Dreambooth: Added option to insert a new input token; removed Dreambooth Plus

2 files changed, 42 insertions, 1029 deletions

diff --git a/dreambooth.py b/dreambooth.py
index d1cf535..da8399f 100644
--- a/dreambooth.py
+++ b/dreambooth.py
@@ -69,6 +69,18 @@ def parse_args():
         help="A token to use as a placeholder for the concept.",
     )
     parser.add_argument(
+        "--placeholder_token",
+        type=str,
+        default="<*>",
+        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(
         "--num_class_images",
         type=int,
         default=400,
@@ -114,7 +126,7 @@ def parse_args():
     parser.add_argument(
         "--max_train_steps",
         type=int,
-        default=3600,
+        default=6000,
         help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
     )
     parser.add_argument(
@@ -131,13 +143,13 @@ def parse_args():
     parser.add_argument(
         "--learning_rate_unet",
         type=float,
-        default=3e-6,
+        default=2e-6,
         help="Initial learning rate (after the potential warmup period) to use.",
     )
     parser.add_argument(
         "--learning_rate_text",
         type=float,
-        default=3e-6,
+        default=2e-6,
         help="Initial learning rate (after the potential warmup period) to use.",
     )
     parser.add_argument(
@@ -476,8 +488,13 @@ class Checkpointer:
 def main():
     args = parse_args()
 
+    instance_identifier = args.instance_identifier
+
+    if args.placeholder_token is not None:
+        instance_identifier = instance_identifier.format(args.placeholder_token)
+
     now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-    basepath = Path(args.output_dir).joinpath(slugify(args.instance_identifier), now)
+    basepath = Path(args.output_dir).joinpath(slugify(instance_identifier), now)
     basepath.mkdir(parents=True, exist_ok=True)
 
     accelerator = Accelerator(
@@ -514,6 +531,25 @@ def main():
         device=accelerator.device
     ) if args.use_ema else None
 
+    if args.initializer_token is not None:
+        # Convert the initializer_token, placeholder_token to ids
+        initializer_token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
+        print(f"Initializer token {args.initializer_token} maps to {len(initializer_token_ids)} embeddings.")
+        initializer_token_ids = torch.tensor(initializer_token_ids[:1])
+
+        # Add the placeholder token in tokenizer
+        num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
+        if num_added_tokens == 0:
+            print(f"Re-using existing token {args.placeholder_token}.")
+        else:
+            print(f"Training new token {args.placeholder_token}.")
+        placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
+
+        text_encoder.resize_token_embeddings(len(tokenizer))
+        token_embeds = text_encoder.get_input_embeddings()
+        initializer_token_embeddings = token_embeds(initializer_token_ids)
+        token_embeds.weight.data[placeholder_token_id] = initializer_token_embeddings
+
     prompt_processor = PromptProcessor(tokenizer, text_encoder)
 
     if args.gradient_checkpointing:
@@ -605,7 +641,7 @@ def main():
         data_file=args.train_data_file,
         batch_size=args.train_batch_size,
         prompt_processor=prompt_processor,
-        instance_identifier=args.instance_identifier,
+        instance_identifier=instance_identifier,
         class_identifier=args.class_identifier,
         class_subdir="cls",
         num_class_images=args.num_class_images,
@@ -735,7 +771,7 @@ def main():
         tokenizer=tokenizer,
         text_encoder=text_encoder,
         output_dir=basepath,
-        instance_identifier=args.instance_identifier,
+        instance_identifier=instance_identifier,
         sample_image_size=args.sample_image_size,
         sample_batch_size=args.sample_batch_size,
         sample_batches=args.sample_batches,
diff --git a/dreambooth_plus.py b/dreambooth_plus.py
deleted file mode 100644
index 413abe3..0000000
--- a/dreambooth_plus.py
+++ /dev/null
@@ -1,1023 +0,0 @@
-import argparse
-import itertools
-import math
-import os
-import datetime
-import logging
-import json
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import LoggerType, set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, UNet2DConditionModel
-from diffusers.optimization import get_scheduler, get_cosine_with_hard_restarts_schedule_with_warmup
-from diffusers.training_utils import EMAModel
-from PIL import Image
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-from slugify import slugify
-
-from schedulers.scheduling_euler_a import EulerAScheduler
-from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
-from data.csv import CSVDataModule
-from models.clip.prompt import PromptProcessor
-
-logger = get_logger(__name__)
-
-
-torch.backends.cuda.matmul.allow_tf32 = True
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Simple example of a training script."
