Move regional prompt concatenation further up the stack. This solves a number of issues.

invokeai/app/invocations/latent.py

@@ -1,5 +1,4 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
 import inspect
 import math
 from contextlib import ExitStack
@@ -9,6 +8,7 @@ from typing import List, Literal, Optional, Union
 import einops
 import numpy as np
 import torch
+import torchvision
 import torchvision.transforms as T
 from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.image_processor import VaeImageProcessor
@@ -44,8 +44,10 @@ from invokeai.backend.model_management.models import ModelType, SilenceWarnings
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
     IPAdapterConditioningInfo,
+    Range,
     SDXLConditioningInfo,
     TextConditioningData,
+    TextConditioningRegions,
 )
 
 from ...backend.model_management.lora import ModelPatcher
@@ -334,7 +336,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
         context: InvocationContext,
         device: torch.device,
         dtype: torch.dtype,
-    ):
+    ) -> tuple[Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]], list[Optional[torch.Tensor]]]:
+        """Get the text embeddings and masks from the input conditioning fields."""
         # Normalize cond_field to a list.
         cond_list = cond_field
         if not isinstance(cond_list, list):
@@ -353,12 +356,111 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         return text_embeddings, text_embeddings_masks
 
+    def _preprocess_regional_prompt_mask(
+        self, mask: Optional[torch.Tensor], target_height: int, target_width: int
+    ) -> torch.Tensor:
+        """Preprocess a regional prompt mask to match the target height and width.
+
+        If mask is None, returns a mask of all ones with the target height and width.
+        If mask is not None, resizes the mask to the target height and width using nearest neighbor interpolation.
+
+        Returns:
+            torch.Tensor: The processed mask. dtype: torch.bool, shape: (1, 1, target_height, target_width).
+        """
+        if mask is None:
+            return torch.ones((1, 1, target_height, target_width), dtype=torch.bool)
+
+        tf = torchvision.transforms.Resize(
+            (target_height, target_width), interpolation=torchvision.transforms.InterpolationMode.NEAREST
+        )
+        mask = mask.unsqueeze(0)  # Shape: (1, h, w) -> (1, 1, h, w)
+        mask = tf(mask)
+
+        return mask
+
+    def concat_regional_text_embeddings(
+        self,
+        text_conditionings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]],
+        masks: Optional[list[Optional[torch.Tensor]]],
+        latent_height: int,
+        latent_width: int,
+    ) -> tuple[Union[BasicConditioningInfo, SDXLConditioningInfo], Optional[TextConditioningRegions]]:
+        """Concatenate regional text embeddings into a single embedding and track the region masks accordingly."""
+        if masks is None:
+            masks = [None] * len(text_conditionings)
+        assert len(text_conditionings) == len(masks)
+
+        is_sdxl = type(text_conditionings[0]) is SDXLConditioningInfo
+
+        all_masks_are_none = all(mask is None for mask in masks)
+
+        text_embedding = []
+        pooled_embedding = None
+        add_time_ids = None
+        cur_text_embedding_len = 0
+        processed_masks = []
+        embedding_ranges = []
+
+        for text_embedding_info, mask in zip(text_conditionings, masks, strict=True):
+            # HACK(ryand): Figure out the intended relationship with CAC. Probably want to raise if more than one text
+            # embedding is passed in and CAC is being used.
+            assert (
+                text_embedding_info.extra_conditioning is None
+                or not text_embedding_info.extra_conditioning.wants_cross_attention_control
+            )
+
+            if is_sdxl:
+                # HACK(ryand): We just use the the first SDXLConditioningInfo's pooled_embeds and add_time_ids. This is
+                # fundamentally an interface issue, as the SDXL Compel nodes are not designed to be used in the way that
+                # we use them for regional prompting. Ideally, the DenoiseLatents invocation should accept a single
+                # pooled_embeds tensor and a list of standard text embeds with region masks. This change would be a
+                # pretty major breaking change to a popular node, so for now we use this hack.
+                #
+                # An improvement could be to use the pooled embeds from the prompt with the largest region, as this is
+                # most likely to be a 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)
+            if not all_masks_are_none:
+                embedding_ranges.append(
+                    Range(
+                        start=cur_text_embedding_len, end=cur_text_embedding_len + text_embedding_info.embeds.shape[1]
+                    )
+                )
+                processed_masks.append(self._preprocess_regional_prompt_mask(mask, latent_height, latent_width))
+
+            cur_text_embedding_len += text_embedding_info.embeds.shape[1]
+
+        text_embedding = torch.cat(text_embedding, dim=1)
+        assert len(text_embedding.shape) == 3  # batch_size, seq_len, token_len
+
+        regions = None
+        if not all_masks_are_none:
+            regions = TextConditioningRegions(masks=torch.cat(processed_masks, dim=1), ranges=embedding_ranges)
+
+        if is_sdxl:
+            return SDXLConditioningInfo(
+                embeds=text_embedding,
+                # TODO(ryand): This should not be hard-coded to None.
+                extra_conditioning=None,
+                pooled_embeds=pooled_embedding,
+                add_time_ids=add_time_ids,
+            ), regions
+        return BasicConditioningInfo(
+            embeds=text_embedding,
+            # TODO(ryand): This should not be hard-coded to None.
+            extra_conditioning=None,
+        ), regions
+
     def get_conditioning_data(
         self,
         context: InvocationContext,
-        scheduler,
         unet,
-        seed,
+        latent_height: int,
+        latent_width: int,
     ) -> TextConditioningData:
         cond_text_embeddings, cond_text_embedding_masks = self._get_text_embeddings_and_masks(
             self.positive_conditioning, context, unet.device, unet.dtype
@@ -366,12 +468,23 @@ class DenoiseLatentsInvocation(BaseInvocation):
         uncond_text_embeddings, uncond_text_embedding_masks = self._get_text_embeddings_and_masks(
             self.negative_conditioning, context, unet.device, unet.dtype
         )
-
+        cond_text_embedding, cond_regions = self.concat_regional_text_embeddings(
+            text_conditionings=cond_text_embeddings,
+            masks=cond_text_embedding_masks,
+            latent_height=latent_height,
+            latent_width=latent_width,
+        )
+        uncond_text_embedding, uncond_regions = self.concat_regional_text_embeddings(
+            text_conditionings=uncond_text_embeddings,
+            masks=uncond_text_embedding_masks,
+            latent_height=latent_height,
+            latent_width=latent_width,
+        )
         conditioning_data = TextConditioningData(
-            uncond_text_embeddings=uncond_text_embeddings,
-            uncond_text_embedding_masks=uncond_text_embedding_masks,
-            cond_text_embeddings=cond_text_embeddings,
-            cond_text_embedding_masks=cond_text_embedding_masks,
+            uncond_text=uncond_text_embedding,
+            cond_text=cond_text_embedding,
+            uncond_regions=uncond_regions,
+            cond_regions=cond_regions,
             guidance_scale=self.cfg_scale,
             guidance_rescale_multiplier=self.cfg_rescale_multiplier,
         )
@@ -761,7 +874,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 )
 
