5 files changed, 70 insertions, 531 deletions

diff --git a/training/common.py b/training/common.py
index 73ce814..b6964a3 100644
--- a/training/common.py
+++ b/training/common.py
@@ -1,52 +1,24 @@
 import math
-from pathlib import Path
 from contextlib import _GeneratorContextManager, nullcontext
-from typing import Callable, Any, Tuple, Union, Literal, Optional, NamedTuple
-import datetime
-import logging
+from typing import Callable, Any, Tuple, Union
 
 import torch
 import torch.nn.functional as F
 from torch.utils.data import DataLoader
 
 from accelerate import Accelerator
-from accelerate.utils import LoggerType, set_seed
 from transformers import CLIPTextModel
 from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel, DPMSolverMultistepScheduler
-from diffusers.optimization import get_scheduler as get_scheduler_, get_cosine_with_hard_restarts_schedule_with_warmup
 
 from tqdm.auto import tqdm
-from slugify import slugify
 
-from data.csv import VlpnDataModule, VlpnDataItem
-from util import load_embeddings_from_dir
 from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
-from models.clip.embeddings import patch_managed_embeddings
+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.optimization import get_one_cycle_schedule
 from training.util import AverageMeter, CheckpointerBase
 
 
-class TrainingSetup(NamedTuple):
-    accelerator: Accelerator
-    tokenizer: MultiCLIPTokenizer
-    text_encoder: CLIPTextModel
-    vae: AutoencoderKL
-    unet: UNet2DConditionModel
-    noise_scheduler: DDPMScheduler
-    checkpoint_scheduler: DPMSolverMultistepScheduler
-    optimizer_class: Callable
-    learning_rate: float
-    weight_dtype: torch.dtype
-    output_dir: Path
-    seed: int
-    train_dataloader: DataLoader
-    val_dataloader: DataLoader
-    placeholder_token: list[str]
-    placeholder_token_ids: list[list[int]]
-
-
 def noop(*args, **kwards):
     pass
 
@@ -59,57 +31,6 @@ def noop_on_log():
     return {}
 
 
-def get_scheduler(
-    id: str,
-    optimizer: torch.optim.Optimizer,
-    num_training_steps_per_epoch: int,
-    gradient_accumulation_steps: int,
-    min_lr: float = 0.04,
-    warmup_func: str = "cos",
-    annealing_func: str = "cos",
-    warmup_exp: int = 1,
-    annealing_exp: int = 1,
-    cycles: int = 1,
-    train_epochs: int = 100,
-    warmup_epochs: int = 10,
-):
-    num_training_steps_per_epoch = math.ceil(
-        num_training_steps_per_epoch / gradient_accumulation_steps
-    ) * gradient_accumulation_steps
-    num_training_steps = train_epochs * num_training_steps_per_epoch
-    num_warmup_steps = warmup_epochs * num_training_steps_per_epoch
-
-    if id == "one_cycle":
-        lr_scheduler = get_one_cycle_schedule(
-            optimizer=optimizer,
-            num_training_steps=num_training_steps,
-            warmup=warmup_func,
-            annealing=annealing_func,
-            warmup_exp=warmup_exp,
-            annealing_exp=annealing_exp,
-            min_lr=min_lr,
-        )
-    elif id == "cosine_with_restarts":
-        if cycles is None:
-            cycles = math.ceil(math.sqrt(((num_training_steps - num_warmup_steps) / num_training_steps_per_epoch)))
-
-        lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
-            optimizer=optimizer,
-            num_warmup_steps=num_warmup_steps,
-            num_training_steps=num_training_steps,
-            num_cycles=cycles,
-        )
-    else:
-        lr_scheduler = get_scheduler_(
-            id,
-            optimizer=optimizer,
-            num_warmup_steps=num_warmup_steps,
-            num_training_steps=num_training_steps,
-        )
-
-    return lr_scheduler
-
-
 def generate_class_images(
     accelerator,
     text_encoder,
@@ -162,194 +83,43 @@ def generate_class_images(
             torch.cuda.empty_cache()
 