-    )
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--train_data_file",
-        type=str,
-        default=None,
-        help="A CSV file containing the training data."
-    )
-    parser.add_argument(
-        "--instance_identifier",
-        type=str,
-        default=None,
-        help="A token to use as a placeholder for the concept.",
-    )
-    parser.add_argument(
-        "--class_identifier",
-        type=str,
-        default=None,
-        help="A token to use as a placeholder for the concept.",
-    )
-    parser.add_argument(
-        "--placeholder_token",
-        type=str,
-        default="<*>",
-        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(
-        "--num_class_images",
-        type=int,
-        default=400,
-        help="How many class images to generate."
-    )
-    parser.add_argument(
-        "--repeats",
-        type=int,
-        default=1,
-        help="How many times to repeat the training data."
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="output/dreambooth-plus",
-        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=4700,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate_unet",
-        type=float,
-        default=2e-6,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--learning_rate_text",
-        type=float,
-        default=2e-6,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=True,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="cosine_with_restarts",
-        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=300,
-        help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--lr_cycles",
-        type=int,
-        default=None,
-        help="Number of restart cycles in the lr scheduler."
-    )
-    parser.add_argument(
-        "--use_ema",
-        action="store_true",
-        default=True,
-        help="Whether to use EMA model."
-    )
-    parser.add_argument(
-        "--ema_inv_gamma",
-        type=float,
-        default=1.0
-    )
-    parser.add_argument(
-        "--ema_power",
-        type=float,
-        default=9 / 10
-    )
-    parser.add_argument(
-        "--ema_max_decay",
-        type=float,
-        default=0.9999
-    )
-    parser.add_argument(
-        "--use_8bit_adam",
-        action="store_true",
-        default=True,
-        help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-    parser.add_argument(
-        "--adam_beta1",
-        type=float,
-        default=0.9,
-        help="The beta1 parameter for the Adam optimizer."
-    )
-    parser.add_argument(
-        "--adam_beta2",
-        type=float,
-        default=0.999,
-        help="The beta2 parameter for the Adam optimizer."
-    )
-    parser.add_argument(
-        "--adam_weight_decay",
-        type=float,
-        default=1e-2,
-        help="Weight decay to use."
-    )
-    parser.add_argument(
-        "--adam_epsilon",
-        type=float,
-        default=1e-08,
-        help="Epsilon value for the Adam optimizer"
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose"
-            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
-            "and an Nvidia Ampere GPU."
-        ),
-    )
-    parser.add_argument(
-        "--checkpoint_frequency",
-        type=int,
-        default=500,
-        help="How often to save a checkpoint and sample image",
-    )
-    parser.add_argument(
-        "--sample_frequency",
-        type=int,
-        default=100,
-        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(
-        "--sample_batches",
-        type=int,
-        default=1,
-        help="Number of sample batches to generate per checkpoint",
-    )
-    parser.add_argument(
-        "--sample_batch_size",
-        type=int,
-        default=1,
-        help="Number of samples to generate per batch",
-    )
-    parser.add_argument(
-        "--train_batch_size",
-        type=int,
-        default=1,
-        help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument(
-        "--sample_steps",
-        type=int,
-        default=30,
-        help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.",
-    )
-    parser.add_argument(
-        "--prior_loss_weight",
-        type=float,
-        default=1.0,
-        help="The weight of prior preservation loss."
-    )
-    parser.add_argument(
-        "--max_grad_norm",
-        default=1.0,
-        type=float,
-        help="Max gradient norm."
-    )
-    parser.add_argument(
-        "--noise_timesteps",
-        type=int,
-        default=1000,
-    )
-    parser.add_argument(
-        "--config",
-        type=str,
-        default=None,
-        help="Path to a JSON configuration file containing arguments for invoking this script."
