From 5d2abb1749b5d2f2f22ad603b5c2bf9182864520 Mon Sep 17 00:00:00 2001 From: Volpeon Date: Tue, 27 Sep 2022 10:03:12 +0200 Subject: More cleanup --- data.py | 145 ------- data/textual_inversion/csv.py | 134 +++++++ main.py | 850 ------------------------------------------ textual_inversion.py | 847 +++++++++++++++++++++++++++++++++++++++++ 4 files changed, 981 insertions(+), 995 deletions(-) delete mode 100644 data.py create mode 100644 data/textual_inversion/csv.py delete mode 100644 main.py create mode 100644 textual_inversion.py diff --git a/data.py b/data.py deleted file mode 100644 index 0d1e96e..0000000 --- a/data.py +++ /dev/null @@ -1,145 +0,0 @@ -import os -import numpy as np -import pandas as pd -import random -import PIL -import pytorch_lightning as pl -from PIL import Image -import torch -from torch.utils.data import Dataset, DataLoader, random_split -from torchvision import transforms - - -class CSVDataModule(pl.LightningDataModule): - def __init__(self, - batch_size, - data_root, - tokenizer, - size=512, - repeats=100, - interpolation="bicubic", - placeholder_token="*", - flip_p=0.5, - center_crop=False): - super().__init__() - - self.data_root = data_root - self.tokenizer = tokenizer - self.size = size - self.repeats = repeats - self.placeholder_token = placeholder_token - self.center_crop = center_crop - self.flip_p = flip_p - self.interpolation = interpolation - - self.batch_size = batch_size - - def prepare_data(self): - metadata = pd.read_csv(f'{self.data_root}/list.csv') - image_paths = [os.path.join(self.data_root, f_path) for f_path in metadata['image'].values] - captions = [caption for caption in metadata['caption'].values] - skips = [skip for skip in metadata['skip'].values] - self.data_full = [(img, cap) for img, cap, skip in zip(image_paths, captions, skips) if skip != "x"] - - def setup(self, stage=None): - train_set_size = int(len(self.data_full) * 0.8) - valid_set_size = len(self.data_full) - train_set_size - self.data_train, self.data_val = random_split(self.data_full, [train_set_size, valid_set_size]) - - train_dataset = CSVDataset(self.data_train, self.tokenizer, size=self.size, repeats=self.repeats, interpolation=self.interpolation, - flip_p=self.flip_p, placeholder_token=self.placeholder_token, center_crop=self.center_crop) - val_dataset = CSVDataset(self.data_val, self.tokenizer, size=self.size, interpolation=self.interpolation, - flip_p=self.flip_p, placeholder_token=self.placeholder_token, center_crop=self.center_crop) - self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True) - self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size) - - def train_dataloader(self): - return self.train_dataloader_ - - def val_dataloader(self): - return self.val_dataloader_ - - -class CSVDataset(Dataset): - def __init__(self, - data, - tokenizer, - size=512, - repeats=1, - interpolation="bicubic", - flip_p=0.5, - placeholder_token="*", - center_crop=False, - ): - - self.data = data - self.tokenizer = tokenizer - - self.num_images = len(self.data) - self._length = self.num_images * repeats - - self.placeholder_token = placeholder_token - - self.size = size - self.center_crop = center_crop - self.interpolation = {"linear": PIL.Image.LINEAR, - "bilinear": PIL.Image.BILINEAR, - "bicubic": PIL.Image.BICUBIC, - "lanczos": PIL.Image.LANCZOS, - }[interpolation] - self.flip = transforms.RandomHorizontalFlip(p=flip_p) - - self.cache = {} - - def __len__(self): - return self._length - - def get_example(self, i, flipped): - image_path, text = self.data[i % self.num_images] - - if image_path in self.cache: - return self.cache[image_path] - - example = {} - image = Image.open(image_path) - - if not image.mode == "RGB": - image = image.convert("RGB") - - text = text.format(self.placeholder_token) - - example["prompt"] = text - example["input_ids"] = self.tokenizer( - text, - padding="max_length", - truncation=True, - max_length=self.tokenizer.model_max_length, - return_tensors="pt", - ).input_ids[0] - - # default to score-sde preprocessing - img = np.array(image).astype(np.uint8) - - if self.center_crop: - crop = min(img.shape[0], img.shape[1]) - h, w, = img.shape[0], img.shape[1] - img = img[(h - crop) // 2:(h + crop) // 2, - (w - crop) // 2:(w + crop) // 2] - - image = Image.fromarray(img) - image = image.resize((self.size, self.size), - resample=self.interpolation) - image = self.flip(image) - image = np.array(image).astype(np.uint8) - image = (image / 127.5 - 1.0).astype(np.float32) - - example["key"] = "-".join([image_path, "-", str(flipped)]) - example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1) - - self.cache[image_path] = example - return example - - def __getitem__(self, i): - flipped = random.choice([False, True]) - example = self.get_example(i, flipped) - return example diff --git a/data/textual_inversion/csv.py b/data/textual_inversion/csv.py new file mode 100644 index 0000000..38ffb6f --- /dev/null +++ b/data/textual_inversion/csv.py @@ -0,0 +1,134 @@ +import os +import numpy as np +import pandas as pd +import random +import PIL +import pytorch_lightning as pl +from PIL import Image +import torch +from torch.utils.data import Dataset, DataLoader, random_split +from torchvision import transforms + + +class CSVDataModule(pl.LightningDataModule): + def __init__(self, + batch_size, + data_root, + tokenizer, + size=512, + repeats=100, + interpolation="bicubic", + placeholder_token="*", + flip_p=0.5, + center_crop=False): + super().__init__() + + self.data_root = data_root + self.tokenizer = tokenizer + self.size = size + self.repeats = repeats + self.placeholder_token = placeholder_token + self.center_crop = center_crop + self.flip_p = flip_p + self.interpolation = interpolation + + self.batch_size = batch_size + + def prepare_data(self): + metadata = pd.read_csv(f'{self.data_root}/list.csv') + image_paths = [os.path.join(self.data_root, f_path) for f_path in metadata['image'].values] + captions = [caption for caption in metadata['caption'].values] + skips = [skip for skip in metadata['skip'].values] + self.data_full = [(img, cap) for img, cap, skip in zip(image_paths, captions, skips) if skip != "x"] + + def setup(self, stage=None): + train_set_size = int(len(self.data_full) * 0.8) + valid_set_size = len(self.data_full) - train_set_size + self.data_train, self.data_val = random_split(self.data_full, [train_set_size, valid_set_size]) + + train_dataset = CSVDataset(self.data_train, self.tokenizer, size=self.size, repeats=self.repeats, interpolation=self.interpolation, + flip_p=self.flip_p, placeholder_token=self.placeholder_token, center_crop=self.center_crop) + val_dataset = CSVDataset(self.data_val, self.tokenizer, size=self.size, interpolation=self.interpolation, + flip_p=self.flip_p, placeholder_token=self.placeholder_token, center_crop=self.center_crop) + self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True) + self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size) + + def train_dataloader(self): + return self.train_dataloader_ + + def val_dataloader(self): + return self.val_dataloader_ + + +class CSVDataset(Dataset): + def __init__(self, + data, + tokenizer, + size=512, + repeats=1, + interpolation="bicubic", + flip_p=0.5, + placeholder_token="*", + center_crop=False, + ): + + self.data = data + self.tokenizer = tokenizer + + self.num_images = len(self.data) + self._length = self.num_images * repeats + + self.placeholder_token = placeholder_token + + self.interpolation = {"linear": PIL.Image.LINEAR, + "bilinear": PIL.Image.BILINEAR, + "bicubic": PIL.Image.BICUBIC, + "lanczos": PIL.Image.LANCZOS, + }[interpolation] + self.image_transforms = transforms.Compose( + [ + transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + self.cache = {} + + def __len__(self): + return self._length + + def get_example(self, i, flipped): + image_path, text = self.data[i % self.num_images] + + if image_path in self.cache: + return self.cache[image_path] + + example = {} + image = Image.open(image_path) + if not image.mode == "RGB": + image = image.convert("RGB") + image = self.image_transforms(image) + + text = text.format(self.placeholder_token) + + example["prompt"] = text + example["input_ids"] = self.tokenizer( + text, + padding="max_length", + truncation=True, + max_length=self.tokenizer.model_max_length, + return_tensors="pt", + ).input_ids[0] + + example["key"] = "-".join([image_path, "-", str(flipped)]) + example["pixel_values"] = image + + self.cache[image_path] = example + return example + + def __getitem__(self, i): + flipped = random.choice([False, True]) + example = self.get_example(i, flipped) + return example diff --git a/main.py b/main.py deleted file mode 100644 index 51b64c1..0000000 --- a/main.py +++ /dev/null @@ -1,850 +0,0 @@ -import argparse -import itertools -import math -import os -import random -import datetime -from pathlib import Path -from typing import Optional - -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, LMSDiscreteScheduler, StableDiffusionPipeline, UNet2DConditionModel -from diffusers.optimization import get_scheduler -from pipelines.stable_diffusion.no_check import NoCheck -from PIL import Image -from tqdm.auto import tqdm -from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer -from slugify import slugify -import json -import os - -from data import CSVDataModule - -logger = get_logger(__name__) - - -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_dir", - type=str, - default=None, - help="A folder containing the training data." - ) - parser.add_argument( - "--placeholder_token", - type=str, - default=None, - help="A token to use as a placeholder for the concept.", - ) - parser.add_argument( - "--initializer_token", - type=str, - default=None, - help="A token to use as initializer word." - ) - parser.add_argument( - "--vectors_per_token", - type=int, - default=1, - help="Vectors per token." - ) - parser.add_argument( - "--repeats", - type=int, - default=100, - help="How many times to repeat the training data.") - parser.add_argument( - "--output_dir", - type=str, - default="text-inversion-model", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument( - "--seed", - type=int, - default=None, - help="A seed for reproducible training.") - parser.add_argument( - "--resolution", - type=int, - default=512, - 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( - "--num_train_epochs", - type=int, - default=100) - parser.add_argument( - "--max_train_steps", - type=int, - default=5000, - 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="constant", - help=( - 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]' - ), - ) - parser.add_argument( - "--lr_warmup_steps", - type=int, - default=500, - help="Number of steps for the warmup in the lr scheduler." - ) - 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( - "--local_rank", - type=int, - default=-1, - help="For distributed training: local_rank" - ) - parser.add_argument( - "--checkpoint_frequency", - type=int, - default=500, - help="How often to save a checkpoint and sample image", - ) - parser.add_argument( - "--sample_image_size", - type=int, - default=512, - help="Size of sample images", - ) - parser.add_argument( - "--stable_sample_batches", - type=int, - default=1, - help="Number of fixed seed sample batches to generate per checkpoint", - ) - parser.add_argument( - "--random_sample_batches", - type=int, - default=1, - help="Number of random seed 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( - "--train_batch_size", - type=int, - default=1, - help="Batch size (per device) for the training dataloader." - ) - parser.add_argument( - "--sample_steps", - type=int, - default=50, - help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.", - ) - 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( - "--resume_checkpoint", - type=str, - default=None, - help="Path to a specific checkpoint to resume training from (ie, logs/token_name/2022-09-22T23-36-27/checkpoints/something.bin)." - ) - parser.add_argument( - "--config", - type=str, - default=None, - help="Path to a JSON configuration file containing arguments for invoking this script. If resume_from is given, its resume.json takes priority over this." - ) - - args = parser.parse_args() - if args.resume_from is not None: - with open(f"{args.resume_from}/resume.json", 'rt') as f: - args = parser.parse_args( - namespace=argparse.Namespace(**json.load(f)["args"])) - elif args.config is not None: - with open(args.config, 'rt') as f: - args = parser.parse_args( - namespace=argparse.Namespace(**json.load(f)["args"])) - - env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) - if env_local_rank != -1 and env_local_rank != args.local_rank: - args.local_rank = env_local_rank - - if args.train_data_dir is None: - raise ValueError("You must specify --train_data_dir") - - if args.pretrained_model_name_or_path is None: - raise ValueError("You must specify --pretrained_model_name_or_path") - - if args.placeholder_token is None: - raise ValueError("You must specify --placeholder_token") - - if args.initializer_token is None: - raise ValueError("You must specify --initializer_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_resume_file(basepath, args, extra={}): - info = {"args": vars(args)} - info["args"].update(extra) - with open(f"{basepath}/resume.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, - placeholder_token, - placeholder_token_id, - output_dir, - sample_image_size, - random_sample_batches, - sample_batch_size, - stable_sample_batches, - seed - ): - self.datamodule = datamodule - self.accelerator = accelerator - self.vae = vae - self.unet = unet - self.tokenizer = tokenizer - 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 - self.random_sample_batches = random_sample_batches - self.sample_batch_size = sample_batch_size - self.stable_sample_batches = stable_sample_batches - - @torch.no_grad() - def checkpoint(self, step, postfix, text_encoder, save_samples=True, path=None): - print("Saving checkpoint for step %d..." % step) - with self.accelerator.autocast(): - if path is None: - checkpoints_path = f"{self.output_dir}/checkpoints" - os.makedirs(checkpoints_path, exist_ok=True) - - unwrapped = self.accelerator.unwrap_model(text_encoder) - - # Save a checkpoint - learned_embeds = unwrapped.get_input_embeddings().weight[self.placeholder_token_id] - learned_embeds_dict = {self.placeholder_token: learned_embeds.detach().cpu()} - - filename = f"%s_%d_%s.bin" % (slugify(self.placeholder_token), step, postfix) - if path is not None: - torch.save(learned_embeds_dict, path) - else: - torch.save(learned_embeds_dict, f"{checkpoints_path}/{filename}") - torch.save(learned_embeds_dict, f"{checkpoints_path}/last.bin") - del unwrapped - del learned_embeds - - @torch.no_grad() - def save_samples(self, mode, step, text_encoder, height, width, guidance_scale, eta, num_inference_steps): - samples_path = f"{self.output_dir}/samples/{mode}" - os.makedirs(samples_path, exist_ok=True) - checker = NoCheck() - - unwrapped = self.accelerator.unwrap_model(text_encoder) - # Save a sample image - pipeline = StableDiffusionPipeline( - text_encoder=unwrapped, - vae=self.vae, - unet=self.unet, - tokenizer=self.tokenizer, - scheduler=LMSDiscreteScheduler( - beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" - ), - safety_checker=NoCheck(), - feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"), - ).to(self.accelerator.device) - pipeline.enable_attention_slicing() - - data = { - "training": self.datamodule.train_dataloader(), - "validation": self.datamodule.val_dataloader(), - }[mode] - - if mode == "validation" and self.stable_sample_batches > 0 and step > 0: - stable_latents = torch.randn( - (self.sample_batch_size, pipeline.unet.in_channels, height // 8, width // 8), - device=pipeline.device, - generator=torch.Generator(device=pipeline.device).manual_seed(self.seed), - ) - - all_samples = [] - filename = f"stable_step_%d.png" % (step) - - data_enum = enumerate(data) - - # Generate and save stable samples - for i in range(0, self.stable_sample_batches): - prompt = [prompt for i, batch in data_enum for j, prompt in enumerate( - batch["prompt"]) if i * data.batch_size + j < self.sample_batch_size] - - with self.accelerator.autocast(): - samples = pipeline( - prompt=prompt, - height=self.sample_image_size, - latents=stable_latents[:len(prompt)], - width=self.sample_image_size, - guidance_scale=guidance_scale, - eta=eta, - num_inference_steps=num_inference_steps, - output_type='pil' - )["sample"] - - all_samples += samples - del samples - - image_grid = make_grid(all_samples, self.stable_sample_batches, self.sample_batch_size) - image_grid.save(f"{samples_path}/{filename}") - - del all_samples - del image_grid - del stable_latents - - all_samples = [] - filename = f"step_%d.png" % (step) - - data_enum = enumerate(data) - - # Generate and save random samples - for i in range(0, self.random_sample_batches): - prompt = [prompt for i, batch in data_enum for j, prompt in enumerate( - batch["prompt"]) if i * data.batch_size + j < self.sample_batch_size] - - with self.accelerator.autocast(): - samples = pipeline( - prompt=prompt, - height=self.sample_image_size, - width=self.sample_image_size, - guidance_scale=guidance_scale, - eta=eta, - num_inference_steps=num_inference_steps, - output_type='pil' - )["sample"] - - all_samples += samples - del samples - - image_grid = make_grid(all_samples, self.random_sample_batches, self.sample_batch_size) - image_grid.save(f"{samples_path}/{filename}") - - del all_samples - del image_grid - - del checker - del unwrapped - del pipeline - torch.cuda.empty_cache() - - -class ImageToLatents(): - def __init__(self, vae): - self.vae = vae - self.encoded_pixel_values_cache = {} - - @torch.no_grad() - def __call__(self, batch): - key = "|".join(batch["key"]) - if self.encoded_pixel_values_cache.get(key, None) is None: - self.encoded_pixel_values_cache[key] = self.vae.encode(batch["pixel_values"]).latent_dist - latents = self.encoded_pixel_values_cache[key].sample().detach().half() * 0.18215 - return latents - - -def main(): - args = parse_args() - - global_step_offset = 0 - if args.resume_from is not None: - basepath = f"{args.resume_from}" - print("Resuming state from %s" % args.resume_from) - with open(f"{basepath}/resume.json", 'r') as f: - state = json.load(f) - global_step_offset = state["args"].get("global_step", 0) - - print("We've trained %d steps so far" % global_step_offset) - else: - now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") - basepath = f"{args.output_dir}/{slugify(args.placeholder_token)}/{now}" - os.makedirs(basepath, 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 - ) - - # If passed along, set the training seed now. - if args.seed is not None: - 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 + '/tokenizer' - ) - - # Add the placeholder token in tokenizer - num_added_tokens = tokenizer.add_tokens(args.placeholder_token) - if num_added_tokens == 0: - raise ValueError( - f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different" - " `placeholder_token` that is not already in the tokenizer." - ) - - # Convert the initializer_token, placeholder_token to ids - initializer_token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False) - # Check if initializer_token is a single token or a sequence of tokens - if args.vectors_per_token % len(initializer_token_ids) != 0: - raise ValueError( - f"vectors_per_token ({args.vectors_per_token}) must be divisible by initializer token ({len(initializer_token_ids)}).") - - initializer_token_ids = torch.tensor(initializer_token_ids) - placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) - - # Load models and create wrapper for stable diffusion - text_encoder = CLIPTextModel.from_pretrained( - args.pretrained_model_name_or_path + '/text_encoder', - ) - vae = AutoencoderKL.from_pretrained( - args.pretrained_model_name_or_path + '/vae', - ) - unet = UNet2DConditionModel.from_pretrained( - args.pretrained_model_name_or_path + '/unet', - ) - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - - slice_size = unet.config.attention_head_dim // 2 - unet.set_attention_slice(slice_size) - - # 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 - - initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids) - - if args.resume_checkpoint is not None: - token_embeds[placeholder_token_id] = torch.load(args.resume_checkpoint)[ - args.placeholder_token] - else: - token_embeds[placeholder_token_id] = initializer_token_embeddings - - # Freeze vae and unet - freeze_params(vae.parameters()) - freeze_params(unet.parameters()) - # Freeze all parameters except for the token embeddings in text encoder - params_to_freeze = itertools.chain( - text_encoder.text_model.encoder.parameters(), - text_encoder.text_model.final_layer_norm.parameters(), - text_encoder.text_model.embeddings.position_embedding.parameters(), - ) - freeze_params(params_to_freeze) - - if args.scale_lr: - args.learning_rate = ( - args.learning_rate * args.gradient_accumulation_steps * - args.train_batch_size * accelerator.num_processes - ) - - # Initialize the optimizer - optimizer = torch.optim.AdamW( - 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, - ) - - # TODO (patil-suraj): laod scheduler using args - noise_scheduler = DDPMScheduler( - beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, tensor_format="pt" - ) - - datamodule = CSVDataModule( - data_root=args.train_data_dir, batch_size=args.train_batch_size, tokenizer=tokenizer, - size=args.resolution, placeholder_token=args.placeholder_token, repeats=args.repeats, - center_crop=args.center_crop) - - datamodule.prepare_data() - datamodule.setup() - - train_dataloader = datamodule.train_dataloader() - val_dataloader = datamodule.val_dataloader() - - checkpointer = Checkpointer( - datamodule=datamodule, - accelerator=accelerator, - vae=vae, - unet=unet, - tokenizer=tokenizer, - 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, - random_sample_batches=args.random_sample_batches, - stable_sample_batches=args.stable_sample_batches, - seed=args.seed - ) - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil( - (len(train_dataloader) + len(val_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 - - lr_scheduler = get_scheduler( - args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_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) - unet.to(accelerator.device) - - # 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) + len(val_dataloader)) / args.gradient_accumulation_steps) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - # Afterwards we recalculate our number of training epochs - args.num_train_epochs = math.ceil( - args.max_train_steps / num_update_steps_per_epoch) - - # 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: - accelerator.init_trackers("textual_inversion", config=vars(args)) - - # Train! - total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps - - logger.info("***** Running training *****") - logger.info(f" Num Epochs = {args.num_train_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 - - imageToLatents = ImageToLatents(vae) - - checkpointer.save_samples( - "validation", - 0, - text_encoder, - args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) - - progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) - progress_bar.set_description("Global steps") - - local_progress_bar = tqdm(range(num_update_steps_per_epoch), disable=not accelerator.is_local_main_process) - local_progress_bar.set_description("Steps") - - try: - for epoch in range(args.num_train_epochs): - local_progress_bar.reset() - - text_encoder.train() - train_loss = 0.0 - - for step, batch in enumerate(train_dataloader): - with accelerator.accumulate(text_encoder): - with accelerator.autocast(): - # Convert images to latent space - latents = imageToLatents(batch) - - # Sample noise that we'll add to the latents - noise = torch.randn(latents.shape).to(latents.device) - bsz = latents.shape[0] - # Sample a random timestep for each image - timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, - (bsz,), device=latents.device).