 
-def train_setup(
-    output_dir: str,
-    project: str,
-    pretrained_model_name_or_path: str,
-    learning_rate: float,
-    data_file: str,
-    gradient_accumulation_steps: int = 1,
-    mixed_precision: Literal["no", "fp16", "bf16"] = "no",
-    seed: Optional[int] = None,
-    vector_shuffle: Union[bool, Literal["all", "trailing", "leading", "between", "off"]] = "auto",
-    vector_dropout: float = 0.1,
-    gradient_checkpointing: bool = True,
-    embeddings_dir: Optional[str] = None,
-    placeholder_token: list[str] = [],
-    initializer_token: list[str] = [],
-    num_vectors: int = 1,
-    scale_lr: bool = False,
-    use_8bit_adam: bool = False,
-    train_batch_size: int = 1,
-    class_image_dir: Optional[str] = None,
-    num_class_images: int = 0,
-    resolution: int = 768,
-    num_buckets: int = 0,
-    progressive_buckets: bool = False,
-    bucket_step_size: int = 64,
-    bucket_max_pixels: Optional[int] = None,
-    tag_dropout: float = 0.1,
-    tag_shuffle: bool = True,
-    data_template: str = "template",
-    valid_set_size: Optional[int] = None,
-    valid_set_repeat: int = 1,
-    data_filter: Optional[Callable[[VlpnDataItem], bool]] = None,
-    sample_batch_size: int = 1,
-    sample_image_size: int = 768,
-    sample_steps: int = 20,
-) -> TrainingSetup:
-    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-    output_dir = Path(output_dir).joinpath(slugify(project), now)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    accelerator = Accelerator(
-        log_with=LoggerType.TENSORBOARD,
-        logging_dir=f"{output_dir}",
-        gradient_accumulation_steps=gradient_accumulation_steps,
-        mixed_precision=mixed_precision
-    )
-
-    logging.basicConfig(filename=output_dir.joinpath("log.txt"), level=logging.DEBUG)
-
-    seed = seed or (torch.random.seed() >> 32)
-    set_seed(seed)
-
-    # Load the tokenizer and add the placeholder token as a additional special token
+def get_models(pretrained_model_name_or_path: str):
     tokenizer = MultiCLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder='tokenizer')
-    tokenizer.set_use_vector_shuffle(vector_shuffle)
-    tokenizer.set_dropout(vector_dropout)
-
-    # Load models and create wrapper for stable diffusion
     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')
-    checkpoint_scheduler = DPMSolverMultistepScheduler.from_pretrained(
+    sample_scheduler = DPMSolverMultistepScheduler.from_pretrained(
         pretrained_model_name_or_path, subfolder='scheduler')
 
     vae.enable_slicing()
     vae.set_use_memory_efficient_attention_xformers(True)
     unet.set_use_memory_efficient_attention_xformers(True)
 
-    if gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-        text_encoder.gradient_checkpointing_enable()
-
     embeddings = patch_managed_embeddings(text_encoder)
 
-    if embeddings_dir is not None:
-        embeddings_dir = Path(embeddings_dir)
-        if not embeddings_dir.exists() or not embeddings_dir.is_dir():
-            raise ValueError("--embeddings_dir must point to an existing directory")
+    return tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings
 
-        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))}")
 
-    # Convert the initializer_token, placeholder_token to ids
+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_token
+        for token in initializer_tokens
     ]
+    placeholder_token_ids = tokenizer.add_multi_tokens(placeholder_tokens, num_vectors)
 
-    placeholder_token_ids = tokenizer.add_multi_tokens(placeholder_token, num_vectors)
     embeddings.resize(len(tokenizer))
 
-    for (new_id, init_ids) in zip(placeholder_token_ids, initializer_token_ids):
-        embeddings.add_embed(new_id, init_ids)
-
-    init_ratios = [
-        f"{len(init_ids)} / {len(new_id)}"
-        for new_id, init_ids in zip(placeholder_token_ids, initializer_token_ids)
-    ]
-
-    print(f"Added {len(placeholder_token_ids)} new tokens: {list(zip(placeholder_token, placeholder_token_ids, init_ratios))}")
+    for (placeholder_token_id, initializer_token_id) in zip(placeholder_token_ids, initializer_token_ids):
+        embeddings.add_embed(placeholder_token_id, initializer_token_id)
 
