Update

4 files changed, 40 insertions, 56 deletions

diff --git a/train_dreambooth.py b/train_dreambooth.py
index d284346..c8f03ea 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -145,12 +145,6 @@ def parse_args():
         help="Tokens to create an alias for.",
     )
     parser.add_argument(
-        "--inverted_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(
@@ -499,6 +493,15 @@ def parse_args():
         help="Embedding dropout probability.",
     )
     parser.add_argument(
+        "--use_emb_decay", action="store_true", help="Whether to use embedding decay."
+    )
+    parser.add_argument(
+        "--emb_decay_target", default=0.4, type=float, help="Embedding decay target."
+    )
+    parser.add_argument(
+        "--emb_decay", default=1e2, type=float, help="Embedding decay factor."
+    )
+    parser.add_argument(
         "--max_grad_norm", default=1.0, type=float, help="Max gradient norm."
     )
     parser.add_argument(
@@ -554,18 +557,6 @@ def parse_args():
             "--placeholder_tokens and --initializer_tokens must have the same number of items"
         )
 
-    if isinstance(args.inverted_initializer_tokens, str):
-        args.inverted_initializer_tokens = [args.inverted_initializer_tokens] * len(
-            args.placeholder_tokens
-        )
-
-    if (
-        isinstance(args.inverted_initializer_tokens, list)
-        and len(args.inverted_initializer_tokens) != 0
-    ):
-        args.placeholder_tokens += [f"inv_{t}" for t in args.placeholder_tokens]
-        args.initializer_tokens += args.inverted_initializer_tokens
-
     if isinstance(args.num_vectors, int):
         args.num_vectors = [args.num_vectors] * len(args.placeholder_tokens)
 
@@ -875,6 +866,11 @@ def main():
         sample_num_batches=args.sample_batches,
         sample_num_steps=args.sample_steps,
         sample_image_size=args.sample_image_size,
+        placeholder_tokens=placeholder_tokens,
+        placeholder_token_ids=placeholder_token_ids,
+        use_emb_decay=args.use_emb_decay,
+        emb_decay_target=args.emb_decay_target,
+        emb_decay=args.emb_decay,
         max_grad_norm=args.max_grad_norm,
     )
 
diff --git a/train_lora.py b/train_lora.py
index 1ff25ff..fbec009 100644
--- a/train_lora.py
+++ b/train_lora.py
@@ -158,12 +158,6 @@ def parse_args():
         help="Tokens to create an alias for.",
     )
     parser.add_argument(
-        "--inverted_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(
@@ -633,18 +627,6 @@ def parse_args():
             "--placeholder_tokens and --initializer_tokens must have the same number of items"
         )
 
-    if isinstance(args.inverted_initializer_tokens, str):
-        args.inverted_initializer_tokens = [args.inverted_initializer_tokens] * len(
-            args.placeholder_tokens
-        )
-
-    if (
-        isinstance(args.inverted_initializer_tokens, list)
-        and len(args.inverted_initializer_tokens) != 0
-    ):
-        args.placeholder_tokens += [f"inv_{t}" for t in args.placeholder_tokens]
-        args.initializer_tokens += args.inverted_initializer_tokens
-
     if isinstance(args.num_vectors, int):
         args.num_vectors = [args.num_vectors] * len(args.placeholder_tokens)
 
diff --git a/train_ti.py b/train_ti.py
index 1dbd637..8c63493 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -112,12 +112,6 @@ def parse_args():
         help="Tokens to create an alias for.",
     )
     parser.add_argument(
-        "--inverted_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(
@@ -545,18 +539,6 @@ def parse_args():
             "--placeholder_tokens and --initializer_tokens must have the same number of items"
         )
 
-    if isinstance(args.inverted_initializer_tokens, str):
-        args.inverted_initializer_tokens = [args.inverted_initializer_tokens] * len(
-            args.placeholder_tokens
-        )
-
-    if (
-        isinstance(args.inverted_initializer_tokens, list)
-        and len(args.inverted_initializer_tokens) != 0
-    ):
-        args.placeholder_tokens += [f"inv_{t}" for t in args.placeholder_tokens]
-        args.initializer_tokens += args.inverted_initializer_tokens
-
     if isinstance(args.num_vectors, int):
         args.num_vectors = [args.num_vectors] * len(args.placeholder_tokens)
 
@@ -872,7 +854,7 @@ def main():
 
     optimizer = create_optimizer(
         text_encoder.text_model.embeddings.token_embedding.parameters(),
-        lr=learning_rate,
+        lr=args.learning_rate,
     )
 
     data_generator = torch.Generator(device="cpu").manual_seed(args.seed)
diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py
index dc19ba3..0f64747 100644
--- a/training/strategy/dreambooth.py
+++ b/training/strategy/dreambooth.py
@@ -30,8 +30,13 @@ def dreambooth_strategy_callbacks(
     sample_output_dir: Path,
     checkpoint_output_dir: Path,
     seed: int,
+    placeholder_tokens: list[str],
+    placeholder_token_ids: list[list[int]],
     train_text_encoder_cycles: int,
     text_encoder_unfreeze_last_n_layers: int = 2,
+    use_emb_decay: bool = False,
+    emb_decay_target: float = 0.4,
+    emb_decay: float = 1e-2,
     max_grad_norm: float = 1.0,
     use_ema: bool = False,
     ema_inv_gamma: float = 1.0,
@@ -112,11 +117,29 @@ def dreambooth_strategy_callbacks(
             params_to_clip.append(text_encoder.parameters())
         accelerator.clip_grad_norm_(itertools.chain(*params_to_clip), max_grad_norm)
 
+        if len(placeholder_tokens) != 0 and use_emb_decay:
+            params = [
+                p
+                for p in text_encoder.text_model.embeddings.parameters()
+                if p.grad is not None
+            ]
+            return torch.stack(params) if len(params) != 0 else None
+
     @torch.no_grad()
-    def on_after_optimize(_, lrs: dict[str, float]):
+    def on_after_optimize(w, lrs: dict[str, float]):
         if ema_unet is not None:
             ema_unet.step(unet.parameters())
 
+        if w is not None and "emb" in lrs:
+            lr = lrs["emb"]
+            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)
+                )
+
     def on_log():
         if ema_unet is not None:
             return {"ema_decay": ema_unet.decay}
@@ -212,6 +235,7 @@ def dreambooth_prepare(
         ]:
             layer.requires_grad_(False)
 
+    text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
     # text_encoder.text_model.embeddings.requires_grad_(False)
 
     return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler