Expanded styles & updated UI

invokeai/app/invocations/ip_adapter.py

@@ -95,7 +95,7 @@ class IPAdapterInvocation(BaseInvocation):
     weight: Union[float, List[float]] = InputField(
         default=1, description="The weight given to the IP-Adapter", title="Weight"
     )
-    method: Literal["full", "style", "composition"] = InputField(
+    method: Literal["full", "style", "composition", "style_strong", "style_precise"] = InputField(
         default="full", description="The method to apply the IP-Adapter"
     )
     begin_step_percent: float = InputField(
@@ -133,12 +133,13 @@ class IPAdapterInvocation(BaseInvocation):
             image_encoder_model_name = image_encoder_starter_model.name
 
         image_encoder_model = self.get_clip_image_encoder(context, image_encoder_model_id, image_encoder_model_name)
+        negative_blocks: List[str] = []
 
         if self.method == "style":
             if ip_adapter_info.base == "sd-1":
                 target_blocks = ["up_blocks.1"]
             elif ip_adapter_info.base == "sdxl":
-                target_blocks = ["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"]
+                target_blocks = ["up_blocks.0.attentions.1"]
             else:
                 raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
         elif self.method == "composition":
@@ -148,6 +149,38 @@ class IPAdapterInvocation(BaseInvocation):
                 target_blocks = ["down_blocks.2.attentions.1"]
             else:
                 raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
+        elif self.method == "style_precise":
+            if ip_adapter_info.base == "sd-1":
+                target_blocks = ["up_blocks.1","down_blocks.2","mid_block"]
+            elif ip_adapter_info.base == "sdxl":
+                target_blocks = ["up_blocks.0.attentions.1","down_blocks.2.attentions.1"]
+            else:
+                raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
+        elif self.method == "style_strong":
+            if ip_adapter_info.base == "sd-1":
+                target_blocks = ["up_blocks.0", "up_blocks.1", "up_blocks.2", "down_blocks.0", "down_blocks.1"]
+            elif ip_adapter_info.base == "sdxl":
+                target_blocks = [
+                    "up_blocks.0.attentions.1",
+                    "up_blocks.1.attentions.1",
+                    "up_blocks.2.attentions.1",
+                    "up_blocks.0.attentions.2",
+                    "up_blocks.1.attentions.2",
+                    "up_blocks.2.attentions.2",
+                    "up_blocks.0.attentions.0",
+                    "up_blocks.1.attentions.0",
+                    "up_blocks.2.attentions.0",
+                    "down_blocks.0.attentions.0",
+                    "down_blocks.0.attentions.1",
+                    "down_blocks.0.attentions.2",
+                    "down_blocks.1.attentions.0",
+                    "down_blocks.1.attentions.1",
+                    "down_blocks.1.attentions.2",
+                    "down_blocks.2.attentions.0",
+                    "down_blocks.2.attentions.2",
+                ]
+            else:
+                raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
         elif self.method == "full":
             target_blocks = ["block"]
         else:

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import math
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from enum import Enum
 from typing import TYPE_CHECKING, List, Optional, Tuple, Union
 
@@ -104,12 +104,24 @@ class IPAdapterConditioningInfo:
 
 @dataclass
 class IPAdapterData:
+    """Data class for IP-Adapter configuration.
+
+    Attributes:
+        ip_adapter_model: The IP-Adapter model to use.
+        ip_adapter_conditioning: The IP-Adapter conditioning data.
+        mask: The mask to apply to the IP-Adapter conditioning.
+        target_blocks: List of target attention block names to apply IP-Adapter to.
+        negative_blocks: List of target attention block names that should use negative attention.
+        weight: The weight to apply to the IP-Adapter conditioning.
+        begin_step_percent: The percentage of steps at which to start applying the IP-Adapter.
+        end_step_percent: The percentage of steps at which to stop applying the IP-Adapter.
+        method: The method to use for applying the IP-Adapter ('full', 'style', 'composition').
+    """
     ip_adapter_model: IPAdapter
     ip_adapter_conditioning: IPAdapterConditioningInfo
     mask: torch.Tensor
     target_blocks: List[str]
-
-    # Either a single weight applied to all steps, or a list of weights for each step.
+    negative_blocks: List[str] = field(default_factory=list)
     weight: Union[float, List[float]] = 1.0
     begin_step_percent: float = 0.0
     end_step_percent: float = 1.0

invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py

@@ -12,8 +12,8 @@ from invokeai.backend.stable_diffusion.diffusion.custom_atttention import (
 
 class UNetIPAdapterData(TypedDict):
     ip_adapter: IPAdapter
-    target_blocks: List[str]
-    method: str
+    target_blocks: List[str]  # Blocks where IP-Adapter should be applied
+    method: str  # Style or other method type
 
 
 class UNetAttentionPatcher:
@@ -44,7 +44,7 @@ class UNetAttentionPatcher:
                     for block in ip_adapter["target_blocks"]:
                         if block in name:
                             skip = False
-                            negative = (ip_adapter["method"] == 'style' and block == "down_blocks.2.attentions.1")
+                            negative = (ip_adapter["method"] == 'style_precise' and block == "down_blocks.2.attentions.1")
                             break
                     ip_adapter_attention_weights: IPAdapterAttentionWeights = IPAdapterAttentionWeights(
                         ip_adapter_weights=ip_adapter_weights, skip=skip, negative=negative

invokeai/frontend/web/src/features/controlLayers/components/IPAdapter/IPAdapterMethod.tsx

@@ -35,6 +35,16 @@ export const IPAdapterMethod = memo(({ method, onChange }: Props) => {
         value: 'composition',
         description: shouldShowModelDescriptions ? t('controlLayers.ipAdapterMethod.compositionDesc') : undefined,
       },
+      {
+        label: t('controlLayers.ipAdapterMethod.styleStrong'),
+        value: 'style_strong',
+        description: shouldShowModelDescriptions ? t('controlLayers.ipAdapterMethod.styleStrongDesc') : undefined,
+      },
+      {
+        label: t('controlLayers.ipAdapterMethod.stylePrecise'),
+        value: 'style_precise',
+        description: shouldShowModelDescriptions ? t('controlLayers.ipAdapterMethod.stylePreciseDesc') : undefined,
+      },
     ],
     [t, shouldShowModelDescriptions]
   );

invokeai/frontend/web/src/features/controlLayers/store/types.ts

@@ -50,7 +50,7 @@ const zCLIPVisionModelV2 = z.enum(['ViT-H', 'ViT-G', 'ViT-L']);
 export type CLIPVisionModelV2 = z.infer<typeof zCLIPVisionModelV2>;
 export const isCLIPVisionModelV2 = (v: unknown): v is CLIPVisionModelV2 => zCLIPVisionModelV2.safeParse(v).success;
 
-const zIPMethodV2 = z.enum(['full', 'style', 'composition']);
+const zIPMethodV2 = z.enum(['full', 'style', 'composition', 'style_strong', 'style_precise']);
 export type IPMethodV2 = z.infer<typeof zIPMethodV2>;
 export const isIPMethodV2 = (v: unknown): v is IPMethodV2 => zIPMethodV2.safeParse(v).success;
 

invokeai/frontend/web/src/features/nodes/types/common.ts

@@ -147,7 +147,7 @@ export const zIPAdapterField = z.object({
   image: zImageField,
   ip_adapter_model: zModelIdentifierField,
   weight: z.number(),
-  method: z.enum(['full', 'style', 'composition']),
+  method: z.enum(['full', 'style', 'composition', 'style_strong', 'style_precise']),
   begin_step_percent: z.number().optional(),
   end_step_percent: z.number().optional(),
 });