Fixed Textual Inversion

3 files changed, 13 insertions, 14 deletions

diff --git a/models/clip/prompt.py b/models/clip/prompt.py
index 9b427a0..da33ecf 100644
--- a/models/clip/prompt.py
+++ b/models/clip/prompt.py
@@ -27,10 +27,10 @@ class PromptProcessor():
     def get_embeddings(self, input_ids: torch.IntTensor, attention_mask=None):
         prompts = input_ids.shape[0]
 
-        input_ids = input_ids.reshape((-1, self.tokenizer.model_max_length)).to(self.text_encoder.device)
+        input_ids = input_ids.view((-1, self.tokenizer.model_max_length)).to(self.text_encoder.device)
         if attention_mask is not None:
-            attention_mask = attention_mask.reshape((-1, self.tokenizer.model_max_length)).to(self.text_encoder.device)
+            attention_mask = attention_mask.view((-1, self.tokenizer.model_max_length)).to(self.text_encoder.device)
 
         text_embeddings = self.text_encoder(input_ids, attention_mask=attention_mask)[0]
-        text_embeddings = text_embeddings.reshape((prompts, -1, text_embeddings.shape[2]))
+        text_embeddings = text_embeddings.view((prompts, -1, text_embeddings.shape[2]))
         return text_embeddings
diff --git a/train_ti.py b/train_ti.py
index bb51dc2..e933c48 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -365,6 +365,7 @@ class Checkpointer(CheckpointerBase):
         tokenizer,
         text_encoder,
         scheduler,
+        text_embeddings,
         instance_identifier,
         placeholder_token,
         placeholder_token_id,
@@ -392,6 +393,7 @@ class Checkpointer(CheckpointerBase):
         self.tokenizer = tokenizer
         self.text_encoder = text_encoder
         self.scheduler = scheduler
+        self.text_embeddings = text_embeddings
 
     @torch.no_grad()
     def checkpoint(self, step, postfix):
@@ -403,8 +405,10 @@ class Checkpointer(CheckpointerBase):
         text_encoder = self.accelerator.unwrap_model(self.text_encoder)
 
         for (placeholder_token, placeholder_token_id) in zip(self.placeholder_token, self.placeholder_token_id):
+            training_token_id = self.text_embeddings.id_mapping[placeholder_token_id]
+
             # Save a checkpoint
-            learned_embeds = text_encoder.get_input_embeddings().weight[placeholder_token_id]
+            learned_embeds = self.text_embeddings.trainable_embedding.weight[training_token_id]
             learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()}
 
             filename = f"%s_%d_%s.bin" % (slugify(placeholder_token), step, postfix)
@@ -543,7 +547,7 @@ def main():
 
     # Initialize the optimizer
     optimizer = optimizer_class(
-        text_encoder.get_input_embeddings().parameters(),  # only optimize the embeddings
+        text_embeddings.trainable_embedding.parameters(),  # only optimize the embeddings
         lr=args.learning_rate,
         betas=(args.adam_beta1, args.adam_beta2),
         weight_decay=args.adam_weight_decay,
@@ -741,6 +745,7 @@ def main():
         tokenizer=tokenizer,
         text_encoder=text_encoder,
         scheduler=checkpoint_scheduler,
+        text_embeddings=text_embeddings,
         instance_identifier=args.instance_identifier,
         placeholder_token=args.placeholder_token,
         placeholder_token_id=placeholder_token_id,
@@ -774,7 +779,6 @@ def main():
             local_progress_bar.reset()
 
             text_encoder.train()
-            train_loss = 0.0
 
             for step, batch in enumerate(train_dataloader):
                 with accelerator.accumulate(text_encoder):
@@ -834,8 +838,6 @@ def main():
                         lr_scheduler.step()
                     optimizer.zero_grad(set_to_none=True)
 
-                    text_embeddings.save()
-
                     avg_loss.update(loss.detach_(), bsz)
                     avg_acc.update(acc.detach_(), bsz)
 
diff --git a/training/ti.py b/training/ti.py
index a5fd8e4..2efd2f2 100644
--- a/training/ti.py
+++ b/training/ti.py
@@ -19,8 +19,8 @@ class TrainableEmbeddings(CLIPTextEmbeddings):
     def __init__(self, config: CLIPTextConfig, new_ids: list[int]):
         super().__init__(config)
 
-        self.token_embedding.requires_grad_(False)
-        self.position_embedding.requires_grad_(False)
+        self.token_embedding.weight.requires_grad = False
+        self.position_embedding.weight.requires_grad = False
 
         self.id_mapping = {new_ids[i]: i for i in range(len(new_ids))}
 
@@ -28,6 +28,7 @@ class TrainableEmbeddings(CLIPTextEmbeddings):
         self.train_indices = indices[torch.isin(indices, torch.tensor(new_ids))]
 
         self.trainable_embedding = nn.Embedding.from_pretrained(self.token_embedding.weight[self.train_indices])
+        self.trainable_embedding.weight.requires_grad = True
 
     def forward(
         self,
@@ -64,7 +65,3 @@ class TrainableEmbeddings(CLIPTextEmbeddings):
         embeddings = inputs_embeds + position_embeddings
 
         return embeddings
-
-    @torch.no_grad()
-    def save(self):
-        self.token_embedding.weight.data[self.train_indices] = self.trainable_embedding.weight.data