-    )
-
-    args = parser.parse_args()
-    if args.config is not None:
-        with open(args.config, 'rt') as f:
-            args = parser.parse_args(
-                namespace=argparse.Namespace(**json.load(f)["args"]))
-
-    if args.train_data_file is None:
-        raise ValueError("You must specify --train_data_file")
-
-    if args.pretrained_model_name_or_path is None:
-        raise ValueError("You must specify --pretrained_model_name_or_path")
-
-    if args.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 save_args(basepath: Path, args, extra={}):
-    info = {"args": vars(args)}
-    info["args"].update(extra)
-    with open(basepath.joinpath("args.json"), "w") as f:
-        json.dump(info, f, indent=4)
-
-
-def freeze_params(params):
-    for param in params:
-        param.requires_grad = False
-
-
-def make_grid(images, rows, cols):
-    w, h = images[0].size
-    grid = Image.new('RGB', size=(cols*w, rows*h))
-    for i, image in enumerate(images):
-        grid.paste(image, box=(i % cols*w, i//cols*h))
-    return grid
-
-
-class Checkpointer:
-    def __init__(
-        self,
-        datamodule,
-        accelerator,
-        vae,
-        unet,
-        ema_unet,
-        tokenizer,
-        text_encoder,
-        instance_identifier,
-        placeholder_token,
-        placeholder_token_id,
-        output_dir: Path,
-        sample_image_size,
-        sample_batches,
-        sample_batch_size,
-        seed
-    ):
-        self.datamodule = datamodule
-        self.accelerator = accelerator
-        self.vae = vae
-        self.unet = unet
-        self.ema_unet = ema_unet
-        self.tokenizer = tokenizer
-        self.text_encoder = text_encoder
-        self.instance_identifier = instance_identifier
-        self.placeholder_token = placeholder_token
-        self.placeholder_token_id = placeholder_token_id
-        self.output_dir = output_dir
-        self.sample_image_size = sample_image_size
-        self.seed = seed or torch.random.seed()
-        self.sample_batches = sample_batches
-        self.sample_batch_size = sample_batch_size
-
-    @torch.no_grad()
-    def checkpoint(self, step, postfix):
-        print("Saving checkpoint for step %d..." % step)
-
-        checkpoints_path = self.output_dir.joinpath("checkpoints")
-        checkpoints_path.mkdir(parents=True, exist_ok=True)
-
-        unwrapped = self.accelerator.unwrap_model(self.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)
-        torch.save(learned_embeds_dict, checkpoints_path.joinpath(filename))
-
-        del unwrapped
-        del learned_embeds
-
-    @torch.no_grad()
-    def save_model(self):
-        print("Saving model...")
-
-        unwrapped_unet = self.accelerator.unwrap_model(
-            self.ema_unet.averaged_model if self.ema_unet is not None else self.unet)
-        unwrapped_text_encoder = self.accelerator.unwrap_model(self.text_encoder)
-        pipeline = VlpnStableDiffusion(
-            text_encoder=unwrapped_text_encoder,
-            vae=self.vae,
-            unet=unwrapped_unet,
-            tokenizer=self.tokenizer,
-            scheduler=PNDMScheduler(
-                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
-            ),
-        )
-        pipeline.save_pretrained(self.output_dir.joinpath("model"))
-
-        del unwrapped_unet
-        del unwrapped_text_encoder
-        del pipeline
-
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-    @torch.no_grad()
-    def save_samples(self, step, height, width, guidance_scale, eta, num_inference_steps):
-        samples_path = Path(self.output_dir).joinpath("samples")
-
-        unwrapped_unet = self.accelerator.unwrap_model(
-            self.ema_unet.averaged_model if self.ema_unet is not None else self.unet)
-        unwrapped_text_encoder = self.accelerator.unwrap_model(self.text_encoder)
-        scheduler = EulerAScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
-        )
-
-        # Save a sample image
-        pipeline = VlpnStableDiffusion(
-            text_encoder=unwrapped_text_encoder,
-            vae=self.vae,
-            unet=unwrapped_unet,
-            tokenizer=self.tokenizer,
-            scheduler=scheduler,
-        ).to(self.accelerator.device)
-        pipeline.set_progress_bar_config(dynamic_ncols=True)
-
-        train_data = self.datamodule.train_dataloader()
-        val_data = self.datamodule.val_dataloader()
-
-        generator = torch.Generator(device=pipeline.device).manual_seed(self.seed)
-        stable_latents = torch.randn(
-            (self.sample_batch_size, pipeline.unet.in_channels, height // 8, width // 8),
-            device=pipeline.device,
-            generator=generator,
-        )
-
-        with torch.inference_mode():
-            for pool, data, latents in [("stable", val_data, stable_latents), ("val", val_data, None), ("train", train_data, None)]:
-                all_samples = []
-                file_path = samples_path.joinpath(pool, f"step_{step}.png")
-                file_path.parent.mkdir(parents=True, exist_ok=True)
-
-                data_enum = enumerate(data)
-
-                for i in range(self.sample_batches):
-                    batches = [batch for j, batch in data_enum if j * data.batch_size < self.sample_batch_size]
-                    prompt = [
-                        prompt.format(self.instance_identifier)
-                        for batch in batches
-                        for prompt in batch["prompts"]
-                    ][:self.sample_batch_size]
-                    nprompt = [
-                        prompt
-                        for batch in batches
-                        for prompt in batch["nprompts"]
-                    ][:self.sample_batch_size]
-
-                    samples = pipeline(
-                        prompt=prompt,
-                        negative_prompt=nprompt,
-                        height=self.sample_image_size,
-                        width=self.sample_image_size,
-                        latents=latents[:len(prompt)] if latents is not None else None,
-                        generator=generator if latents is not None else None,
-                        guidance_scale=guidance_scale,
-                        eta=eta,
-                        num_inference_steps=num_inference_steps,
-                        output_type='pil'
-                    ).images
-
-                    all_samples += samples
-
-                    del samples
-
-                image_grid = make_grid(all_samples, self.sample_batches, self.sample_batch_size)
-                image_grid.save(file_path)
-
-                del all_samples
-                del image_grid
-
-        del unwrapped_unet
-        del unwrapped_text_encoder
-        del scheduler
-        del pipeline
-        del generator
-        del stable_latents
-
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-
-def main():
-    args = parse_args()
-
-    global_step_offset = 0
-    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-    basepath = Path(args.output_dir).joinpath(slugify(args.placeholder_token), now)
-    basepath.mkdir(parents=True, exist_ok=True)
-
-    accelerator = Accelerator(
-        log_with=LoggerType.TENSORBOARD,
-        logging_dir=f"{basepath}",
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision
-    )
-
-    logging.basicConfig(filename=basepath.joinpath("log.txt"), level=logging.DEBUG)
-
-    save_args(basepath, args)
-
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    args.instance_identifier = args.instance_identifier.format(args.placeholder_token)
-
-    # Load the tokenizer and add the placeholder token as a additional special token
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
-
-    # Convert the initializer_token, placeholder_token to ids
-    initializer_token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
-    print(f"Initializer token maps to {len(initializer_token_ids)} embeddings.")
-    initializer_token_ids = torch.tensor(initializer_token_ids[:1])
-
-    # Add the placeholder token in tokenizer
-    num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
-    if num_added_tokens == 0:
-        print(f"Re-using existing token {args.placeholder_token}.")
-    else:
-        print(f"Training new token {args.placeholder_token}.")
-
-    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, subfolder='text_encoder')
-    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
-    unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder='unet')
-
-    ema_unet = EMAModel(
-        unet,
-        inv_gamma=args.ema_inv_gamma,
-        power=args.ema_power,
-        max_value=args.ema_max_decay,
-        device=accelerator.device
-    ) if args.use_ema else None
-
-    prompt_processor = PromptProcessor(tokenizer, text_encoder)
-
-    if args.gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-        text_encoder.gradient_checkpointing_enable()
-
-    # 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
-    original_token_embeds = token_embeds.detach().clone().to(accelerator.device)
-    initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids)
-    token_embeds[placeholder_token_id] = initializer_token_embeddings
-
-    # Freeze vae and unet
-    freeze_params(vae.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_unet = (
-            args.learning_rate_unet * args.gradient_accumulation_steps *
-            args.train_batch_size * accelerator.num_processes
-        )
-        args.learning_rate_text = (
-            args.learning_rate_text * args.gradient_accumulation_steps *
-            args.train_batch_size * accelerator.num_processes
-        )
-
-    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
-    if args.use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.")
