11 files changed, 1534 insertions, 279 deletions

diff --git a/infer.py b/infer.py
index 36b5a2c..2b07b21 100644
--- a/infer.py
+++ b/infer.py
@@ -214,10 +214,21 @@ def load_embeddings(pipeline, embeddings_dir):
 def create_pipeline(model, dtype):
     print("Loading Stable Diffusion pipeline...")
 
-    pipeline = VlpnStableDiffusion.from_pretrained(model, torch_dtype=dtype)
+    tokenizer = MultiCLIPTokenizer.from_pretrained(model, subfolder='tokenizer', torch_dtype=dtype)
+    text_encoder = CLIPTextModel.from_pretrained(model, subfolder='text_encoder', torch_dtype=dtype)
+    vae = AutoencoderKL.from_pretrained(model, subfolder='vae', torch_dtype=dtype)
+    unet = UNet2DConditionModel.from_pretrained(model, subfolder='unet', torch_dtype=dtype)
+    scheduler = DDIMScheduler.from_pretrained(model, subfolder='scheduler', torch_dtype=dtype)
 
-    patch_managed_embeddings(pipeline.text_encoder)
+    patch_managed_embeddings(text_encoder)
 
+    pipeline = VlpnStableDiffusion(
+        text_encoder=text_encoder,
+        vae=vae,
+        unet=unet,
+        tokenizer=tokenizer,
+        scheduler=scheduler,
+    )
     pipeline.enable_xformers_memory_efficient_attention()
     pipeline.enable_vae_slicing()
     pipeline.to("cuda")
diff --git a/pipelines/stable_diffusion/vlpn_stable_diffusion.py b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
index a5cfc60..43141bd 100644
--- a/pipelines/stable_diffusion/vlpn_stable_diffusion.py
+++ b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
@@ -52,7 +52,6 @@ class VlpnStableDiffusion(DiffusionPipeline):
             EulerAncestralDiscreteScheduler,
             DPMSolverMultistepScheduler,
         ],
-        **kwargs,
     ):
         super().__init__()
 
