1 files changed, 847 insertions, 0 deletions

diff --git a/textual_inversion.py b/textual_inversion.py
new file mode 100644
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+++ 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()