-
-        optimizer_class = bnb.optim.AdamW8bit
-    else:
-        optimizer_class = torch.optim.AdamW
-
-    # Initialize the optimizer
-    optimizer = optimizer_class(
-        [
-            {
-                'params': unet.parameters(),
-                'lr': args.learning_rate_unet,
-            },
-            {
-                'params': text_encoder.get_input_embeddings().parameters(),
-                'lr': args.learning_rate_text,
-            }
-        ],
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    noise_scheduler = DDPMScheduler(
-        beta_start=0.00085,
-        beta_end=0.012,
-        beta_schedule="scaled_linear",
-        num_train_timesteps=args.noise_timesteps
-    )
-
-    weight_dtype = torch.float32
-    if args.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif args.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    def collate_fn(examples):
-        prompts = [example["prompts"] for example in examples]
-        nprompts = [example["nprompts"] for example in examples]
-        input_ids = [example["instance_prompt_ids"] for example in examples]
-        pixel_values = [example["instance_images"] for example in examples]
-
-        # concat class and instance examples for prior preservation
-        if args.num_class_images != 0 and "class_prompt_ids" in examples[0]:
-            input_ids += [example["class_prompt_ids"] for example in examples]
-            pixel_values += [example["class_images"] for example in examples]
-
-        pixel_values = torch.stack(pixel_values)
-        pixel_values = pixel_values.to(dtype=weight_dtype, memory_format=torch.contiguous_format)
-
-        input_ids = prompt_processor.unify_input_ids(input_ids)
-
-        batch = {
-            "prompts": prompts,
-            "nprompts": nprompts,
-            "input_ids": input_ids,
-            "pixel_values": pixel_values,
-        }
-        return batch
-
-    datamodule = CSVDataModule(
-        data_file=args.train_data_file,
-        batch_size=args.train_batch_size,
-        prompt_processor=prompt_processor,
-        instance_identifier=args.instance_identifier,
-        class_identifier=args.class_identifier,
-        class_subdir="cls",
-        num_class_images=args.num_class_images,
-        size=args.resolution,
-        repeats=args.repeats,
-        center_crop=args.center_crop,
-        valid_set_size=args.sample_batch_size*args.sample_batches,
-        collate_fn=collate_fn
-    )
-
-    datamodule.prepare_data()
-    datamodule.setup()
-
-    if args.num_class_images != 0:
-        missing_data = [item for item in datamodule.data_train if not item.class_image_path.exists()]
-
-        if len(missing_data) != 0:
-            batched_data = [
-                missing_data[i:i+args.sample_batch_size]
-                for i in range(0, len(missing_data), args.sample_batch_size)
-            ]
-
-            scheduler = EulerAScheduler(
-                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
-            )
-
-            pipeline = VlpnStableDiffusion(
-                text_encoder=text_encoder,
-                vae=vae,
-                unet=unet,
-                tokenizer=tokenizer,
-                scheduler=scheduler,
-            ).to(accelerator.device)
-            pipeline.set_progress_bar_config(dynamic_ncols=True)
-
-            with torch.inference_mode():
-                for batch in batched_data:
-                    image_name = [item.class_image_path for item in batch]
-                    prompt = [item.prompt.format(args.class_identifier) for item in batch]
-                    nprompt = [item.nprompt for item in batch]
-
-                    images = pipeline(
-                        prompt=prompt,
-                        negative_prompt=nprompt,
-                        num_inference_steps=args.sample_steps
-                    ).images
-
-                    for i, image in enumerate(images):
-                        image.save(image_name[i])
-
-            del pipeline
-
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-    train_dataloader = datamodule.train_dataloader()
-    val_dataloader = datamodule.val_dataloader()
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-    num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    if args.lr_scheduler == "cosine_with_restarts":
-        lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
-            optimizer=optimizer,
-            num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
-            num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
-            num_cycles=args.lr_cycles or math.ceil(math.sqrt(
-                ((args.max_train_steps - args.lr_warmup_steps) / num_update_steps_per_epoch))),
-        )
-    else:
-        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, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
-        text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler
-    )
-
-    # Move vae and unet to device
-    vae.to(accelerator.device, dtype=weight_dtype)
-
-    # Keep vae and unet in eval mode as we don't train these
-    vae.eval()
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-
-    num_val_steps_per_epoch = len(val_dataloader)
-    num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-    val_steps = num_val_steps_per_epoch * num_epochs
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        accelerator.init_trackers("dreambooth_plus", 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 = {num_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    # Only show the progress bar once on each machine.