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..d8644c4
--- /dev/null
+++ b/train.py
@@ -0,0 +1,672 @@
+import argparse
+import datetime
+import logging
+from pathlib import Path
+
+import torch
+import torch.utils.checkpoint
+
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import LoggerType, set_seed
+from slugify import slugify
+
+from data.csv import VlpnDataModule, VlpnDataItem
+from util import load_config, load_embeddings_from_dir
+
+from trainer.ti import TextualInversionTrainingStrategy
+from trainer.base import Trainer
+from training.optimization import get_scheduler
+from training.util import save_args, generate_class_images, add_placeholder_tokens, get_models
+
+logger = get_logger(__name__)
+
+
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.benchmark = True
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Simple example of a training script."
+    )
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--train_data_file",
+        type=str,
+        default=None,
+        help="A CSV file containing the training data."
+    )
+    parser.add_argument(
+        "--train_data_template",
+        type=str,
+        default="template",
+    )
+    parser.add_argument(
+        "--project",
+        type=str,
+        default=None,
+        help="The name of the current project.",
+    )
+    parser.add_argument(
+        "--placeholder_tokens",
+        type=str,
+        nargs='*',
+        help="A token to use as a placeholder for the concept.",
+    )
+    parser.add_argument(
+        "--initializer_tokens",
+        type=str,
+        nargs='*',
+        help="A token to use as initializer word."
+    )
+    parser.add_argument(
+        "--num_vectors",
+        type=int,
+        nargs='*',
+        help="Number of vectors per embedding."
+    )
+    parser.add_argument(
+        "--num_class_images",
+        type=int,
+        default=1,
+        help="How many class images to generate."
+    )
+    parser.add_argument(
+        "--class_image_dir",
+        type=str,
+        default="cls",
+        help="The directory where class images will be saved.",
+    )
+    parser.add_argument(
+        "--exclude_collections",
+        type=str,
+        nargs='*',
+        help="Exclude all items with a listed collection.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="output/text-inversion",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--embeddings_dir",
+        type=str,
+        default=None,
+        help="The embeddings directory where Textual Inversion embeddings are stored.",
+    )
+    parser.add_argument(
+        "--collection",
+        type=str,
+        nargs='*',
+        help="A collection to filter the dataset.",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=None,
+        help="A seed for reproducible training."
+    )
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=768,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--num_buckets",
+        type=int,
+        default=0,
+        help="Number of aspect ratio buckets in either direction.",
+    )
+    parser.add_argument(
+        "--progressive_buckets",
+        action="store_true",
+        help="Include images in smaller buckets as well.",
+    )
+    parser.add_argument(
+        "--bucket_step_size",
+        type=int,
+        default=64,
+        help="Step size between buckets.",
+    )
+    parser.add_argument(
+        "--bucket_max_pixels",
+        type=int,
+        default=None,
+        help="Maximum pixels per bucket.",
+    )
+    parser.add_argument(
+        "--tag_dropout",
+        type=float,
+        default=0,
+        help="Tag dropout probability.",
+    )
+    parser.add_argument(
+        "--no_tag_shuffle",
+        action="store_true",
+        help="Shuffle tags.",
+    )
+    parser.add_argument(
+        "--vector_dropout",
+        type=int,
+        default=0,
+        help="Vector dropout probability.",
+    )
+    parser.add_argument(
+        "--vector_shuffle",
+        type=str,
+        default="auto",
+        help='Vector shuffling algorithm. Choose between ["all", "trailing", "leading", "between", "auto", "off"]',
+    )
+    parser.add_argument(
+        "--num_train_epochs",
+        type=int,
+        default=100
+    )
+    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(
+        "--find_lr",
+        action="store_true",
+        help="Automatically find a learning rate (no training).",
+    )
+    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",
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="one_cycle",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup", "one_cycle"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_epochs",
+        type=int,
+        default=10,
+        help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument(
+        "--lr_cycles",
+        type=int,
+        default=None,
+        help="Number of restart cycles in the lr scheduler."
+    )
+    parser.add_argument(
+        "--lr_warmup_func",
+        type=str,
+        default="cos",
+        help='Choose between ["linear", "cos"]'
+    )
+    parser.add_argument(
+        "--lr_warmup_exp",
+        type=int,
+        default=1,
+        help='If lr_warmup_func is "cos", exponent to modify the function'
+    )
+    parser.add_argument(
+        "--lr_annealing_func",
+        type=str,
+        default="cos",
+        help='Choose between ["linear", "half_cos", "cos"]'
+    )
+    parser.add_argument(
+        "--lr_annealing_exp",
+        type=int,
+        default=1,
+        help='If lr_annealing_func is "half_cos" or "cos", exponent to modify the function'
+    )
+    parser.add_argument(
+        "--lr_min_lr",
+        type=float,
+        default=0.04,
+        help="Minimum learning rate in the lr scheduler."
+    )
+    parser.add_argument(
+        "--use_ema",
+        action="store_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=1
+    )
+    parser.add_argument(
+        "--ema_max_decay",
+        type=float,
+        default=0.9999
+    )
+    parser.add_argument(
+        "--use_8bit_adam",
+        action="store_true",
+        help="Whether or not to use 8-bit Adam from bitsandbytes."
+    )
+    parser.add_argument(
+        "--adam_beta1",
+        type=float,
+        default=0.9,
+        help="The beta1 parameter for the Adam optimizer."
+    )
+    parser.add_argument(
+        "--adam_beta2",
+        type=float,
+        default=0.999,
+        help="The beta2 parameter for the Adam optimizer."
+    )
+    parser.add_argument(
+        "--adam_weight_decay",
+        type=float,
+        default=0,
+        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(
+        "--adam_amsgrad",
+        type=bool,
+        default=False,
+        help="Amsgrad value for the Adam optimizer"
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose"
+            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+            "and an Nvidia Ampere GPU."
+        ),
+    )
+    parser.add_argument(
+        "--checkpoint_frequency",
+        type=int,
+        default=5,
+        help="How often to save a checkpoint and sample image (in epochs)",
+    )
+    parser.add_argument(
+        "--sample_frequency",
+        type=int,
+        default=1,
+        help="How often to save a checkpoint and sample image (in epochs)",
+    )
+    parser.add_argument(
+        "--sample_image_size",
+        type=int,
+        default=768,
+        help="Size of sample images",
+    )
+    parser.add_argument(
+        "--sample_batches",
+        type=int,
+        default=1,
+        help="Number of sample batches to generate per checkpoint",
+    )
+    parser.add_argument(
+        "--sample_batch_size",
+        type=int,
+        default=1,
+        help="Number of samples to generate per batch",
+    )
+    parser.add_argument(
+        "--valid_set_size",
+        type=int,
+        default=None,
+        help="Number of images in the validation dataset."
+    )
+    parser.add_argument(
+        "--valid_set_repeat",
+        type=int,
+        default=1,
+        help="Times the images in the validation dataset are repeated."
+    )
+    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=20,
+        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(
+        "--emb_decay_target",
+        default=0.4,
+        type=float,
+        help="Embedding decay target."
+    )
+    parser.add_argument(
+        "--emb_decay_factor",
+        default=0,
+        type=float,
+        help="Embedding decay factor."
+    )
+    parser.add_argument(
+        "--emb_decay_start",
+        default=1e-4,
+        type=float,
+        help="Embedding decay start offset."
+    )
+    parser.add_argument(
+        "--noise_timesteps",
+        type=int,
+        default=1000,
+    )
+    parser.add_argument(
+        "--resume_from",
+        type=str,
+        default=None,
+        help="Path to a directory to resume training from (ie, logs/token_name/2022-09-22T23-36-27)"
+    )
+    parser.add_argument(
+        "--global_step",
+        type=int,
+        default=0,
+    )
+    parser.add_argument(
+        "--config",
+        type=str,
+        default=None,
+        help="Path to a JSON configuration file containing arguments for invoking this script."
+    )
+
+    args = parser.parse_args()
+    if args.config is not None:
+        args = load_config(args.config)
+        args = parser.parse_args(namespace=argparse.Namespace(**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.project is None:
+        raise ValueError("You must specify --project")
+
+    if isinstance(args.placeholder_tokens, str):
+        args.placeholder_tokens = [args.placeholder_tokens]
+
+    if len(args.placeholder_tokens) == 0:
+        args.placeholder_tokens = [f"<*{i}>" for i in range(args.initializer_tokens)]
+
+    if isinstance(args.initializer_tokens, str):
+        args.initializer_tokens = [args.initializer_tokens] * len(args.placeholder_tokens)
+
+    if len(args.initializer_tokens) == 0:
+        raise ValueError("You must specify --initializer_tokens")
+
+    if len(args.placeholder_tokens) != len(args.initializer_tokens):
+        raise ValueError("--placeholder_tokens and --initializer_tokens must have the same number of items")
+
+    if args.num_vectors is None:
+        args.num_vectors = 1
+
+    if isinstance(args.num_vectors, int):
+        args.num_vectors = [args.num_vectors] * len(args.initializer_tokens)
+
+    if len(args.placeholder_tokens) != len(args.num_vectors):
+        raise ValueError("--placeholder_tokens and --num_vectors must have the same number of items")
+
+    if isinstance(args.collection, str):
+        args.collection = [args.collection]
+
+    if isinstance(args.exclude_collections, str):
+        args.exclude_collections = [args.exclude_collections]
+
+    if args.output_dir is None:
+        raise ValueError("You must specify --output_dir")
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    global_step_offset = args.global_step
+    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+    output_dir = Path(args.output_dir).joinpath(slugify(args.project), now)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    accelerator = Accelerator(
+        log_with=LoggerType.TENSORBOARD,
+        logging_dir=f"{output_dir}",
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision
+    )
+
+    logging.basicConfig(filename=output_dir.joinpath("log.txt"), level=logging.DEBUG)
+
+    if args.seed is None:
+        args.seed = torch.random.seed() >> 32
+
+    set_seed(args.seed)
+
+    save_args(output_dir, args)
+
+    tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings = get_models(
+        args.pretrained_model_name_or_path)
+
+    tokenizer.set_use_vector_shuffle(args.vector_shuffle)
+    tokenizer.set_dropout(args.vector_dropout)
+
+    vae.enable_slicing()
+    vae.set_use_memory_efficient_attention_xformers(True)
+    unet.set_use_memory_efficient_attention_xformers(True)
+
+    if args.gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+        text_encoder.gradient_checkpointing_enable()
+
+    if args.embeddings_dir is not None:
+        embeddings_dir = Path(args.embeddings_dir)
+        if not embeddings_dir.exists() or not embeddings_dir.is_dir():
+            raise ValueError("--embeddings_dir must point to an existing directory")
+
+        added_tokens, added_ids = load_embeddings_from_dir(tokenizer, embeddings, embeddings_dir)
+        print(f"Added {len(added_tokens)} tokens from embeddings dir: {list(zip(added_tokens, added_ids))}")
+
+    placeholder_token_ids, initializer_token_ids = add_placeholder_tokens(
+        tokenizer=tokenizer,
+        embeddings=embeddings,
+        placeholder_tokens=args.placeholder_tokens,
+        initializer_tokens=args.initializer_tokens,
+        num_vectors=args.num_vectors
+    )
+
+    if len(placeholder_token_ids) != 0:
+        initializer_token_id_lens = [len(id) for id in initializer_token_ids]
+        placeholder_token_stats = list(zip(args.placeholder_tokens, placeholder_token_ids, initializer_token_id_lens))
+        print(f"Added {len(placeholder_token_ids)} new tokens: {placeholder_token_stats}")
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps *
+            args.train_batch_size * accelerator.num_processes
+        )
+
+    if args.find_lr:
+        args.learning_rate = 1e-5
+
+    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
+
+    optimizer = optimizer_class(
+        text_encoder.text_model.embeddings.temp_token_embedding.parameters(),
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+        amsgrad=args.adam_amsgrad,
+    )
+
+    weight_dtype = torch.float32
+    if args.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif args.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    def keyword_filter(item: VlpnDataItem):
+        cond1 = any(
+            keyword in part
+            for keyword in args.placeholder_tokens
+            for part in item.prompt
+        )
+        cond3 = args.collection is None or args.collection in item.collection
+        cond4 = args.exclude_collections is None or not any(
+            collection in item.collection
+            for collection in args.exclude_collections
+        )
+        return cond1 and cond3 and cond4
+
+    datamodule = VlpnDataModule(
+        data_file=args.train_data_file,
+        batch_size=args.train_batch_size,
+        tokenizer=tokenizer,
+        class_subdir=args.class_image_dir,
+        num_class_images=args.num_class_images,
+        size=args.resolution,
+        num_buckets=args.num_buckets,
+        progressive_buckets=args.progressive_buckets,
+        bucket_step_size=args.bucket_step_size,
+        bucket_max_pixels=args.bucket_max_pixels,
+        dropout=args.tag_dropout,
+        shuffle=not args.no_tag_shuffle,
+        template_key=args.train_data_template,
+        valid_set_size=args.valid_set_size,
+        valid_set_repeat=args.valid_set_repeat,
+        seed=args.seed,
+        filter=keyword_filter,
+        dtype=weight_dtype
+    )
+    datamodule.setup()
+
+    train_dataloader = datamodule.train_dataloader
+    val_dataloader = datamodule.val_dataloader
+
+    if args.num_class_images != 0:
+        generate_class_images(
+            accelerator,
+            text_encoder,
+            vae,
+            unet,
+            tokenizer,
+            sample_scheduler,
+            datamodule.data_train,
+            args.sample_batch_size,
+            args.sample_image_size,
+            args.sample_steps
+        )
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        optimizer=optimizer,
+        num_training_steps_per_epoch=len(train_dataloader),
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        min_lr=args.lr_min_lr,
+        warmup_func=args.lr_warmup_func,
+        annealing_func=args.lr_annealing_func,
+        warmup_exp=args.lr_warmup_exp,
+        annealing_exp=args.lr_annealing_exp,
+        cycles=args.lr_cycles,
+        train_epochs=args.num_train_epochs,
+        warmup_epochs=args.lr_warmup_epochs,
+    )
+
+    trainer = Trainer(
+        accelerator=accelerator,
+        unet=unet,
+        text_encoder=text_encoder,
+        tokenizer=tokenizer,
+        vae=vae,
+        noise_scheduler=noise_scheduler,
+        sample_scheduler=sample_scheduler,
+        train_dataloader=train_dataloader,
+        val_dataloader=val_dataloader,
+        dtype=weight_dtype,
+    )
+
+    trainer(
+        strategy_class=TextualInversionTrainingStrategy,
+        optimizer=optimizer,
+        lr_scheduler=lr_scheduler,
+        num_train_epochs=args.num_train_epochs,
+        sample_frequency=args.sample_frequency,
+        checkpoint_frequency=args.checkpoint_frequency,
+        global_step_offset=global_step_offset,
+        prior_loss_weight=args.prior_loss_weight,
+        output_dir=output_dir,
+        placeholder_tokens=args.placeholder_tokens,
+        placeholder_token_ids=placeholder_token_ids,
+        learning_rate=args.learning_rate,
+        sample_steps=args.sample_steps,
+        sample_image_size=args.sample_image_size,
+        sample_batch_size=args.sample_batch_size,
+        sample_batches=args.sample_batches,
+        seed=args.seed,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/train_dreambooth.py b/train_dreambooth.py
index 53776ba..71bad7e 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -20,10 +20,9 @@ from slugify import slugify
 from util import load_config, load_embeddings_from_dir
 from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
 from data.csv import VlpnDataModule, VlpnDataItem
-from training.common import loss_step, train_loop, generate_class_images, add_placeholder_tokens, get_models
 from training.optimization import get_scheduler
 from training.lr import LRFinder
-from training.util import CheckpointerBase, EMAModel, save_args
+from training.util import CheckpointerBase, EMAModel, save_args, generate_class_images, add_placeholder_tokens, get_models
 from models.clip.tokenizer import MultiCLIPTokenizer
 
 logger = get_logger(__name__)
diff --git a/train_ti.py b/train_ti.py
index 8631892..deed84c 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -19,10 +19,11 @@ from slugify import slugify
 from util import load_config, load_embeddings_from_dir
 from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
 from data.csv import VlpnDataModule, VlpnDataItem
-from training.common import loss_step, train_loop, generate_class_images, add_placeholder_tokens, get_models
+from trainer.base import Checkpointer
+from training.functional import loss_step, train_loop, generate_class_images, add_placeholder_tokens, get_models
 from training.optimization import get_scheduler
 from training.lr import LRFinder
-from training.util import CheckpointerBase, EMAModel, save_args
+from training.util import EMAModel, save_args
 from models.clip.tokenizer import MultiCLIPTokenizer
 
 logger = get_logger(__name__)
@@ -480,38 +481,20 @@ def parse_args():
     return args
 
 
-class Checkpointer(CheckpointerBase):
+class TextualInversionCheckpointer(Checkpointer):
     def __init__(
         self,
-        weight_dtype: torch.dtype,
-        accelerator: Accelerator,
-        vae: AutoencoderKL,
-        unet: UNet2DConditionModel,
-        tokenizer: MultiCLIPTokenizer,
-        text_encoder: CLIPTextModel,
         ema_embeddings: EMAModel,
-        scheduler,
-        placeholder_tokens,
-        placeholder_token_ids,
         *args,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(*args, **kwargs)
 
-        self.weight_dtype = weight_dtype
-        self.accelerator = accelerator
-        self.vae = vae
-        self.unet = unet
-        self.tokenizer = tokenizer
-        self.text_encoder = text_encoder
         self.ema_embeddings = ema_embeddings
-        self.scheduler = scheduler
-        self.placeholder_tokens = placeholder_tokens
-        self.placeholder_token_ids = placeholder_token_ids
 
     @torch.no_grad()
     def checkpoint(self, step, postfix):
-        print("Saving checkpoint for step %d..." % step)
+        print(f"Saving checkpoint for step {step}...")
 
         checkpoints_path = self.output_dir.joinpath("checkpoints")
         checkpoints_path.mkdir(parents=True, exist_ok=True)
@@ -519,7 +502,8 @@ class Checkpointer(CheckpointerBase):
         text_encoder = self.accelerator.unwrap_model(self.text_encoder)
 
         ema_context = self.ema_embeddings.apply_temporary(
-            text_encoder.text_model.embeddings.temp_token_embedding.parameters()) if self.ema_embeddings is not None else nullcontext()
+            text_encoder.text_model.embeddings.temp_token_embedding.parameters()
+        ) if self.ema_embeddings is not None else nullcontext()
 
         with ema_context:
             for (token, ids) in zip(self.placeholder_tokens, self.placeholder_token_ids):
@@ -528,42 +512,14 @@ class Checkpointer(CheckpointerBase):
                     checkpoints_path.joinpath(f"{slugify(token)}_{step}_{postfix}.bin")
                 )
 
