From 1a829bb998f3f28f35f8d3801d1447e9c3abea10 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Thu, 9 Mar 2023 18:04:11 -0800
Subject: [PATCH] pipeline: remove code for legacy model

---
 invokeai/backend/generate.py                  | 12 ----------
 .../stable_diffusion/diffusers_pipeline.py    | 24 -------------------
 2 files changed, 36 deletions(-)

diff --git a/invokeai/backend/generate.py b/invokeai/backend/generate.py
index ee5241bca1..35dba41ffb 100644
--- a/invokeai/backend/generate.py
+++ b/invokeai/backend/generate.py
@@ -495,18 +495,6 @@ class Generate:
             torch.cuda.reset_peak_memory_stats()
 
         results = list()
-        init_image = None
-        mask_image = None
-
-        try:
-            if (
-                self.free_gpu_mem
-                and self.model.cond_stage_model.device != self.model.device
-            ):
-                self.model.cond_stage_model.device = self.model.device
-                self.model.cond_stage_model.to(self.model.device)
-        except AttributeError:
-            pass
 
         try:
             uc, c, extra_conditioning_info = get_uc_and_c_and_ec(
diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
index c97b122728..51e7b1ee1d 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -54,16 +54,6 @@ class PipelineIntermediateState:
     attention_map_saver: Optional[AttentionMapSaver] = None
 
 
-# copied from configs/stable-diffusion/v1-inference.yaml
-_default_personalization_config_params = dict(
-    placeholder_strings=["*"],
-    initializer_wods=["sculpture"],
-    per_image_tokens=False,
-    num_vectors_per_token=1,
-    progressive_words=False,
-)
-
-
 @dataclass
 class AddsMaskLatents:
     """Add the channels required for inpainting model input.
@@ -917,20 +907,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             device=self._model_group.device_for(self.unet),
         )
 
-    @property
-    def cond_stage_model(self):
-        return self.embeddings_provider
-
-    @torch.inference_mode()
-    def _tokenize(self, prompt: Union[str, List[str]]):
-        return self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-
     @property
     def channels(self) -> int:
         """Compatible with DiffusionWrapper"""