-    vae.requires_grad_(False)
-    unet.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-
-    if scale_lr:
-        learning_rate = (
-            learning_rate * gradient_accumulation_steps *
-            train_batch_size * accelerator.num_processes
-        )
-
-    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
-    if 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
-
-    weight_dtype = torch.float32
-    if mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    datamodule = VlpnDataModule(
-        data_file=data_file,
-        batch_size=train_batch_size,
-        tokenizer=tokenizer,
-        class_subdir=class_image_dir,
-        num_class_images=num_class_images,
-        size=resolution,
-        num_buckets=num_buckets,
-        progressive_buckets=progressive_buckets,
-        bucket_step_size=bucket_step_size,
-        bucket_max_pixels=bucket_max_pixels,
-        dropout=tag_dropout,
-        shuffle=tag_shuffle,
-        template_key=data_template,
-        valid_set_size=valid_set_size,
-        valid_set_repeat=valid_set_repeat,
-        seed=seed,
-        filter=data_filter,
-        dtype=weight_dtype
-    )
-    datamodule.setup()
-
-    train_dataloader = datamodule.train_dataloader
-    val_dataloader = datamodule.val_dataloader
-
-    train_dataloader, val_dataloader = accelerator.prepare(train_dataloader, val_dataloader)
-
-    if num_class_images != 0:
-        generate_class_images(
-            accelerator,
-            text_encoder,
-            vae,
-            unet,
-            tokenizer,
-            checkpoint_scheduler,
-            datamodule.data_train,
-            sample_batch_size,
-            sample_image_size,
-            sample_steps
-        )
-
-    return TrainingSetup(
-        accelerator=accelerator,
-        tokenizer=tokenizer,
-        text_encoder=text_encoder,
-        vae=vae,
-        unet=unet,
-        noise_scheduler=noise_scheduler,
-        checkpoint_scheduler=checkpoint_scheduler,
-        optimizer_class=optimizer_class,
-        learning_rate=learning_rate,
-        output_dir=output_dir,
-        weight_dtype=weight_dtype,
-        seed=seed,
-        train_dataloader=train_dataloader,
-        val_dataloader=val_dataloader,
-        placeholder_token=placeholder_token,
-        placeholder_token_ids=placeholder_token_ids
-    )
+    return placeholder_token_ids
 