-        del text_encoder
-
-    @torch.no_grad()
+    @torch.inference_mode()
     def save_samples(self, step):
-        unet = self.accelerator.unwrap_model(self.unet)
-        text_encoder = self.accelerator.unwrap_model(self.text_encoder)
-
         ema_context = self.ema_embeddings.apply_temporary(
-            text_encoder.text_model.embeddings.temp_token_embedding.parameters()) if self.ema_embeddings is not None else nullcontext()
+            self.text_encoder.text_model.embeddings.temp_token_embedding.parameters()
+        ) if self.ema_embeddings is not None else nullcontext()
 
         with ema_context:
-            orig_unet_dtype = unet.dtype
-            orig_text_encoder_dtype = text_encoder.dtype
-
-            unet.to(dtype=self.weight_dtype)
-            text_encoder.to(dtype=self.weight_dtype)
-
-            pipeline = VlpnStableDiffusion(
-                text_encoder=text_encoder,
-                vae=self.vae,
-                unet=self.unet,
-                tokenizer=self.tokenizer,
-                scheduler=self.scheduler,
-            ).to(self.accelerator.device)
-            pipeline.set_progress_bar_config(dynamic_ncols=True)
-
-            super().save_samples(pipeline, step)
-
-            unet.to(dtype=orig_unet_dtype)
-            text_encoder.to(dtype=orig_text_encoder_dtype)
-
-        del text_encoder
-        del pipeline
-
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
+            super().save_samples(step)
 
 
 def main():
@@ -806,8 +762,8 @@ def main():
         args.seed,
     )
 
