_training() - quit() - - -if __name__ == "__main__": - main() diff --git a/train_dreambooth.py b/train_dreambooth.py new file mode 100644 index 0000000..3eecf9c --- /dev/null +++ b/train_dreambooth.py @@ -0,0 +1,1133 @@ +import argparse +import itertools +import math +import datetime +import logging +import json +from pathlib import Path + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint + +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import LoggerType, set_seed +from diffusers import AutoencoderKL, DDPMScheduler, DPMSolverMultistepScheduler, UNet2DConditionModel +from diffusers.optimization import get_scheduler, get_cosine_with_hard_restarts_schedule_with_warmup +from diffusers.training_utils import EMAModel +from PIL import Image +from tqdm.auto import tqdm +from transformers import CLIPTextModel, CLIPTokenizer +from slugify import slugify + +from common import load_text_embeddings +from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion +from pipelines.util import set_use_memory_efficient_attention_xformers +from data.csv import CSVDataModule +from training.optimization import get_one_cycle_schedule +from models.clip.prompt import PromptProcessor + +logger = get_logger(__name__) + + +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cudnn.benchmark = True + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Simple example of a training script." + ) + parser.add_argument( + "--pretrained_model_name_or_path", + type=str, + default=None, + help="Path to pretrained model or model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--tokenizer_name", + type=str, + default=None, + help="Pretrained tokenizer name or path if not the same as model_name", + ) + parser.add_argument( + "--train_data_file", + type=str, + default=None, + help="A folder containing the training data." + ) + parser.add_argument( + "--train_data_template", + type=str, + default="template", + ) + parser.add_argument( + "--instance_identifier", + type=str, + default=None, + help="A token to use as a placeholder for the concept.", + ) + parser.add_argument( + "--class_identifier", + type=str, + default=None, + help="A token to use as a placeholder for the concept.", + ) + parser.add_argument( + "--placeholder_token", + type=str, + nargs='*', + default=[], + help="A token to use as a placeholder for the concept.", + ) + parser.add_argument( + "--initializer_token", + type=str, + nargs='*', + default=[], + help="A token to use as initializer word." + ) + parser.add_argument( + "--train_text_encoder", + action="store_true", + default=True, + help="Whether to train the whole text encoder." + ) + parser.add_argument( + "--train_text_encoder_epochs", + default=999999, + help="Number of epochs the text encoder will be trained." + ) + parser.add_argument( + "--tag_dropout", + type=float, + default=0.1, + help="Tag dropout probability.", + ) + parser.add_argument( + "--num_class_images", + type=int, + default=400, + help="How many class images to generate." + ) + parser.add_argument( + "--repeats", + type=int, + default=1, + help="How many times to repeat the training data." + ) + parser.add_argument( + "--output_dir", + type=str, + default="output/dreambooth", + help="The output directory where the model predictions and checkpoints will be written.", + ) + parser.add_argument( + "--embeddings_dir", + type=str, + default=None, + help="The embeddings directory where Textual Inversion embeddings are stored.", + ) + parser.add_argument( + "--mode", + type=str, + default=None, + help="A mode to filter the dataset.", + ) + parser.add_argument( + "--seed", + type=int, + default=None, + help="A seed for reproducible training." + ) + parser.add_argument( + "--resolution", + type=int, + default=768, + help=( + "The resolution for input images, all the images in the train/validation dataset will be resized to this" + " resolution" + ), + ) + parser.add_argument( + "--center_crop", + action="store_true", + help="Whether to center crop images before resizing to resolution" + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=0, + help=( + "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main" + " process." + ), + ) + parser.add_argument( + "--num_train_epochs", + type=int, + default=100 + ) + parser.add_argument( + "--max_train_steps", + type=int, + default=None, + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", + ) + parser.add_argument( + "--gradient_accumulation_steps", + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.", + ) + parser.add_argument( + "--gradient_checkpointing", + action="store_true", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + ) + parser.add_argument( + "--learning_rate_unet", + type=float, + default=2e-6, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--learning_rate_text", + type=float, + default=2e-6, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--scale_lr", + action="store_true", + default=True, + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + ) + parser.add_argument( + "--lr_scheduler", + type=str, + default="one_cycle", + help=( + 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup", "one_cycle"]' + ), + ) + parser.add_argument( + "--lr_warmup_epochs", + type=int, + default=10, + help="Number of steps for the warmup in the lr scheduler." + ) + parser.add_argument( + "--lr_cycles", + type=int, + default=None, + help="Number of restart cycles in the lr scheduler (if supported)." + ) + parser.add_argument( + "--use_ema", + action="store_true", + default=True, + help="Whether to use EMA model." + ) + parser.add_argument( + "--ema_inv_gamma", + type=float, + default=1.0 + ) + parser.add_argument( + "--ema_power", + type=float, + default=6/7 + ) + parser.add_argument( + "--ema_max_decay", + type=float, + default=0.9999 + ) + parser.add_argument( + "--use_8bit_adam", + action="store_true", + default=True, + help="Whether or not to use 8-bit Adam from bitsandbytes." + ) + parser.add_argument( + "--adam_beta1", + type=float, + default=0.9, + help="The beta1 parameter for the Adam optimizer." + ) + parser.add_argument( + "--adam_beta2", + type=float, + default=0.999, + help="The beta2 parameter for the Adam optimizer." + ) + parser.add_argument( + "--adam_weight_decay", + type=float, + default=1e-2, + help="Weight decay to use." + ) + parser.add_argument( + "--adam_epsilon", + type=float, + default=1e-08, + help="Epsilon value for the Adam optimizer" + ) + parser.add_argument( + "--mixed_precision", + type=str, + default="no", + choices=["no", "fp16", "bf16"], + help=( + "Whether to use mixed precision. Choose" + "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." + "and an Nvidia Ampere GPU." + ), + ) + parser.add_argument( + "--sample_frequency", + type=int, + default=1, + help="How often to save a checkpoint and sample image", + ) + parser.add_argument( + "--sample_image_size", + type=int, + default=768, + help="Size of sample images", + ) + parser.add_argument( + "--sample_batches", + type=int, + default=1, + help="Number of sample batches to generate per checkpoint", + ) + parser.add_argument( + "--sample_batch_size", + type=int, + default=1, + help="Number of samples to generate per