Merge branch 'main' into feat/nodes/invocation-cache

Set final l2i to intermediate so the save image node is the only one outputting a final image.

invokeai/app/api/dependencies.py

@@ -9,6 +9,7 @@ from invokeai.app.services.boards import BoardService, BoardServiceDependencies
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
 from invokeai.app.services.images import ImageService, ImageServiceDependencies
+from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
 from invokeai.app.services.resource_name import SimpleNameService
 from invokeai.app.services.urls import LocalUrlService
 from invokeai.backend.util.logging import InvokeAILogger
@@ -126,6 +127,7 @@ class ApiDependencies:
             configuration=config,
             performance_statistics=InvocationStatsService(graph_execution_manager),
             logger=logger,
+            invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size),
         )
 
         create_system_graphs(services.graph_library)

invokeai/app/cli_app.py

@@ -1,5 +1,7 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
+
 from .services.config import InvokeAIAppConfig
 
 # parse_args() must be called before any other imports. if it is not called first, consumers of the config
@@ -309,6 +311,7 @@ def invoke_cli():
         performance_statistics=InvocationStatsService(graph_execution_manager),
         logger=logger,
         configuration=config,
+        invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size),
     )
 
     system_graphs = create_system_graphs(services.graph_library)

invokeai/app/invocations/baseinvocation.py

@@ -67,7 +67,6 @@ class FieldDescriptions:
     width = "Width of output (px)"
     height = "Height of output (px)"
     control = "ControlNet(s) to apply"
-    ip_adapter = "IP-Adapter to apply"
     denoised_latents = "Denoised latents tensor"
     latents = "Latents tensor"
     strength = "Strength of denoising (proportional to steps)"
@@ -156,7 +155,6 @@ class UIType(str, Enum):
     VaeModel = "VaeModelField"
     LoRAModel = "LoRAModelField"
     ControlNetModel = "ControlNetModelField"
-    IPAdapterModel = "IPAdapterModelField"
     UNet = "UNetField"
     Vae = "VaeField"
     CLIP = "ClipField"
@@ -570,7 +568,24 @@ class BaseInvocation(ABC, BaseModel):
                     raise RequiredConnectionException(self.__fields__["type"].default, field_name)
                 elif _input == Input.Any:
                     raise MissingInputException(self.__fields__["type"].default, field_name)
-        return self.invoke(context)
+        output: BaseInvocationOutput
+        if self.use_cache:
+            key = context.services.invocation_cache.create_key(self)
+            cached_value = context.services.invocation_cache.get(key)
+            if cached_value is None:
+                context.services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
+                output = self.invoke(context)
+                context.services.invocation_cache.save(key, output)
+                return output
+            else:
+                context.services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
+                return cached_value
+        else:
+            context.services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
+            return self.invoke(context)
+
+    def get_type(self) -> str:
+        return self.__fields__["type"].default
 
     id: str = Field(
         description="The id of this instance of an invocation. Must be unique among all instances of invocations."
@@ -583,6 +598,7 @@ class BaseInvocation(ABC, BaseModel):
         description="The workflow to save with the image",
         ui_type=UIType.WorkflowField,
     )
+    use_cache: bool = InputField(default=True, description="Whether or not to use the cache")
 
     @validator("workflow", pre=True)
     def validate_workflow_is_json(cls, v):
@@ -606,6 +622,7 @@ def invocation(
     tags: Optional[list[str]] = None,
     category: Optional[str] = None,
     version: Optional[str] = None,
+    use_cache: Optional[bool] = True,
 ) -> Callable[[Type[GenericBaseInvocation]], Type[GenericBaseInvocation]]:
     """
     Adds metadata to an invocation.
@@ -638,6 +655,8 @@ def invocation(
             except ValueError as e:
                 raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
             cls.UIConfig.version = version
+        if use_cache is not None:
+            cls.__fields__["use_cache"].default = use_cache
 
         # Add the invocation type to the pydantic model of the invocation
         invocation_type_annotation = Literal[invocation_type]  # type: ignore

invokeai/app/invocations/collections.py

@@ -56,6 +56,7 @@ class RangeOfSizeInvocation(BaseInvocation):
     tags=["range", "integer", "random", "collection"],
     category="collections",
     version="1.0.0",
+    use_cache=False,
 )
 class RandomRangeInvocation(BaseInvocation):
     """Creates a collection of random numbers"""

invokeai/app/invocations/compel.py

@@ -7,14 +7,14 @@ from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 
 from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
+from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import (
     BasicConditioningInfo,
-    ExtraConditioningInfo,
     SDXLConditioningInfo,
 )
 
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.model_management.models import ModelNotFoundException, ModelType
+from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
 from ...backend.util.devices import torch_dtype
 from .baseinvocation import (
     BaseInvocation,
@@ -99,15 +99,14 @@ class CompelInvocation(BaseInvocation):
                 # print(traceback.format_exc())
                 print(f'Warn: trigger: "{trigger}" not found')
 
-        with (
-            ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),
-            ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
-                tokenizer,
-                ti_manager,
-            ),
-            ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
-            text_encoder_info as text_encoder,
-        ):
+        with ModelPatcher.apply_lora_text_encoder(
+            text_encoder_info.context.model, _lora_loader()
+        ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
+            tokenizer,
+            ti_manager,
+        ), ModelPatcher.apply_clip_skip(
+            text_encoder_info.context.model, self.clip.skipped_layers
+        ), text_encoder_info as text_encoder:
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -123,7 +122,7 @@ class CompelInvocation(BaseInvocation):
 
             c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
 
-            ec = ExtraConditioningInfo(
+            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
                 tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                 cross_attention_control_args=options.get("cross_attention_control", None),
             )
@@ -214,15 +213,14 @@ class SDXLPromptInvocationBase:
                 # print(traceback.format_exc())
                 print(f'Warn: trigger: "{trigger}" not found')
 
-        with (
-            ModelPatcher.apply_lora(text_encoder_info.context.model, _lora_loader(), lora_prefix),
-            ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
-                tokenizer,
-                ti_manager,
-            ),
-            ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
-            text_encoder_info as text_encoder,
-        ):
+        with ModelPatcher.apply_lora(
+            text_encoder_info.context.model, _lora_loader(), lora_prefix
+        ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
+            tokenizer,
+            ti_manager,
+        ), ModelPatcher.apply_clip_skip(
+            text_encoder_info.context.model, clip_field.skipped_layers
+        ), text_encoder_info as text_encoder:
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -246,7 +244,7 @@ class SDXLPromptInvocationBase:
             else:
                 c_pooled = None
 
-            ec = ExtraConditioningInfo(
+            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
                 tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                 cross_attention_control_args=options.get("cross_attention_control", None),
             )
@@ -438,11 +436,9 @@ def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, trun
         raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
 
     text_fragments = [
-        (
-            x.text
-            if type(x) is Fragment
-            else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
-        )
+        x.text
+        if type(x) is Fragment
+        else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
         for x in parsed_prompt.children
     ]
     text = " ".join(text_fragments)

invokeai/app/invocations/image.py

@@ -965,3 +965,42 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
             width=image_dto.width,
             height=image_dto.height,
         )
+
+
+@invocation(
+    "save_image",
+    title="Save Image",
+    tags=["primitives", "image"],
+    category="primitives",
+    version="1.0.0",
+    use_cache=False,
+)
+class SaveImageInvocation(BaseInvocation):
+    """Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
+
+    image: ImageField = InputField(description="The image to load")
+    metadata: CoreMetadata = InputField(
+        default=None,
+        description=FieldDescriptions.core_metadata,
+        ui_hidden=True,
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+
+        image_dto = context.services.images.create(
+            image=image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            metadata=self.metadata.dict() if self.metadata else None,
+            workflow=self.workflow,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )

invokeai/app/invocations/ip_adapter.py

@@ -1,105 +0,0 @@
-import os
-from builtins import float
-from typing import List, Union
-
-from pydantic import BaseModel, Field
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    FieldDescriptions,
-    Input,
-    InputField,
-    InvocationContext,
-    OutputField,
-    UIType,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.primitives import ImageField
-from invokeai.backend.model_management.models.base import BaseModelType, ModelType
-from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
-
-
-class IPAdapterModelField(BaseModel):
-    model_name: str = Field(description="Name of the IP-Adapter model")
-    base_model: BaseModelType = Field(description="Base model")
-
-
-class CLIPVisionModelField(BaseModel):
-    model_name: str = Field(description="Name of the CLIP Vision image encoder model")
-    base_model: BaseModelType = Field(description="Base model (usually 'Any')")
-
-
-class IPAdapterField(BaseModel):
-    image: ImageField = Field(description="The IP-Adapter image prompt.")
-    ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
-    image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
-    weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
-    # weight: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
-    begin_step_percent: float = Field(
-        default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
-    )
-    end_step_percent: float = Field(
-        default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
-    )
-
-
-@invocation_output("ip_adapter_output")
-class IPAdapterOutput(BaseInvocationOutput):
-    # Outputs
-    ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
-
-
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.0.0")
-class IPAdapterInvocation(BaseInvocation):
-    """Collects IP-Adapter info to pass to other nodes."""
-
-    # Inputs
-    image: ImageField = InputField(description="The IP-Adapter image prompt.")
-    ip_adapter_model: IPAdapterModelField = InputField(
-        description="The IP-Adapter model.",
-        title="IP-Adapter Model",
-        input=Input.Direct,
-    )
-
-    # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
-    weight: Union[float, List[float]] = InputField(
-        default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
-    )
-
-    begin_step_percent: float = InputField(
-        default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
-    )
-    end_step_percent: float = InputField(
-        default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
-    )
-
-    def invoke(self, context: InvocationContext) -> IPAdapterOutput:
-        # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
-        ip_adapter_info = context.services.model_manager.model_info(
-            self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
-        )
-        # HACK(ryand): This is bad for a couple of reasons: 1) we are bypassing the model manager to read the model
-        # directly, and 2) we are reading from disk every time this invocation is called without caching the result.
-        # A better solution would be to store the image encoder model reference in the IP-Adapter model info, but this
-        # is currently messy due to differences between how the model info is generated when installing a model from
-        # disk vs. downloading the model.
-        image_encoder_model_id = get_ip_adapter_image_encoder_model_id(
-            os.path.join(context.services.configuration.get_config().models_path, ip_adapter_info["path"])
-        )
-        image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
-        image_encoder_model = CLIPVisionModelField(
-            model_name=image_encoder_model_name,
-            base_model=BaseModelType.Any,
-        )
-        return IPAdapterOutput(
-            ip_adapter=IPAdapterField(
-                image=self.image,
-                ip_adapter_model=self.ip_adapter_model,
-                image_encoder_model=image_encoder_model,
-                weight=self.weight,
-                begin_step_percent=self.begin_step_percent,
-                end_step_percent=self.end_step_percent,
-            ),
-        )

invokeai/app/invocations/latent.py

@@ -8,7 +8,6 @@ import numpy as np
 import torch
 import torchvision.transforms as T
 from diffusers.image_processor import VaeImageProcessor
-from diffusers.models import UNet2DConditionModel
 from diffusers.models.attention_processor import (
     AttnProcessor2_0,
     LoRAAttnProcessor2_0,
@@ -20,7 +19,6 @@ from diffusers.schedulers import SchedulerMixin as Scheduler
 from pydantic import validator
 from torchvision.transforms.functional import resize as tv_resize
 
-from invokeai.app.invocations.ip_adapter import IPAdapterField
 from invokeai.app.invocations.metadata import CoreMetadata
 from invokeai.app.invocations.primitives import (
     DenoiseMaskField,
@@ -33,17 +31,15 @@ from invokeai.app.invocations.primitives import (
 )
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
-from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
 from invokeai.backend.model_management.models import ModelType, SilenceWarnings
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData, IPAdapterConditioningInfo
 