-    checkpointer = Checkpointer(
-        weight_dtype=weight_dtype,
+    checkpointer = TextualInversionCheckpointer(
+        dtype=weight_dtype,
         train_dataloader=train_dataloader,
         val_dataloader=val_dataloader,
         accelerator=accelerator,
@@ -816,7 +772,7 @@ def main():
         tokenizer=tokenizer,
         text_encoder=text_encoder,
         ema_embeddings=ema_embeddings,
-        scheduler=sample_scheduler,
+        sample_scheduler=sample_scheduler,
         placeholder_tokens=args.placeholder_tokens,
         placeholder_token_ids=placeholder_token_ids,
         output_dir=output_dir,
diff --git a/trainer/base.py b/trainer/base.py
new file mode 100644
index 0000000..e700dd6
--- /dev/null
+++ b/trainer/base.py
@@ -0,0 +1,544 @@
+from pathlib import Path
+import math
+from contextlib import contextmanager
+from typing import Type, Optional
+import itertools
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+
+from accelerate import Accelerator
+from transformers import CLIPTextModel
+from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel, DPMSolverMultistepScheduler
+
+from tqdm.auto import tqdm
+from PIL import Image
+
+from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
+from models.clip.tokenizer import MultiCLIPTokenizer
+from models.clip.util import get_extended_embeddings
+from training.util import AverageMeter
+
+
+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,
+        accelerator: Accelerator,
+        vae: AutoencoderKL,
+        unet: UNet2DConditionModel,
+        text_encoder: CLIPTextModel,
+        tokenizer: MultiCLIPTokenizer,
+        sample_scheduler,
+        dtype,
+        train_dataloader: DataLoader,
+        val_dataloader: DataLoader,
+        output_dir: Path,
+        sample_steps: int = 20,
+        sample_guidance_scale: float = 7.5,
+        sample_image_size: int = 768,
+        sample_batches: int = 1,
+        sample_batch_size: int = 1,
+        seed: Optional[int] = None,
+        *args,
+        **kwargs,
+    ):
+        self.accelerator = accelerator
+        self.vae = vae
+        self.unet = unet
+        self.text_encoder = text_encoder
+        self.tokenizer = tokenizer
+        self.sample_scheduler = sample_scheduler
+        self.dtype = dtype
+        self.train_dataloader = train_dataloader
+        self.val_dataloader = val_dataloader
+        self.output_dir = output_dir
+        self.sample_steps = sample_steps
+        self.sample_guidance_scale = sample_guidance_scale
+        self.sample_image_size = sample_image_size
+        self.sample_batches = sample_batches
+        self.sample_batch_size = sample_batch_size
+        self.seed = seed if seed is not None else torch.random.seed()
+
+    @torch.no_grad()
+    def checkpoint(self, step: int, postfix: str):
+        pass
+
+    @torch.inference_mode()
+    def save_samples(self, step: int):
+        print(f"Saving samples for step {step}...")
+
+        samples_path = self.output_dir.joinpath("samples")
+
+        grid_cols = min(self.sample_batch_size, 4)
+        grid_rows = (self.sample_batches * self.sample_batch_size) // grid_cols
+
+        unet = self.accelerator.unwrap_model(self.unet)
+        text_encoder = self.accelerator.unwrap_model(self.text_encoder)
+
+        orig_unet_dtype = unet.dtype
+        orig_text_encoder_dtype = text_encoder.dtype
+
+        unet.to(dtype=self.dtype)
+        text_encoder.to(dtype=self.dtype)
+
+        pipeline = VlpnStableDiffusion(
+            text_encoder=text_encoder,
+            vae=self.vae,
+            unet=self.unet,
+            tokenizer=self.tokenizer,
+            scheduler=self.sample_scheduler,
+        ).to(self.accelerator.device)
+        pipeline.set_progress_bar_config(dynamic_ncols=True)
+
+        generator = torch.Generator(device=self.accelerator.device).manual_seed(self.seed)
+
+        for pool, data, gen in [
+            ("stable", self.val_dataloader, generator),
+            ("val", self.val_dataloader, None),
+            ("train", self.train_dataloader, None)
+        ]:
+            all_samples = []
+            file_path = samples_path.joinpath(pool, f"step_{step}.jpg")
+            file_path.parent.mkdir(parents=True, exist_ok=True)
+
+            batches = list(itertools.islice(itertools.cycle(data), self.sample_batch_size * self.sample_batches))
+            prompt_ids = [
+                prompt
+                for batch in batches
+                for prompt in batch["prompt_ids"]
+            ]
+            nprompt_ids = [
+                prompt
+                for batch in batches
+                for prompt in batch["nprompt_ids"]
+            ]
+
+            for i in range(self.sample_batches):
+                start = i * self.sample_batch_size
+                end = (i + 1) * self.sample_batch_size
+                prompt = prompt_ids[start:end]
+                nprompt = nprompt_ids[start:end]
+
+                samples = pipeline(
+                    prompt=prompt,
+                    negative_prompt=nprompt,
+                    height=self.sample_image_size,
+                    width=self.sample_image_size,
+                    generator=gen,
+                    guidance_scale=self.sample_guidance_scale,
+                    num_inference_steps=self.sample_steps,
+                    output_type='pil'
+                ).images
+
+                all_samples += samples
+
+            image_grid = make_grid(all_samples, grid_rows, grid_cols)
+            image_grid.save(file_path, quality=85)
+
+        unet.to(dtype=orig_unet_dtype)
+        text_encoder.to(dtype=orig_text_encoder_dtype)
+
+        del unet
+        del text_encoder
+        del generator
+        del pipeline
+
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+
+class TrainingStrategy():
+    def __init__(
+        self,
+        tokenizer: MultiCLIPTokenizer,
+        *args,
+        **kwargs,
+    ):
+        self.tokenizer = tokenizer
+        self.checkpointer = Checkpointer(tokenizer=tokenizer, *args, **kwargs)
+
+    @property
+    def main_model(self) -> nn.Module:
+        ...
+
+    @contextmanager
+    def on_train(self, epoch: int):
+        try:
+            self.tokenizer.train()
+            yield
+        finally:
+            pass
+
+    @contextmanager
+    def on_eval(self):
+        try:
+            self.tokenizer.eval()
+            yield
+        finally:
+            pass
+
+    def on_before_optimize(self, epoch: int):
+        ...
+
+    def on_after_optimize(self, lr: float):
+        ...
+
+    def on_log():
+        return {}
+
+
+def loss_step(
+    vae: AutoencoderKL,
+    unet: UNet2DConditionModel,
+    text_encoder: CLIPTextModel,
+    seed: int,
+    noise_scheduler,
+    prior_loss_weight: float,
+    step: int,
+    batch: dict,
+    eval: bool = False
+):
+    # Convert images to latent space
+    latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach()
+    latents = latents * 0.18215
+
+    generator = torch.Generator(device=latents.device).manual_seed(seed + step) if eval else None
+
+    # Sample noise that we'll add to the latents
+    noise = torch.randn(
+        latents.shape,
+        dtype=latents.dtype,
+        layout=latents.layout,
+        device=latents.device,
+        generator=generator
+    )
+    bsz = latents.shape[0]
+    # Sample a random timestep for each image
+    timesteps = torch.randint(
+        0,
+        noise_scheduler.config.num_train_timesteps,
+        (bsz,),
+        generator=generator,
+        device=latents.device,
+    )
+    timesteps = timesteps.long()
+
+    # Add noise to the latents according to the noise magnitude at each timestep
+    # (this is the forward diffusion process)
+    noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+    noisy_latents = noisy_latents.to(dtype=unet.dtype)
+
+    # Get the text embedding for conditioning
+    encoder_hidden_states = get_extended_embeddings(
+        text_encoder,
+        batch["input_ids"],
+        batch["attention_mask"]
+    )
+    encoder_hidden_states = encoder_hidden_states.to(dtype=unet.dtype)
+
+    # Predict the noise residual
+    model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+    # Get the target for loss depending on the prediction type
+    if noise_scheduler.config.prediction_type == "epsilon":
+        target = noise
+    elif noise_scheduler.config.prediction_type == "v_prediction":
+        target = noise_scheduler.get_velocity(latents, noise, timesteps)
+    else:
+        raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+
+    if batch["with_prior"].all():
+        # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
+        model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
+        target, target_prior = torch.chunk(target, 2, dim=0)
+
+        # Compute instance loss
+        loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+        # Compute prior loss
+        prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
+
+        # Add the prior loss to the instance loss.
+        loss = loss + prior_loss_weight * prior_loss
+    else:
+        loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+    acc = (model_pred == target).float().mean()
+
+    return loss, acc, bsz
+
+
+def train_loop(
+    strategy: TrainingStrategy,
+    accelerator: Accelerator,
+    vae: AutoencoderKL,
+    unet: UNet2DConditionModel,
+    text_encoder: CLIPTextModel,
+    train_dataloader: DataLoader,
+    val_dataloader: DataLoader,
+    seed: int,
+    optimizer: torch.optim.Optimizer,
+    lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
+    noise_scheduler,
+    prior_loss_weight: float = 1.0,
+    sample_frequency: int = 10,
+    checkpoint_frequency: int = 50,
+    global_step_offset: int = 0,
+    num_epochs: int = 100,
+):
+    num_training_steps_per_epoch = math.ceil(
+        len(train_dataloader) / accelerator.gradient_accumulation_steps
+    )
+    num_val_steps_per_epoch = len(val_dataloader)
+
+    num_training_steps = num_training_steps_per_epoch * num_epochs
+    num_val_steps = num_val_steps_per_epoch * num_epochs
+
+    global_step = 0
+
+    avg_loss = AverageMeter()
+    avg_acc = AverageMeter()
+
+    avg_loss_val = AverageMeter()
+    avg_acc_val = AverageMeter()
+
+    max_acc_val = 0.0
+
+    local_progress_bar = tqdm(
+        range(num_training_steps_per_epoch + num_val_steps_per_epoch),
+        disable=not accelerator.is_local_main_process,
+        dynamic_ncols=True
+    )
+    local_progress_bar.set_description(f"Epoch 1 / {num_epochs}")
+
+    global_progress_bar = tqdm(
+        range(num_training_steps + num_val_steps),
+        disable=not accelerator.is_local_main_process,
+        dynamic_ncols=True
+    )
+    global_progress_bar.set_description("Total progress")
+
+    loss_step_ = partial(
+        loss_step,
+        vae,
+        unet,
+        text_encoder,
+        seed,
+        noise_scheduler,
+        prior_loss_weight
+    )
+
+    try:
+        for epoch in range(num_epochs):
+            if accelerator.is_main_process:
+                if epoch % sample_frequency == 0 and epoch != 0:
+                    strategy.checkpointer.save_samples(global_step + global_step_offset)
+
+                if epoch % checkpoint_frequency == 0 and epoch != 0:
+                    strategy.checkpointer.checkpoint(global_step + global_step_offset, "training")
+
+            local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}")
+            local_progress_bar.reset()
+
+            strategy.main_model.train()
+
+            with strategy.on_train(epoch):
+                for step, batch in enumerate(train_dataloader):
+                    with accelerator.accumulate(strategy.main_model):
+                        loss, acc, bsz = loss_step_(step, batch)
+
+                        accelerator.backward(loss)
+
+                        strategy.on_before_optimize(epoch)
+
+                        optimizer.step()
+                        lr_scheduler.step()
+                        optimizer.zero_grad(set_to_none=True)
+
+                        avg_loss.update(loss.detach_(), bsz)
+                        avg_acc.update(acc.detach_(), bsz)
+
+                    # Checks if the accelerator has performed an optimization step behind the scenes
