3 files changed, 5 insertions, 24 deletions

diff --git a/models/clip/embeddings.py b/models/clip/embeddings.py
index 60c1b20..840f8ae 100644
--- a/models/clip/embeddings.py
+++ b/models/clip/embeddings.py
@@ -2,7 +2,6 @@ from typing import Union, Optional
 from pathlib import Path
 
 import torch
-import torch.nn as nn
 
 from safetensors import safe_open
 from safetensors.torch import save_file
@@ -64,6 +63,7 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
 
         token_ids = torch.tensor(token_ids, dtype=torch.long)
 
+        self.token_embedding.mark_trainable(token_ids)
         self.token_embedding.weight.data[token_ids] = initializer
 
     def load_embed(self, input_ids: list[int], filename: Path):
diff --git a/models/lora.py b/models/lora.py
index c0f74a6..98d4d2c 100644
--- a/models/lora.py
+++ b/models/lora.py
@@ -83,11 +83,11 @@ class LoraEmbedding(nn.Embedding, LoraLayer):
         if new_ids.shape[0] == 0:
             return
 
-        n = self.trainable_ids.shape[0]
-        self.trainable_ids[new_ids] = torch.arange(n, n + new_ids.shape[0])
+        n1 = self.lora_A.shape[1]
+        n2 = n1 + new_ids.shape[0]
+        self.trainable_ids[new_ids] = torch.arange(n1, n2)
 
-        lora_A = nn.Parameter(self.weight.new_zeros((self.trainable_ids.shape[0], 0)))
-        lora_A.data[:n] = self.lora_A.data
+        lora_A = nn.Parameter(self.weight.new_zeros((self.r, n2)))
         self.lora_A = lora_A
 
     def reset_parameters(self):
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 49236c6..f0b84b5 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -104,28 +104,10 @@ def textual_inversion_strategy_callbacks(
             yield
 
     @torch.no_grad()
-    def on_before_optimize(epoch: int):
-        if use_emb_decay:
-            params = [
-                p
-                for p in text_encoder.text_model.embeddings.token_embedding.parameters()
-                if p.grad is not None
-            ]
-            return torch.stack(params) if len(params) != 0 else None
-
-    @torch.no_grad()
     def on_after_optimize(w, lrs: dict[str, float]):
         if ema_embeddings is not None:
             ema_embeddings.step(text_encoder.text_model.embeddings.token_embedding.parameters())
 
-        if use_emb_decay and w is not None:
-            lr = lrs["emb"] if "emb" in lrs else lrs["0"]
-            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_embeddings is not None:
             return {"ema_decay": ema_embeddings.decay}
@@ -166,7 +148,6 @@ def textual_inversion_strategy_callbacks(
     return TrainingCallbacks(
         on_train=on_train,
         on_eval=on_eval,
-        on_before_optimize=on_before_optimize,
         on_after_optimize=on_after_optimize,
         on_log=on_log,
         on_checkpoint=on_checkpoint,