diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index ef17962f89..7593b34142 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -171,7 +171,7 @@ class TextToLatentsInvocation(BaseInvocation):
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
         self, context: InvocationContext, intermediate_state: PipelineIntermediateState
-    ) -> None:  
+    ) -> None:
         if (context.services.queue.is_canceled(context.graph_execution_state_id)):
             raise CanceledException
 
@@ -185,7 +185,7 @@ class TextToLatentsInvocation(BaseInvocation):
 
         diffusers_step_callback_adapter(sample, step, steps=self.steps, id=self.id, context=context)
 
-    
+
     def get_model(self, model_manager: ModelManager) -> StableDiffusionGeneratorPipeline:
         model_info = choose_model(model_manager, self.model)
         model_name = model_info['model_name']
@@ -195,7 +195,7 @@ class TextToLatentsInvocation(BaseInvocation):
             model=model,
             scheduler_name=self.scheduler
         )
-        
+
         if isinstance(model, DiffusionPipeline):
             for component in [model.unet, model.vae]:
                 configure_model_padding(component,
@@ -292,57 +292,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
         initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
             latent, device=model.device, dtype=latent.dtype
         )
-        
-        timesteps, _ = model.get_img2img_timesteps(
-            self.steps,
-            self.strength,
-            device=model.device,
-        )
 
-        result_latents, result_attention_map_saver = model.latents_from_embeddings(
-            latents=initial_latents,
-            timesteps=timesteps,
-            noise=noise,
-            num_inference_steps=self.steps,
-            conditioning_data=conditioning_data,
-            callback=step_callback
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
-
-
-class LatentsToLatentsInvocation(TextToLatentsInvocation):
-    """Generates latents using latents as base image."""
-
-    type: Literal["l2l"] = "l2l"
-
-    # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
-    strength: float = Field(default=0.5, description="The strength of the latents to use")
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
-        latent = context.services.latents.get(self.latents.latents_name)
-
-        def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, state)
-
-        model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(model)
-
-        # TODO: Verify the noise is the right size
-
-        initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
-            latent, device=model.device, dtype=latent.dtype
-        )
-        
         timesteps, _ = model.get_img2img_timesteps(
             self.steps,
             self.strength,