+                    if accelerator.sync_gradients:
+                        strategy.on_after_optimize(lr_scheduler.get_last_lr()[0])
+
+                        local_progress_bar.update(1)
+                        global_progress_bar.update(1)
+
+                        global_step += 1
+
+                    logs = {
+                        "train/loss": avg_loss.avg.item(),
+                        "train/acc": avg_acc.avg.item(),
+                        "train/cur_loss": loss.item(),
+                        "train/cur_acc": acc.item(),
+                        "lr": lr_scheduler.get_last_lr()[0],
+                    }
+                    logs.update(strategy.on_log())
+
+                    accelerator.log(logs, step=global_step)
+
+                    local_progress_bar.set_postfix(**logs)
+
+                    if global_step >= num_training_steps:
+                        break
+
+            accelerator.wait_for_everyone()
+
+            strategy.main_model.eval()
+
+            cur_loss_val = AverageMeter()
+            cur_acc_val = AverageMeter()
+
+            with torch.inference_mode(), strategy.on_eval():
+                for step, batch in enumerate(val_dataloader):
+                    loss, acc, bsz = loss_step_(step, batch, True)
+
+                    loss = loss.detach_()
+                    acc = acc.detach_()
+
+                    cur_loss_val.update(loss, bsz)
+                    cur_acc_val.update(acc, bsz)
+
+                    avg_loss_val.update(loss, bsz)
+                    avg_acc_val.update(acc, bsz)
+
+                    local_progress_bar.update(1)
+                    global_progress_bar.update(1)
+
+                    logs = {
+                        "val/loss": avg_loss_val.avg.item(),
+                        "val/acc": avg_acc_val.avg.item(),
+                        "val/cur_loss": loss.item(),
+                        "val/cur_acc": acc.item(),
+                    }
+                    local_progress_bar.set_postfix(**logs)
+
+            logs["val/cur_loss"] = cur_loss_val.avg.item()
+            logs["val/cur_acc"] = cur_acc_val.avg.item()
+
+            accelerator.log(logs, step=global_step)
+
+            local_progress_bar.clear()
+            global_progress_bar.clear()
+
+            if accelerator.is_main_process:
+                if avg_acc_val.avg.item() > max_acc_val:
+                    accelerator.print(
+                        f"Global step {global_step}: Validation accuracy reached new maximum: {max_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}")
+                    strategy.checkpointer.checkpoint(global_step + global_step_offset, "milestone")
+                    max_acc_val = avg_acc_val.avg.item()
+
+        # Create the pipeline using using the trained modules and save it.
+        if accelerator.is_main_process:
+            print("Finished!")
+            strategy.checkpointer.checkpoint(global_step + global_step_offset, "end")
+            strategy.checkpointer.save_samples(global_step + global_step_offset)
+            accelerator.end_training()
+
+    except KeyboardInterrupt:
+        if accelerator.is_main_process:
+            print("Interrupted")
+            strategy.checkpointer.checkpoint(global_step + global_step_offset, "end")
+            accelerator.end_training()
+
+
+class Trainer():
+    def __init__(
+        self,
+        accelerator: Accelerator,
+        unet: UNet2DConditionModel,
+        text_encoder: CLIPTextModel,
+        tokenizer: MultiCLIPTokenizer,
+        vae: AutoencoderKL,
+        noise_scheduler: DDPMScheduler,
+        sample_scheduler: DPMSolverMultistepScheduler,
+        train_dataloader: DataLoader,
+        val_dataloader: DataLoader,
+        dtype: torch.dtype,
+    ):
+        self.accelerator = accelerator
+        self.unet = unet
+        self.text_encoder = text_encoder
+        self.tokenizer = tokenizer
+        self.vae = vae
+        self.noise_scheduler = noise_scheduler
+        self.sample_scheduler = sample_scheduler
+        self.train_dataloader = train_dataloader
+        self.val_dataloader = val_dataloader
+        self.dtype = dtype
+
+    def __call__(
+        self,
+        strategy_class: Type[TrainingStrategy],
+        optimizer,
+        lr_scheduler,
+        num_train_epochs: int = 100,
+        sample_frequency: int = 20,
+        checkpoint_frequency: int = 50,
+        global_step_offset: int = 0,
+        prior_loss_weight: float = 0,
+        seed: Optional[int] = None,
+        **kwargs,
+    ):
+        unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = self.accelerator.prepare(
+            self.unet, self.text_encoder, optimizer, self.train_dataloader, self.val_dataloader, lr_scheduler
+        )
+
+        self.vae.to(self.accelerator.device, dtype=self.dtype)
+
+        for model in (unet, text_encoder, self.vae):
+            model.requires_grad_(False)
+            model.eval()
+
+        if seed is None:
+            seed = torch.random.seed()
+
+        strategy = strategy_class(
+            accelerator=self.accelerator,
+            vae=self.vae,
+            unet=unet,
+            text_encoder=text_encoder,
+            tokenizer=self.tokenizer,
+            sample_scheduler=self.sample_scheduler,
+            train_dataloader=train_dataloader,
+            val_dataloader=val_dataloader,
+            dtype=self.dtype,
+            seed=seed,
+            **kwargs
+        )
+
+        if self.accelerator.is_main_process:
+            self.accelerator.init_trackers("textual_inversion")
+
+        train_loop(
+            strategy=strategy,
+            accelerator=self.accelerator,
+            vae=self.vae,
+            unet=unet,
+            text_encoder=text_encoder,
+            train_dataloader=train_dataloader,
+            val_dataloader=val_dataloader,
+            seed=seed,
+            optimizer=optimizer,
+            lr_scheduler=lr_scheduler,
+            noise_scheduler=self.noise_scheduler,
+            prior_loss_weight=prior_loss_weight,
+            sample_frequency=sample_frequency,
+            checkpoint_frequency=checkpoint_frequency,
+            global_step_offset=global_step_offset,
+            num_epochs=num_train_epochs,
+        )
+
+        self.accelerator.free_memory()
diff --git a/trainer/dreambooth.py b/trainer/dreambooth.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/trainer/dreambooth.py
diff --git a/trainer/ti.py b/trainer/ti.py
new file mode 100644
index 0000000..15cf747
--- /dev/null
+++ b/trainer/ti.py
@@ -0,0 +1,164 @@
+from contextlib import contextmanager, nullcontext
+
+import torch
+
+from slugify import slugify
+
+from diffusers import UNet2DConditionModel
+from transformers import CLIPTextModel
+
+from trainer.base import TrainingStrategy, Checkpointer
+from training.util import EMAModel
+
+
+class TextualInversionCheckpointer(Checkpointer):
+    def __init__(
+        self,
+        ema_embeddings: EMAModel,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.ema_embeddings = ema_embeddings
+
+    @torch.no_grad()
+    def checkpoint(self, step, postfix):
+        print(f"Saving checkpoint for step {step}...")
+
+        checkpoints_path = self.output_dir.joinpath("checkpoints")
+        checkpoints_path.mkdir(parents=True, exist_ok=True)
+
+        text_encoder = self.accelerator.unwrap_model(self.text_encoder)
+
+        ema_context = self.ema_embeddings.apply_temporary(
+            text_encoder.text_model.embeddings.temp_token_embedding.parameters()
+        ) if self.ema_embeddings is not None else nullcontext()
+
+        with ema_context:
+            for (token, ids) in zip(self.placeholder_tokens, self.placeholder_token_ids):
+                text_encoder.text_model.embeddings.save_embed(
+                    ids,
+                    checkpoints_path.joinpath(f"{slugify(token)}_{step}_{postfix}.bin")
+                )
+
+    @torch.inference_mode()
+    def save_samples(self, step):
+        ema_context = self.ema_embeddings.apply_temporary(
+            self.text_encoder.text_model.embeddings.temp_token_embedding.parameters()
+        ) if self.ema_embeddings is not None else nullcontext()
+
+        with ema_context:
+            super().save_samples(step)
+
+
+class TextualInversionTrainingStrategy(TrainingStrategy):
+    def __init__(
+        self,
+        unet: UNet2DConditionModel,
+        text_encoder: CLIPTextModel,
+        placeholder_tokens: list[str],
+        placeholder_token_ids: list[list[int]],
+        learning_rate: float,
+        gradient_checkpointing: bool = False,
+        use_emb_decay: bool = False,
+        emb_decay_target: float = 0.4,
+        emb_decay_factor: float = 1,
+        emb_decay_start: float = 1e-4,
+        use_ema: bool = False,
+        ema_inv_gamma: float = 1.0,
+        ema_power: int = 1,
+        ema_max_decay: float = 0.9999,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(
+            unet=unet,
+            text_encoder=text_encoder,
+            *args,
+            **kwargs
+        )
+
+        self.text_encoder = text_encoder
+        self.unet = unet
+
+        self.placeholder_tokens = placeholder_tokens
+        self.placeholder_token_ids = placeholder_token_ids
+
+        self.gradient_checkpointing = gradient_checkpointing
+
+        self.learning_rate = learning_rate
+        self.use_emb_decay = use_emb_decay
+        self.emb_decay_target = emb_decay_target
+        self.emb_decay_factor = emb_decay_factor
+        self.emb_decay_start = emb_decay_start
+
+        self.text_encoder.text_model.embeddings.temp_token_embedding.requires_grad_(True)
+
+        self.ema_embeddings = None
+
+        if use_ema:
+            self.ema_embeddings = EMAModel(
+                self.text_encoder.text_model.embeddings.temp_token_embedding.parameters(),
+                inv_gamma=ema_inv_gamma,
+                power=ema_power,
+                max_value=ema_max_decay,
+            )
+
+        self.checkpointer = TextualInversionCheckpointer(
+            unet=unet,
+            text_encoder=text_encoder,
+            ema_embeddings=self.ema_embeddings,
+            *args,
+            **kwargs
+        )
+
+    @property
+    def main_model(self):
+        return self.text_encoder
+
+    @contextmanager
+    def on_train(self, epoch: int):
+        try:
+            if self.gradient_checkpointing:
+                self.unet.train()
+
+            with super().on_eval():
+                yield
+        finally:
+            pass
+
+    @contextmanager
+    def on_eval(self):
+        try:
+            if self.gradient_checkpointing:
+                self.unet.eval()
+
+            ema_context = self.ema_embeddings.apply_temporary(
+                self.text_encoder.text_model.embeddings.temp_token_embedding.parameters()
+            ) if self.ema_embeddings is not None else nullcontext()
+
+            with ema_context, super().on_eval():
+                yield
+        finally:
+            pass
+
+    @torch.no_grad()
+    def on_after_optimize(self, lr: float):
+        if self.use_emb_decay:
+            self.text_encoder.text_model.embeddings.normalize(
+                self.emb_decay_target,
+                min(1.0, max(0.0, self.emb_decay_factor * ((lr - self.emb_decay_start) / (self.learning_rate - self.emb_decay_start))))
+            )
+
+        if self.ema_embeddings is not None:
+            self.ema_embeddings.step(self.text_encoder.text_model.embeddings.temp_token_embedding.parameters())
+
+    def on_log(self):
+        log = super().on_log()
+        added = {}
+
+        if self.ema_embeddings is not None:
+            added = {"ema_decay": self.ema_embeddings.decay}
+
+        return log.update(added)
diff --git a/training/common.py b/training/functional.py
index 5d1e3f9..2d81eca 100644
--- a/training/common.py
+++ b/training/functional.py
@@ -16,19 +16,14 @@ from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
 from models.clip.embeddings import ManagedCLIPTextEmbeddings, patch_managed_embeddings
 from models.clip.util import get_extended_embeddings
 from models.clip.tokenizer import MultiCLIPTokenizer
-from training.util import AverageMeter, CheckpointerBase
+from training.util import AverageMeter
+from trainer.base import Checkpointer
 