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 = text_encoder(batch["input_ids"])[0] - - # Predict the noise residual - noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - - loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() - - accelerator.backward(loss) - - # Zero out the gradients for all token embeddings except the newly added - # embeddings for the concept, as we only want to optimize the concept embeddings - if accelerator.num_processes > 1: - grads = text_encoder.module.get_input_embeddings().weight.grad - else: - grads = text_encoder.get_input_embeddings().weight.grad - # Get the index for tokens that we want to zero the grads for - index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id - grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0) - - optimizer.step() - if not accelerator.optimizer_step_was_skipped: - lr_scheduler.step() - optimizer.zero_grad() - - loss = loss.detach().item() - train_loss += loss - - # Checks if the accelerator has performed an optimization step behind the scenes - if accelerator.sync_gradients: - progress_bar.update(1) - local_progress_bar.update(1) - - global_step += 1 - - if global_step % args.checkpoint_frequency == 0 and global_step > 0 and accelerator.is_main_process: - progress_bar.clear() - local_progress_bar.clear() - - checkpointer.checkpoint(global_step + global_step_offset, "training", text_encoder) - save_resume_file(basepath, args, { - "global_step": global_step + global_step_offset, - "resume_checkpoint": f"{basepath}/checkpoints/last.bin" - }) - checkpointer.save_samples( - "training", - global_step + global_step_offset, - text_encoder, - args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) - - logs = {"mode": "training", "loss": loss, "lr": lr_scheduler.get_last_lr()[0]} - local_progress_bar.set_postfix(**logs) - - if global_step >= args.max_train_steps: - break - - train_loss /= len(train_dataloader) - - text_encoder.eval() - val_loss = 0.0 - - for step, batch in enumerate(val_dataloader): - with torch.no_grad(), accelerator.autocast(): - latents = imageToLatents(batch) - - noise = torch.randn(latents.shape).to(latents.device) - bsz = latents.shape[0] - timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, - (bsz,), device=latents.device).long() - - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - - encoder_hidden_states = text_encoder(batch["input_ids"])[0] - - noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - - noise_pred, noise = accelerator.gather_for_metrics((noise_pred, noise)) - - loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() - - loss = loss.detach().item() - val_loss += loss - - if accelerator.sync_gradients: - progress_bar.update(1) - local_progress_bar.update(1) - - logs = {"mode": "validation", "loss": loss} - local_progress_bar.set_postfix(**logs) - - val_loss /= len(val_dataloader) - - accelerator.log({"train/loss": train_loss, "val/loss": val_loss}, step=global_step) - - progress_bar.clear() - local_progress_bar.clear() - - if min_val_loss > val_loss: - accelerator.print(f"Validation loss reached new minimum: {min_val_loss:.2e} -> {val_loss:.2e}") - checkpointer.checkpoint(global_step + global_step_offset, "milestone", text_encoder) - min_val_loss = val_loss - - checkpointer.save_samples( - "validation", - global_step + global_step_offset, - text_encoder, - args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) - - accelerator.wait_for_everyone() - - # 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", - text_encoder, - path=f"{basepath}/learned_embeds.bin" - ) - - save_resume_file(basepath, args, { - "global_step": global_step + global_step_offset, - "resume_checkpoint": f"{basepath}/checkpoints/last.bin" - }) - - 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", text_encoder) - save_resume_file(basepath, args, { - "global_step": global_step + global_step_offset, - "resume_checkpoint": f"{basepath}/checkpoints/last.bin" - }) - accelerator.end_training() - quit() - - -if __name__ == "__main__": - main() diff --git a/textual_inversion.py b/textual_inversion.py new file mode 100644 index 0000000..aa8e744 --- /dev/null +++ b/textual_inversion.py @@ -0,0 +1,847 @@ +import argparse +import itertools +import math +import os +import datetime + +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, LMSDiscreteScheduler, StableDiffusionPipeline, UNet2DConditionModel +from diffusers.optimization import get_scheduler +from pipelines.stable_diffusion.no_check import NoCheck +from PIL import Image +from tqdm.auto import tqdm +from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer +from slugify import slugify +import json +import os + +from data.textual_inversion.csv import CSVDataModule + +logger = get_logger(__name__) + + +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_dir", + type=str, + default=None, + help="A folder containing the training data." + ) + parser.add_argument( + "--placeholder_token", + type=str, + default=None, + help="A token to use as a placeholder for the concept.", + ) + parser.add_argument( + "--initializer_token", + type=str, + default=None, + help="A token to use as initializer word." + ) + parser.add_argument( + "--vectors_per_token", + type=int, + default=1, + help="Vectors per token." + ) + parser.add_argument( + "--repeats", + type=int, + default=100, + help="How many times to repeat the training data.") + parser.add_argument( + "--output_dir", + type=str, + default="text-inversion-model", + help="The output directory where the model predictions and checkpoints will be written.", + ) + parser.add_argument( + "--seed", + type=int, + default=None, + help="A seed for reproducible training.") + parser.add_argument( + "--resolution", + type=int, + default=512, + 