1 files changed, 168 insertions, 0 deletions

diff --git a/trainer_old/ti.py b/trainer_old/ti.py
new file mode 100644
index 0000000..66393af
--- /dev/null
+++ b/trainer_old/ti.py
@@ -0,0 +1,168 @@
+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,
+        placeholder_tokens: list[str],
+        placeholder_token_ids: list[list[int]],
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.ema_embeddings = ema_embeddings
+        self.placeholder_tokens = placeholder_tokens
+        self.placeholder_token_ids = placeholder_token_ids
+
+    @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.no_grad()
+    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)