 
-def noop(*args, **kwards):
-    pass
-
-
-def noop_ctx(*args, **kwards):
-    return nullcontext()
-
-
-def noop_on_log():
-    return {}
+def const(result=None):
+    def fn(*args, **kwargs):
+        return result
+    return fn
 
 
 def generate_class_images(
@@ -210,7 +205,7 @@ def train_loop(
     optimizer: torch.optim.Optimizer,
     lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
     model: torch.nn.Module,
-    checkpointer: CheckpointerBase,
+    checkpointer: Checkpointer,
     train_dataloader: DataLoader,
     val_dataloader: DataLoader,
     loss_step: Union[Callable[[int, Any], Tuple[Any, Any, int]], Callable[[int, Any, bool], Tuple[Any, Any, int]]],
@@ -218,11 +213,11 @@ def train_loop(
     checkpoint_frequency: int = 50,
     global_step_offset: int = 0,
     num_epochs: int = 100,
-    on_log: Callable[[], dict[str, Any]] = noop_on_log,
-    on_train: Callable[[int], _GeneratorContextManager] = noop_ctx,
-    on_before_optimize: Callable[[int], None] = noop,
-    on_after_optimize: Callable[[float], None] = noop,
-    on_eval: Callable[[], _GeneratorContextManager] = noop_ctx
+    on_log: Callable[[], dict[str, Any]] = const({}),
+    on_train: Callable[[int], _GeneratorContextManager] = const(nullcontext()),
+    on_before_optimize: Callable[[int], None] = const(),
+    on_after_optimize: Callable[[float], None] = const(),
+    on_eval: Callable[[], _GeneratorContextManager] = const(nullcontext())
 ):
     num_training_steps_per_epoch = math.ceil(len(train_dataloader) / accelerator.gradient_accumulation_steps)
     num_val_steps_per_epoch = len(val_dataloader)
diff --git a/training/lora.py b/training/lora.py
deleted file mode 100644
index 3857d78..0000000
--- a/training/lora.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import torch
-import torch.nn as nn
-
-from diffusers import ModelMixin, ConfigMixin
-from diffusers.configuration_utils import register_to_config
-from diffusers.models.cross_attention import CrossAttention
-from diffusers.utils.import_utils import is_xformers_available
-
-
-if is_xformers_available():
-    import xformers
-    import xformers.ops
-else:
-    xformers = None
-
-
-class LoRALinearLayer(nn.Module):
-    def __init__(self, in_features, out_features, rank=4):
-        super().__init__()
-
-        if rank > min(in_features, out_features):
-            raise ValueError(
-                f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}"
-            )
-
-        self.lora_down = nn.Linear(in_features, rank, bias=False)
-        self.lora_up = nn.Linear(rank, out_features, bias=False)
-        self.scale = 1.0
-
-        nn.init.normal_(self.lora_down.weight, std=1 / rank)
-        nn.init.zeros_(self.lora_up.weight)
-
-    def forward(self, hidden_states):
-        down_hidden_states = self.lora_down(hidden_states)
-        up_hidden_states = self.lora_up(down_hidden_states)
-
-        return up_hidden_states
-
-
-class LoRACrossAttnProcessor(nn.Module):
-    def __init__(self, hidden_size, cross_attention_dim=None, rank=4):
-        super().__init__()
-
-        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size)
-        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
-        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
-        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size)
-
-    def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0):
-        batch_size, sequence_length, _ = hidden_states.shape
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
-
-        query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
-        query = attn.head_to_batch_dim(query)
-
-        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
-
-        key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
-
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        return hidden_states
-
-
-class LoRAXFormersCrossAttnProcessor(nn.Module):
-    def __init__(self, hidden_size, cross_attention_dim, rank=4):
-        super().__init__()
-
-        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size)
-        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
-        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
-        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size)
-
-    def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0):
-        batch_size, sequence_length, _ = hidden_states.shape
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
-
-        query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
-        query = attn.head_to_batch_dim(query).contiguous()
-
-        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
-
-        key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
-
-        key = attn.head_to_batch_dim(key).contiguous()
-        value = attn.head_to_batch_dim(value).contiguous()
-
-        hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        return hidden_states
diff --git a/training/util.py b/training/util.py
index 781cf04..a292edd 100644
--- a/training/util.py
+++ b/training/util.py
@@ -1,12 +1,40 @@
 from pathlib import Path
 import json
 import copy
-import itertools
-from typing import Iterable, Optional
+from typing import Iterable, Union
 from contextlib import contextmanager
 
