Update

1 files changed, 36 insertions, 1 deletions

diff --git a/training/strategy/lora.py b/training/strategy/lora.py
index 209785a..d51a2f3 100644
--- a/training/strategy/lora.py
+++ b/training/strategy/lora.py
@@ -14,6 +14,8 @@ from diffusers import AutoencoderKL, UNet2DConditionModel, DPMSolverMultistepSch
 from peft import get_peft_model_state_dict
 from safetensors.torch import save_file
 
+from slugify import slugify
+
 from models.clip.tokenizer import MultiCLIPTokenizer
 from training.functional import TrainingStrategy, TrainingCallbacks, save_samples
 
@@ -30,6 +32,11 @@ def lora_strategy_callbacks(
     sample_output_dir: Path,
     checkpoint_output_dir: Path,
     seed: int,
+    placeholder_tokens: list[str],
+    placeholder_token_ids: list[list[int]],
+    use_emb_decay: bool = False,
+    emb_decay_target: float = 0.4,
+    emb_decay: float = 1e-2,
     max_grad_norm: float = 1.0,
     sample_batch_size: int = 1,
     sample_num_batches: int = 1,
@@ -77,6 +84,22 @@ def lora_strategy_callbacks(
             max_grad_norm
         )
 
+        if use_emb_decay:
+            return torch.stack([
+                p
+                for p in text_encoder.text_model.embeddings.token_override_embedding.params
+                if p.grad is not None
+            ])
+
+    @torch.no_grad()
+    def on_after_optimize(w, lr: float):
+        if use_emb_decay:
+            lambda_ = emb_decay * lr
+
+            if lambda_ != 0:
+                norm = w[:, :].norm(dim=-1, keepdim=True)
+                w[:].add_((w[:] / norm.clamp_min(1e-12)) * lambda_ * (emb_decay_target - norm))
+
     @torch.no_grad()
     def on_checkpoint(step, postfix):
         if postfix != "end":
@@ -87,6 +110,12 @@ def lora_strategy_callbacks(
         unet_ = accelerator.unwrap_model(unet, keep_fp32_wrapper=False)
         text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=False)
 
+        for (token, ids) in zip(placeholder_tokens, placeholder_token_ids):
+            text_encoder_.text_model.embeddings.save_embed(
+                ids,
+                checkpoint_output_dir / f"{slugify(token)}_{step}_{postfix}.bin"
+            )
+
         lora_config = {}
         state_dict = get_peft_model_state_dict(unet_, state_dict=accelerator.get_state_dict(unet_))
         lora_config["peft_config"] = unet_.get_peft_config_as_dict(inference=True)
@@ -126,6 +155,7 @@ def lora_strategy_callbacks(
         on_train=on_train,
         on_eval=on_eval,
         on_before_optimize=on_before_optimize,
+        on_after_optimize=on_after_optimize,
         on_checkpoint=on_checkpoint,
         on_sample=on_sample,
     )
@@ -141,7 +171,12 @@ def lora_prepare(
     lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
     **kwargs
 ):
-    return accelerator.prepare(text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler) + ({},)
+    text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
+        text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler)
+
+    text_encoder.text_model.embeddings.token_override_embedding.params.requires_grad_(True)
+
+    return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {}
 
 
 lora_strategy = TrainingStrategy(