Reverted modularization mostly

3 files changed, 0 insertions, 284 deletions

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
-    )