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( + "--num_train_epochs", + type=int, + default=100) + parser.add_argument( + "--max_train_steps", + type=int, + default=5000, + 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="constant", + help=( + 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]' + ), + ) + parser.add_argument( + "--lr_warmup_steps", + type=int, + default=500, + help="Number of steps for the warmup in the lr scheduler." + ) + 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( + "--local_rank", + type=int, + default=-1, + help="For distributed training: local_rank" + ) + parser.add_argument( + "--checkpoint_frequency", + type=int, + default=500, + help="How often to save a checkpoint and sample image", + ) + parser.add_argument( + "--sample_image_size", + type=int, + default=512, + help="Size of sample images", + ) + parser.add_argument( + "--stable_sample_batches", + type=int, + default=1, + help="Number of fixed seed sample batches to generate per checkpoint", + ) + parser.add_argument( + "--random_sample_batches", + type=int, + default=1, + help="Number of random seed 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( + "--train_batch_size", + type=int, + default=1, + help="Batch size (per device) for the training dataloader." + ) + parser.add_argument( + "--sample_steps", + type=int, + default=50, + help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.", + ) + 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( + "--resume_checkpoint", + type=str, + default=None, + help="Path to a specific checkpoint to resume training from (ie, logs/token_name/2022-09-22T23-36-27/checkpoints/something.bin)." + ) + parser.add_argument( + "--config", + type=str, + default=None, + help="Path to a JSON configuration file containing arguments for invoking this script. If resume_from is given, its resume.json takes priority over this." + ) + + args = parser.parse_args() + if args.resume_from is not None: + with open(f"{args.resume_from}/resume.json", 'rt') as f: + args = parser.parse_args( + namespace=argparse.Namespace(**json.load(f)["args"])) + elif args.config is not None: + with open(args.config, 'rt') as f: + args = parser.parse_args( + namespace=argparse.Namespace(**json.load(f)["args"])) + + env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) + if env_local_rank != -1 and env_local_rank != args.local_rank: + args.local_rank = env_local_rank + + if args.train_data_dir is None: + raise ValueError("You must specify --train_data_dir") + + if args.pretrained_model_name_or_path is None: + raise ValueError("You must specify --pretrained_model_name_or_path") + + if args.placeholder_token is None: + raise ValueError("You must specify --placeholder_token") + + if args.initializer_token is None: + raise ValueError("You must specify --initializer_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_resume_file(basepath, args, extra={}): + info = {"args": vars(args)} + info["args"].update(extra) + with open(f"{basepath}/resume.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, + placeholder_token, + placeholder_token_id, + output_dir, + sample_image_size, + random_sample_batches, + sample_batch_size, + stable_sample_batches, + seed + ): + self.datamodule = datamodule + self.accelerator = accelerator + self.vae = vae + self.unet = unet + self.tokenizer = tokenizer + 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 + self.random_sample_batches = random_sample_batches + self.sample_batch_size = sample_batch_size + self.stable_sample_batches = stable_sample_batches + + @torch.no_grad() + def checkpoint(self, step, postfix, text_encoder, save_samples=True, path=None): + print("Saving checkpoint for step %d..." % step) + with self.accelerator.autocast(): + if path is None: + checkpoints_path = f"{self.output_dir}/checkpoints" + os.makedirs(checkpoints_path, exist_ok=True) + + unwrapped = self.accelerator.unwrap_model(text_encoder) + + # Save a checkpoint + learned_embeds = unwrapped.get_input_embeddings().weight[self.placeholder_token_id] + learned_embeds_dict = {self.placeholder_token: learned_embeds.detach().cpu()} + + filename = f"%s_%d_%s.bin" % (slugify(self.placeholder_token), step, postfix) + if path is not None: + torch.save(learned_embeds_dict, path) + else: + torch.save(learned_embeds_dict, f"{checkpoints_path}/{filename}") + torch.save(learned_embeds_dict, f"{checkpoints_path}/last.bin") + del unwrapped + del learned_embeds + + @torch.no_grad() + def save_samples(self, mode, step, text_encoder, height, width, guidance_scale, eta, num_inference_steps): + samples_path = f"{self.output_dir}/samples/{mode}" + os.makedirs(samples_path, exist_ok=True) + checker = NoCheck() + + unwrapped = self.accelerator.unwrap_model(text_encoder) + # Save a sample image + pipeline = StableDiffusionPipeline( + text_encoder=unwrapped, + vae=self.vae, + unet=self.unet, + tokenizer=self.tokenizer, + scheduler=LMSDiscreteScheduler( + beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" + ), + safety_checker=NoCheck(), + feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"), + ).to(self.accelerator.device) + pipeline.enable_attention_slicing() + + data = { + "training": self.datamodule.train_dataloader(), + "validation": self.datamodule.val_dataloader(), + }[mode] + + if mode == "validation" and self.stable_sample_batches > 0 and step > 0: + stable_latents = torch.randn( + (self.sample_batch_size, pipeline.unet.in_channels, height // 8, width // 8), + device=pipeline.device, + generator=torch.Generator(device=pipeline.device).manual_seed(self.seed), + ) + + all_samples = [] + filename = f"stable_step_%d.png" % (step) + + data_enum = enumerate(data) + + # Generate and save stable samples + for i in range(0, self.stable_sample_batches): + prompt = [prompt for i, batch in data_enum for j, prompt in enumerate( + batch["prompt"]) if i * data.batch_size + j < self.sample_batch_size] + + with