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.model_management.models import BaseModelType
 from ...backend.model_management.seamless import set_seamless
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
+    ConditioningData,
     ControlNetData,
-    IPAdapterData,
     StableDiffusionGeneratorPipeline,
     image_resized_to_grid_as_tensor,
 )
@@ -72,6 +68,7 @@ if choose_torch_device() == torch.device("mps"):
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
+
 SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
 
 
@@ -194,7 +191,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.1.0",
+    version="1.0.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -222,12 +219,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
         input=Input.Connection,
         ui_order=5,
     )
-    ip_adapter: Optional[IPAdapterField] = InputField(
-        description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection, ui_order=6
-    )
     latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
     denoise_mask: Optional[DenoiseMaskField] = InputField(
-        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=7
+        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=6
     )
 
     @validator("cfg_scale")
@@ -329,6 +323,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
     def prep_control_data(
         self,
         context: InvocationContext,
+        # really only need model for dtype and device
+        model: StableDiffusionGeneratorPipeline,
         control_input: Union[ControlField, List[ControlField]],
         latents_shape: List[int],
         exit_stack: ExitStack,
@@ -348,107 +344,57 @@ class DenoiseLatentsInvocation(BaseInvocation):
         else:
             control_list = None
         if control_list is None:
-            return None
-        # After above handling, any control that is not None should now be of type list[ControlField].
-
-        # FIXME: add checks to skip entry if model or image is None
-        #        and if weight is None, populate with default 1.0?
-        controlnet_data = []
-        for control_info in control_list:
-            control_model = exit_stack.enter_context(
-                context.services.model_manager.get_model(
-                    model_name=control_info.control_model.model_name,
-                    model_type=ModelType.ControlNet,
-                    base_model=control_info.control_model.base_model,
-                    context=context,
+            control_data = None
+            # from above handling, any control that is not None should now be of type list[ControlField]
+        else:
+            # FIXME: add checks to skip entry if model or image is None
+            #        and if weight is None, populate with default 1.0?
+            control_data = []
+            control_models = []
+            for control_info in control_list:
+                control_model = exit_stack.enter_context(
+                    context.services.model_manager.get_model(
+                        model_name=control_info.control_model.model_name,
+                        model_type=ModelType.ControlNet,
+                        base_model=control_info.control_model.base_model,
+                        context=context,
+                    )
                 )
-            )
 
-            # control_models.append(control_model)
-            control_image_field = control_info.image
-            input_image = context.services.images.get_pil_image(control_image_field.image_name)
-            # self.image.image_type, self.image.image_name
-            # FIXME: still need to test with different widths, heights, devices, dtypes
-            #        and add in batch_size, num_images_per_prompt?
-            #        and do real check for classifier_free_guidance?
-            # prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
-            control_image = prepare_control_image(
-                image=input_image,
-                do_classifier_free_guidance=do_classifier_free_guidance,
-                width=control_width_resize,
-                height=control_height_resize,
-                # batch_size=batch_size * num_images_per_prompt,
-                # num_images_per_prompt=num_images_per_prompt,
-                device=control_model.device,
-                dtype=control_model.dtype,
-                control_mode=control_info.control_mode,
-                resize_mode=control_info.resize_mode,
-            )
-            control_item = ControlNetData(
-                model=control_model,  # model object
-                image_tensor=control_image,
-                weight=control_info.control_weight,
-                begin_step_percent=control_info.begin_step_percent,
-                end_step_percent=control_info.end_step_percent,
-                control_mode=control_info.control_mode,
-                # any resizing needed should currently be happening in prepare_control_image(),
-                #    but adding resize_mode to ControlNetData in case needed in the future
-                resize_mode=control_info.resize_mode,
-            )
-            controlnet_data.append(control_item)
-            # MultiControlNetModel has been refactored out, just need list[ControlNetData]
-
-        return controlnet_data
-
-    def prep_ip_adapter_data(
-        self,
-        context: InvocationContext,
-        ip_adapter: Optional[IPAdapterField],
-        conditioning_data: ConditioningData,
-        unet: UNet2DConditionModel,
-        exit_stack: ExitStack,
-    ) -> Optional[IPAdapterData]:
-        """If IP-Adapter is enabled, then this function loads the requisite models, and adds the image prompt embeddings
-        to the `conditioning_data` (in-place).
-        """
-        if ip_adapter is None:
-            return None
-
-        image_encoder_model_info = context.services.model_manager.get_model(
-            model_name=ip_adapter.image_encoder_model.model_name,
-            model_type=ModelType.CLIPVision,
-            base_model=ip_adapter.image_encoder_model.base_model,
-            context=context,
-        )
-
-        ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
-            context.services.model_manager.get_model(
-                model_name=ip_adapter.ip_adapter_model.model_name,
-                model_type=ModelType.IPAdapter,
-                base_model=ip_adapter.ip_adapter_model.base_model,
-                context=context,
-            )
-        )
-
-        input_image = context.services.images.get_pil_image(ip_adapter.image.image_name)
-
-        # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
-        # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
-        with image_encoder_model_info as image_encoder_model:
-            # Get image embeddings from CLIP and ImageProjModel.
-            image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
-                input_image, image_encoder_model
-            )
-            conditioning_data.ip_adapter_conditioning = IPAdapterConditioningInfo(
-                image_prompt_embeds, uncond_image_prompt_embeds
-            )
-
-        return IPAdapterData(
-            ip_adapter_model=ip_adapter_model,
-            weight=ip_adapter.weight,
-            begin_step_percent=ip_adapter.begin_step_percent,
-            end_step_percent=ip_adapter.end_step_percent,
-        )
+                control_models.append(control_model)
+                control_image_field = control_info.image
+                input_image = context.services.images.get_pil_image(control_image_field.image_name)
+                # self.image.image_type, self.image.image_name
+                # FIXME: still need to test with different widths, heights, devices, dtypes
+                #        and add in batch_size, num_images_per_prompt?
+                #        and do real check for classifier_free_guidance?
+                # prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
+                control_image = prepare_control_image(
+                    image=input_image,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                    width=control_width_resize,
+                    height=control_height_resize,
+                    # batch_size=batch_size * num_images_per_prompt,
+                    # num_images_per_prompt=num_images_per_prompt,
+                    device=control_model.device,
+                    dtype=control_model.dtype,
+                    control_mode=control_info.control_mode,
+                    resize_mode=control_info.resize_mode,
+                )
+                control_item = ControlNetData(
+                    model=control_model,
+                    image_tensor=control_image,
+                    weight=control_info.control_weight,
+                    begin_step_percent=control_info.begin_step_percent,
+                    end_step_percent=control_info.end_step_percent,
+                    control_mode=control_info.control_mode,
+                    # any resizing needed should currently be happening in prepare_control_image(),
+                    #    but adding resize_mode to ControlNetData in case needed in the future
+                    resize_mode=control_info.resize_mode,
+                )
+                control_data.append(control_item)
+                # MultiControlNetModel has been refactored out, just need list[ControlNetData]
+        return control_data
 
     # original idea by https://github.com/AmericanPresidentJimmyCarter
     # TODO: research more for second order schedulers timesteps
@@ -542,12 +488,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 **self.unet.unet.dict(),
                 context=context,
             )
-            with (
-                ExitStack() as exit_stack,
-                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),
-                set_seamless(unet_info.context.model, **self.unet.seamless.dict()),
-                unet_info as unet,
-            ):
+            with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
+                unet_info.context.model, _lora_loader()
+            ), set_seamless(unet_info.context.model, self.unet.seamless_axes), unet_info as unet:
                 latents = latents.to(device=unet.device, dtype=unet.dtype)
                 if noise is not None:
                     noise = noise.to(device=unet.device, dtype=unet.dtype)
@@ -566,7 +509,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 pipeline = self.create_pipeline(unet, scheduler)
                 conditioning_data = self.get_conditioning_data(context, scheduler, unet, seed)
 
-                controlnet_data = self.prep_control_data(
+                control_data = self.prep_control_data(
+                    model=pipeline,
                     context=context,
                     control_input=self.control,
                     latents_shape=latents.shape,
@@ -575,14 +519,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     exit_stack=exit_stack,
                 )
 
-                ip_adapter_data = self.prep_ip_adapter_data(
-                    context=context,
-                    ip_adapter=self.ip_adapter,
-                    conditioning_data=conditioning_data,
-                    unet=unet,
-                    exit_stack=exit_stack,
-                )
-
                 num_inference_steps, timesteps, init_timestep = self.init_scheduler(
                     scheduler,
                     device=unet.device,
@@ -601,8 +537,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     masked_latents=masked_latents,
                     num_inference_steps=num_inference_steps,
                     conditioning_data=conditioning_data,
-                    control_data=controlnet_data,  # list[ControlNetData],
-                    ip_adapter_data=ip_adapter_data,  # IPAdapterData,
+                    control_data=control_data,  # list[ControlNetData]
                     callback=step_callback,
                 )
 
@@ -648,7 +583,7 @@ class LatentsToImageInvocation(BaseInvocation):
             context=context,
         )
 
-        with set_seamless(vae_info.context.model, **self.vae.seamless.dict()), vae_info as vae:
+        with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
             latents = latents.to(vae.device)
             if self.fp32:
                 vae.to(dtype=torch.float32)

invokeai/app/invocations/math.py

@@ -54,7 +54,14 @@ class DivideInvocation(BaseInvocation):
         return IntegerOutput(value=int(self.a / self.b))
 
 
-@invocation("rand_int", title="Random Integer", tags=["math", "random"], category="math", version="1.0.0")
+@invocation(
+    "rand_int",
+    title="Random Integer",
+    tags=["math", "random"],
+    category="math",
+    version="1.0.0",
+    use_cache=False,
+)
 class RandomIntInvocation(BaseInvocation):
     """Outputs a single random integer."""
 

invokeai/app/invocations/model.py

@@ -18,13 +18,6 @@ from .baseinvocation import (
 )
 
 
-class SeamlessSettings(BaseModel):
-    axes: List[str] = Field(description="Axes('x' and 'y') to which apply seamless")
-    skipped_layers: int = Field(description="How much down layers skip when applying seamless")
-    skip_second_resnet: bool = Field(description="Skip or not second resnet in down blocks when applying seamless")
-    skip_conv2: bool = Field(description="Skip or not conv2 in down blocks when applying seamless")
-
-
 class ModelInfo(BaseModel):
     model_name: str = Field(description="Info to load submodel")
     base_model: BaseModelType = Field(description="Base model")
@@ -40,7 +33,7 @@ class UNetField(BaseModel):
     unet: ModelInfo = Field(description="Info to load unet submodel")
     scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
     loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
-    seamless: Optional[SeamlessSettings] = Field(default=None, description="Seamless settings applied to model")
+    seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
 class ClipField(BaseModel):
@@ -53,7 +46,7 @@ class ClipField(BaseModel):
 class VaeField(BaseModel):
     # TODO: better naming?
     vae: ModelInfo = Field(description="Info to load vae submodel")
-    seamless: Optional[SeamlessSettings] = Field(default=None, description="Seamless settings applied to model")
+    seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
 @invocation_output("model_loader_output")
@@ -395,11 +388,6 @@ class SeamlessModeInvocation(BaseInvocation):
     )
     seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
     seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")
-    skipped_layers: int = InputField(default=0, input=Input.Any, description="How much model's down layers to skip")
-    skip_second_resnet: bool = InputField(
-        default=True, input=Input.Any, description="Skip or not second resnet in down layers"
-    )
-    skip_conv2: bool = InputField(default=True, input=Input.Any, description="Skip or not conv2 in down layers")
 
     def invoke(self, context: InvocationContext) -> SeamlessModeOutput:
         # Conditionally append 'x' and 'y' based on seamless_x and seamless_y
@@ -414,18 +402,8 @@ class SeamlessModeInvocation(BaseInvocation):
             seamless_axes_list.append("y")
 
         if unet is not None:
-            unet.seamless = SeamlessSettings(
-                axes=seamless_axes_list,
-                skipped_layers=self.skipped_layers,
-                skip_second_resnet=self.skip_second_resnet,
-                skip_conv2=self.skip_conv2,
-            )
+            unet.seamless_axes = seamless_axes_list
         if vae is not None:
-            vae.seamless = SeamlessSettings(
-                axes=seamless_axes_list,
-                skipped_layers=self.skipped_layers,
-                skip_second_resnet=self.skip_second_resnet,
-                skip_conv2=self.skip_conv2,
-            )
+            vae.seamless_axes = seamless_axes_list
 
         return SeamlessModeOutput(unet=unet, vae=vae)

invokeai/app/invocations/onnx.py

@@ -95,10 +95,9 @@ class ONNXPromptInvocation(BaseInvocation):
                     print(f'Warn: trigger: "{trigger}" not found')
             if loras or ti_list:
                 text_encoder.release_session()
-            with (
-                ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras),
-                ONNXModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager),
-            ):
+            with ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras), ONNXModelPatcher.apply_ti(
+                orig_tokenizer, text_encoder, ti_list
+            ) as (tokenizer, ti_manager):
                 text_encoder.create_session()
 
                 # copy from

invokeai/app/invocations/prompt.py

@@ -10,7 +10,14 @@ from invokeai.app.invocations.primitives import StringCollectionOutput
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, invocation
 
 
-@invocation("dynamic_prompt", title="Dynamic Prompt", tags=["prompt", "collection"], category="prompt", version="1.0.0")
+@invocation(
+    "dynamic_prompt",
+    title="Dynamic Prompt",
+    tags=["prompt", "collection"],
+    category="prompt",
+    version="1.0.0",
+    use_cache=False,
+)
 class DynamicPromptInvocation(BaseInvocation):
     """Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator"""
 

invokeai/app/services/config/invokeai_config.py

@@ -253,6 +253,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     attention_type      : Literal[tuple(["auto", "normal", "xformers", "sliced", "torch-sdp"])] = Field(default="auto", description="Attention type", category="Generation", )
     attention_slice_size: Literal[tuple(["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8])] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
     force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
+    node_cache_size     : int = Field(default=512, description="How many cached nodes to keep in memory", category="Generation", )
 
     # NODES
     allow_nodes         : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", category="Nodes")

invokeai/app/services/invocation_cache/invocation_cache_base.py

@@ -0,0 +1,29 @@
+from abc import ABC, abstractmethod
+from typing import Optional, Union
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
+
+
+class InvocationCacheBase(ABC):
+    """Base class for invocation caches."""
+
+    @abstractmethod
+    def get(self, key: Union[int, str]) -> Optional[BaseInvocationOutput]:
+        """Retrieves and invocation output from the cache"""
+        pass
+
+    @abstractmethod
+    def save(self, key: Union[int, str], value: BaseInvocationOutput) -> None:
+        """Stores an invocation output in the cache"""
+        pass
+
+    @abstractmethod
+    def delete(self, key: Union[int, str]) -> None:
+        """Deleted an invocation output from the cache"""
+        pass
+
+    @classmethod
+    @abstractmethod
+    def create_key(cls, value: BaseInvocation) -> Union[int, str]:
+        """Creates the cache key for an invocation"""
+        pass

invokeai/app/services/invocation_cache/invocation_cache_memory.py

@@ -0,0 +1,34 @@
+from queue import Queue
+from typing import Optional, Union
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
+from invokeai.app.services.invocation_cache.invocation_cache_base import InvocationCacheBase
+
+
+class MemoryInvocationCache(InvocationCacheBase):
+    __cache: dict[Union[int, str], BaseInvocationOutput]
+    __max_cache_size: int
+    __cache_ids: Queue
+
+    def __init__(self, max_cache_size: int = 512) -> None:
+        self.__cache = dict()
+        self.__max_cache_size = max_cache_size
+        self.__cache_ids = Queue()
+
+    def get(self, key: Union[int, str]) -> Optional[BaseInvocationOutput]:
+        return self.__cache.get(key, None)
+
+    def save(self, key: Union[int, str], value: BaseInvocationOutput) -> None:
+        if key not in self.__cache:
+            self.__cache[key] = value
+            self.__cache_ids.put(key)
+            if self.__cache_ids.qsize() > self.__max_cache_size:
+                self.__cache.pop(self.__cache_ids.get())
+
+    def delete(self, key: Union[int, str]) -> None:
+        if key in self.__cache:
+            del self.__cache[key]
+
+    @classmethod
+    def create_key(cls, value: BaseInvocation) -> Union[int, str]:
+        return hash(value.json(exclude={"id"}))

invokeai/app/services/invocation_services.py

@@ -12,6 +12,7 @@ if TYPE_CHECKING:
     from invokeai.app.services.events import EventServiceBase
     from invokeai.app.services.graph import GraphExecutionState, LibraryGraph
     from invokeai.app.services.images import ImageServiceABC
+    from invokeai.app.services.invocation_cache.invocation_cache_base import InvocationCacheBase
     from invokeai.app.services.invocation_queue import InvocationQueueABC
     from invokeai.app.services.invocation_stats import InvocationStatsServiceBase
     from invokeai.app.services.invoker import InvocationProcessorABC
@@ -37,6 +38,7 @@ class InvocationServices:
     processor: "InvocationProcessorABC"
     performance_statistics: "InvocationStatsServiceBase"
     queue: "InvocationQueueABC"
+    invocation_cache: "InvocationCacheBase"
 
     def __init__(
         self,
@@ -53,6 +55,7 @@ class InvocationServices:
         processor: "InvocationProcessorABC",
         performance_statistics: "InvocationStatsServiceBase",
         queue: "InvocationQueueABC",
+        invocation_cache: "InvocationCacheBase",
     ):
         self.board_images = board_images
         self.boards = boards
@@ -68,3 +71,4 @@ class InvocationServices:
         self.processor = processor
         self.performance_statistics = performance_statistics
         self.queue = queue
+        self.invocation_cache = invocation_cache

invokeai/backend/install/model_install_backend.py

@@ -326,16 +326,6 @@ class ModelInstall(object):
                     elif f"learned_embeds.{suffix}" in files:
                         location = self._download_hf_model(repo_id, [f"learned_embeds.{suffix}"], staging)
                         break
-                    elif "image_encoder.txt" in files and f"ip_adapter.{suffix}" in files:  # IP-Adapter
-                        files = ["image_encoder.txt", f"ip_adapter.{suffix}"]
-                        location = self._download_hf_model(repo_id, files, staging)
-                        break
-                    elif f"model.{suffix}" in files and "config.json" in files:
-                        # This elif-condition is pretty fragile, but it is intended to handle CLIP Vision models hosted
-                        # by InvokeAI for use with IP-Adapters.
-                        files = ["config.json", f"model.{suffix}"]
-                        location = self._download_hf_model(repo_id, files, staging)
-                        break
             if not location:
                 logger.warning(f"Could not determine type of repo {repo_id}. Skipping install.")
                 return {}
@@ -544,17 +534,14 @@ def hf_download_with_resume(
         logger.info(f"{model_name}: Downloading...")
 
     try:
-        with (
-            open(model_dest, open_mode) as file,
-            tqdm(
-                desc=model_name,
-                initial=exist_size,
-                total=total + exist_size,
-                unit="iB",
-                unit_scale=True,
-                unit_divisor=1000,
-            ) as bar,
-        ):
+        with open(model_dest, open_mode) as file, tqdm(
+            desc=model_name,
+            initial=exist_size,
+            total=total + exist_size,
+            unit="iB",
+            unit_scale=True,
+            unit_divisor=1000,
+        ) as bar:
             for data in resp.iter_content(chunk_size=1024):
                 size = file.write(data)
                 bar.update(size)

invokeai/backend/ip_adapter/README.md

@@ -1,45 +0,0 @@
-# IP-Adapter Model Formats
-
-The official IP-Adapter models are released here: [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter)
-
-This official model repo does not integrate well with InvokeAI's current approach to model management, so we have defined a new file structure for IP-Adapter models. The InvokeAI format is described below.
-
-## CLIP Vision Models
-
-CLIP Vision models are organized in `diffusers`` format. The expected directory structure is:
-
-```bash
-ip_adapter_sd_image_encoder/
-├── config.json
-└── model.safetensors
-```
-
-## IP-Adapter Models
-
-IP-Adapter models are stored in a directory containing two files
-- `image_encoder.txt`: A text file containing the model identifier for the CLIP Vision encoder that is intended to be used with this IP-Adapter model.
-- `ip_adapter.bin`: The IP-Adapter weights.
-
-Sample directory structure:
-```bash
-ip_adapter_sd15/
-├── image_encoder.txt
-└── ip_adapter.bin
-```
-
-### Why save the weights in a .safetensors file?
-
-The weights in `ip_adapter.bin` are stored in a nested dict, which is not supported by `safetensors`. This could be solved by splitting `ip_adapter.bin` into multiple files, but for now we have decided to maintain consistency with the checkpoint structure used in the official [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter) repo.
-
-## InvokeAI Hosted IP-Adapters
-
-Image Encoders:
-- [InvokeAI/ip_adapter_sd_image_encoder](https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder)
-- [InvokeAI/ip_adapter_sdxl_image_encoder](https://huggingface.co/InvokeAI/ip_adapter_sdxl_image_encoder)
-
-IP-Adapters:
-- [InvokeAI/ip_adapter_sd15](https://huggingface.co/InvokeAI/ip_adapter_sd15)
-- [InvokeAI/ip_adapter_plus_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_sd15)
-- [InvokeAI/ip_adapter_plus_face_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15)
-- [InvokeAI/ip_adapter_sdxl](https://huggingface.co/InvokeAI/ip_adapter_sdxl)
-- Not yet supported: [InvokeAI/ip_adapter_sdxl_vit_h](https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h)

invokeai/backend/ip_adapter/attention_processor.py

@@ -1,162 +0,0 @@
-# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
-#   and modified as needed
-
-# tencent-ailab comment:
-# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from diffusers.models.attention_processor import AttnProcessor2_0 as DiffusersAttnProcessor2_0
-
-
-# Create a version of AttnProcessor2_0 that is a sub-class of nn.Module. This is required for IP-Adapter state_dict
-# loading.
-class AttnProcessor2_0(DiffusersAttnProcessor2_0, nn.Module):
-    def __init__(self):
-        DiffusersAttnProcessor2_0.__init__(self)
-        nn.Module.__init__(self)
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-        ip_adapter_image_prompt_embeds=None,
-    ):
-        """Re-definition of DiffusersAttnProcessor2_0.__call__(...) that accepts and ignores the
-        ip_adapter_image_prompt_embeds parameter.
-        """
-        return DiffusersAttnProcessor2_0.__call__(
-            self, attn, hidden_states, encoder_hidden_states, attention_mask, temb
-        )
-
-
-class IPAttnProcessor2_0(torch.nn.Module):
-    r"""
-    Attention processor for IP-Adapater for PyTorch 2.0.
-    Args:
-        hidden_size (`int`):
-            The hidden size of the attention layer.
-        cross_attention_dim (`int`):
-            The number of channels in the `encoder_hidden_states`.
-        scale (`float`, defaults to 1.0):
-            the weight scale of image prompt.
-    """
-
-    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0):
-        super().__init__()
-
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.scale = scale
-
-        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-        ip_adapter_image_prompt_embeds=None,
-    ):
-        if encoder_hidden_states is not None:
-            # If encoder_hidden_states is not None, then we are doing cross-attention, not self-attention. In this case,
-            # we will apply IP-Adapter conditioning. We validate the inputs for IP-Adapter conditioning here.
-            assert ip_adapter_image_prompt_embeds is not None
-            # The batch dimensions should match.
-            assert ip_adapter_image_prompt_embeds.shape[0] == encoder_hidden_states.shape[0]
-            # The channel dimensions should match.
-            assert ip_adapter_image_prompt_embeds.shape[2] == encoder_hidden_states.shape[2]
-            ip_hidden_states = ip_adapter_image_prompt_embeds
-
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        if ip_hidden_states is not None:
-            ip_key = self.to_k_ip(ip_hidden_states)
-            ip_value = self.to_v_ip(ip_hidden_states)
-
-            ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-            ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-            # the output of sdp = (batch, num_heads, seq_len, head_dim)
-            # TODO: add support for attn.scale when we move to Torch 2.1
-            ip_hidden_states = F.scaled_dot_product_attention(
-                query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
-            )
-
-            ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-            ip_hidden_states = ip_hidden_states.to(query.dtype)
-
-            hidden_states = hidden_states + self.scale * ip_hidden_states
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states

invokeai/backend/ip_adapter/ip_adapter.py

@@ -1,217 +0,0 @@
-# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
-#   and modified as needed
-
-from contextlib import contextmanager
-from typing import Optional, Union
-
-import torch
-from diffusers.models import UNet2DConditionModel
-from PIL import Image
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
-
-from .attention_processor import AttnProcessor2_0, IPAttnProcessor2_0
-from .resampler import Resampler
-
-
-class ImageProjModel(torch.nn.Module):
-    """Image Projection Model"""
-
-    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
-        super().__init__()
-
-        self.cross_attention_dim = cross_attention_dim
-        self.clip_extra_context_tokens = clip_extra_context_tokens
-        self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
-        self.norm = torch.nn.LayerNorm(cross_attention_dim)
-
-    @classmethod
-    def from_state_dict(cls, state_dict: dict[torch.Tensor], clip_extra_context_tokens=4):
-        """Initialize an ImageProjModel from a state_dict.
-
-        The cross_attention_dim and clip_embeddings_dim are inferred from the shape of the tensors in the state_dict.
-
-        Args:
-            state_dict (dict[torch.Tensor]): The state_dict of model weights.
-            clip_extra_context_tokens (int, optional): Defaults to 4.
-
-        Returns:
-            ImageProjModel
-        """
-        cross_attention_dim = state_dict["norm.weight"].shape[0]
-        clip_embeddings_dim = state_dict["proj.weight"].shape[-1]
-
-        model = cls(cross_attention_dim, clip_embeddings_dim, clip_extra_context_tokens)
-
-        model.load_state_dict(state_dict)
-        return model
-
-    def forward(self, image_embeds):
-        embeds = image_embeds
-        clip_extra_context_tokens = self.proj(embeds).reshape(
-            -1, self.clip_extra_context_tokens, self.cross_attention_dim
-        )
-        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
-        return clip_extra_context_tokens
-
-
-class IPAdapter:
-    """IP-Adapter: https://arxiv.org/pdf/2308.06721.pdf"""
-
-    def __init__(
-        self,
-        state_dict: dict[torch.Tensor],
-        device: torch.device,
-        dtype: torch.dtype = torch.float16,
-        num_tokens: int = 4,
-    ):
-        self.device = device
-        self.dtype = dtype
-
-        self._num_tokens = num_tokens
-
-        self._clip_image_processor = CLIPImageProcessor()
-
-        self._state_dict = state_dict
-
-        self._image_proj_model = self._init_image_proj_model(self._state_dict["image_proj"])
-
-        # The _attn_processors will be initialized later when we have access to the UNet.
-        self._attn_processors = None
-
-    def to(self, device: torch.device, dtype: Optional[torch.dtype] = None):
-        self.device = device
-        if dtype is not None:
-            self.dtype = dtype
-
-        self._image_proj_model.to(device=self.device, dtype=self.dtype)
-        if self._attn_processors is not None:
-            torch.nn.ModuleList(self._attn_processors.values()).to(device=self.device, dtype=self.dtype)
-
-    def _init_image_proj_model(self, state_dict):
-        return ImageProjModel.from_state_dict(state_dict, self._num_tokens).to(self.device, dtype=self.dtype)
-
-    def _prepare_attention_processors(self, unet: UNet2DConditionModel):
-        """Prepare a dict of attention processors that can later be injected into a unet, and load the IP-Adapter
-        attention weights into them.
-
-        Note that the `unet` param is only used to determine attention block dimensions and naming.
-        TODO(ryand): As a future improvement, this could all be inferred from the state_dict when the IPAdapter is
-        intialized.
-        """
-        attn_procs = {}
-        for name in unet.attn_processors.keys():
-            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
-            if name.startswith("mid_block"):
-                hidden_size = unet.config.block_out_channels[-1]
-            elif name.startswith("up_blocks"):
-                block_id = int(name[len("up_blocks.")])
-                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
-            elif name.startswith("down_blocks"):
-                block_id = int(name[len("down_blocks.")])
-                hidden_size = unet.config.block_out_channels[block_id]
-            if cross_attention_dim is None:
-                attn_procs[name] = AttnProcessor2_0()
-            else:
-                attn_procs[name] = IPAttnProcessor2_0(
-                    hidden_size=hidden_size,
-                    cross_attention_dim=cross_attention_dim,
-                    scale=1.0,
-                ).to(self.device, dtype=self.dtype)
-
-        ip_layers = torch.nn.ModuleList(attn_procs.values())
-        ip_layers.load_state_dict(self._state_dict["ip_adapter"])
-        self._attn_processors = attn_procs
-        self._state_dict = None
-
-    # @genomancer: pushed scaling back out into its own method (like original Tencent implementation)
-    #      which makes implementing begin_step_percent and end_step_percent easier
-    #  but based on self._attn_processors (ala @Ryan) instead of original Tencent unet.attn_processors,
-    #      which should make it easier to implement multiple IPAdapters
-    def set_scale(self, scale):
-        if self._attn_processors is not None:
-            for attn_processor in self._attn_processors.values():
-                if isinstance(attn_processor, IPAttnProcessor2_0):
-                    attn_processor.scale = scale
-
-    @contextmanager
-    def apply_ip_adapter_attention(self, unet: UNet2DConditionModel, scale: float):
-        """A context manager that patches `unet` with this IP-Adapter's attention processors while it is active.
-
-        Yields:
-            None
-        """
-        if self._attn_processors is None:
-            # We only have to call _prepare_attention_processors(...) once, and then the result is cached and can be
-            # used on any UNet model (with the same dimensions).
-            self._prepare_attention_processors(unet)
-
-        # Set scale
-        self.set_scale(scale)
-        # for attn_processor in self._attn_processors.values():
-        #     if isinstance(attn_processor, IPAttnProcessor2_0):
-        #         attn_processor.scale = scale
-
-        orig_attn_processors = unet.attn_processors
-
-        # Make a (moderately-) shallow copy of the self._attn_processors dict, because unet.set_attn_processor(...)
-        # actually pops elements from the passed dict.
-        ip_adapter_attn_processors = {k: v for k, v in self._attn_processors.items()}
-
-        try:
-            unet.set_attn_processor(ip_adapter_attn_processors)
-            yield None
-        finally:
-            unet.set_attn_processor(orig_attn_processors)
-
-    @torch.inference_mode()
-    def get_image_embeds(self, pil_image, image_encoder: CLIPVisionModelWithProjection):
-        if isinstance(pil_image, Image.Image):
-            pil_image = [pil_image]
-        clip_image = self._clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-        clip_image_embeds = image_encoder(clip_image.to(self.device, dtype=self.dtype)).image_embeds
-        image_prompt_embeds = self._image_proj_model(clip_image_embeds)
-        uncond_image_prompt_embeds = self._image_proj_model(torch.zeros_like(clip_image_embeds))
-        return image_prompt_embeds, uncond_image_prompt_embeds
-
-
-class IPAdapterPlus(IPAdapter):
-    """IP-Adapter with fine-grained features"""
-
-    def _init_image_proj_model(self, state_dict):
-        return Resampler.from_state_dict(
-            state_dict=state_dict,
-            depth=4,
-            dim_head=64,
-            heads=12,
-            num_queries=self._num_tokens,
-            ff_mult=4,
-        ).to(self.device, dtype=self.dtype)
-
-    @torch.inference_mode()
-    def get_image_embeds(self, pil_image, image_encoder: CLIPVisionModelWithProjection):
-        if isinstance(pil_image, Image.Image):
-            pil_image = [pil_image]
-        clip_image = self._clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-        clip_image = clip_image.to(self.device, dtype=self.dtype)
-        clip_image_embeds = image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
-        image_prompt_embeds = self._image_proj_model(clip_image_embeds)
-        uncond_clip_image_embeds = image_encoder(torch.zeros_like(clip_image), output_hidden_states=True).hidden_states[
-            -2
-        ]
-        uncond_image_prompt_embeds = self._image_proj_model(uncond_clip_image_embeds)
-        return image_prompt_embeds, uncond_image_prompt_embeds
-
-
-def build_ip_adapter(
-    ip_adapter_ckpt_path: str, device: torch.device, dtype: torch.dtype = torch.float16
-) -> Union[IPAdapter, IPAdapterPlus]:
-    state_dict = torch.load(ip_adapter_ckpt_path, map_location="cpu")
-
-    # Determine if the state_dict is from an IPAdapter or IPAdapterPlus based on the image_proj weights that it
-    # contains.
-    is_plus = "proj.weight" not in state_dict["image_proj"]
-
-    if is_plus:
-        return IPAdapterPlus(state_dict, device=device, dtype=dtype)
-    else:
-        return IPAdapter(state_dict, device=device, dtype=dtype)