batch", + ) + parser.add_argument( + "--valid_set_size", + type=int, + default=None, + help="Number of images in the validation dataset." + ) + parser.add_argument( + "--train_batch_size", + type=int, + default=1, + help="Batch size (per device) for the training dataloader." + ) + parser.add_argument( + "--sample_steps", + type=int, + default=15, + help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.", + ) + parser.add_argument( + "--prior_loss_weight", + type=float, + default=1.0, + help="The weight of prior preservation loss." + ) + parser.add_argument( + "--max_grad_norm", + default=1.0, + type=float, + help="Max gradient norm." + ) + parser.add_argument( + "--noise_timesteps", + type=int, + default=1000, + ) + parser.add_argument( + "--config", + type=str, + default=None, + help="Path to a JSON configuration file containing arguments for invoking this script." + ) + + args = parser.parse_args() + if args.config is not None: + with open(args.config, 'rt') as f: + args = parser.parse_args( + namespace=argparse.Namespace(**json.load(f)["args"])) + + if args.train_data_file is None: + raise ValueError("You must specify --train_data_file") + + if args.pretrained_model_name_or_path is None: + raise ValueError("You must specify --pretrained_model_name_or_path") + + if args.instance_identifier is None: + raise ValueError("You must specify --instance_identifier") + + if isinstance(args.initializer_token, str): + args.initializer_token = [args.initializer_token] + + if isinstance(args.placeholder_token, str): + args.placeholder_token = [args.placeholder_token] + + if len(args.placeholder_token) == 0: + args.placeholder_token = [f"<*{i}>" for i in range(len(args.initializer_token))] + + if len(args.placeholder_token) != len(args.initializer_token): + raise ValueError("Number of items in --placeholder_token and --initializer_token must match") + + if args.output_dir is None: + raise ValueError("You must specify --output_dir") + + return args + + +def save_args(basepath: Path, args, extra={}): + info = {"args": vars(args)} + info["args"].update(extra) + with open(basepath.joinpath("args.json"), "w") as f: + json.dump(info, f, indent=4) + + +def freeze_params(params): + for param in params: + param.requires_grad = False + + +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 + + +class AverageMeter: + def __init__(self, name=None): + self.name = name + self.reset() + + def reset(self): + self.sum = self.count = self.avg = 0 + + def update(self, val, n=1): + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + +class Checkpointer: + def __init__( + self, + datamodule, + accelerator, + vae, + unet, + ema_unet, + tokenizer, + text_encoder, + scheduler, + output_dir: Path, + instance_identifier, + placeholder_token, + placeholder_token_id, + sample_image_size, + sample_batches, + sample_batch_size, + seed + ): + self.datamodule = datamodule + self.accelerator = accelerator + self.vae = vae + self.unet = unet + self.ema_unet = ema_unet + self.tokenizer = tokenizer + self.text_encoder = text_encoder + self.scheduler = scheduler + self.output_dir = output_dir + self.instance_identifier = instance_identifier + self.placeholder_token = placeholder_token + self.placeholder_token_id = placeholder_token_id + self.sample_image_size = sample_image_size + self.seed = seed or torch.random.seed() + self.sample_batches = sample_batches + self.sample_batch_size = sample_batch_size + + @torch.no_grad() + def save_model(self): + print("Saving model...") + + unet = self.ema_unet.averaged_model if self.ema_unet is not None else self.accelerator.unwrap_model(self.unet) + text_encoder = self.accelerator.unwrap_model(self.text_encoder) + + pipeline = VlpnStableDiffusion( + text_encoder=text_encoder, + vae=self.vae, + unet=unet, + tokenizer=self.tokenizer, + scheduler=self.scheduler, + ) + pipeline.save_pretrained(self.output_dir.joinpath("model")) + + del unet + del text_encoder + del pipeline + + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + @torch.no_grad() + def save_samples(self, step, num_inference_steps, guidance_scale=7.5, eta=0.0): + samples_path = Path(self.output_dir).joinpath("samples") + + unet = self.ema_unet.averaged_model if self.ema_unet is not None else self.accelerator.unwrap_model(self.unet) + text_encoder = self.accelerator.unwrap_model(self.text_encoder) + + pipeline = VlpnStableDiffusion( + text_encoder=text_encoder, + vae=self.vae, + unet=unet, + tokenizer=self.tokenizer, + scheduler=self.scheduler, + ).to(self.accelerator.device) + pipeline.set_progress_bar_config(dynamic_ncols=True) + + train_data = self.datamodule.train_dataloader() + val_data = self.datamodule.val_dataloader() + + generator = torch.Generator(device=pipeline.device).manual_seed(self.seed) + stable_latents = torch.randn( + (self.sample_batch_size, pipeline.unet.in_channels, self.sample_image_size // 8, self.sample_image_size // 8), + device=pipeline.device, + generator=generator, + ) + + with torch.autocast("cuda"), torch.inference_mode(): + for pool, data, latents in [("stable", val_data, stable_latents), ("val", val_data, None), ("train", train_data, None)]: + all_samples = [] + file_path = samples_path.joinpath(pool, f"step_{step}.jpg") + file_path.parent.mkdir(parents=True, exist_ok=True) + + data_enum = enumerate(data) + + batches = [ + batch + for j, batch in data_enum + if j * data.batch_size < self.sample_batch_size * self.sample_batches + ] + prompts = [ + prompt.format(identifier=self.instance_identifier) + for batch in batches + for prompt in batch["prompts"] + ] + nprompts = [ + prompt + for batch in batches + for prompt in batch["nprompts"] + ] + + for i in range(self.sample_batches): + prompt = prompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] + nprompt = nprompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] + + samples = pipeline( + prompt=prompt, + negative_prompt=nprompt, + height=self.sample_image_size, + width=self.sample_image_size, + image=latents[:len(prompt)] if latents is not None else None, + generator=generator if latents is not None else None, + guidance_scale=guidance_scale, + eta=eta, + num_inference_steps=num_inference_steps, + output_type='pil' + ).images + + all_samples += samples + + del samples + + image_grid = make_grid(all_samples, self.sample_batches, self.sample_batch_size) + image_grid.save(file_path, quality=85) + + del all_samples + del image_grid + + del unet + del text_encoder + del pipeline + del generator + del stable_latents + + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def main(): + args = parse_args() + + if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1: + raise ValueError( + "Gradient accumulation is not supported when training the text encoder in distributed training. " + "Please set gradient_accumulation_steps to 1. This feature will be supported in the future." + ) + + instance_identifier = args.instance_identifier + + if len(args.placeholder_token) != 0: + instance_identifier = instance_identifier.format(args.placeholder_token[0]) + + now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") + basepath = Path(args.output_dir).joinpath(slugify(instance_identifier), now) + basepath.mkdir(parents=True, exist_ok=True) + + accelerator = Accelerator( + log_with=LoggerType.TENSORBOARD, + logging_dir=f"{basepath}", + gradient_accumulation_steps=args.gradient_accumulation_steps, + mixed_precision=args.mixed_precision + ) + + logging.basicConfig(filename=basepath.joinpath("log.txt"), level=logging.DEBUG) + + args.seed = args.seed or (torch.random.seed() >> 32) + set_seed(args.seed) + + save_args(basepath, args) + + # Load the tokenizer and add the placeholder token as a additional special token + if args.tokenizer_name: + tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) + elif args.pretrained_model_name_or_path: + tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer') + + # Load models and create wrapper for stable diffusion + text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder') + vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae') + unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet') + noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder='scheduler') + checkpoint_scheduler = DPMSolverMultistepScheduler.from_pretrained( + args.pretrained_model_name_or_path, subfolder='scheduler') + + vae.enable_slicing() + set_use_memory_efficient_attention_xformers(unet, True) + set_use_memory_efficient_attention_xformers(vae, True) + + if args.gradient_checkpointing: + unet.enable_gradient_checkpointing() + text_encoder.gradient_checkpointing_enable() + + ema_unet = None + if args.use_ema: + ema_unet = EMAModel( + unet, + inv_gamma=args.ema_inv_gamma, + power=args.ema_power, + max_value=args.ema_max_decay, + device=accelerator.device + ) + + # Freeze text_encoder and vae + vae.requires_grad_(False) + + if args.embeddings_dir is not None: + embeddings_dir = Path(args.embeddings_dir) + if not embeddings_dir.exists() or not embeddings_dir.is_dir(): + raise ValueError("--embeddings_dir must point to an existing directory") + added_tokens = load_text_embeddings(tokenizer, text_encoder, embeddings_dir) + print(f"Added {len(added_tokens)} tokens from embeddings dir: {added_tokens}") + + if len(args.placeholder_token) != 0: + # Convert the initializer_token, placeholder_token to ids + initializer_token_ids = torch.stack([ + torch.tensor(tokenizer.encode(token, add_special_tokens=False)[:1]) + for token in args.initializer_token + ]) + + num_added_tokens = tokenizer.add_tokens(args.placeholder_token) + print(f"Added {num_added_tokens} new tokens.") + + placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) + + # Resize the token embeddings as we are adding new special tokens to the tokenizer + text_encoder.resize_token_embeddings(len(tokenizer)) + + token_embeds = text_encoder.get_input_embeddings().weight.data + original_token_embeds = token_embeds.clone().to(accelerator.device) + initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids) + + for (token_id, embeddings) in zip(placeholder_token_id, initializer_token_embeddings): + token_embeds[token_id] = embeddings + else: + placeholder_token_id = [] + + if args.train_text_encoder: + print(f"Training entire text encoder.") + else: + print(f"Training added text embeddings") + + freeze_params(itertools.chain( + text_encoder.text_model.encoder.parameters(), + text_encoder.text_model.final_layer_norm.parameters(), + text_encoder.text_model.embeddings.position_embedding.parameters(), + )) + + index_fixed_tokens = torch.arange(len(tokenizer)) + index_fixed_tokens = index_fixed_tokens[~torch.isin(index_fixed_tokens, torch.tensor(placeholder_token_id))] + + prompt_processor = PromptProcessor(tokenizer, text_encoder) + + if args.scale_lr: + args.learning_rate_unet = ( + args.learning_rate_unet * args.gradient_accumulation_steps * + args.train_batch_size * accelerator.num_processes + ) + args.learning_rate_text = ( + args.learning_rate_text * args.gradient_accumulation_steps * + args.train_batch_size * accelerator.num_processes + ) + + # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs + if args.use_8bit_adam: + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.") + + optimizer_class = bnb.optim.AdamW8bit + else: + optimizer_class = torch.optim.AdamW + + if args.train_text_encoder: + text_encoder_params_to_optimize = text_encoder.parameters() + else: + text_encoder_params_to_optimize = text_encoder.get_input_embeddings().parameters() + + # Initialize the optimizer + optimizer = optimizer_class( + [ + { + 'params': unet.parameters(), + 'lr': args.learning_rate_unet, + }, + { + 'params': text_encoder_params_to_optimize, + 'lr': args.learning_rate_text, + } + ], + betas=(args.adam_beta1, args.adam_beta2), + weight_decay=args.adam_weight_decay, + eps=args.adam_epsilon, + ) + + weight_dtype = torch.float32 + if args.mixed_precision == "fp16": + weight_dtype = torch.float16 + elif args.mixed_precision == "bf16": + weight_dtype = torch.bfloat16 + + def collate_fn(examples): + prompts = [example["prompts"] for example in examples] + nprompts = [example["nprompts"] for example in 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 + if args.num_class_images != 0 and "class_prompt_ids" in examples[0]: + 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(dtype=weight_dtype, memory_format=torch.contiguous_format) + + inputs = prompt_processor.unify_input_ids(input_ids) + + batch = { + "prompts": prompts, + "nprompts": nprompts, + "input_ids": inputs.input_ids, + "pixel_values": pixel_values, + "attention_mask": inputs.attention_mask, + } + return batch + + datamodule = CSVDataModule( + data_file=args.train_data_file, + batch_size=args.train_batch_size, + prompt_processor=prompt_processor, + instance_identifier=instance_identifier, + class_identifier=args.class_identifier, + class_subdir="cls", + num_class_images=args.num_class_images, + size=args.resolution, + repeats=args.repeats, + mode=args.mode, + dropout=args.tag_dropout, + center_crop=args.center_crop, + template_key=args.train_data_template, + valid_set_size=args.valid_set_size, + num_workers=args.dataloader_num_workers, + collate_fn=collate_fn + ) + + datamodule.prepare_data() + datamodule.setup() + + if args.num_class_images != 0: + missing_data = [item for item in datamodule.data_train if not item.class_image_path.exists()] + + if len(missing_data) != 0: + batched_data = [ + missing_data[i:i+args.sample_batch_size] + for i in range(0, len(missing_data), args.sample_batch_size) + ] + + pipeline = VlpnStableDiffusion( + text_encoder=text_encoder, + vae=vae, + unet=unet, + tokenizer=tokenizer, + scheduler=checkpoint_scheduler, + ).to(accelerator.device) + pipeline.set_progress_bar_config(dynamic_ncols=True) + + with torch.autocast("cuda"), torch.inference_mode(): + for batch in batched_data: + image_name = [item.class_image_path for item in batch] + prompt = [item.prompt.format(identifier=args.class_identifier) for item in batch] + nprompt = [item.nprompt for item in batch] + + images = pipeline( + prompt=prompt, + negative_prompt=nprompt, + num_inference_steps=args.