self.accelerator.autocast(): + samples = pipeline( + prompt=prompt, + height=self.sample_image_size, + latents=stable_latents[:len(prompt)], + width=self.sample_image_size, + guidance_scale=guidance_scale, + eta=eta, + num_inference_steps=num_inference_steps, + output_type='pil' + )["sample"] + + all_samples += samples + del samples + + image_grid = make_grid(all_samples, self.stable_sample_batches, self.sample_batch_size) + image_grid.save(f"{samples_path}/{filename}") + + del all_samples + del image_grid + del stable_latents + + all_samples = [] + filename = f"step_%d.png" % (step) + + data_enum = enumerate(data) + + # Generate and save random samples + for i in range(0, self.random_sample_batches): + prompt = [prompt for i, batch in data_enum for j, prompt in enumerate( + batch["prompt"]) if i * data.batch_size + j < self.sample_batch_size] + + with self.accelerator.autocast(): + samples = pipeline( + prompt=prompt, + height=self.sample_image_size, + width=self.sample_image_size, + guidance_scale=guidance_scale, + eta=eta, + num_inference_steps=num_inference_steps, + output_type='pil' + )["sample"] + + all_samples += samples + del samples + + image_grid = make_grid(all_samples, self.random_sample_batches, self.sample_batch_size) + image_grid.save(f"{samples_path}/{filename}") + + del all_samples + del image_grid + + del checker + del unwrapped + del pipeline + torch.cuda.empty_cache() + + +class ImageToLatents(): + def __init__(self, vae): + self.vae = vae + self.encoded_pixel_values_cache = {} + + @torch.no_grad() + def __call__(self, batch): + key = "|".join(batch["key"]) + if self.encoded_pixel_values_cache.get(key, None) is None: + self.encoded_pixel_values_cache[key] = self.vae.encode(batch["pixel_values"]).latent_dist + latents = self.encoded_pixel_values_cache[key].sample().detach().half() * 0.18215 + return latents + + +def main(): + args = parse_args() + + global_step_offset = 0 + if args.resume_from is not None: + basepath = f"{args.resume_from}" + print("Resuming state from %s" % args.resume_from) + with open(f"{basepath}/resume.json", 'r') as f: + state = json.load(f) + global_step_offset = state["args"].get("global_step", 0) + + print("We've trained %d steps so far" % global_step_offset) + else: + now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") + basepath = f"{args.output_dir}/{slugify(args.placeholder_token)}/{now}" + os.makedirs(basepath, 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 + ) + + # If passed along, set the training seed now. + if args.seed is not None: + 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 + '/tokenizer' + ) + + # Add the placeholder token in tokenizer + num_added_tokens = tokenizer.add_tokens(args.placeholder_token) + if num_added_tokens == 0: + raise ValueError( + f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different" + " `placeholder_token` that is not already in the tokenizer." + ) + + # Convert the initializer_token, placeholder_token to ids + initializer_token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False) + # Check if initializer_token is a single token or a sequence of tokens + if args.vectors_per_token % len(initializer_token_ids) != 0: + raise ValueError( + f"vectors_per_token ({args.vectors_per_token}) must be divisible by initializer token ({len(initializer_token_ids)}).") + + initializer_token_ids = torch.tensor(initializer_token_ids) + placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) + + # Load models and create wrapper for stable diffusion + text_encoder = CLIPTextModel.from_pretrained( + args.pretrained_model_name_or_path + '/text_encoder', + ) + vae = AutoencoderKL.from_pretrained( + args.pretrained_model_name_or_path + '/vae', + ) + unet = UNet2DConditionModel.from_pretrained( + args.pretrained_model_name_or_path + '/unet', + ) + + if args.gradient_checkpointing: + unet.enable_gradient_checkpointing() + + slice_size = unet.config.attention_head_dim // 2 + unet.set_attention_slice(slice_size) + + # 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 + + initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids) + + if args.resume_checkpoint is not None: + token_embeds[placeholder_token_id] = torch.load(args.resume_checkpoint)[ + args.placeholder_token] + else: + token_embeds[placeholder_token_id] = initializer_token_embeddings + + # Freeze vae and unet + freeze_params(vae.parameters()) + freeze_params(unet.parameters()) + # Freeze all parameters except for the token embeddings in text encoder + params_to_freeze = itertools.chain( + text_encoder.text_model.encoder.parameters(), + text_encoder.text_model.final_layer_norm.parameters(), + text_encoder.text_model.embeddings.position_embedding.parameters(), + ) + freeze_params(params_to_freeze) + + if args.scale_lr: + args.learning_rate = ( + args.learning_rate * args.gradient_accumulation_steps * + args.train_batch_size * accelerator.num_processes + ) + + # Initialize the optimizer + optimizer = torch.optim.AdamW( + 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, + ) + + # TODO (patil-suraj): laod scheduler using args + noise_scheduler = DDPMScheduler( + beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, tensor_format="pt" + ) + + datamodule = CSVDataModule( + data_root=args.train_data_dir, batch_size=args.train_batch_size, tokenizer=tokenizer, + size=args.resolution, placeholder_token=args.placeholder_token, repeats=args.repeats, + center_crop=args.center_crop) + + datamodule.prepare_data() + datamodule.setup() + + train_dataloader = datamodule.train_dataloader() + val_dataloader = datamodule.val_dataloader() + + checkpointer = Checkpointer( + datamodule=datamodule, + accelerator=accelerator, + vae=vae, + unet=unet, + tokenizer=tokenizer, + 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, + random_sample_batches=args.random_sample_batches, + stable_sample_batches=args.stable_sample_batches, + seed=args.seed + ) + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil( + (len(train_dataloader) + len(val_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 + + lr_scheduler = get_scheduler( + args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_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) + unet.to(accelerator.device) + + # 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) + len(val_dataloader)) / args.gradient_accumulation_steps) + if overrode_max_train_steps: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + # Afterwards we recalculate our number of training epochs + args.num_train_epochs = math.ceil( + args.max_train_steps / num_update_steps_per_epoch) + + # 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: + accelerator.init_trackers("textual_inversion", config=vars(args)) + + # Train! + total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps + + logger.info("***** Running training *****") + logger.info(f" Num Epochs = {args.num_train_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 + + imageToLatents = ImageToLatents(vae) + + checkpointer.save_samples( + "validation", + 0, + text_encoder, + args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) + + progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) + progress_bar.set_description("Global steps") + + local_progress_bar = tqdm(range(num_update_steps_per_epoch), disable=not accelerator.is_local_main_process) + local_progress_bar.set_description("Steps") + + try: + for epoch in range(args.num_train_epochs): + local_progress_bar.reset() + + text_encoder.train() + train_loss = 0.0 + + for step, batch in enumerate(train_dataloader): + with accelerator.accumulate(text_encoder): + with accelerator.autocast(): + # Convert images to latent space + latents = imageToLatents(batch) + + # Sample noise that we'll add to the latents + noise = torch.randn(latents.shape).to(latents.device) + bsz = latents.shape[0] + # Sample a random timestep for each image + timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, + (bsz,), device=latents.device).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 = text_encoder(batch["input_ids"])[0] + + # Predict the noise residual + noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample + + loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() + + accelerator.backward(loss) + + # Zero out the gradients for all token embeddings except the newly added + # embeddings for the concept, as we only want to optimize the concept embeddings + if accelerator.num_processes > 1: + grads = text_encoder.module.get_input_embeddings().weight.grad + else: + grads = text_encoder.get_input_embeddings().weight.grad + # Get the index for tokens that we want to zero the grads for + index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id + grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0) + + optimizer.step() + if not accelerator.optimizer_step_was_skipped: + lr_scheduler.step() + optimizer.zero_grad() + + loss = loss.detach().item() + train_loss += loss + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + progress_bar.update(1) + local_progress_bar.update(1) + + global_step += 1 + + if global_step % args.checkpoint_frequency == 0 and global_step > 0 and accelerator.is_main_process: + progress_bar.clear() + local_progress_bar.clear() + + checkpointer.checkpoint(global_step + global_step_offset, "training", text_encoder) + save_resume_file(basepath, args, { + "global_step": global_step + global_step_offset, + "resume_checkpoint": f"{basepath}/checkpoints/last.bin" + }) + checkpointer.save_samples( + "training", + global_step + global_step_offset, + text_encoder, + args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) + + logs = {"mode": "training", "loss": loss, "lr": lr_scheduler.get_last_lr()[0]} + local_progress_bar.set_postfix(**logs) + + if global_step >= args.max_train_steps: + break + + train_loss /= len(train_dataloader) + + text_encoder.eval() + val_loss = 0.0 + + for step, batch in enumerate(val_dataloader): + with torch.no_grad(), accelerator.autocast(): + latents = imageToLatents(batch) + + noise = torch.randn(latents.shape).to(latents.device) + bsz = latents.shape[0] + timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, + (bsz,), device=latents.device).long() + + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + encoder_hidden_states = text_encoder(batch["input_ids"])[0] + + noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample + + noise_pred, noise = accelerator.gather_for_metrics((noise_pred, noise)) + + loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() + + loss = loss.detach().item() + val_loss += loss + + if accelerator.sync_gradients: + progress_bar.update(1) + local_progress_bar.update(1) + + logs = {"mode": "validation", "loss": loss} + local_progress_bar.set_postfix(**logs) + + val_loss /= len(val_dataloader) + + accelerator.log({"train/loss": train_loss, "val/loss": val_loss}, step=global_step) + + progress_bar.clear() + local_progress_bar.clear() + + if min_val_loss > val_loss: + accelerator.print(f"Validation loss reached new minimum: {min_val_loss:.2e} -> {val_loss:.2e}") + checkpointer.checkpoint(global_step + global_step_offset, "milestone", text_encoder) + min_val_loss = val_loss + + checkpointer.save_samples( + "validation", + global_step + global_step_offset, + text_encoder, + args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) + + accelerator.wait_for_everyone() + + # 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", + text_encoder, + path=f"{basepath}/learned_embeds.bin" + ) + + save_resume_file(basepath, args, { + "global_step": global_step + global_step_offset, + "resume_checkpoint": f"{basepath}/checkpoints/last.bin" + }) + + 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", text_encoder) + save_resume_file(basepath, args, { + "global_step": global_step + global_step_offset, + "resume_checkpoint": f"{basepath}/checkpoints/last.bin" + }) + accelerator.end_training() + quit() + + +if __name__ == "__main__": + main() -- cgit v1.2.3-70-g09d2