-
-    global_step = 0
-    min_val_loss = np.inf
-
-    checkpointer = Checkpointer(
-        datamodule=datamodule,
-        accelerator=accelerator,
-        vae=vae,
-        unet=unet,
-        ema_unet=ema_unet,
-        tokenizer=tokenizer,
-        text_encoder=text_encoder,
-        instance_identifier=args.instance_identifier,
-        placeholder_token=args.placeholder_token,
-        placeholder_token_id=placeholder_token_id,
-        output_dir=basepath,
-        sample_image_size=args.sample_image_size,
-        sample_batch_size=args.sample_batch_size,
-        sample_batches=args.sample_batches,
-        seed=args.seed
-    )
-
-    if accelerator.is_main_process:
-        checkpointer.save_samples(
-            0,
-            args.resolution, args.resolution, 7.5, 0.0, args.sample_steps)
-
-    local_progress_bar = tqdm(
-        range(num_update_steps_per_epoch + num_val_steps_per_epoch),
-        disable=not accelerator.is_local_main_process,
-        dynamic_ncols=True
-    )
-    local_progress_bar.set_description("Epoch X / Y")
-
-    global_progress_bar = tqdm(
-        range(args.max_train_steps + val_steps),
-        disable=not accelerator.is_local_main_process,
-        dynamic_ncols=True
-    )
-    global_progress_bar.set_description("Total progress")
-
-    try:
-        for epoch in range(num_epochs):
-            local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}")
-            local_progress_bar.reset()
-
-            text_encoder.train()
-            train_loss = 0.0
-
-            sample_checkpoint = False
-
-            for step, batch in enumerate(train_dataloader):
-                with accelerator.accumulate(itertools.chain(unet, text_encoder)):
-                    # Convert images to latent space
-                    latents = vae.encode(batch["pixel_values"]).latent_dist.sample()
-                    latents = latents * 0.18215
-
-                    # Sample noise that we'll add to the latents
-                    noise = torch.randn_like(latents)
-                    bsz = latents.shape[0]
-                    # Sample a random timestep for each image
-                    timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps,
-                                              (bsz,), device=latents.device)
-                    timesteps = timesteps.long()
-
-                    # Add noise to the latents according to the noise magnitude at each timestep
-                    # (this is the forward diffusion process)
-                    noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                    # Get the text embedding for conditioning
-                    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"])
-
-                    # Predict the noise residual
-                    noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
-                    if args.num_class_images != 0:
-                        # Chunk the noise and noise_pred into two parts and compute the loss on each part separately.
-                        noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
-                        noise, noise_prior = torch.chunk(noise, 2, dim=0)
-
-                        # Compute instance loss
-                        loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="none").mean([1, 2, 3]).mean()
-
-                        # Compute prior loss
-                        prior_loss = F.mse_loss(noise_pred_prior.float(), noise_prior.float(), reduction="mean")
-
-                        # Add the prior loss to the instance loss.