 import torch
-from PIL import Image
+
+from transformers import CLIPTextModel
+from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel, DPMSolverMultistepScheduler
+
+from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
+from models.clip.tokenizer import MultiCLIPTokenizer
+from models.clip.embeddings import ManagedCLIPTextEmbeddings, patch_managed_embeddings
+
+
+class TrainingStrategy():
+    @property
+    def main_model(self) -> torch.nn.Module:
+        ...
+
+    @contextmanager
+    def on_train(self, epoch: int):
+        yield
+
+    @contextmanager
+    def on_eval(self):
+        yield
+
+    def on_before_optimize(self, epoch: int):
+        ...
+
+    def on_after_optimize(self, lr: float):
+        ...
+
+    def on_log():
+        return {}
 
 
 def save_args(basepath: Path, args, extra={}):
@@ -16,113 +44,107 @@ def save_args(basepath: Path, args, extra={}):
         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
+def generate_class_images(
+    accelerator,
+    text_encoder,
+    vae,
+    unet,
+    tokenizer,
+    scheduler,
+    data_train,
+    sample_batch_size,
+    sample_image_size,
+    sample_steps
+):
+    missing_data = [item for item in data_train if not item.class_image_path.exists()]
 
+    if len(missing_data) == 0:
+        return
 
-class AverageMeter:
-    def __init__(self, name=None):
-        self.name = name
-        self.reset()
+    batched_data = [
+        missing_data[i:i+sample_batch_size]
+        for i in range(0, len(missing_data), sample_batch_size)
+    ]
 
-    def reset(self):
-        self.sum = self.count = self.avg = 0
+    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)
 
-    def update(self, val, n=1):
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
+    with torch.inference_mode():
+        for batch in batched_data:
+            image_name = [item.class_image_path for item in batch]
+            prompt = [item.cprompt for item in batch]
+            nprompt = [item.nprompt for item in batch]
 
+            images = pipeline(
+                prompt=prompt,
+                negative_prompt=nprompt,
+                height=sample_image_size,
+                width=sample_image_size,
+                num_inference_steps=sample_steps
+            ).images
 
-class CheckpointerBase:
-    def __init__(
-        self,
-        train_dataloader,
-        val_dataloader,
-        output_dir: Path,
-        sample_steps: int = 20,
-        sample_guidance_scale: float = 7.5,
-        sample_image_size: int = 768,
-        sample_batches: int = 1,
-        sample_batch_size: int = 1,
-        seed: Optional[int] = None
-    ):
-        self.train_dataloader = train_dataloader
-        self.val_dataloader = val_dataloader
-        self.output_dir = output_dir
-        self.sample_image_size = sample_image_size
-        self.sample_steps = sample_steps
-        self.sample_guidance_scale = sample_guidance_scale
-        self.sample_batches = sample_batches
-        self.sample_batch_size = sample_batch_size
-        self.seed = seed if seed is not None else torch.random.seed()
+            for i, image in enumerate(images):
+                image.save(image_name[i])
 
-    @torch.no_grad()
-    def checkpoint(self, step: int, postfix: str):
-        pass
+    del pipeline
 
-    @torch.inference_mode()
-    def save_samples(self, pipeline, step: int):
-        samples_path = Path(self.output_dir).joinpath("samples")
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
 
-        generator = torch.Generator(device=pipeline.device).manual_seed(self.seed)
 
-        grid_cols = min(self.sample_batch_size, 4)
-        grid_rows = (self.sample_batches * self.sample_batch_size) // grid_cols
+def get_models(pretrained_model_name_or_path: str):
+    tokenizer = MultiCLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder='tokenizer')
+    text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder='text_encoder')
+    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder='vae')
+    unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder='unet')
+    noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder='scheduler')
+    sample_scheduler = DPMSolverMultistepScheduler.from_pretrained(
+        pretrained_model_name_or_path, subfolder='scheduler')
 
-        for pool, data, gen in [
-            ("stable", self.val_dataloader, generator),
-            ("val", self.val_dataloader, None),
-            ("train", self.train_dataloader, None)
-        ]:
-            all_samples = []
-            file_path = samples_path.joinpath(pool, f"step_{step}.jpg")
-            file_path.parent.mkdir(parents=True, exist_ok=True)
+    embeddings = patch_managed_embeddings(text_encoder)
 
-            batches = list(itertools.islice(itertools.cycle(data), self.sample_batch_size * self.sample_batches))
-            prompt_ids = [
-                prompt
-                for batch in batches
-                for prompt in batch["prompt_ids"]
-            ]
-            nprompt_ids = [
-                prompt
-                for batch in batches
-                for prompt in batch["nprompt_ids"]
-            ]
+    return tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings
 
-            for i in range(self.sample_batches):
-                start = i * self.sample_batch_size
-                end = (i + 1) * self.sample_batch_size
-                prompt = prompt_ids[start:end]
-                nprompt = nprompt_ids[start:end]
 
-                samples = pipeline(
-                    prompt=prompt,
-                    negative_prompt=nprompt,
-                    height=self.sample_image_size,
-                    width=self.sample_image_size,
-                    generator=gen,
-                    guidance_scale=self.sample_guidance_scale,
-                    num_inference_steps=self.sample_steps,
-                    output_type='pil'
-                ).images
+def add_placeholder_tokens(
+    tokenizer: MultiCLIPTokenizer,
+    embeddings: ManagedCLIPTextEmbeddings,
+    placeholder_tokens: list[str],
+    initializer_tokens: list[str],
+    num_vectors: Union[list[int], int]
+):
+    initializer_token_ids = [
+        tokenizer.encode(token, add_special_tokens=False)
+        for token in initializer_tokens
+    ]
+    placeholder_token_ids = tokenizer.add_multi_tokens(placeholder_tokens, num_vectors)
 
-                all_samples += samples
+    embeddings.resize(len(tokenizer))
 
-                del samples
+    for (placeholder_token_id, initializer_token_id) in zip(placeholder_token_ids, initializer_token_ids):
+        embeddings.add_embed(placeholder_token_id, initializer_token_id)
 
-            image_grid = make_grid(all_samples, grid_rows, grid_cols)
-            image_grid.save(file_path, quality=85)
+    return placeholder_token_ids, initializer_token_ids
 
-            del all_samples
-            del image_grid
 
-        del generator
+class AverageMeter:
+    def __init__(self, name=None):
+        self.name = name
+        self.reset()
+
+    def reset(self):
+        self.sum = self.count = self.avg = 0
+
+    def update(self, val, n=1):
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
 
 
 # Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14