 
 def loss_step(
diff --git a/training/modules/dreambooth.py b/training/modules/dreambooth.py
deleted file mode 100644
index e69de29..0000000
--- a/training/modules/dreambooth.py
+++ /dev/null
diff --git a/training/modules/lora.py b/training/modules/lora.py
deleted file mode 100644
index e69de29..0000000
--- a/training/modules/lora.py
+++ /dev/null
diff --git a/training/modules/ti.py b/training/modules/ti.py
deleted file mode 100644
index 2db6f88..0000000
--- a/training/modules/ti.py
+++ /dev/null
@@ -1,284 +0,0 @@
-from typing import Literal
-from functools import partial
-from contextlib import contextmanager, nullcontext
-
-import torch
-
-from slugify import slugify
-
-from accelerate import Accelerator
-from transformers import CLIPTextModel
-from diffusers import AutoencoderKL, UNet2DConditionModel
-
-from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
-from models.clip.tokenizer import MultiCLIPTokenizer
-
-from training.common import TrainingSetup, get_scheduler, train_loop, loss_step
-from training.util import EMAModel, CheckpointerBase
-
-
-class Checkpointer(CheckpointerBase):
-    def __init__(
-        self,
-        accelerator: Accelerator,
-        vae: AutoencoderKL,
-        unet: UNet2DConditionModel,
-        tokenizer: MultiCLIPTokenizer,
-        text_encoder: CLIPTextModel,
-        ema_embeddings: EMAModel,
-        weight_dtype: torch.dtype,
-        scheduler,
-        placeholder_token,
-        placeholder_token_ids,
-        *args,
-        **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_token = placeholder_token
-        self.placeholder_token_ids = placeholder_token_ids
-
-    @torch.no_grad()
-    def checkpoint(self, step, postfix):
-        print("Saving checkpoint for step %d..." % step)
-
-        checkpoints_path = self.output_dir.joinpath("checkpoints")
-        checkpoints_path.mkdir(parents=True, exist_ok=True)
-
-        text_encoder = self.accelerator.unwrap_model(self.text_encoder)
-
-        ema_context = nullcontext()
-        if self.ema_embeddings is not None:
-            ema_context = self.ema_embeddings.apply_temporary(
-                text_encoder.text_model.embeddings.temp_token_embedding.parameters())
-
-        with ema_context:
-            for (token, ids) in zip(self.placeholder_token, self.placeholder_token_ids):
-                text_encoder.text_model.embeddings.save_embed(
-                    ids,
-                    checkpoints_path.joinpath(f"{slugify(token)}_{step}_{postfix}.bin")
-                )
-
-        del text_encoder
-
-    @torch.no_grad()
-    def save_samples(self, step, num_inference_steps, guidance_scale=7.5, eta=0.0):
-        text_encoder = self.accelerator.unwrap_model(self.text_encoder)
-
-        ema_context = nullcontext()
-        if self.ema_embeddings is not None:
-            ema_context = self.ema_embeddings.apply_temporary(
-                text_encoder.text_model.embeddings.temp_token_embedding.parameters())
-
-        with ema_context:
-            orig_dtype = text_encoder.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, num_inference_steps, guidance_scale, eta)
-
-            text_encoder.to(dtype=orig_dtype)
-
-        del text_encoder
-        del pipeline
-
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-
-def train_ti(
-    setup: TrainingSetup,
-    num_train_epochs: int = 100,
-    num_class_images: int = 0,
-    prior_loss_weight: float = 1.0,
-    use_ema: bool = False,
-    ema_inv_gamma: float = 1.0,
-    ema_power: float = 4/5,
-    ema_max_decay: float = .9999,
-    adam_beta1: float = 0.9,
-    adam_beta2: float = 0.999,
-    adam_weight_decay: float = 0,
-    adam_epsilon: float = 1e-08,
-    adam_amsgrad: bool = False,
-    lr_scheduler: Literal[
-        "linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup", "one_cycle"
-    ] = "one_cycle",
-    lr_min_lr: float = 0.04,
-    lr_warmup_func: Literal["linear", "cos"] = "cos",
-    lr_annealing_func: Literal["linear", "half_cos", "cos"] = "cos",
-    lr_warmup_exp: int = 1,
-    lr_annealing_exp: int = 1,
-    lr_cycles: int = 1,
-    lr_warmup_epochs: int = 10,
-    emb_decay_target: float = 0.4,
-    emb_decay_factor: float = 1,
-    emb_decay_start: float = 1e-4,
-    sample_image_size: int = 768,
-    sample_batch_size: int = 1,
-    sample_batches: int = 1,
-    sample_frequency: int = 10,
-    sample_steps: int = 20,
-    checkpoint_frequency: int = 50,
-    global_step_offset: int = 0,
-):
-    if use_ema:
-        ema_embeddings = EMAModel(
-            setup.text_encoder.text_model.embeddings.temp_token_embedding.parameters(),
-            inv_gamma=ema_inv_gamma,
-            power=ema_power,
-            max_value=ema_max_decay,
-        )
-    else:
-        ema_embeddings = None
-
-    setup.text_encoder.requires_grad_(True)
-    setup.text_encoder.text_model.encoder.requires_grad_(False)
-    setup.text_encoder.text_model.final_layer_norm.requires_grad_(False)
-    setup.text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
-    setup.text_encoder.text_model.embeddings.token_embedding.requires_grad_(False)
-
-    # Initialize the optimizer
-    optimizer = setup.optimizer_class(
-        setup.text_encoder.text_model.embeddings.temp_token_embedding.parameters(),
-        lr=setup.learning_rate,
-        betas=(adam_beta1, adam_beta2),
-        weight_decay=adam_weight_decay,
-        eps=adam_epsilon,
-        amsgrad=adam_amsgrad,
-    )
-
-    lr_scheduler = get_scheduler(
-        lr_scheduler,
-        optimizer=optimizer,
-        min_lr=lr_min_lr,
-        warmup_func=lr_warmup_func,
-        annealing_func=lr_annealing_func,
-        warmup_exp=lr_warmup_exp,
-        annealing_exp=lr_annealing_exp,
-        cycles=lr_cycles,
-        train_epochs=num_train_epochs,
-        warmup_epochs=lr_warmup_epochs,
-        num_training_steps_per_epoch=len(setup.train_dataloader),
-        gradient_accumulation_steps=setup.accelerator.gradient_accumulation_steps
-    )
-
-    text_encoder, optimizer, lr_scheduler = setup.accelerator.prepare(
-        setup.text_encoder, optimizer, lr_scheduler
-    )
-
-    # Move vae and unet to device
-    setup.vae.to(setup.accelerator.device, dtype=setup.weight_dtype)
-    setup.unet.to(setup.accelerator.device, dtype=setup.weight_dtype)
-
-    if use_ema:
-        ema_embeddings.to(setup.accelerator.device)
-
-    setup.unet.train()
-
-    @contextmanager
-    def on_train(epoch: int):
-        try:
-            setup.tokenizer.train()
-            yield
-        finally:
-            pass
-
-    @contextmanager
-    def on_eval():
-        try:
-            setup.tokenizer.eval()
-
-            ema_context = nullcontext()
-            if use_ema:
-                ema_context = ema_embeddings.apply_temporary(
-                    text_encoder.text_model.embeddings.temp_token_embedding.parameters())
-
-            with ema_context:
-                yield
-        finally:
-            pass
-
-    @torch.no_grad()
-    def on_after_optimize(lr: float):
-        text_encoder.text_model.embeddings.normalize(
-            emb_decay_target,
-            min(1.0, max(0.0, emb_decay_factor * ((lr - emb_decay_start) / (setup.learning_rate - emb_decay_start))))
-        )
-
-        if use_ema:
-            ema_embeddings.step(text_encoder.text_model.embeddings.temp_token_embedding.parameters())
-
-    def on_log():
-        if use_ema:
-            return {"ema_decay": ema_embeddings.decay}
-        return {}
-
-    loss_step_ = partial(
-        loss_step,
-        setup.vae,
-        setup.noise_scheduler,
-        setup.unet,
-        text_encoder,
-        num_class_images != 0,
-        prior_loss_weight,
-        setup.seed,
-    )
-
-    checkpointer = Checkpointer(
-        accelerator=setup.accelerator,
-        vae=setup.vae,
-        unet=setup.unet,
-        tokenizer=setup.tokenizer,
-        text_encoder=text_encoder,
-        ema_embeddings=ema_embeddings,
-        weight_dtype=setup.weight_dtype,
-        scheduler=setup.checkpoint_scheduler,
-        placeholder_token=setup.placeholder_token,
-        placeholder_token_ids=setup.placeholder_token_ids,
-        train_dataloader=setup.train_dataloader,
-        val_dataloader=setup.val_dataloader,
-        output_dir=setup.output_dir,
-        seed=setup.seed,
-        sample_image_size=sample_image_size,
-        sample_batch_size=sample_batch_size,
-        sample_batches=sample_batches
-    )
-
-    if setup.accelerator.is_main_process:
-        setup.accelerator.init_trackers("textual_inversion")
-
-    train_loop(
-        accelerator=setup.accelerator,
-        optimizer=optimizer,
-        lr_scheduler=lr_scheduler,
-        model=text_encoder,
-        checkpointer=checkpointer,
-        train_dataloader=setup.train_dataloader,
-        val_dataloader=setup.val_dataloader,
-        loss_step=loss_step_,
-        sample_frequency=sample_frequency,
-        sample_steps=sample_steps,
-        checkpoint_frequency=checkpoint_frequency,
-        global_step_offset=global_step_offset,
-        num_epochs=num_train_epochs,
-        on_log=on_log,
-        on_train=on_train,
-        on_after_optimize=on_after_optimize,
-        on_eval=on_eval
-    )
diff --git a/training/optimization.py b/training/optimization.py
index dd84f9c..5db7794 100644
--- a/training/optimization.py
+++ b/training/optimization.py
@@ -5,6 +5,8 @@ from functools import partial
 import torch
 from torch.optim.lr_scheduler import LambdaLR
 