-                        loss = loss + args.prior_loss_weight * prior_loss
-                    else:
-                        loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="mean")
-
-                    accelerator.backward(loss)
-
-                    # Keep the token embeddings fixed except the newly added
-                    # embeddings for the concept, as we only want to optimize the concept embeddings
-                    if accelerator.num_processes > 1:
-                        token_embeds = text_encoder.module.get_input_embeddings().weight
-                    else:
-                        token_embeds = text_encoder.get_input_embeddings().weight
-
-                    # Get the index for tokens that we want to freeze
-                    index_fixed_tokens = torch.arange(len(tokenizer)) != placeholder_token_id
-                    token_embeds.data[index_fixed_tokens, :] = original_token_embeds[index_fixed_tokens, :]
-
-                    if accelerator.sync_gradients:
-                        accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
-
-                    optimizer.step()
-                    if not accelerator.optimizer_step_was_skipped:
-                        lr_scheduler.step()
-                    optimizer.zero_grad(set_to_none=True)
-
-                    loss = loss.detach().item()
-                    train_loss += loss
-
-                # Checks if the accelerator has performed an optimization step behind the scenes
-                if accelerator.sync_gradients:
-                    if args.use_ema:
-                        ema_unet.step(unet)
-
-                    local_progress_bar.update(1)
-                    global_progress_bar.update(1)
-
-                    global_step += 1
-
-                    if global_step % args.sample_frequency == 0:
-                        sample_checkpoint = True
-
-                    if global_step % args.checkpoint_frequency == 0 and global_step > 0 and accelerator.is_main_process:
-                        local_progress_bar.clear()
-                        global_progress_bar.clear()
-
-                        checkpointer.checkpoint(global_step + global_step_offset, "training")
-
-                logs = {
-                    "train/loss": loss,
-                    "lr/unet": lr_scheduler.get_last_lr()[0],
-                    "lr/text": lr_scheduler.get_last_lr()[1]
-                }
-                if args.use_ema:
-                    logs["ema_decay"] = ema_unet.decay
-
-                accelerator.log(logs, step=global_step)
-
-                local_progress_bar.set_postfix(**logs)
-
-                if global_step >= args.max_train_steps:
-                    break
-
-            train_loss /= len(train_dataloader)
-
-            accelerator.wait_for_everyone()
-
-            text_encoder.eval()
-            val_loss = 0.0
-
-            for step, batch in enumerate(val_dataloader):
-                with torch.no_grad():
-                    latents = vae.encode(batch["pixel_values"]).latent_dist.sample()
-                    latents = latents * 0.18215
-
-                    noise = torch.randn_like(latents)
-                    bsz = latents.shape[0]
-                    timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps,
-                                              (bsz,), device=latents.device)
-                    timesteps = timesteps.long()
-
-                    noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"])
-
-                    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.float(), noise.float(), reduction="mean")
-
-                    loss = loss.detach().item()
-                    val_loss += loss
-
-                if accelerator.sync_gradients:
-                    local_progress_bar.update(1)
-                    global_progress_bar.update(1)
-
-                logs = {"val/loss": loss}
-                local_progress_bar.set_postfix(**logs)
-
-            val_loss /= len(val_dataloader)
-
-            accelerator.log({"val/loss": val_loss}, step=global_step)
-
-            local_progress_bar.clear()
-            global_progress_bar.clear()
-
-            if min_val_loss > val_loss:
-                accelerator.print(
-                    f"Global step {global_step}: Validation loss reached new minimum: {min_val_loss:.2e} -> {val_loss:.2e}")
-                checkpointer.checkpoint(global_step + global_step_offset, "milestone")
-                min_val_loss = val_loss
-
-            if sample_checkpoint and accelerator.is_main_process:
-                checkpointer.save_samples(
-                    global_step + global_step_offset,
-                    args.resolution, args.resolution, 7.5, 0.0, args.sample_steps)
-
-        # Create the pipeline using using the trained modules and save it.
-        if accelerator.is_main_process:
-            print("Finished! Saving final checkpoint and resume state.")
-            checkpointer.checkpoint(global_step + global_step_offset, "end")
-            checkpointer.save_model()
-            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")
-            checkpointer.save_model()
-            accelerator.end_training()
-        quit()
-
-
-if __name__ == "__main__":
-    main()