invokeai/backend/ip_adapter/resampler.py

@@ -1,158 +0,0 @@
-# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
-
-# tencent ailab comment: modified from
-# https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
-import math
-
-import torch
-import torch.nn as nn
-
-
-# FFN
-def FeedForward(dim, mult=4):
-    inner_dim = int(dim * mult)
-    return nn.Sequential(
-        nn.LayerNorm(dim),
-        nn.Linear(dim, inner_dim, bias=False),
-        nn.GELU(),
-        nn.Linear(inner_dim, dim, bias=False),
-    )
-
-
-def reshape_tensor(x, heads):
-    bs, length, width = x.shape
-    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
-    x = x.view(bs, length, heads, -1)
-    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
-    x = x.transpose(1, 2)
-    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
-    x = x.reshape(bs, heads, length, -1)
-    return x
-
-
-class PerceiverAttention(nn.Module):
-    def __init__(self, *, dim, dim_head=64, heads=8):
-        super().__init__()
-        self.scale = dim_head**-0.5
-        self.dim_head = dim_head
-        self.heads = heads
-        inner_dim = dim_head * heads
-
-        self.norm1 = nn.LayerNorm(dim)
-        self.norm2 = nn.LayerNorm(dim)
-
-        self.to_q = nn.Linear(dim, inner_dim, bias=False)
-        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
-        self.to_out = nn.Linear(inner_dim, dim, bias=False)
-
-    def forward(self, x, latents):
-        """
-        Args:
-            x (torch.Tensor): image features
-                shape (b, n1, D)
-            latent (torch.Tensor): latent features
-                shape (b, n2, D)
-        """
-        x = self.norm1(x)
-        latents = self.norm2(latents)
-
-        b, l, _ = latents.shape
-
-        q = self.to_q(latents)
-        kv_input = torch.cat((x, latents), dim=-2)
-        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
-
-        q = reshape_tensor(q, self.heads)
-        k = reshape_tensor(k, self.heads)
-        v = reshape_tensor(v, self.heads)
-
-        # attention
-        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
-        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
-        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
-        out = weight @ v
-
-        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
-
-        return self.to_out(out)
-
-
-class Resampler(nn.Module):
-    def __init__(
-        self,
-        dim=1024,
-        depth=8,
-        dim_head=64,
-        heads=16,
-        num_queries=8,
-        embedding_dim=768,
-        output_dim=1024,
-        ff_mult=4,
-    ):
-        super().__init__()
-
-        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
-
-        self.proj_in = nn.Linear(embedding_dim, dim)
-
-        self.proj_out = nn.Linear(dim, output_dim)
-        self.norm_out = nn.LayerNorm(output_dim)
-
-        self.layers = nn.ModuleList([])
-        for _ in range(depth):
-            self.layers.append(
-                nn.ModuleList(
-                    [
-                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
-                        FeedForward(dim=dim, mult=ff_mult),
-                    ]
-                )
-            )
-
-    @classmethod
-    def from_state_dict(cls, state_dict: dict[torch.Tensor], depth=8, dim_head=64, heads=16, num_queries=8, ff_mult=4):
-        """A convenience function that initializes a Resampler from a state_dict.
-
-        Some of the shape parameters are inferred from the state_dict (e.g. dim, embedding_dim, etc.). At the time of
-        writing, we did not have a need for inferring ALL of the shape parameters from the state_dict, but this would be
-        possible if needed in the future.
-
-        Args:
-            state_dict (dict[torch.Tensor]): The state_dict to load.
-            depth (int, optional):
-            dim_head (int, optional):
-            heads (int, optional):
-            ff_mult (int, optional):
-
-        Returns:
-            Resampler
-        """
-        dim = state_dict["latents"].shape[2]
-        num_queries = state_dict["latents"].shape[1]
-        embedding_dim = state_dict["proj_in.weight"].shape[-1]
-        output_dim = state_dict["norm_out.weight"].shape[0]
-
-        model = cls(
-            dim=dim,
-            depth=depth,
-            dim_head=dim_head,
-            heads=heads,
-            num_queries=num_queries,
-            embedding_dim=embedding_dim,
-            output_dim=output_dim,
-            ff_mult=ff_mult,
-        )
-        model.load_state_dict(state_dict)
-        return model
-
-    def forward(self, x):
-        latents = self.latents.repeat(x.size(0), 1, 1)
-
-        x = self.proj_in(x)
-
-        for attn, ff in self.layers:
-            latents = attn(x, latents) + latents
-            latents = ff(latents) + latents
-
-        latents = self.proj_out(latents)
-        return self.norm_out(latents)

invokeai/backend/model_management/model_manager.py

@@ -25,7 +25,6 @@ Models are described using four attributes:
          ModelType.Lora -- a LoRA or LyCORIS fine-tune
          ModelType.TextualInversion -- a textual inversion embedding
          ModelType.ControlNet -- a ControlNet model
-         ModelType.IPAdapter -- an IPAdapter model
 
   3) BaseModelType -- an enum indicating the stable diffusion base model, one of:
          BaseModelType.StableDiffusion1
@@ -1001,8 +1000,8 @@ class ModelManager(object):
                                 new_models_found = True
                             except DuplicateModelException as e:
                                 self.logger.warning(e)
-                            except InvalidModelException as e:
-                                self.logger.warning(f"Not a valid model: {model_path}. {e}")
+                            except InvalidModelException:
+                                self.logger.warning(f"Not a valid model: {model_path}")
                             except NotImplementedError as e:
                                 self.logger.warning(e)
 

invokeai/backend/model_management/model_probe.py

@@ -8,8 +8,6 @@ import torch
 from diffusers import ConfigMixin, ModelMixin
 from picklescan.scanner import scan_file_path
 
-from invokeai.backend.model_management.models.ip_adapter import IPAdapterModelFormat
-
 from .models import (
     BaseModelType,
     InvalidModelException,
@@ -54,7 +52,6 @@ class ModelProbe(object):
         "StableDiffusionXLInpaintPipeline": ModelType.Main,
         "AutoencoderKL": ModelType.Vae,
         "ControlNetModel": ModelType.ControlNet,
-        "CLIPVisionModelWithProjection": ModelType.CLIPVision,
     }
 
     @classmethod
@@ -121,18 +118,14 @@ class ModelProbe(object):
                     and prediction_type == SchedulerPredictionType.VPrediction
                 ),
                 format=format,
-                image_size=(
-                    1024
-                    if (base_type in {BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner})
-                    else (
-                        768
-                        if (
-                            base_type == BaseModelType.StableDiffusion2
-                            and prediction_type == SchedulerPredictionType.VPrediction
-                        )
-                        else 512
-                    )
-                ),
+                image_size=1024
+                if (base_type in {BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner})
+                else 768
+                if (
+                    base_type == BaseModelType.StableDiffusion2
+                    and prediction_type == SchedulerPredictionType.VPrediction
+                )
+                else 512,
             )
         except Exception:
             raise
@@ -184,10 +177,9 @@ class ModelProbe(object):
                 return ModelType.ONNX
             if (folder_path / "learned_embeds.bin").exists():
                 return ModelType.TextualInversion
+
             if (folder_path / "pytorch_lora_weights.bin").exists():
                 return ModelType.Lora
-            if (folder_path / "image_encoder.txt").exists():
-                return ModelType.IPAdapter
 
             i = folder_path / "model_index.json"
             c = folder_path / "config.json"
@@ -196,12 +188,7 @@ class ModelProbe(object):
             if config_path:
                 with open(config_path, "r") as file:
                     conf = json.load(file)
-                if "_class_name" in conf:
-                    class_name = conf["_class_name"]
-                elif "architectures" in conf:
-                    class_name = conf["architectures"][0]
-                else:
-                    class_name = None
+                class_name = conf["_class_name"]
 
         if class_name and (type := cls.CLASS2TYPE.get(class_name)):
             return type
@@ -379,16 +366,6 @@ class ControlNetCheckpointProbe(CheckpointProbeBase):
         raise InvalidModelException("Unable to determine base type for {self.checkpoint_path}")
 
 
-class IPAdapterCheckpointProbe(CheckpointProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        raise NotImplementedError()
-
-
-class CLIPVisionCheckpointProbe(CheckpointProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        raise NotImplementedError()
-
-
 ########################################################
 # classes for probing folders
 #######################################################
@@ -508,13 +485,11 @@ class ControlNetFolderProbe(FolderProbeBase):
         base_model = (
             BaseModelType.StableDiffusion1
             if dimension == 768
-            else (
-                BaseModelType.StableDiffusion2
-                if dimension == 1024
-                else BaseModelType.StableDiffusionXL
-                if dimension == 2048
-                else None
-            )
+            else BaseModelType.StableDiffusion2
+            if dimension == 1024
+            else BaseModelType.StableDiffusionXL
+            if dimension == 2048
+            else None
         )
         if not base_model:
             raise InvalidModelException(f"Unable to determine model base for {self.folder_path}")
@@ -534,47 +509,15 @@ class LoRAFolderProbe(FolderProbeBase):
         return LoRACheckpointProbe(model_file, None).get_base_type()
 
 
-class IPAdapterFolderProbe(FolderProbeBase):
-    def get_format(self) -> str:
-        return IPAdapterModelFormat.InvokeAI.value
-
-    def get_base_type(self) -> BaseModelType:
-        model_file = self.folder_path / "ip_adapter.bin"
-        if not model_file.exists():
-            raise InvalidModelException("Unknown IP-Adapter model format.")
-
-        state_dict = torch.load(model_file, map_location="cpu")
-        cross_attention_dim = state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[-1]
-        if cross_attention_dim == 768:
-            return BaseModelType.StableDiffusion1
-        elif cross_attention_dim == 1024:
-            return BaseModelType.StableDiffusion2
-        elif cross_attention_dim == 2048:
-            return BaseModelType.StableDiffusionXL
-        else:
-            raise InvalidModelException(f"IP-Adapter had unexpected cross-attention dimension: {cross_attention_dim}.")
-
-
-class CLIPVisionFolderProbe(FolderProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        return BaseModelType.Any
-
-
 ############## register probe classes ######
 ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.Vae, VaeFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.Lora, LoRAFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInversionFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.IPAdapter, IPAdapterFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.CLIPVision, CLIPVisionFolderProbe)
-
 ModelProbe.register_probe("checkpoint", ModelType.Main, PipelineCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.Vae, VaeCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.Lora, LoRACheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.TextualInversion, TextualInversionCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.ControlNet, ControlNetCheckpointProbe)
-ModelProbe.register_probe("checkpoint", ModelType.IPAdapter, IPAdapterCheckpointProbe)
-ModelProbe.register_probe("checkpoint", ModelType.CLIPVision, CLIPVisionCheckpointProbe)
-
 ModelProbe.register_probe("onnx", ModelType.ONNX, ONNXFolderProbe)

invokeai/backend/model_management/model_search.py

@@ -79,7 +79,7 @@ class ModelSearch(ABC):
                         self._models_found += 1
                         self._scanned_dirs.add(path)
                     except Exception as e:
-                        self.logger.warning(f"Failed to process '{path}': {e}")
+                        self.logger.warning(str(e))
 
             for f in files:
                 path = Path(root) / f
@@ -90,7 +90,7 @@ class ModelSearch(ABC):
                         self.on_model_found(path)
                         self._models_found += 1
                     except Exception as e:
-                        self.logger.warning(f"Failed to process '{path}': {e}")
+                        self.logger.warning(str(e))
 
 
 class FindModels(ModelSearch):

invokeai/backend/model_management/models/__init__.py

@@ -18,9 +18,7 @@ from .base import (  # noqa: F401
     SilenceWarnings,
     SubModelType,
 )
-from .clip_vision import CLIPVisionModel
 from .controlnet import ControlNetModel  # TODO:
-from .ip_adapter import IPAdapterModel
 from .lora import LoRAModel
 from .sdxl import StableDiffusionXLModel
 from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
@@ -36,8 +34,6 @@ MODEL_CLASSES = {
         ModelType.Lora: LoRAModel,
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
-        ModelType.IPAdapter: IPAdapterModel,
-        ModelType.CLIPVision: CLIPVisionModel,
     },
     BaseModelType.StableDiffusion2: {
         ModelType.ONNX: ONNXStableDiffusion2Model,
@@ -46,8 +42,6 @@ MODEL_CLASSES = {
         ModelType.Lora: LoRAModel,
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
-        ModelType.IPAdapter: IPAdapterModel,
-        ModelType.CLIPVision: CLIPVisionModel,
     },
     BaseModelType.StableDiffusionXL: {
         ModelType.Main: StableDiffusionXLModel,
@@ -57,8 +51,6 @@ MODEL_CLASSES = {
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
         ModelType.ONNX: ONNXStableDiffusion2Model,
-        ModelType.IPAdapter: IPAdapterModel,
-        ModelType.CLIPVision: CLIPVisionModel,
     },
     BaseModelType.StableDiffusionXLRefiner: {
         ModelType.Main: StableDiffusionXLModel,
@@ -68,19 +60,6 @@ MODEL_CLASSES = {
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
         ModelType.ONNX: ONNXStableDiffusion2Model,
-        ModelType.IPAdapter: IPAdapterModel,
-        ModelType.CLIPVision: CLIPVisionModel,
-    },
-    BaseModelType.Any: {
-        ModelType.CLIPVision: CLIPVisionModel,
-        # The following model types are not expected to be used with BaseModelType.Any.
-        ModelType.ONNX: ONNXStableDiffusion2Model,
-        ModelType.Main: StableDiffusion2Model,
-        ModelType.Vae: VaeModel,
-        ModelType.Lora: LoRAModel,
-        ModelType.ControlNet: ControlNetModel,
-        ModelType.TextualInversion: TextualInversionModel,
-        ModelType.IPAdapter: IPAdapterModel,
     },
     # BaseModelType.Kandinsky2_1: {
     #    ModelType.Main: Kandinsky2_1Model,