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() + + train_dataloader = datamodule.train_dataloader() + val_dataloader = datamodule.val_dataloader() + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + overrode_max_train_steps = True + num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + + warmup_steps = args.lr_warmup_epochs * num_update_steps_per_epoch * args.gradient_accumulation_steps + + if args.lr_scheduler == "one_cycle": + lr_scheduler = get_one_cycle_schedule( + optimizer=optimizer, + num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, + ) + elif args.lr_scheduler == "cosine_with_restarts": + lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup( + optimizer=optimizer, + num_warmup_steps=warmup_steps, + num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, + num_cycles=args.lr_cycles or math.ceil(math.sqrt( + ((args.max_train_steps - warmup_steps) / num_update_steps_per_epoch))), + ) + else: + lr_scheduler = get_scheduler( + args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=warmup_steps, + num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, + ) + + unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare( + unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler + ) + + # Move text_encoder and vae to device + vae.to(accelerator.device, dtype=weight_dtype) + + # Keep text_encoder and vae in eval mode as we don't train these + vae.eval() + + # We need to recalculate our total training steps as the size of the training dataloader may have changed. + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if overrode_max_train_steps: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + + num_val_steps_per_epoch = len(val_dataloader) + num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + val_steps = num_val_steps_per_epoch * num_epochs + + # We need to initialize the trackers we use, and also store our configuration. + # The trackers initializes automatically on the main process. + if accelerator.is_main_process: + config = vars(args).copy() + config["initializer_token"] = " ".join(config["initializer_token"]) + config["placeholder_token"] = " ".join(config["placeholder_token"]) + accelerator.init_trackers("dreambooth", config=config) + + # Train! + total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps + + logger.info("***** Running training *****") + logger.info(f" Num Epochs = {num_epochs}") + logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") + logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") + logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + logger.info(f" Total optimization steps = {args.max_train_steps}") + # Only show the progress bar once on each machine. + + global_step = 0 + + avg_loss = AverageMeter() + avg_acc = AverageMeter() + + avg_loss_val = AverageMeter() + avg_acc_val = AverageMeter() + + max_acc_val = 0.0 + + checkpointer = Checkpointer( + datamodule=datamodule, + accelerator=accelerator, + vae=vae, + unet=unet, + ema_unet=ema_unet, + tokenizer=tokenizer, + text_encoder=text_encoder, + scheduler=checkpoint_scheduler, + output_dir=basepath, + instance_identifier=instance_identifier, + placeholder_token=args.placeholder_token, + placeholder_token_id=placeholder_token_id, + sample_image_size=args.sample_image_size, + sample_batch_size=args.sample_batch_size, + sample_batches=args.sample_batches, + seed=args.seed + ) + + if accelerator.is_main_process: + checkpointer.save_samples(0, args.sample_steps) + + local_progress_bar = tqdm( + range(num_update_steps_per_epoch + num_val_steps_per_epoch), + disable=not accelerator.is_local_main_process, + dynamic_ncols=True + ) + local_progress_bar.set_description("Epoch X / Y") + + global_progress_bar = tqdm( + range(args.max_train_steps + val_steps), + disable=not accelerator.is_local_main_process, + dynamic_ncols=True + ) + global_progress_bar.set_description("Total progress") + + try: + for epoch in range(num_epochs): + local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}") + local_progress_bar.reset() + + unet.train() + + if epoch < args.train_text_encoder_epochs: + text_encoder.train() + elif epoch == args.train_text_encoder_epochs: + freeze_params(text_encoder.parameters()) + + sample_checkpoint = False + + for step, batch in enumerate(train_dataloader): + with accelerator.accumulate(unet): + # Convert images to latent space + latents = vae.encode(batch["pixel_values"]).latent_dist.sample() + latents = latents * 0.18215 + + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) + bsz = latents.shape[0] + # Sample a random timestep for each image + timesteps = torch.randint(0, 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 = noise_scheduler.add_noise(latents, noise, timesteps) + + # Get the text embedding for conditioning + encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"]) + + # 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 args.num_class_images != 0: + # 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="none").mean([1, 2, 3]).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 + args.prior_loss_weight * prior_loss + else: + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + + acc = (model_pred == latents).float().mean() + + accelerator.backward(loss) + + if accelerator.sync_gradients: + params_to_clip = ( + itertools.chain(unet.parameters(), text_encoder.parameters()) + if args.train_text_encoder and epoch < args.train_text_encoder_epochs + else unet.parameters() + ) + accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) + + optimizer.step() + if not accelerator.optimizer_step_was_skipped: + lr_scheduler.step() + if args.use_ema: + ema_unet.step(unet) + optimizer.zero_grad(set_to_none=True) + + if not args.train_text_encoder: + # Let's make sure we don't update any embedding weights besides the newly added token + with torch.no_grad(): + text_encoder.get_input_embeddings( + ).weight[index_fixed_tokens] = original_token_embeds[index_fixed_tokens] + + 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: + 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/unet": lr_scheduler.get_last_lr()[0], + "lr/text": lr_scheduler.get_last_lr()[1] + } + if args.use_ema: + logs["ema_decay"] = 1 - ema_unet.decay + + accelerator.log(logs, step=global_step) + + local_progress_bar.set_postfix(**logs) + + if global_step >= args.max_train_steps: + break + + accelerator.wait_for_everyone() + + unet.eval() + text_encoder.eval() + + with torch.inference_mode(): + for step, batch in enumerate(val_dataloader): + latents = vae.encode(batch["pixel_values"]).latent_dist.sample() + latents = latents * 0.18215 + + noise = torch.randn_like(latents) + bsz = latents.shape[0] + timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, + (bsz,), device=latents.device) + timesteps = timesteps.long() + + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"]) + + 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}") + + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + + acc = (model_pred == latents).float().mean() + + avg_loss_val.update(loss.detach_(), bsz) + avg_acc_val.update(acc.detach_(), bsz) + + if accelerator.sync_gradients: + 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) + + accelerator.log({ + "val/loss": avg_loss_val.avg.item(), + "val/acc": avg_acc_val.avg.item(), + }, step=global_step) + + local_progress_bar.clear() + global_progress_bar.clear() + + 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}") + max_acc_val = avg_acc_val.avg.item() + + if accelerator.is_main_process: + if (epoch + 