+from diffusers.optimization import get_scheduler as get_scheduler_, get_cosine_with_hard_restarts_schedule_with_warmup
+
 
 class OneCyclePhase(NamedTuple):
     step_min: int
@@ -83,3 +85,54 @@ def get_one_cycle_schedule(
         return phase.min + phase.func((current_step - phase.step_min) / (phase.step_max - phase.step_min)) * (phase.max - phase.min)
 
     return LambdaLR(optimizer, lr_lambda, last_epoch)
+
+
+def get_scheduler(
+    id: str,
+    optimizer: torch.optim.Optimizer,
+    num_training_steps_per_epoch: int,
+    gradient_accumulation_steps: int,
+    min_lr: float = 0.04,
+    warmup_func: str = "cos",
+    annealing_func: str = "cos",
+    warmup_exp: int = 1,
+    annealing_exp: int = 1,
+    cycles: int = 1,
+    train_epochs: int = 100,
+    warmup_epochs: int = 10,
+):
+    num_training_steps_per_epoch = math.ceil(
+        num_training_steps_per_epoch / gradient_accumulation_steps
+    ) * gradient_accumulation_steps
+    num_training_steps = train_epochs * num_training_steps_per_epoch
+    num_warmup_steps = warmup_epochs * num_training_steps_per_epoch
+
+    if id == "one_cycle":
+        lr_scheduler = get_one_cycle_schedule(
+            optimizer=optimizer,
+            num_training_steps=num_training_steps,
+            warmup=warmup_func,
+            annealing=annealing_func,
+            warmup_exp=warmup_exp,
+            annealing_exp=annealing_exp,
+            min_lr=min_lr,
+        )
+    elif id == "cosine_with_restarts":
+        if cycles is None:
+            cycles = math.ceil(math.sqrt(((num_training_steps - num_warmup_steps) / num_training_steps_per_epoch)))
+
+        lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
+            optimizer=optimizer,
+            num_warmup_steps=num_warmup_steps,
+            num_training_steps=num_training_steps,
+            num_cycles=cycles,
+        )
+    else:
+        lr_scheduler = get_scheduler_(
+            id,
+            optimizer=optimizer,
+            num_warmup_steps=num_warmup_steps,
+            num_training_steps=num_training_steps,
+        )
+
+    return lr_scheduler