invokeai/backend/model_management/models/base.py

@@ -36,7 +36,6 @@ class ModelNotFoundException(Exception):
 
 
 class BaseModelType(str, Enum):
-    Any = "any"  # For models that are not associated with any particular base model.
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"
     StableDiffusionXL = "sdxl"
@@ -51,8 +50,6 @@ class ModelType(str, Enum):
     Lora = "lora"
     ControlNet = "controlnet"  # used by model_probe
     TextualInversion = "embedding"
-    IPAdapter = "ip_adapter"
-    CLIPVision = "clip_vision"
 
 
 class SubModelType(str, Enum):

invokeai/backend/model_management/models/clip_vision.py

@@ -1,82 +0,0 @@
-import os
-from enum import Enum
-from typing import Literal, Optional
-
-import torch
-from transformers import CLIPVisionModelWithProjection
-
-from invokeai.backend.model_management.models.base import (
-    BaseModelType,
-    InvalidModelException,
-    ModelBase,
-    ModelConfigBase,
-    ModelType,
-    SubModelType,
-    calc_model_size_by_data,
-    calc_model_size_by_fs,
-    classproperty,
-)
-
-
-class CLIPVisionModelFormat(str, Enum):
-    Diffusers = "diffusers"
-
-
-class CLIPVisionModel(ModelBase):
-    class DiffusersConfig(ModelConfigBase):
-        model_format: Literal[CLIPVisionModelFormat.Diffusers]
-
-    def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
-        assert model_type == ModelType.CLIPVision
-        super().__init__(model_path, base_model, model_type)
-
-        self.model_size = calc_model_size_by_fs(self.model_path)
-
-    @classmethod
-    def detect_format(cls, path: str) -> str:
-        if not os.path.exists(path):
-            raise ModuleNotFoundError(f"No CLIP Vision model at path '{path}'.")
-
-        if os.path.isdir(path) and os.path.exists(os.path.join(path, "config.json")):
-            return CLIPVisionModelFormat.Diffusers
-
-        raise InvalidModelException(f"Unexpected CLIP Vision model format: {path}")
-
-    @classproperty
-    def save_to_config(cls) -> bool:
-        return True
-
-    def get_size(self, child_type: Optional[SubModelType] = None) -> int:
-        if child_type is not None:
-            raise ValueError("There are no child models in a CLIP Vision model.")
-
-        return self.model_size
-
-    def get_model(
-        self,
-        torch_dtype: Optional[torch.dtype],
-        child_type: Optional[SubModelType] = None,
-    ) -> CLIPVisionModelWithProjection:
-        if child_type is not None:
-            raise ValueError("There are no child models in a CLIP Vision model.")
-
-        model = CLIPVisionModelWithProjection.from_pretrained(self.model_path, torch_dtype=torch_dtype)
-
-        # Calculate a more accurate model size.
-        self.model_size = calc_model_size_by_data(model)
-
-        return model
-
-    @classmethod
-    def convert_if_required(
-        cls,
-        model_path: str,
-        output_path: str,
-        config: ModelConfigBase,
-        base_model: BaseModelType,
-    ) -> str:
-        format = cls.detect_format(model_path)
-        if format == CLIPVisionModelFormat.Diffusers:
-            return model_path
-        else:
-            raise ValueError(f"Unsupported format: '{format}'.")

invokeai/backend/model_management/models/ip_adapter.py

@@ -1,92 +0,0 @@
-import os
-import typing
-from enum import Enum
-from typing import Literal, Optional
-
-import torch
-
-from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus, build_ip_adapter
-from invokeai.backend.model_management.models.base import (
-    BaseModelType,
-    InvalidModelException,
-    ModelBase,
-    ModelConfigBase,
-    ModelType,
-    SubModelType,
-    classproperty,
-)
-
-
-class IPAdapterModelFormat(str, Enum):
-    # The custom IP-Adapter model format defined by InvokeAI.
-    InvokeAI = "invokeai"
-
-
-class IPAdapterModel(ModelBase):
-    class InvokeAIConfig(ModelConfigBase):
-        model_format: Literal[IPAdapterModelFormat.InvokeAI]
-
-    def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
-        assert model_type == ModelType.IPAdapter
-        super().__init__(model_path, base_model, model_type)
-
-        self.model_size = os.path.getsize(self.model_path)
-
-    @classmethod
-    def detect_format(cls, path: str) -> str:
-        if not os.path.exists(path):
-            raise ModuleNotFoundError(f"No IP-Adapter model at path '{path}'.")
-
-        if os.path.isdir(path):
-            model_file = os.path.join(path, "ip_adapter.bin")
-            image_encoder_config_file = os.path.join(path, "image_encoder.txt")
-            if os.path.exists(model_file) and os.path.exists(image_encoder_config_file):
-                return IPAdapterModelFormat.InvokeAI
-
-        raise InvalidModelException(f"Unexpected IP-Adapter model format: {path}")
-
-    @classproperty
-    def save_to_config(cls) -> bool:
-        return True
-
-    def get_size(self, child_type: Optional[SubModelType] = None) -> int:
-        if child_type is not None:
-            raise ValueError("There are no child models in an IP-Adapter model.")
-
-        return self.model_size
-
-    def get_model(
-        self,
-        torch_dtype: Optional[torch.dtype],
-        child_type: Optional[SubModelType] = None,
-    ) -> typing.Union[IPAdapter, IPAdapterPlus]:
-        if child_type is not None:
-            raise ValueError("There are no child models in an IP-Adapter model.")
-
-        return build_ip_adapter(
-            ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"), device="cpu", dtype=torch_dtype
-        )
-
-    @classmethod
-    def convert_if_required(
-        cls,
-        model_path: str,
-        output_path: str,
-        config: ModelConfigBase,
-        base_model: BaseModelType,
-    ) -> str:
-        format = cls.detect_format(model_path)
-        if format == IPAdapterModelFormat.InvokeAI:
-            return model_path
-        else:
-            raise ValueError(f"Unsupported format: '{format}'.")
-
-
-def get_ip_adapter_image_encoder_model_id(model_path: str):
-    """Read the ID of the image encoder associated with the IP-Adapter at `model_path`."""
-    image_encoder_config_file = os.path.join(model_path, "image_encoder.txt")
-
-    with open(image_encoder_config_file, "r") as f:
-        image_encoder_model = f.readline().strip()
-
-    return image_encoder_model

invokeai/backend/model_management/seamless.py

@@ -25,55 +25,71 @@ def _conv_forward_asymmetric(self, input, weight, bias):
 
 
 @contextmanager
-def set_seamless(
-    model: Union[UNet2DConditionModel, AutoencoderKL],
-    axes: List[str],
-    skipped_layers: int,
-    skip_second_resnet: bool,
-    skip_conv2: bool,
-):
+def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
     try:
         to_restore = []
 
         for m_name, m in model.named_modules():
-            if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                continue
-
-            if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
-                # down_blocks.1.resnets.1.conv1
-                _, block_num, _, resnet_num, submodule_name = m_name.split(".")
-                block_num = int(block_num)
-                resnet_num = int(resnet_num)
-
-                # if block_num >= seamless_down_blocks:
-                if block_num >= len(model.down_blocks) - skipped_layers:
+            if isinstance(model, UNet2DConditionModel):
+                if ".attentions." in m_name:
                     continue
 
-                if resnet_num > 0 and skip_second_resnet:
+                if ".resnets." in m_name:
+                    if ".conv2" in m_name:
+                        continue
+                    if ".conv_shortcut" in m_name:
+                        continue
+
+            """
+            if isinstance(model, UNet2DConditionModel):
+                if False and ".upsamplers." in m_name:
                     continue
 
-                if submodule_name == "conv2" and skip_conv2:
+                if False and ".downsamplers." in m_name:
                     continue
 
-            m.asymmetric_padding_mode = {}
-            m.asymmetric_padding = {}
-            m.asymmetric_padding_mode["x"] = "circular" if ("x" in axes) else "constant"
-            m.asymmetric_padding["x"] = (
-                m._reversed_padding_repeated_twice[0],
-                m._reversed_padding_repeated_twice[1],
-                0,
-                0,
-            )
-            m.asymmetric_padding_mode["y"] = "circular" if ("y" in axes) else "constant"
-            m.asymmetric_padding["y"] = (
-                0,
-                0,
-                m._reversed_padding_repeated_twice[2],
-                m._reversed_padding_repeated_twice[3],
-            )
+                if True and ".resnets." in m_name:
+                    if True and ".conv1" in m_name:
+                        if False and "down_blocks" in m_name:
+                            continue
+                        if False and "mid_block" in m_name:
+                            continue
+                        if False and "up_blocks" in m_name:
+                            continue
 
-            to_restore.append((m, m._conv_forward))
-            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+                    if True and ".conv2" in m_name:
+                        continue
+
+                    if True and ".conv_shortcut" in m_name:
+                        continue
+
+                if True and ".attentions." in m_name:
+                    continue
+
+                if False and m_name in ["conv_in", "conv_out"]:
+                    continue
+            """
+
+            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                m.asymmetric_padding_mode = {}
+                m.asymmetric_padding = {}
+                m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
+                m.asymmetric_padding["x"] = (
+                    m._reversed_padding_repeated_twice[0],
+                    m._reversed_padding_repeated_twice[1],
+                    0,
+                    0,
+                )
+                m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
+                m.asymmetric_padding["y"] = (
+                    0,
+                    0,
+                    m._reversed_padding_repeated_twice[2],
+                    m._reversed_padding_repeated_twice[3],
+                )
+
+                to_restore.append((m, m._conv_forward))
+                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
 
         yield
 

invokeai/backend/stable_diffusion/__init__.py

@@ -1,6 +1,15 @@
 """
 Initialization file for the invokeai.backend.stable_diffusion package
 """
-from .diffusers_pipeline import PipelineIntermediateState, StableDiffusionGeneratorPipeline  # noqa: F401
+from .diffusers_pipeline import (  # noqa: F401
+    ConditioningData,
+    PipelineIntermediateState,
+    StableDiffusionGeneratorPipeline,
+)
 from .diffusion import InvokeAIDiffuserComponent  # noqa: F401
 from .diffusion.cross_attention_map_saving import AttentionMapSaver  # noqa: F401
+from .diffusion.shared_invokeai_diffusion import (  # noqa: F401
+    BasicConditioningInfo,
+    PostprocessingSettings,
+    SDXLConditioningInfo,
+)

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 
-import math
-from contextlib import nullcontext
-from dataclasses import dataclass
+import dataclasses
+import inspect
+from dataclasses import dataclass, field
 from typing import Any, Callable, List, Optional, Union
 
 import einops
@@ -23,11 +23,9 @@ from pydantic import Field
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData
 
 from ..util import auto_detect_slice_size, normalize_device
-from .diffusion import AttentionMapSaver, InvokeAIDiffuserComponent
+from .diffusion import AttentionMapSaver, BasicConditioningInfo, InvokeAIDiffuserComponent, PostprocessingSettings
 
 
 @dataclass
@@ -97,7 +95,7 @@ class AddsMaskGuidance:
         # Mask anything that has the same shape as prev_sample, return others as-is.
         return output_class(
             {
-                k: self.apply_mask(v, self._t_for_field(k, t)) if are_like_tensors(prev_sample, v) else v
+                k: (self.apply_mask(v, self._t_for_field(k, t)) if are_like_tensors(prev_sample, v) else v)
                 for k, v in step_output.items()
             }
         )
@@ -164,13 +162,39 @@ class ControlNetData:
 
