From cfba51aed5011ccc0a7444dafdc656afb30d0506 Mon Sep 17 00:00:00 2001
From: Ryan Dick <ryanjdick3@gmail.com>
Date: Tue, 27 Feb 2024 19:23:20 -0500
Subject: [PATCH] Removed unused function: _prepare_text_embeddings(...)

---
 .../diffusion/shared_invokeai_diffusion.py    | 84 +------------------
 1 file changed, 1 insertion(+), 83 deletions(-)

diff --git a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
index 9b7c0b7fd8..940eafe69c 100644
--- a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
+++ b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
@@ -2,7 +2,7 @@ from __future__ import annotations
 
 import math
 from contextlib import contextmanager
-from typing import Any, Callable, Optional, Tuple, Union
+from typing import Any, Callable, Optional, Union
 
 import torch
 import torchvision
@@ -467,88 +467,6 @@ class InvokeAIDiffuserComponent:
 
         return mask
 
-    def _prepare_text_embeddings(
-        self,
-        text_embeddings: list[Union[BasicConditioningInfo, SDXLConditioningInfo]],
-        masks: list[Optional[torch.Tensor]],
-        target_height: int,
-        target_width: int,
-    ) -> Tuple[Union[BasicConditioningInfo, SDXLConditioningInfo], Optional[RegionalPromptData]]:
-        """Prepare text embeddings and associated masks for use in the UNet forward pass.
-
-        - Concatenates the text embeddings into a single tensor (returned as a single BasicConditioningInfo or
-          SDXLConditioningInfo).
-        - Preprocesses the masks to match the target height and width, and stacks them into a single tensor.
-        - If all masks are None, skips all mask processing.
-
-        Returns:
-            Tuple[Union[BasicConditioningInfo, SDXLConditioningInfo], Optional[RegionalPromptData]]:
-                (text_embedding, regional_prompt_data)
-                - text_embedding: The concatenated text embeddings.
-                - regional_prompt_data: The processed masks and embedding ranges, or None if all masks are None.
-        """
-        is_sdxl = type(text_embeddings[0]) is SDXLConditioningInfo
-
-        all_masks_are_none = all(mask is None for mask in masks)
-
-        text_embedding = []
-        pooled_embedding = None
-        add_time_ids = None
-        processed_masks = []
-        cur_text_embedding_len = 0
-        embedding_ranges: list[Range] = []
-
-        for text_embedding_info, mask in zip(text_embeddings, masks, strict=True):
-            # HACK(ryand): Figure out the intended relationship between CAC and other conditioning features.
-            assert (
-                text_embedding_info.extra_conditioning is None
-                or not text_embedding_info.extra_conditioning.wants_cross_attention_control
-            )
-
-            if is_sdxl:
-                # We just use the the first SDXLConditioningInfo's pooled_embeds and add_time_ids.
-                # TODO(ryand): Think about this some more. If we can't use the pooled_embeds and add_time_ids from all
-                # the conditioning info, then we shouldn't allow it to be passed in.
-                # How does Compel handle this? Options that come to mind:
-                # - Blend the pooled_embeds and add_time_ids from all of the text embeddings.
-                # - Use the pooled_embeds and add_time_ids from the text embedding with the largest mask area, since
-                #   this is likely the global prompt.
-                if pooled_embedding is None:
-                    pooled_embedding = text_embedding_info.pooled_embeds
-                if add_time_ids is None:
-                    add_time_ids = text_embedding_info.add_time_ids
-
-            text_embedding.append(text_embedding_info.embeds)
-            embedding_ranges.append(
-                Range(start=cur_text_embedding_len, end=cur_text_embedding_len + text_embedding_info.embeds.shape[1])
-            )
-            cur_text_embedding_len += text_embedding_info.embeds.shape[1]
-
-            if not all_masks_are_none:
-                processed_masks.append(self._preprocess_regional_prompt_mask(mask, target_height, target_width))
-
-        text_embedding = torch.cat(text_embedding, dim=1)
-        assert len(text_embedding.shape) == 3  # batch_size, seq_len, token_len
-
-        regional_prompt_data = None
-        if not all_masks_are_none:
-            # TODO(ryand): Think about at what point a batch dimension should be added to the masks.
-            processed_masks = torch.cat(processed_masks, dim=1)
-
-            regional_prompt_data = RegionalPromptData(masks=processed_masks, embedding_ranges=embedding_ranges)
-
-        if is_sdxl:
-            return SDXLConditioningInfo(
-                embeds=text_embedding,
-                extra_conditioning=None,
-                pooled_embeds=pooled_embedding,
-                add_time_ids=add_time_ids,
-            ), regional_prompt_data
-        return BasicConditioningInfo(
-            embeds=text_embedding,
-            extra_conditioning=None,
-        ), regional_prompt_data
-
     def _apply_standard_conditioning(
         self,
         x,