                 pipeline = self.create_pipeline(unet, scheduler)
-                conditioning_data = self.get_conditioning_data(context, scheduler, unet, seed)
+                _, _, latent_height, latent_width = latents.shape
+                conditioning_data = self.get_conditioning_data(
+                    context=context, unet=unet, latent_height=latent_height, latent_width=latent_width
+                )
 
                 controlnet_data = self.prep_control_data(
                     context=context,

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -411,13 +411,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if timesteps.shape[0] == 0:
             return latents
 
-        extra_conditioning_info = conditioning_data.cond_text_embeddings[0].extra_conditioning
+        extra_conditioning_info = conditioning_data.cond_text.extra_conditioning
         use_cross_attention_control = (
             extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control
         )
         use_ip_adapter = ip_adapter_data is not None
         # HACK(ryand): Fix this logic.
-        use_regional_prompting = len(conditioning_data.cond_text_embeddings) > 1
+        use_regional_prompting = conditioning_data.cond_regions is not None
         if sum([use_cross_attention_control, use_ip_adapter, use_regional_prompting]) > 1:
             raise Exception(
                 "Cross-attention control, IP-Adapter, and regional prompting cannot be used simultaneously (yet)."

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -8,6 +8,11 @@ from .cross_attention_control import Arguments
 
 @dataclass
 class ExtraConditioningInfo:
+    """Extra conditioning information produced by Compel.
+
+    This is used for prompt-to-prompt cross-attention control (a.k.a. `.swap()` in Compel).
+    """
+
     tokens_count_including_eos_bos: int
     cross_attention_control_args: Optional[Arguments] = None
 
@@ -54,20 +59,48 @@ class IPAdapterConditioningInfo:
 
 
 @dataclass
-class TextConditioningData:
-    uncond_text_embeddings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]]
-    uncond_text_embedding_masks: list[Optional[torch.Tensor]]
-    cond_text_embeddings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]]
-    cond_text_embedding_masks: list[Optional[torch.Tensor]]
+class Range:
+    start: int
+    end: int
 
-    """
-    Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-    `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
-    Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
-    images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
-    """
-    guidance_scale: Union[float, List[float]]
-    """ for models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7 .
-     ref [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf)
-    """
-    guidance_rescale_multiplier: float = 0
+
+class TextConditioningRegions:
+    def __init__(self, masks: torch.Tensor, ranges: list[Range]):
+        # A binary mask indicating the regions of the image that the prompt should be applied to.
+        # Shape: (1, num_prompts, height, width)
+        # Dtype: torch.bool
+        self.masks = masks
+
+        # A list of ranges indicating the start and end indices of the embeddings that corresponding mask applies to.
+        # ranges[i] contains the embedding range for the i'th prompt / mask.
+        self.ranges = ranges
+
+        assert self.masks.shape[1] == len(self.ranges)
+
+
+class TextConditioningData:
+    def __init__(
+        self,
+        uncond_text: Union[BasicConditioningInfo, SDXLConditioningInfo],
+        cond_text: Union[BasicConditioningInfo, SDXLConditioningInfo],
+        uncond_regions: Optional[TextConditioningRegions],
+        cond_regions: Optional[TextConditioningRegions],
+        guidance_scale: Union[float, List[float]],
+        guidance_rescale_multiplier: float = 0,
+    ):
+        self.uncond_text = uncond_text
+        self.cond_text = cond_text
+        self.uncond_regions = uncond_regions
+        self.cond_regions = cond_regions
+        # Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+        # `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
+        # Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
+        # images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
+        self.guidance_scale = guidance_scale
+        # For models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7.
+        # See [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+        self.guidance_rescale_multiplier = guidance_rescale_multiplier
+
+    def is_sdxl(self):
+        assert isinstance(self.uncond_text, SDXLConditioningInfo) == isinstance(self.cond_text, SDXLConditioningInfo)
+        return isinstance(self.cond_text, SDXLConditioningInfo)

invokeai/backend/stable_diffusion/diffusion/regional_prompt_attention.py

@@ -1,5 +1,4 @@
 from contextlib import contextmanager
-from dataclasses import dataclass
 from typing import Optional
 
 import torch
@@ -8,11 +7,9 @@ from diffusers import UNet2DConditionModel
 from diffusers.models.attention_processor import Attention, AttnProcessor2_0
 from diffusers.utils import USE_PEFT_BACKEND
 
-
-@dataclass
-class Range:
-    start: int
-    end: int
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
+    TextConditioningRegions,
+)
 
 
 class RegionalPromptData:
@@ -20,10 +17,9 @@ class RegionalPromptData:
         self._attn_masks_by_seq_len = attn_masks_by_seq_len
 
     @classmethod
-    def from_masks_and_ranges(
+    def from_regions(
         cls,
-        masks: list[torch.Tensor],
-        embedding_ranges: list[list[Range]],
+        regions: list[TextConditioningRegions],
         key_seq_len: int,
         # TODO(ryand): Pass in a list of downscale factors?
         max_downscale_factor: int = 8,
@@ -31,14 +27,8 @@ class RegionalPromptData:
         """Construct a `RegionalPromptData` object.
 
         Args:
-            masks (list[torch.Tensor]): masks[i] contains the regions masks for the i'th sample in the batch.
-                The shape of masks[i] is (num_prompts, height, width). The mask is set to 1.0 in regions where the
-                prompt should be applied, and 0.0 elsewhere.
-
-            embedding_ranges (list[list[Range]]): embedding_ranges[i][j] contains the embedding range for the j'th
-                prompt in the i'th batch sample. masks[i][j, ...] is applied to the embeddings in:
-                encoder_hidden_states[i, embedding_ranges[j].start:embedding_ranges[j].end, :].
-
+            regions (list[TextConditioningRegions]): regions[i] contains the prompt regions for the i'th sample in the
+                batch.
             key_seq_len (int): The sequence length of the expected prompt embeddings (which act as the key in the
                 cross-attention layers). This is most likely equal to the max embedding range end, but we pass it
                 explicitly to be sure.
@@ -48,11 +38,11 @@ class RegionalPromptData:
         # batch_attn_mask_by_seq_len[b][s] contains the attention mask for the b'th batch sample with a query sequence
         # length of s.
         batch_attn_masks_by_seq_len: list[dict[int, torch.Tensor]] = []
-        for batch_masks, batch_ranges in zip(masks, embedding_ranges, strict=True):
+        for batch_sample_regions in regions:
             batch_attn_masks_by_seq_len.append({})
 
             # Convert the bool masks to float masks so that max pooling can be applied.
-            batch_masks = batch_masks.to(dtype=torch.float32)
+            batch_masks = batch_sample_regions.masks.to(dtype=torch.float32)
 
             # Downsample the spatial dimensions by factors of 2 until max_downscale_factor is reached.
             downscale_factor = 1
@@ -69,7 +59,7 @@ class RegionalPromptData:
                 # TODO(ryand): What device / dtype should this be?
                 attn_mask = torch.zeros((1, query_seq_len, key_seq_len))
 
-                for prompt_idx, embedding_range in enumerate(batch_ranges):
+                for prompt_idx, embedding_range in enumerate(batch_sample_regions.ranges):
                     attn_mask[0, :, embedding_range.start : embedding_range.end] = batch_query_masks[
                         :, prompt_idx, :, :
                     ]

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -5,19 +5,18 @@ from contextlib import contextmanager
 from typing import Any, Callable, Optional, Union
 
 import torch
-import torchvision
 from diffusers import UNet2DConditionModel
 from typing_extensions import TypeAlias
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
-    BasicConditioningInfo,
     ExtraConditioningInfo,
     IPAdapterConditioningInfo,
-    SDXLConditioningInfo,
+    Range,
     TextConditioningData,
+    TextConditioningRegions,
 )
-from invokeai.backend.stable_diffusion.diffusion.regional_prompt_attention import Range, RegionalPromptData
+from invokeai.backend.stable_diffusion.diffusion.regional_prompt_attention import RegionalPromptData
 
 from .cross_attention_control import (
     CrossAttentionType,
@@ -36,143 +35,6 @@ ModelForwardCallback: TypeAlias = Union[
 ]
 
 
-class RegionalTextConditioningInfo:
-    def __init__(
-        self,
-        text_conditioning: Union[BasicConditioningInfo, SDXLConditioningInfo],
-        masks: Optional[torch.Tensor] = None,
-        embedding_ranges: Optional[list[Range]] = None,
-    ):
-        """Initialize a RegionalTextConditioningInfo.
-
-        Args:
-            text_conditioning (Union[BasicConditioningInfo, SDXLConditioningInfo]): The text conditioning embeddings
-                after concatenating the embeddings for all regions.
-            masks (Optional[torch.Tensor], optional): Shape: (1, num_regions, h, w).
-            embedding_ranges (Optional[list[Range]], optional): The embedding range for each region.
-        """
-        self.text_conditioning = text_conditioning
-        self.masks = masks
-        self.embedding_ranges = embedding_ranges
-
-        assert (self.masks is None) == (self.embedding_ranges is None)
-        if self.masks is not None:
-            assert self.masks.shape[1] == len(self.embedding_ranges)
-
-    def has_region_masks(self):
-        if self.masks is None:
-            return False
-        return any(mask is not None for mask in self.masks)
-
-    def is_sdxl(self):
-        return isinstance(self.text_conditioning, SDXLConditioningInfo)
-
-    @classmethod
-    def _preprocess_regional_prompt_mask(
-        cls, mask: Optional[torch.Tensor], target_height: int, target_width: int
-    ) -> torch.Tensor:
-        """Preprocess a regional prompt mask to match the target height and width.
-
-        If mask is None, returns a mask of all ones with the target height and width.
-        If mask is not None, resizes the mask to the target height and width using nearest neighbor interpolation.
-
-        Returns:
-            torch.Tensor: The processed mask. dtype: torch.bool, shape: (1, 1, target_height, target_width).
-        """
-        if mask is None:
-            return torch.ones((1, 1, target_height, target_width), dtype=torch.bool)
-
-        tf = torchvision.transforms.Resize(
-            (target_height, target_width), interpolation=torchvision.transforms.InterpolationMode.NEAREST
-        )
-        mask = mask.unsqueeze(0)  # Shape: (1, h, w) -> (1, 1, h, w)
-        mask = tf(mask)
-
-        return mask
-
-    @classmethod
-    def from_text_conditioning_and_masks(
-        cls,
-        text_conditionings: list[Union[BasicConditioningInfo, SDXLConditioningInfo]],
-        masks: Optional[list[Optional[torch.Tensor]]],
-        latent_height: int,
-        latent_width: int,
-    ):
-        if masks is None:
-            masks = [None] * len(text_conditionings)
-        assert len(text_conditionings) == len(masks)
-
-        is_sdxl = type(text_conditionings[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_conditionings, 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:
-                # HACK(ryand): We just use the the first SDXLConditioningInfo's pooled_embeds and add_time_ids. This is
-                # fundamentally an interface issue, as the SDXL Compel nodes are not designed to be used in the way that
-                # we use them for regional prompting. Ideally, the DenoiseLatents invocation should accept a single
-                # pooled_embeds tensor and a list of standard text embeds with region masks. This change would be a
-                # pretty major breaking change to a popular node, so for now we use this hack.
-                #
-                # An improvement could be to use the pooled embeds from the prompt with the largest region, as this is
-                # most likely to be a 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(cls._preprocess_regional_prompt_mask(mask, latent_height, latent_width))
-
-        text_embedding = torch.cat(text_embedding, dim=1)
-        assert len(text_embedding.shape) == 3  # batch_size, seq_len, token_len
-
-        if not all_masks_are_none:
-            processed_masks = torch.cat(processed_masks, dim=1)
-        else:
-            processed_masks = None
-            embedding_ranges = None
-
-        if is_sdxl:
-            return cls(
-                text_conditioning=SDXLConditioningInfo(
-                    embeds=text_embedding,
-                    extra_conditioning=None,
-                    pooled_embeds=pooled_embedding,
-                    add_time_ids=add_time_ids,
-                ),
-                masks=processed_masks,
-                embedding_ranges=embedding_ranges,
-            )
-        return cls(
-            text_conditioning=BasicConditioningInfo(
-                embeds=text_embedding,
-                extra_conditioning=None,
-            ),
-            masks=processed_masks,
-            embedding_ranges=embedding_ranges,
-        )
-
-
 class InvokeAIDiffuserComponent:
     """
     The aim of this component is to provide a single place for code that can be applied identically to
@@ -233,10 +95,6 @@ class InvokeAIDiffuserComponent:
         conditioning_data: TextConditioningData,
     ):
         down_block_res_samples, mid_block_res_sample = None, None
-        # HACK(ryan): Currently, we just take the first text embedding if there's more than one. We should probably
-        # concatenate all of the embeddings for the ControlNet, but not apply embedding masks.
-        uncond_text_embeddings = conditioning_data.uncond_text_embeddings[0]
-        cond_text_embeddings = conditioning_data.cond_text_embeddings[0]
 
         # control_data should be type List[ControlNetData]
         # this loop covers both ControlNet (one ControlNetData in list)
@@ -267,25 +125,30 @@ class InvokeAIDiffuserComponent:
                 added_cond_kwargs = None
 
                 if cfg_injection:  # only applying ControlNet to conditional instead of in unconditioned
-                    if type(cond_text_embeddings) is SDXLConditioningInfo:
+                    if conditioning_data.is_sdxl():
                         added_cond_kwargs = {
-                            "text_embeds": cond_text_embeddings.pooled_embeds,
-                            "time_ids": cond_text_embeddings.add_time_ids,
+                            "text_embeds": conditioning_data.cond_text.pooled_embeds,
+                            "time_ids": conditioning_data.cond_text.add_time_ids,
                         }
-                    encoder_hidden_states = cond_text_embeddings.embeds
+                    encoder_hidden_states = conditioning_data.cond_text.embeds
                     encoder_attention_mask = None
                 else:
-                    if type(cond_text_embeddings) is SDXLConditioningInfo:
+                    if conditioning_data.is_sdxl():
                         added_cond_kwargs = {
                             "text_embeds": torch.cat(
-                                [uncond_text_embeddings.pooled_embeds, cond_text_embeddings.pooled_embeds], dim=0
+                                [
+                                    conditioning_data.uncond_text.pooled_embeds,
+                                    conditioning_data.cond_text.pooled_embeds,
+                                ],
+                                dim=0,
                             ),
                             "time_ids": torch.cat(
-                                [uncond_text_embeddings.add_time_ids, cond_text_embeddings.add_time_ids], dim=0
+                                [conditioning_data.uncond_text.add_time_ids, conditioning_data.cond_text.add_time_ids],
+                                dim=0,
                             ),
                         }
                     (encoder_hidden_states, encoder_attention_mask) = self._concat_conditionings_for_batch(
-                        uncond_text_embeddings.embeds, cond_text_embeddings.embeds
+                        conditioning_data.uncond_text.embeds, conditioning_data.cond_text.embeds
                     )
                 if isinstance(control_datum.weight, list):
                     # if controlnet has multiple weights, use the weight for the current step
@@ -440,52 +303,7 @@ class InvokeAIDiffuserComponent:
         x_twice = torch.cat([x] * 2)
         sigma_twice = torch.cat([sigma] * 2)
 
-        # TODO(ryand): We currently call from_text_conditioning_and_masks(...) and from_masks_and_ranges(...) for every
-        # denoising step. The text conditionings and masks are not changing from step-to-step, so this really only needs
-        # to be done once. While this seems painfully inefficient, the time spent is typically negligible compared to
-        # the forward inference pass of the UNet. The main reason that this hasn't been moved up to eliminate redundancy
-        # is that it is slightly awkward to handle both standard conditioning and sequential conditioning further up the
-        # stack.
         cross_attention_kwargs = None
-        _, _, h, w = x.shape
-        cond_text = RegionalTextConditioningInfo.from_text_conditioning_and_masks(
-            text_conditionings=conditioning_data.cond_text_embeddings,
-            masks=conditioning_data.cond_text_embedding_masks,
-            latent_height=h,
-            latent_width=w,
-        )
-        uncond_text = RegionalTextConditioningInfo.from_text_conditioning_and_masks(
-            text_conditionings=conditioning_data.uncond_text_embeddings,
-            masks=conditioning_data.uncond_text_embedding_masks,
-            latent_height=h,
-            latent_width=w,
-        )
-
-        if cond_text.has_region_masks() or uncond_text.has_region_masks():
-            masks = []
-            embedding_ranges = []
-            for c in [uncond_text, cond_text]:
-                if c.has_region_masks():
-                    masks.append(c.masks)
-                    embedding_ranges.append(c.embedding_ranges)
-                else:
-                    # Create a dummy mask and range for text conditioning that doesn't have region masks.
-                    masks.append(torch.ones((1, 1, h, w), dtype=torch.bool))
-                    embedding_ranges.append([Range(start=0, end=c.text_conditioning.embeds.shape[1])])
-
-            # The key_seq_len will be the maximum sequence length of all the conditioning embeddings. All other
-            # embeddings will be padded to match this length.
-            key_seq_len = 0
-            for c in [uncond_text, cond_text]:
-                _, seq_len, _ = c.text_conditioning.embeds.shape
-                if seq_len > key_seq_len:
-                    key_seq_len = seq_len
-
-            cross_attention_kwargs = {
-                "regional_prompt_data": RegionalPromptData.from_masks_and_ranges(
-                    masks=masks, embedding_ranges=embedding_ranges, key_seq_len=key_seq_len
-                )
-            }
 
         # TODO(ryand): Figure out interactions between regional prompting and IP-Adapter conditioning.
         if ip_adapter_conditioning is not None:
@@ -499,20 +317,45 @@ class InvokeAIDiffuserComponent:
                 ]
             }
 
+        uncond_text = conditioning_data.uncond_text
+        cond_text = conditioning_data.cond_text
+
         added_cond_kwargs = None
-        if cond_text.is_sdxl():
+        if conditioning_data.is_sdxl():
             added_cond_kwargs = {
-                "text_embeds": torch.cat(
-                    [uncond_text.text_conditioning.pooled_embeds, cond_text.text_conditioning.pooled_embeds], dim=0
-                ),
-                "time_ids": torch.cat(
-                    [uncond_text.text_conditioning.add_time_ids, cond_text.text_conditioning.add_time_ids], dim=0
-                ),
+                "text_embeds": torch.cat([uncond_text.pooled_embeds, cond_text.pooled_embeds], dim=0),
+                "time_ids": torch.cat([uncond_text.add_time_ids, cond_text.add_time_ids], dim=0),
             }
 
         both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(
-            uncond_text.text_conditioning.embeds, cond_text.text_conditioning.embeds
+            uncond_text.embeds, cond_text.embeds
         )
+
+        if conditioning_data.cond_regions is not None or conditioning_data.uncond_regions is not None:
+            # TODO(ryand): We currently call from_regions(...) for every denoising step. The text conditionings and
+            # masks are not changing from step-to-step, so this really only needs to be done once. While this seems
+            # painfully inefficient, the time spent is typically negligible compared to the forward inference pass of
+            # the UNet. The main reason that this hasn't been moved up to eliminate redundancy is that it is slightly
+            # awkward to handle both standard conditioning and sequential conditioning further up the stack.
+            regions = []
+            for c, r in [
+                (conditioning_data.uncond_text, conditioning_data.uncond_regions),
+                (conditioning_data.cond_text, conditioning_data.cond_regions),
+            ]:
+                if r is None:
+                    # Create a dummy mask and range for text conditioning that doesn't have region masks.
+                    _, _, h, w = x.shape
+                    r = TextConditioningRegions(
+                        masks=torch.ones((1, 1, h, w), dtype=torch.bool),
+                        ranges=[Range(start=0, end=c.embeds.shape[1])],
+                    )
+                regions.append(r)
+
+            _, key_seq_len, _ = both_conditionings.shape
+            cross_attention_kwargs = {
+                "regional_prompt_data": RegionalPromptData.from_regions(regions=regions, key_seq_len=key_seq_len)
+            }
+
         both_results = self.model_forward_callback(
             x_twice,
             sigma_twice,
@@ -542,9 +385,6 @@ class InvokeAIDiffuserComponent:
         slower execution speed.
         """
 
-        assert len(conditioning_data.cond_text_embeddings) == 1
-        text_embeddings = conditioning_data.cond_text_embeddings[0]
-
         # Since we are running the conditioned and unconditioned passes sequentially, we need to split the ControlNet
         # and T2I-Adapter residuals into two chunks.
         uncond_down_block, cond_down_block = None, None
@@ -602,18 +442,17 @@ class InvokeAIDiffuserComponent:
 
         # Prepare SDXL conditioning kwargs for the unconditioned pass.
         added_cond_kwargs = None
-        is_sdxl = type(text_embeddings) is SDXLConditioningInfo
-        if is_sdxl:
+        if conditioning_data.is_sdxl():
             added_cond_kwargs = {
-                "text_embeds": conditioning_data.uncond_text_embeddings.pooled_embeds,
-                "time_ids": conditioning_data.uncond_text_embeddings.add_time_ids,
+                "text_embeds": conditioning_data.uncond_text.pooled_embeds,
+                "time_ids": conditioning_data.uncond_text.add_time_ids,
             }
 
         # Run unconditioned UNet denoising (i.e. negative prompt).
         unconditioned_next_x = self.model_forward_callback(
             x,
             sigma,
-            conditioning_data.uncond_text_embeddings.embeds,
+            conditioning_data.uncond_text.embeds,
             cross_attention_kwargs=cross_attention_kwargs,
             down_block_additional_residuals=uncond_down_block,
             mid_block_additional_residual=uncond_mid_block,
@@ -644,17 +483,17 @@ class InvokeAIDiffuserComponent:
 
         # Prepare SDXL conditioning kwargs for the conditioned pass.
         added_cond_kwargs = None
-        if is_sdxl:
+        if conditioning_data.is_sdxl():
             added_cond_kwargs = {
-                "text_embeds": text_embeddings.pooled_embeds,
-                "time_ids": text_embeddings.add_time_ids,
+                "text_embeds": conditioning_data.cond_text.pooled_embeds,
+                "time_ids": conditioning_data.cond_text.add_time_ids,
             }
 
         # Run conditioned UNet denoising (i.e. positive prompt).
         conditioned_next_x = self.model_forward_callback(
             x,
             sigma,
-            text_embeddings.embeds,
+            conditioning_data.cond_text.embeds,
             cross_attention_kwargs=cross_attention_kwargs,
             down_block_additional_residuals=cond_down_block,
             mid_block_additional_residual=cond_mid_block,