diff --git a/extensions_built_in/sd_trainer/SDTrainer.py b/extensions_built_in/sd_trainer/SDTrainer.py
index 196e1323..f8e7b8ec 100644
--- a/extensions_built_in/sd_trainer/SDTrainer.py
+++ b/extensions_built_in/sd_trainer/SDTrainer.py
@@ -1930,9 +1930,8 @@ class SDTrainer(BaseSDTrainProcess):
         encoded = {}
         for source_name, prompts in source_prompts.items():
             with self._activator_mode('full'):
-                active_embeds = self.sd.encode_prompt(
+                active_embeds = self.sd.get_prompt_embeds(
                     prompts,
-                    long_prompts=self.do_long_prompts,
                     return_taps=context_config.enabled,
                 )
                 active_pred = self.predict_noise(
@@ -1946,9 +1945,8 @@ class SDTrainer(BaseSDTrainProcess):
                 )
             with self._activator_mode('activator_bypass'):
                 with torch.no_grad():
-                    bypass_embeds = self.sd.encode_prompt(
+                    bypass_embeds = self.sd.get_prompt_embeds(
                         prompts,
-                        long_prompts=self.do_long_prompts,
                         return_taps=context_config.enabled,
                     ).detach()
                     bypass_pred = self.predict_noise(
diff --git a/testing/test_sd_trainer_three_phase_runtime.py b/testing/test_sd_trainer_three_phase_runtime.py
index 80e00571..2b71ec09 100644
--- a/testing/test_sd_trainer_three_phase_runtime.py
+++ b/testing/test_sd_trainer_three_phase_runtime.py
@@ -331,7 +331,7 @@ class ThreePhaseRuntimeTest(unittest.TestCase):
         trainer.do_long_prompts = False
         trainer.additional_logs = {}
         trainer.sd = SimpleNamespace(
-            encode_prompt=lambda prompts, **kwargs: _FakeEmbeds(len(prompts)),
+            get_prompt_embeds=lambda prompts, **kwargs: _FakeEmbeds(len(prompts)),
             get_loss_target=lambda noise, batch, timesteps: noise + 1,
         )
         calls = []
@@ -397,7 +397,7 @@ class ThreePhaseRuntimeTest(unittest.TestCase):
             return embeds
 
         trainer.sd = SimpleNamespace(
-            encode_prompt=encode,
+            get_prompt_embeds=encode,
             get_loss_target=lambda noise, batch, timesteps: noise,
         )
         trainer.predict_noise = lambda noisy_latents, **kwargs: (