1) % args.sample_frequency == 0: + checkpointer.save_samples(global_step, args.sample_steps) + + # Create the pipeline using using the trained modules and save it. + if accelerator.is_main_process: + print("Finished! Saving final checkpoint and resume state.") + checkpointer.save_model() + + accelerator.end_training() + + except KeyboardInterrupt: + if accelerator.is_main_process: + print("Interrupted, saving checkpoint and resume state...") + checkpointer.save_model() + accelerator.end_training() + quit() + + +if __name__ == "__main__": + main() diff --git a/train_ti.py b/train_ti.py new file mode 100644 index 0000000..dbfe58c --- /dev/null +++ b/train_ti.py @@ -0,0 +1,1032 @@ +import argparse +import itertools +import math +import os +import datetime +import logging +import json +from pathlib import Path + +import numpy as np +import torch +import torch.nn.functional as F +import torch.utils.checkpoint + +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import LoggerType, set_seed +from diffusers import AutoencoderKL, DDPMScheduler, DPMSolverMultistepScheduler, UNet2DConditionModel +from diffusers.optimization import get_scheduler, get_cosine_with_hard_restarts_schedule_with_warmup +from PIL import Image +from tqdm.auto import tqdm +from transformers import CLIPTextModel, CLIPTokenizer +from slugify import slugify + +from common import load_text_embeddings, load_text_embedding +from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion +from pipelines.util import set_use_memory_efficient_attention_xformers +from data.csv import CSVDataModule, CSVDataItem +from training.optimization import get_one_cycle_schedule +from models.clip.prompt import PromptProcessor + +logger = get_logger(__name__) + + +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cudnn.benchmark = True + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Simple example of a training script." + ) + parser.add_argument( + "--pretrained_model_name_or_path", + type=str, + default=None, + help="Path to pretrained model or model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--tokenizer_name", + type=str, + default=None, + help="Pretrained tokenizer name or path if not the same as model_name", + ) + parser.add_argument( + "--train_data_file", + type=str, + default=None, + help="A CSV file containing the training data." + ) + parser.add_argument( + "--train_data_template", + type=str, + default="template", + ) + parser.add_argument( + "--instance_identifier", + type=str, + default=None, + help="A token to use as a placeholder for the concept.", + ) + parser.add_argument( + "--class_identifier", + type=str, + default=None, + help="A token to use as a placeholder for the concept.", + ) + parser.add_argument( + "--placeholder_token", + type=str, + nargs='*', + help="A token to use as a placeholder for the concept.", + ) + parser.add_argument( + "--initializer_token", + type=str, + nargs='*', + help="A token to use as initializer word." + ) + parser.add_argument( + "--num_class_images", + type=int, + default=400, + help="How many class images to generate." + ) + parser.add_argument( + "--repeats", + type=int, + default=1, + help="How many times to repeat the training data." + ) + parser.add_argument( + "--output_dir", + type=str, + default="output/text-inversion", + help="The output directory where the model predictions and checkpoints will be written.", + ) + parser.add_argument( + "--embeddings_dir", + type=str, + default=None, + help="The embeddings directory where Textual Inversion embeddings are stored.", + ) + parser.add_argument( + "--mode", + type=str, + default=None, + help="A mode to filter the dataset.", + ) + parser.add_argument( + "--seed", + type=int, + default=None, + help="A seed for reproducible training.") + parser.add_argument( + "--resolution", + type=int, + default=768, + help=( + "The resolution for input images, all the images in the train/validation dataset will be resized to this" + " resolution" + ), + ) + parser.add_argument( + "--center_crop", + action="store_true", + help="Whether to center crop images before resizing to resolution" + ) + parser.add_argument( + "--tag_dropout", + type=float, + default=0, + help="Tag dropout probability.", + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=0, + help=( + "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main" + " process." + ), + ) + parser.add_argument( + "--num_train_epochs", + type=int, + default=100 + ) + parser.add_argument( + "--max_train_steps", + type=int, + default=None, + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", + ) + parser.add_argument( + "--gradient_accumulation_steps", + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.", + ) + parser.add_argument( + "--gradient_checkpointing", + action="store_true", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--scale_lr", + action="store_true", + default=True, + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + ) + parser.add_argument( + "--lr_scheduler", + type=str, + default="one_cycle", + help=( + 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup", "one_cycle"]' + ), + ) + parser.add_argument( + "--lr_warmup_epochs", + type=int, + default=10, + help="Number of steps for the warmup in the lr scheduler." + ) + parser.add_argument( + "--lr_cycles", + type=int, + default=None, + help="Number of restart cycles in the lr scheduler." + ) + parser.add_argument( + "--use_8bit_adam", + action="store_true", + help="Whether or not to use 8-bit Adam from bitsandbytes." + ) + parser.add_argument( + "--adam_beta1", + type=float, + default=0.9, + help="The beta1 parameter for the Adam optimizer." + ) + parser.add_argument( + "--adam_beta2", + type=float, + default=0.999, + help="The beta2 parameter for the Adam optimizer." + ) + parser.add_argument( + "--adam_weight_decay", + type=float, + default=1e-2, + help="Weight decay to use." + ) + parser.add_argument( + "--adam_epsilon", + type=float, + default=1e-08, + help="Epsilon value for the Adam optimizer" + ) + parser.add_argument( + "--mixed_precision", + type=str, + default="no", + choices=["no", "fp16", "bf16"], + help=( + "Whether to use mixed precision. Choose" + "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." + "and an Nvidia Ampere GPU." + ), + ) + parser.add_argument( + "--checkpoint_frequency", + type=int, + default=5, + help="How often to save a checkpoint and sample image (in epochs)", + ) + parser.add_argument( + "--sample_frequency", + type=int, + default=1, + help="How often to save a checkpoint and sample image (in epochs)", + ) + parser.add_argument( + "--sample_image_size", + type=int, + default=768, + help="Size of sample images", + ) + parser.add_argument( + "--sample_batches", + type=int, + default=1, + help="Number of sample batches to generate per checkpoint", + ) + parser.add_argument( + "--sample_batch_size", + type=int, + default=1, + help="Number of samples to generate per batch", + ) + parser.add_argument( + "--valid_set_size", + type=int, + default=None, + help="Number of images in the validation dataset." + ) + parser.add_argument( + "--train_batch_size", + type=int, + default=1, + help="Batch size (per device) for the training dataloader." + ) + parser.add_argument( + "--sample_steps", + type=int, + default=15, + help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.", + ) + parser.add_argument( + "--prior_loss_weight", + type=float, + default=1.0, + help="The weight of prior preservation loss." + ) + parser.add_argument( + "--noise_timesteps", + type=int, + default=1000, + ) + parser.add_argument( + "--resume_from", + type=str, + default=None, + help="Path to a directory to resume training from (ie, logs/token_name/2022-09-22T23-36-27)" + ) + parser.add_argument( + "--global_step", + type=int, + default=0, + ) + parser.add_argument( + "--config", + type=str, + default=None, + help="Path to a JSON configuration file containing arguments for invoking this script." + ) + + args = parser.parse_args() + if args.config is not None: + with open(args.config, 'rt') as f: + args = parser.parse_args( + namespace=argparse.Namespace(**json.load(f)["args"])) + + if args.train_data_file is None: + raise ValueError("You must specify --train_data_file") + + if args.pretrained_model_name_or_path is None: + raise ValueError("You must specify --pretrained_model_name_or_path") + + if isinstance(args.initializer_token, str): + args.initializer_token = [args.initializer_token] + + if len(args.initializer_token) == 0: + raise ValueError("You must specify --initializer_token") + + if isinstance(args.placeholder_token, str): + args.placeholder_token = [args.placeholder_token] + + if len(args.placeholder_token) == 0: + args.placeholder_token = [f"<*{i}>" for i in range(args.initializer_token)] + + if len(args.placeholder_token) != len(args.initializer_token): + raise ValueError("You must specify --placeholder_token") + + if args.output_dir is None: + raise ValueError("You must specify --output_dir") + + return args + + +def freeze_params(params): + for param in params: + param.requires_grad = False + + +def save_args(basepath: Path, args, extra={}): + info = {"args": vars(args)} + info["args"].update(extra) + with open(basepath.joinpath("args.json"), "w") as f: + json.dump(info, f, indent=4) + + +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 + + +class Checkpointer: + def __init__( + self, + datamodule, + accelerator, + vae, + unet, + tokenizer, + text_encoder, + scheduler, + instance_identifier, + placeholder_token, + placeholder_token_id, + output_dir: Path, + sample_image_size, + sample_batches, + sample_batch_size, + seed + ): + self.datamodule = datamodule + self.accelerator = accelerator + self.vae = vae + self.unet = unet + self.tokenizer = tokenizer + self.text_encoder = text_encoder + self.scheduler = scheduler + self.instance_identifier = instance_identifier + self.placeholder_token = placeholder_token + self.placeholder_token_id = placeholder_token_id + self.output_dir = output_dir + self.sample_image_size = sample_image_size + self.seed = seed or torch.random.seed() + self.sample_batches = sample_batches + self.sample_batch_size = sample_batch_size + + @torch.no_grad() + def checkpoint(self, step, postfix): + print("Saving checkpoint for step %d..." % step) + + checkpoints_path = self.output_dir.joinpath("checkpoints") + checkpoints_path.mkdir(parents=True, exist_ok=True) + + text_encoder = self.accelerator.unwrap_model(self.text_encoder) + + for (placeholder_token, placeholder_token_id) in zip(self.placeholder_token, self.placeholder_token_id): + # Save a checkpoint + learned_embeds = text_encoder.get_input_embeddings().weight[placeholder_token_id] + learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()} + + filename = f"%s_%d_%s.bin" % (slugify(placeholder_token), step, postfix) + torch.save(learned_embeds_dict, checkpoints_path.joinpath(filename)) + + del text_encoder + del learned_embeds + + @torch.no_grad() + def save_samples(self, step, height, width, guidance_scale, eta, num_inference_steps): + samples_path = Path(self.output_dir).joinpath("samples") + + text_encoder = self.accelerator.unwrap_model(self.text_encoder) + + # Save a sample image + pipeline = VlpnStableDiffusion( + text_encoder=text_encoder, + vae=self.vae, + unet=self.unet, + tokenizer=self.tokenizer, + scheduler=self.scheduler, + ).to(self.accelerator.device) + pipeline.set_progress_bar_config(dynamic_ncols=True) + + train_data = self.datamodule.train_dataloader() + val_data = self.datamodule.val_dataloader() + + generator = torch.Generator(device=pipeline.device).manual_seed(self.seed) + stable_latents = torch.randn( + (self.sample_batch_size, pipeline.unet.in_channels, height // 8, width // 8), + device=pipeline.device, + generator=generator, + ) + + with torch.autocast("cuda"), torch.inference_mode(): + for pool, data, latents in [("stable", val_data, stable_latents), ("val", val_data, None), ("train", train_data, None)]: + all_samples = [] + file_path = samples_path.joinpath(pool, f"step_{step}.jpg") + file_path.parent.mkdir(parents=True, exist_ok=True) + + data_enum = enumerate(data) + + batches = [ + batch + for j, batch in data_enum + if j * data.batch_size < self.sample_batch_size * self.sample_batches + ] + prompts = [ + prompt.format(identifier=self.instance_identifier) + for batch in batches + for prompt in batch["prompts"] + ] + nprompts = [ + prompt + for batch in batches + for prompt in batch["nprompts"] + ] + + for i in range(self.sample_batches): + prompt = prompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] + nprompt = nprompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] + + samples = pipeline( + prompt=prompt, + negative_prompt=nprompt, + height=self.sample_image_size, + width=self.sample_image_size, + image=latents[:len(prompt)] if latents is not None else None, + generator=generator if latents is not None else None, + guidance_scale=guidance_scale, + eta=eta, + num_inference_steps=num_inference_steps, + output_type='pil' + ).images + + all_samples += samples + + del samples + + image_grid = make_grid(all_samples, self.sample_batches, self.sample_batch_size) + image_grid.save(file_path, quality=85) + + del all_samples + del image_grid + + del text_encoder + del pipeline + del generator + del stable_latents + + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def main(): + args = parse_args() + + instance_identifier = args.instance_identifier + + if len(args.placeholder_token) != 0: + instance_identifier = instance_identifier.format(args.placeholder_token[0]) + + global_step_offset = args.global_step + now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") + basepath = Path(args.output_dir).joinpath(slugify(instance_identifier), now) + basepath.mkdir(parents=True, exist_ok=True) + + accelerator = Accelerator( + log_with=LoggerType.TENSORBOARD, + logging_dir=f"{basepath}", + gradient_accumulation_steps=args.gradient_accumulation_steps, + mixed_precision=args.mixed_precision + ) + + logging.basicConfig(filename=basepath.joinpath("log.txt"), level=logging.DEBUG) + + args.seed = args.seed or (torch.random.seed() >> 32) + set_seed(args.seed) + + # Load the tokenizer and add the placeholder token as a additional special token + if args.tokenizer_name: + tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) + elif args.pretrained_model_name_or_path: + tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer') + + # Load models and create wrapper for stable diffusion + text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder') + vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae') + unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet') + noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder='scheduler') + checkpoint_scheduler = DPMSolverMultistepScheduler.from_pretrained( + args.pretrained_model_name_or_path, subfolder='scheduler') + + vae.enable_slicing() + set_use_memory_efficient_attention_xformers(unet, True) + set_use_memory_efficient_attention_xformers(vae, True) + + if args.gradient_checkpointing: + unet.enable_gradient_checkpointing() + text_encoder.gradient_checkpointing_enable() + + if args.embeddings_dir is not None: + embeddings_dir = Path(args.embeddings_dir) + if not embeddings_dir.exists() or not embeddings_dir.is_dir(): + raise ValueError("--embeddings_dir must point to an existing directory") + added_tokens_from_dir = load_text_embeddings(tokenizer, text_encoder, embeddings_dir) + print(f"Added {len(added_tokens_from_dir)} tokens from embeddings dir: {added_tokens_from_dir}") + + # Convert the initializer_token, placeholder_token to ids + initializer_token_ids = torch.stack([ + torch.tensor(tokenizer.encode(token, add_special_tokens=False)[:1]) + for token in args.initializer_token + ]) + + num_added_tokens = tokenizer.add_tokens(args.placeholder_token) + print(f"Added {num_added_tokens} new tokens.") + + placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) + + # Resize the token embeddings as we are adding new special tokens to the tokenizer + text_encoder.resize_token_embeddings(len(tokenizer)) + + # Initialise the newly added placeholder token with the embeddings of the initializer token + token_embeds = text_encoder.get_input_embeddings().weight.data + + if args.resume_from is not None: + resumepath = Path(args.resume_from).joinpath("checkpoints") + + for (token_id, token) in zip(placeholder_token_id, args.placeholder_token): + load_text_embedding(token_embeds, token_id, resumepath.joinpath(f"{token}_{args.global_step}_end.bin")) + + original_token_embeds = token_embeds.clone().to(accelerator.device) + + initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids) + for (token_id, embeddings) in zip(placeholder_token_id, initializer_token_embeddings): + token_embeds[token_id] = embeddings + + index_fixed_tokens = torch.arange(len(tokenizer)) + index_fixed_tokens = index_fixed_tokens[~torch.isin(index_fixed_tokens, torch.tensor(placeholder_token_id))] + + # Freeze vae and unet + freeze_params(vae.parameters()) + freeze_params(unet.parameters()) + # Freeze all parameters except for the token embeddings in text encoder + freeze_params(itertools.chain( + text_encoder.text_model.encoder.parameters(), + text_encoder.text_model.final_layer_norm.parameters(), + text_encoder.text_model.embeddings.position_embedding.parameters(), + )) + + prompt_processor = PromptProcessor(tokenizer, text_encoder) + + if args.scale_lr: + args.learning_rate = ( + args.learning_rate * args.gradient_accumulation_steps * + args.train_batch_size * accelerator.num_processes + ) + + # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs + if args.use_8bit_adam: + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.") + + optimizer_class = bnb.optim.AdamW8bit + else: + optimizer_class = torch.optim.AdamW + + # Initialize the optimizer + optimizer = optimizer_class( + text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings + lr=args.learning_rate, + betas=(args.adam_beta1, args.adam_beta2), + weight_decay=args.adam_weight_decay, + eps=args.adam_epsilon, + ) + + weight_dtype = torch.float32 + if args.mixed_precision == "fp16": + weight_dtype = torch.float16 + elif args.mixed_precision == "bf16": + weight_dtype = torch.bfloat16 + + def keyword_filter(item: CSVDataItem): + return any(keyword in item.prompt for keyword in args.placeholder_token) + + def collate_fn(examples): + prompts = [example["prompts"] for example in examples] + nprompts = [example["nprompts"] for example in 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 + if args.num_class_images != 0 and "class_prompt_ids" in examples[0]: + 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(dtype=weight_dtype, memory_format=torch.contiguous_format) + + inputs = prompt_processor.unify_input_ids(input_ids) + + batch = { + "prompts": prompts, + "nprompts": nprompts, + "input_ids": inputs.input_ids, + "pixel_values": pixel_values, + "attention_mask": inputs.attention_mask, + } + return batch + + datamodule = CSVDataModule( + data_file=args.train_data_file, + batch_size=args.train_batch_size, + prompt_processor=prompt_processor, + instance_identifier=args.instance_identifier, + class_identifier=args.class_identifier, + class_subdir="cls", + num_class_images=args.num_class_images, + size=args.resolution, + repeats=args.repeats, + mode=args.mode, + dropout=args.tag_dropout, + center_crop=args.center_crop, + template_key=args.train_data_template, + valid_set_size=args.valid_set_size, + num_workers=args.dataloader_num_workers, + filter=keyword_filter, + collate_fn=collate_fn + ) + + datamodule.prepare_data() + datamodule.setup() + + if args.num_class_images != 0: + missing_data = [item for item in datamodule.data_train if not item.class_image_path.exists()] + + if len(missing_data) != 0: + batched_data = [ + missing_data[i:i+args.sample_batch_size] + for i in range(0, len(missing_data), args.sample_batch_size) + ] + + pipeline = VlpnStableDiffusion( + text_encoder=text_encoder, + vae=vae, + unet=unet, + tokenizer=tokenizer, + scheduler=checkpoint_scheduler, + ).to(accelerator.device) + pipeline.set_progress_bar_config(dynamic_ncols=True) + + with torch.autocast("cuda"), torch.inference_mode(): + for batch in batched_data: + image_name = [item.class_image_path for item in batch] + prompt = [item.prompt.format(identifier=args.class_identifier) for item in batch] + nprompt = [item.nprompt for item in batch] + + images = pipeline( + prompt=prompt, + negative_prompt=nprompt, + num_inference_steps=args.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() + + train_dataloader = datamodule.train_dataloader() + val_dataloader = datamodule.val_dataloader() + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + overrode_max_train_steps = True + num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + + warmup_steps = args.lr_warmup_epochs * num_update_steps_per_epoch * args.gradient_accumulation_steps + + if args.lr_scheduler == "one_cycle": + lr_scheduler = get_one_cycle_schedule( + optimizer=optimizer, + num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, + ) + elif args.lr_scheduler == "cosine_with_restarts": + lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup( + optimizer=optimizer, + num_warmup_steps=warmup_steps, + num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, + num_cycles=args.lr_cycles or math.ceil(math.sqrt( + ((args.max_train_steps - warmup_steps) / num_update_steps_per_epoch))), + ) + else: + lr_scheduler = get_scheduler( + args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=warmup_steps, + num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, + ) + + text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare( + text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler + ) + + # Move vae and unet to device + vae.to(accelerator.device, dtype=weight_dtype) + unet.to(accelerator.device, dtype=weight_dtype) + + # Keep vae and unet in eval mode as we don't train these + vae.eval() + unet.eval() + + # We need to recalculate our total training steps as the size of the training dataloader may have changed. + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if overrode_max_train_steps: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + + num_val_steps_per_epoch = len(val_dataloader) + num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + val_steps = num_val_steps_per_epoch * num_epochs + + # We need to initialize the trackers we use, and also store our configuration. + # The trackers initializes automatically on the main process. + if accelerator.is_main_process: + config = vars(args).copy() + config["initializer_token"] = " ".join(config["initializer_token"]) + config["placeholder_token"] = " ".join(config["placeholder_token"]) + accelerator.init_trackers("textual_inversion", config=config) + + # Train! + total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps + + logger.info("***** Running training *****") + logger.info(f" Num Epochs = {num_epochs}") + logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") + logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") + logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + logger.info(f" Total optimization steps = {args.max_train_steps}") + # Only show the progress bar once on each machine. + + global_step = 0 + min_val_loss = np.inf + + checkpointer = Checkpointer( + datamodule=datamodule, + accelerator=accelerator, + vae=vae, + unet=unet, + tokenizer=tokenizer, + text_encoder=text_encoder, + scheduler=checkpoint_scheduler, + instance_identifier=args.instance_identifier, + placeholder_token=args.placeholder_token, + placeholder_token_id=placeholder_token_id, + output_dir=basepath, + sample_image_size=args.sample_image_size, + sample_batch_size=args.sample_batch_size, + sample_batches=args.sample_batches, + seed=args.seed + ) + + if accelerator.is_main_process: + checkpointer.save_samples( + 0, + args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) + + local_progress_bar = tqdm( + range(num_update_steps_per_epoch + num_val_steps_per_epoch), + disable=not accelerator.is_local_main_process, + dynamic_ncols=True + ) + local_progress_bar.set_description("Epoch X / Y") + + global_progress_bar = tqdm( + range(args.max_train_steps + val_steps), + disable=not accelerator.is_local_main_process, + dynamic_ncols=True + ) + global_progress_bar.set_description("Total progress") + + try: + for epoch in range(num_epochs): + local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}") + local_progress_bar.reset() + + text_encoder.train() + train_loss = 0.0 + + for step, batch in enumerate(train_dataloader): + with accelerator.accumulate(text_encoder): + # Convert images to latent space + latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach() + latents = latents * 0.18215 + + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) + bsz = latents.shape[0] + # Sample a random timestep for each image + timesteps = torch.randint(0, 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 = noise_scheduler.add_noise(latents, noise, timesteps) + + # Get the text embedding for conditioning + encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"]) + encoder_hidden_states = encoder_hidden_states.to(dtype=weight_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 args.num_class_images != 0: + # 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="none").mean([1, 2, 3]).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 + args.prior_loss_weight * prior_loss + else: + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + + accelerator.backward(loss) + + optimizer.step() + if not accelerator.optimizer_step_was_skipped: + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + + # Let's make sure we don't update any embedding weights besides the newly added token + with torch.no_grad(): + text_encoder.get_input_embeddings( + ).weight[index_fixed_tokens] = original_token_embeds[index_fixed_tokens] + + loss = loss.detach().item() + train_loss += loss + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + local_progress_bar.update(1) + global_progress_bar.update(1) + + global_step += 1 + + logs = {"train/loss": loss, "lr": lr_scheduler.get_last_lr()[0]} + + accelerator.log(logs, step=global_step) + + local_progress_bar.set_postfix(**logs) + + if global_step >= args.max_train_steps: + break + + train_loss /= len(train_dataloader) + + accelerator.wait_for_everyone() + + text_encoder.eval() + val_loss = 0.0 + + with torch.inference_mode(): + for step, batch in enumerate(val_dataloader): + latents = vae.encode(batch["pixel_values"]).latent_dist.sample() + latents = latents * 0.18215 + + noise = torch.randn_like(latents) + bsz = latents.shape[0] + timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, + (bsz,), device=latents.device) + timesteps = timesteps.long() + + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"]) + encoder_hidden_states = encoder_hidden_states.to(dtype=weight_dtype) + + 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}") + + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + + loss = loss.detach().item() + val_loss += loss + + if accelerator.sync_gradients: + local_progress_bar.update(1) + global_progress_bar.update(1) + + logs = {"val/loss": loss} + local_progress_bar.set_postfix(**logs) + + val_loss /= len(val_dataloader) + + accelerator.log({"val/loss": val_loss}, step=global_step) + + local_progress_bar.clear() + global_progress_bar.clear() + + if accelerator.is_main_process: + if min_val_loss > val_loss: + accelerator.print( + f"Global step {global_step}: Validation loss reached new minimum: {min_val_loss:.2e} -> {val_loss:.2e}") + checkpointer.checkpoint(global_step + global_step_offset, "milestone") + min_val_loss = val_loss + + if (epoch + 1) % args.checkpoint_frequency == 0: + checkpointer.checkpoint(global_step + global_step_offset, "training") + save_args(basepath, args, { + "global_step": global_step + global_step_offset + }) + + if (epoch + 1) % args.sample_frequency == 0: + checkpointer.save_samples( + global_step + global_step_offset, + args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) + + # Create the pipeline using using the trained modules and save it. + if accelerator.is_main_process: + print("Finished! Saving final checkpoint and resume state.") + checkpointer.checkpoint(global_step + global_step_offset, "end") + save_args(basepath, args, { + "global_step": global_step + global_step_offset + }) + accelerator.end_training() + + except KeyboardInterrupt: + if accelerator.is_main_process: + print("Interrupted, saving checkpoint and resume state...") + checkpointer.checkpoint(global_step + global_step_offset, "end") + save_args(basepath, args, { + "global_step": global_step + global_step_offset + }) + accelerator.end_training() + quit() + + +if __name__ == "__main__": + main() -- cgit v1.2.3-70-g09d2