 
 @dataclass
-class IPAdapterData:
-    ip_adapter_model: IPAdapter = Field(default=None)
-    # TODO: change to polymorphic so can do different weights per step (once implemented...)
-    weight: Union[float, List[float]] = Field(default=1.0)
-    # weight: float = Field(default=1.0)
-    begin_step_percent: float = Field(default=0.0)
-    end_step_percent: float = Field(default=1.0)
+class ConditioningData:
+    unconditioned_embeddings: BasicConditioningInfo
+    text_embeddings: BasicConditioningInfo
+    guidance_scale: Union[float, List[float]]
+    """
+    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.
+    """
+    extra: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo] = None
+    scheduler_args: dict[str, Any] = field(default_factory=dict)
+    """
+    Additional arguments to pass to invokeai_diffuser.do_latent_postprocessing().
+    """
+    postprocessing_settings: Optional[PostprocessingSettings] = None
+
+    @property
+    def dtype(self):
+        return self.text_embeddings.dtype
+
+    def add_scheduler_args_if_applicable(self, scheduler, **kwargs):
+        scheduler_args = dict(self.scheduler_args)
+        step_method = inspect.signature(scheduler.step)
+        for name, value in kwargs.items():
+            try:
+                step_method.bind_partial(**{name: value})
+            except TypeError:
+                # FIXME: don't silently discard arguments
+                pass  # debug("%s does not accept argument named %r", scheduler, name)
+            else:
+                scheduler_args[name] = value
+        return dataclasses.replace(self, scheduler_args=scheduler_args)
 
 
 @dataclass
@@ -253,7 +277,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         )
         self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
         self.control_model = control_model
-        self.use_ip_adapter = False
 
     def _adjust_memory_efficient_attention(self, latents: torch.Tensor):
         """
@@ -326,7 +349,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         additional_guidance: List[Callable] = None,
         callback: Callable[[PipelineIntermediateState], None] = None,
         control_data: List[ControlNetData] = None,
-        ip_adapter_data: Optional[IPAdapterData] = None,
         mask: Optional[torch.Tensor] = None,
         masked_latents: Optional[torch.Tensor] = None,
         seed: Optional[int] = None,
@@ -378,7 +400,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 conditioning_data,
                 additional_guidance=additional_guidance,
                 control_data=control_data,
-                ip_adapter_data=ip_adapter_data,
                 callback=callback,
             )
         finally:
@@ -398,7 +419,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         *,
         additional_guidance: List[Callable] = None,
         control_data: List[ControlNetData] = None,
-        ip_adapter_data: Optional[IPAdapterData] = None,
         callback: Callable[[PipelineIntermediateState], None] = None,
     ):
         self._adjust_memory_efficient_attention(latents)
@@ -411,26 +431,12 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if timesteps.shape[0] == 0:
             return latents, attention_map_saver
 
-        if conditioning_data.extra is not None and conditioning_data.extra.wants_cross_attention_control:
-            attn_ctx = self.invokeai_diffuser.custom_attention_context(
-                self.invokeai_diffuser.model,
-                extra_conditioning_info=conditioning_data.extra,
-                step_count=len(self.scheduler.timesteps),
-            )
-            self.use_ip_adapter = False
-        elif ip_adapter_data is not None:
-            # TODO(ryand): Should we raise an exception if both custom attention and IP-Adapter attention are active?
-            # As it is now, the IP-Adapter will silently be skipped.
-            weight = ip_adapter_data.weight[0] if isinstance(ip_adapter_data.weight, List) else ip_adapter_data.weight
-            attn_ctx = ip_adapter_data.ip_adapter_model.apply_ip_adapter_attention(
-                unet=self.invokeai_diffuser.model,
-                scale=weight,
-            )
-            self.use_ip_adapter = True
-        else:
-            attn_ctx = nullcontext()
-
-        with attn_ctx:
+        extra_conditioning_info = conditioning_data.extra
+        with self.invokeai_diffuser.custom_attention_context(
+            self.invokeai_diffuser.model,
+            extra_conditioning_info=extra_conditioning_info,
+            step_count=len(self.scheduler.timesteps),
+        ):
             if callback is not None:
                 callback(
                     PipelineIntermediateState(
@@ -453,7 +459,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     total_step_count=len(timesteps),
                     additional_guidance=additional_guidance,
                     control_data=control_data,
-                    ip_adapter_data=ip_adapter_data,
                 )
                 latents = step_output.prev_sample
 
@@ -499,7 +504,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         total_step_count: int,
         additional_guidance: List[Callable] = None,
         control_data: List[ControlNetData] = None,
-        ip_adapter_data: Optional[IPAdapterData] = None,
     ):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
@@ -510,24 +514,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         #     i.e. before or after passing it to InvokeAIDiffuserComponent
         latent_model_input = self.scheduler.scale_model_input(latents, timestep)
 
-        # handle IP-Adapter
-        if self.use_ip_adapter and ip_adapter_data is not None:  # somewhat redundant but logic is clearer
-            first_adapter_step = math.floor(ip_adapter_data.begin_step_percent * total_step_count)
-            last_adapter_step = math.ceil(ip_adapter_data.end_step_percent * total_step_count)
-            weight = (
-                ip_adapter_data.weight[step_index]
-                if isinstance(ip_adapter_data.weight, List)
-                else ip_adapter_data.weight
-            )
-            if step_index >= first_adapter_step and step_index <= last_adapter_step:
-                # only apply IP-Adapter if current step is within the IP-Adapter's begin/end step range
-                # ip_adapter_data.ip_adapter_model.set_scale(ip_adapter_data.weight)
-                ip_adapter_data.ip_adapter_model.set_scale(weight)
-            else:
-                # otherwise, set IP-Adapter scale to 0, so it has no effect
-                ip_adapter_data.ip_adapter_model.set_scale(0.0)
-
-        # handle ControlNet(s)
         # default is no controlnet, so set controlnet processing output to None
         controlnet_down_block_samples, controlnet_mid_block_sample = None, None
         if control_data is not None:

invokeai/backend/stable_diffusion/diffusion/__init__.py

@@ -3,4 +3,9 @@ Initialization file for invokeai.models.diffusion
 """
 from .cross_attention_control import InvokeAICrossAttentionMixin  # noqa: F401
 from .cross_attention_map_saving import AttentionMapSaver  # noqa: F401
-from .shared_invokeai_diffusion import InvokeAIDiffuserComponent  # noqa: F401
+from .shared_invokeai_diffusion import (  # noqa: F401
+    BasicConditioningInfo,
+    InvokeAIDiffuserComponent,
+    PostprocessingSettings,
+    SDXLConditioningInfo,
+)

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -1,101 +0,0 @@
-import dataclasses
-import inspect
-from dataclasses import dataclass, field
-from typing import Any, List, Optional, Union
-
-import torch
-
-from .cross_attention_control import Arguments
-
-
-@dataclass
-class ExtraConditioningInfo:
-    tokens_count_including_eos_bos: int
-    cross_attention_control_args: Optional[Arguments] = None
-
-    @property
-    def wants_cross_attention_control(self):
-        return self.cross_attention_control_args is not None
-
-
-@dataclass
-class BasicConditioningInfo:
-    embeds: torch.Tensor
-    # TODO(ryand): Right now we awkwardly copy the extra conditioning info from here up to `ConditioningData`. This
-    # should only be stored in one place.
-    extra_conditioning: Optional[ExtraConditioningInfo]
-    # weight: float
-    # mode: ConditioningAlgo
-
-    def to(self, device, dtype=None):
-        self.embeds = self.embeds.to(device=device, dtype=dtype)
-        return self
-
-
-@dataclass
-class SDXLConditioningInfo(BasicConditioningInfo):
-    pooled_embeds: torch.Tensor
-    add_time_ids: torch.Tensor
-
-    def to(self, device, dtype=None):
-        self.pooled_embeds = self.pooled_embeds.to(device=device, dtype=dtype)
-        self.add_time_ids = self.add_time_ids.to(device=device, dtype=dtype)
-        return super().to(device=device, dtype=dtype)
-
-
-@dataclass(frozen=True)
-class PostprocessingSettings:
-    threshold: float
-    warmup: float
-    h_symmetry_time_pct: Optional[float]
-    v_symmetry_time_pct: Optional[float]
-
-
-@dataclass
-class IPAdapterConditioningInfo:
-    cond_image_prompt_embeds: torch.Tensor
-    """IP-Adapter image encoder conditioning embeddings.
-    Shape: (batch_size, num_tokens, encoding_dim).
-    """
-    uncond_image_prompt_embeds: torch.Tensor
-    """IP-Adapter image encoding embeddings to use for unconditional generation.
-    Shape: (batch_size, num_tokens, encoding_dim).
-    """
-
-
-@dataclass
-class ConditioningData:
-    unconditioned_embeddings: BasicConditioningInfo
-    text_embeddings: BasicConditioningInfo
-    guidance_scale: Union[float, List[float]]
-    """
-    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.
-    """
-    extra: Optional[ExtraConditioningInfo] = None
-    scheduler_args: dict[str, Any] = field(default_factory=dict)
-    """
-    Additional arguments to pass to invokeai_diffuser.do_latent_postprocessing().
-    """
-    postprocessing_settings: Optional[PostprocessingSettings] = None
-
-    ip_adapter_conditioning: Optional[IPAdapterConditioningInfo] = None
-
-    @property
-    def dtype(self):
-        return self.text_embeddings.dtype
-
-    def add_scheduler_args_if_applicable(self, scheduler, **kwargs):
-        scheduler_args = dict(self.scheduler_args)
-        step_method = inspect.signature(scheduler.step)
-        for name, value in kwargs.items():
-            try:
-                step_method.bind_partial(**{name: value})
-            except TypeError:
-                # FIXME: don't silently discard arguments
-                pass  # debug("%s does not accept argument named %r", scheduler, name)
-            else:
-                scheduler_args[name] = value
-        return dataclasses.replace(self, scheduler_args=scheduler_args)

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -376,11 +376,11 @@ def get_cross_attention_modules(model, which: CrossAttentionType) -> list[tuple[
         # non-fatal error but .swap() won't work.
         logger.error(
             f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model "
-            f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching "
-            "failed or some assumption has changed about the structure of the model itself. Please fix the "
-            f"monkey-patching, and/or update the {expected_count} above to an appropriate number, and/or find and "
-            "inform someone who knows what it means. This error is non-fatal, but it is likely that .swap() and "
-            "attention map display will not work properly until it is fixed."
+            + f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed "
+            + "or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, "
+            + f"and/or update the {expected_count} above to an appropriate number, and/or find and inform someone who knows "
+            + "what it means. This error is non-fatal, but it is likely that .swap() and attention map display will not "
+            + "work properly until it is fixed."
         )
     return attention_module_tuples
 
@@ -577,7 +577,6 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
         attention_mask=None,
         # kwargs
         swap_cross_attn_context: SwapCrossAttnContext = None,
-        **kwargs,
     ):
         attention_type = CrossAttentionType.SELF if encoder_hidden_states is None else CrossAttentionType.TOKENS
 

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import math
 from contextlib import contextmanager
+from dataclasses import dataclass
 from typing import Any, Callable, Optional, Union
 
 import torch
@@ -9,14 +10,9 @@ 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 (
-    ConditioningData,
-    ExtraConditioningInfo,
-    PostprocessingSettings,
-    SDXLConditioningInfo,
-)
 
 from .cross_attention_control import (
+    Arguments,
     Context,
     CrossAttentionType,
     SwapCrossAttnContext,
@@ -35,6 +31,37 @@ ModelForwardCallback: TypeAlias = Union[
 ]
 
