[flux lora training] fix t5 training bug

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -881,7 +881,7 @@ def save_embeddings(self, file_path: str):
         for idx, text_encoder in enumerate(self.text_encoders):
             train_ids = self.train_ids if idx == 0 else self.train_ids_t5
             embeds = (
-                text_encoder.text_model.embeddings.token_embedding if idx == 0 else text_encoder.encoder.embed_tokens
+                text_encoder.text_model.embeddings.token_embedding if idx == 0 else text_encoder.shared
             )
             assert embeds.weight.data.shape[0] == len(self.tokenizers[idx]), "Tokenizers should be the same."
             new_token_embeddings = embeds.weight.data[train_ids]
@@ -905,7 +905,7 @@ def device(self):
     def retract_embeddings(self):
         for idx, text_encoder in enumerate(self.text_encoders):
             embeds = (
-                text_encoder.text_model.embeddings.token_embedding if idx == 0 else text_encoder.encoder.embed_tokens
+                text_encoder.text_model.embeddings.token_embedding if idx == 0 else text_encoder.shared
             )
             index_no_updates = self.embeddings_settings[f"index_no_updates_{idx}"]
             embeds.weight.data[index_no_updates] = (
@@ -1749,7 +1749,7 @@ def load_model_hook(models, input_dir):
         if args.enable_t5_ti:  # whether to do pivotal tuning/textual inversion for T5 as well
             text_lora_parameters_two = []
             for name, param in text_encoder_two.named_parameters():
-                if "token_embedding" in name:
+                if "shared" in name:
                     # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
                     param.data = param.to(dtype=torch.float32)
                     param.requires_grad = True