Remove AddConditioningMaskInvocaton.

invokeai/app/invocations/compel.py

@@ -5,7 +5,15 @@ from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIComponent
+from invokeai.app.invocations.fields import (
+    ConditioningField,
+    FieldDescriptions,
+    Input,
+    InputField,
+    MaskField,
+    OutputField,
+    UIComponent,
+)
 from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.ti_utils import generate_ti_list
@@ -36,7 +44,7 @@ from .model import ClipField
     title="Prompt",
     tags=["prompt", "compel"],
     category="conditioning",
-    version="1.0.1",
+    version="1.1.0",
 )
 class CompelInvocation(BaseInvocation):
     """Parse prompt using compel package to conditioning."""
@@ -51,6 +59,10 @@ class CompelInvocation(BaseInvocation):
         description=FieldDescriptions.clip,
         input=Input.Connection,
     )
+    mask: Optional[MaskField] = InputField(
+        default=None, description="A mask defining the region that this conditioning prompt applies to."
+    )
+    positive_cross_attn_mask_score: float = InputField(default=0.0, description="")
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
@@ -118,7 +130,13 @@ class CompelInvocation(BaseInvocation):
 
         conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput.build(conditioning_name)
+        return ConditioningOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+                mask=self.mask,
+                positive_cross_attn_mask_score=self.positive_cross_attn_mask_score,
+            )
+        )
 
 
 class SDXLPromptInvocationBase:
@@ -232,7 +250,7 @@ class SDXLPromptInvocationBase:
     title="SDXL Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.0.1",
+    version="1.1.0",
 )
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -256,6 +274,11 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
     clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
 
+    mask: Optional[MaskField] = InputField(
+        default=None, description="A mask defining the region that this conditioning prompt applies to."
+    )
+    positive_cross_attn_mask_score: float = InputField(default=0.0, description="")
+
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
         c1, c1_pooled, ec1 = self.run_clip_compel(
@@ -317,7 +340,13 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
         conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput.build(conditioning_name)
+        return ConditioningOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+                mask=self.mask,
+                positive_cross_attn_mask_score=self.positive_cross_attn_mask_score,
+            )
+        )
 
 
 @invocation(
@@ -366,7 +395,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
 
         conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput.build(conditioning_name)
+        return ConditioningOutput(conditioning=ConditioningField(conditioning_name=conditioning_name))
 
 
 @invocation_output("clip_skip_output")

invokeai/app/invocations/conditioning.py

@@ -6,27 +6,7 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
 )
 from invokeai.app.invocations.fields import InputField, WithMetadata
-from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, MaskField, MaskOutput
-
-
-@invocation(
-    "add_conditioning_mask",
-    title="Add Conditioning Mask",
-    tags=["conditioning"],
-    category="conditioning",
-    version="1.0.0",
-)
-class AddConditioningMaskInvocation(BaseInvocation):
-    """Add a mask to an existing conditioning tensor."""
-
-    conditioning: ConditioningField = InputField(description="The conditioning tensor to add a mask to.")
-    mask: MaskField = InputField(description="A mask to add to the conditioning tensor.")
-    positive_cross_attn_mask_score: float = InputField(default=0.0, description="")
-
-    def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        self.conditioning.mask = self.mask
-        self.conditioning.positive_cross_attn_mask_score = self.positive_cross_attn_mask_score
-        return ConditioningOutput(conditioning=self.conditioning)
+from invokeai.app.invocations.primitives import MaskField, MaskOutput
 
 
 @invocation(

invokeai/app/invocations/primitives.py

@@ -427,10 +427,6 @@ class ConditioningOutput(BaseInvocationOutput):
 
     conditioning: ConditioningField = OutputField(description=FieldDescriptions.cond)
 
-    @classmethod
-    def build(cls, conditioning_name: str) -> "ConditioningOutput":
-        return cls(conditioning=ConditioningField(conditioning_name=conditioning_name))
-
 
 @invocation_output("conditioning_collection_output")
 class ConditioningCollectionOutput(BaseInvocationOutput):

invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py

@@ -47,7 +47,7 @@ class RegionalPromptData:
         # - Scale by region size.
         self.negative_cross_attn_mask_score = -10000
         # self.positive_cross_attn_mask_score = 0.0
-        self.positive_self_attn_mask_score = 2.0
+        self.positive_self_attn_mask_score = 1.0
         self.self_attn_mask_end_step_percent = 0.3
         # This one is for regional prompting in general, so should be set on the DenoiseLatents node.
         self.self_attn_score_range = 3.0
@@ -233,6 +233,7 @@ class RegionalPromptData:
                 prompt_query_mask = batch_sample_query_masks[0, prompt_idx, :, 0]  # Shape: (query_seq_len,)
                 # Multiply a (1, query_seq_len) mask by a (query_seq_len, 1) mask to get a (query_seq_len,
                 # query_seq_len) mask.
+                # TODO(ryand): Is += really the best option here?
                 attn_mask[batch_idx, :, :] += (
                     prompt_query_mask.unsqueeze(0) * prompt_query_mask.unsqueeze(1) * self.positive_self_attn_mask_score
                 )