 
+@dataclass
+class BasicConditioningInfo:
+    embeds: torch.Tensor
+    extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo]
+    # weight: float
+    # mode: ConditioningAlgo
+
+    def to(self, device, dtype=None):
+        self.embeds = self.embeds.to(device=device, dtype=dtype)
+        return self
+
+
+@dataclass
+class SDXLConditioningInfo(BasicConditioningInfo):
+    pooled_embeds: torch.Tensor
+    add_time_ids: torch.Tensor
+
+    def to(self, device, dtype=None):
+        self.pooled_embeds = self.pooled_embeds.to(device=device, dtype=dtype)
+        self.add_time_ids = self.add_time_ids.to(device=device, dtype=dtype)
+        return super().to(device=device, dtype=dtype)
+
+
+@dataclass(frozen=True)
+class PostprocessingSettings:
+    threshold: float
+    warmup: float
+    h_symmetry_time_pct: Optional[float]
+    v_symmetry_time_pct: Optional[float]
+
+
 class InvokeAIDiffuserComponent:
     """
     The aim of this component is to provide a single place for code that can be applied identically to
@@ -48,6 +75,15 @@ class InvokeAIDiffuserComponent:
     debug_thresholding = False
     sequential_guidance = False
 
+    @dataclass
+    class ExtraConditioningInfo:
+        tokens_count_including_eos_bos: int
+        cross_attention_control_args: Optional[Arguments] = None
+
+        @property
+        def wants_cross_attention_control(self):
+            return self.cross_attention_control_args is not None
+
     def __init__(
         self,
         model,
@@ -67,26 +103,30 @@ class InvokeAIDiffuserComponent:
     @contextmanager
     def custom_attention_context(
         self,
-        unet: UNet2DConditionModel,
+        unet: UNet2DConditionModel,  # note: also may futz with the text encoder depending on requested LoRAs
         extra_conditioning_info: Optional[ExtraConditioningInfo],
         step_count: int,
     ):
-        old_attn_processors = unet.attn_processors
+        old_attn_processors = None
+        if extra_conditioning_info and (extra_conditioning_info.wants_cross_attention_control):
+            old_attn_processors = unet.attn_processors
+            # Load lora conditions into the model
+            if extra_conditioning_info.wants_cross_attention_control:
+                self.cross_attention_control_context = Context(
+                    arguments=extra_conditioning_info.cross_attention_control_args,
+                    step_count=step_count,
+                )
+                setup_cross_attention_control_attention_processors(
+                    unet,
+                    self.cross_attention_control_context,
+                )
 
         try:
-            self.cross_attention_control_context = Context(
-                arguments=extra_conditioning_info.cross_attention_control_args,
-                step_count=step_count,
-            )
-            setup_cross_attention_control_attention_processors(
-                unet,
-                self.cross_attention_control_context,
-            )
-
             yield None
         finally:
             self.cross_attention_control_context = None
-            unet.set_attn_processor(old_attn_processors)
+            if old_attn_processors is not None:
+                unet.set_attn_processor(old_attn_processors)
             # TODO resuscitate attention map saving
             # self.remove_attention_map_saving()
 
@@ -336,24 +376,11 @@ class InvokeAIDiffuserComponent:
 
     # methods below are called from do_diffusion_step and should be considered private to this class.
 
-    def _apply_standard_conditioning(self, x, sigma, conditioning_data: ConditioningData, **kwargs):
-        """Runs the conditioned and unconditioned UNet forward passes in a single batch for faster inference speed at
-        the cost of higher memory usage.
-        """
+    def _apply_standard_conditioning(self, x, sigma, conditioning_data, **kwargs):
+        # fast batched path
         x_twice = torch.cat([x] * 2)
         sigma_twice = torch.cat([sigma] * 2)
 
-        cross_attention_kwargs = None
-        if conditioning_data.ip_adapter_conditioning is not None:
-            cross_attention_kwargs = {
-                "ip_adapter_image_prompt_embeds": torch.cat(
-                    [
-                        conditioning_data.ip_adapter_conditioning.uncond_image_prompt_embeds,
-                        conditioning_data.ip_adapter_conditioning.cond_image_prompt_embeds,
-                    ]
-                )
-            }
-
         added_cond_kwargs = None
         if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
             added_cond_kwargs = {
@@ -381,7 +408,6 @@ class InvokeAIDiffuserComponent:
             x_twice,
             sigma_twice,
             both_conditionings,
-            cross_attention_kwargs=cross_attention_kwargs,
             encoder_attention_mask=encoder_attention_mask,
             added_cond_kwargs=added_cond_kwargs,
             **kwargs,
@@ -393,12 +419,9 @@ class InvokeAIDiffuserComponent:
         self,
         x: torch.Tensor,
         sigma,
-        conditioning_data: ConditioningData,
+        conditioning_data,
         **kwargs,
     ):
-        """Runs the conditioned and unconditioned UNet forward passes sequentially for lower memory usage at the cost of
-        slower execution speed.
-        """
         # low-memory sequential path
         uncond_down_block, cond_down_block = None, None
         down_block_additional_residuals = kwargs.pop("down_block_additional_residuals", None)
@@ -414,13 +437,6 @@ class InvokeAIDiffuserComponent:
         if mid_block_additional_residual is not None:
             uncond_mid_block, cond_mid_block = mid_block_additional_residual.chunk(2)
 
-        # Run unconditional UNet denoising.
-        cross_attention_kwargs = None
-        if conditioning_data.ip_adapter_conditioning is not None:
-            cross_attention_kwargs = {
-                "ip_adapter_image_prompt_embeds": conditioning_data.ip_adapter_conditioning.uncond_image_prompt_embeds
-            }
-
         added_cond_kwargs = None
         is_sdxl = type(conditioning_data.text_embeddings) is SDXLConditioningInfo
         if is_sdxl:
@@ -433,21 +449,12 @@ class InvokeAIDiffuserComponent:
             x,
             sigma,
             conditioning_data.unconditioned_embeddings.embeds,
-            cross_attention_kwargs=cross_attention_kwargs,
             down_block_additional_residuals=uncond_down_block,
             mid_block_additional_residual=uncond_mid_block,
             added_cond_kwargs=added_cond_kwargs,
             **kwargs,
         )
 
-        # Run conditional UNet denoising.
-        cross_attention_kwargs = None
-        if conditioning_data.ip_adapter_conditioning is not None:
-            cross_attention_kwargs = {
-                "ip_adapter_image_prompt_embeds": conditioning_data.ip_adapter_conditioning.cond_image_prompt_embeds
-            }
-
-        added_cond_kwargs = None
         if is_sdxl:
             added_cond_kwargs = {
                 "text_embeds": conditioning_data.text_embeddings.pooled_embeds,
@@ -458,7 +465,6 @@ class InvokeAIDiffuserComponent:
             x,
             sigma,
             conditioning_data.text_embeddings.embeds,
-            cross_attention_kwargs=cross_attention_kwargs,
             down_block_additional_residuals=cond_down_block,
             mid_block_additional_residual=cond_mid_block,
             added_cond_kwargs=added_cond_kwargs,

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-374b1ae5.js

@@ -1,4 +1,4 @@
-import{v as m,hj as Je,u as y,Y as Xa,hk as Ja,a7 as ua,ab as d,hl as b,hm as o,hn as Qa,ho as h,hp as fa,hq as Za,hr as eo,aE as ro,hs as ao,a4 as oo,ht as to}from"./index-f6c3f475.js";import{s as ha,n as t,t as io,o as ma,p as no,q as ga,v as ya,w as pa,x as lo,y as Sa,z as xa,A as xr,B as so,D as co,E as bo,F as $a,G as ka,H as _a,J as vo,K as wa,L as uo,M as fo,N as ho,O as mo,Q as za,R as go,S as yo,T as po,U as So,V as xo,W as $o,e as ko,X as _o}from"./menu-c9cc8c3d.js";var Ca=String.raw,Aa=Ca`
+import{v as m,h5 as Je,u as y,Y as Xa,h6 as Ja,a7 as ua,ab as d,h7 as b,h8 as o,h9 as Qa,ha as h,hb as fa,hc as Za,hd as eo,aE as ro,he as ao,a4 as oo,hf as to}from"./index-f83c2c5c.js";import{s as ha,n as t,t as io,o as ma,p as no,q as ga,v as ya,w as pa,x as lo,y as Sa,z as xa,A as xr,B as so,D as co,E as bo,F as $a,G as ka,H as _a,J as vo,K as wa,L as uo,M as fo,N as ho,O as mo,Q as za,R as go,S as yo,T as po,U as So,V as xo,W as $o,e as ko,X as _o}from"./menu-31376327.js";var Ca=String.raw,Aa=Ca`
   :root,
   :host {
     --chakra-vh: 100vh;

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-f6c3f475.js"></script>
+    <script type="module" crossorigin src="./assets/index-f83c2c5c.js"></script>
   </head>
 
   <body dir="ltr">

invokeai/frontend/web/public/locales/en.json

@@ -49,7 +49,6 @@
         "close": "Close",
         "communityLabel": "Community",
         "controlNet": "Controlnet",
-        "ipAdapter": "IP Adapter",
         "darkMode": "Dark Mode",
         "discordLabel": "Discord",
         "dontAskMeAgain": "Don't ask me again",
@@ -192,11 +191,7 @@
         "showAdvanced": "Show Advanced",
         "toggleControlNet": "Toggle this ControlNet",
         "w": "W",
-        "weight": "Weight",
-        "enableIPAdapter": "Enable IP Adapter",
-        "ipAdapterModel": "Adapter Model",
-        "resetIPAdapterImage": "Reset IP Adapter Image",
-        "ipAdapterImageFallback": "No IP Adapter Image Selected"
+        "weight": "Weight"
     },
     "embedding": {
         "addEmbedding": "Add Embedding",
@@ -1041,7 +1036,6 @@
         "serverError": "Server Error",
         "setCanvasInitialImage": "Set as canvas initial image",
         "setControlImage": "Set as control image",
-        "setIPAdapterImage": "Set as IP Adapter Image",
         "setInitialImage": "Set as initial image",
         "setNodeField": "Set as node field",
         "tempFoldersEmptied": "Temp Folder Emptied",

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted.ts

@@ -1,8 +1,5 @@
 import { resetCanvas } from 'features/canvas/store/canvasSlice';
-import {
-  controlNetReset,
-  ipAdapterStateReset,
-} from 'features/controlNet/store/controlNetSlice';
+import { controlNetReset } from 'features/controlNet/store/controlNetSlice';
 import { getImageUsage } from 'features/deleteImageModal/store/selectors';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
 import { clearInitialImage } from 'features/parameters/store/generationSlice';
@@ -21,7 +18,6 @@ export const addDeleteBoardAndImagesFulfilledListener = () => {
       let wasCanvasReset = false;
       let wasNodeEditorReset = false;
       let wasControlNetReset = false;
-      let wasIPAdapterReset = false;
 
       const state = getState();
       deleted_images.forEach((image_name) => {
@@ -46,11 +42,6 @@ export const addDeleteBoardAndImagesFulfilledListener = () => {
           dispatch(controlNetReset());
           wasControlNetReset = true;
         }
-
-        if (imageUsage.isIPAdapterImage && !wasIPAdapterReset) {
-          dispatch(ipAdapterStateReset());
-          wasIPAdapterReset = true;
-        }
       });
     },
   });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDeleted.ts

@@ -3,7 +3,6 @@ import { resetCanvas } from 'features/canvas/store/canvasSlice';
 import {
   controlNetImageChanged,
   controlNetProcessedImageChanged,
-  ipAdapterImageChanged,
 } from 'features/controlNet/store/controlNetSlice';
 import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions';
 import { isModalOpenChanged } from 'features/deleteImageModal/store/slice';
@@ -111,14 +110,6 @@ export const addRequestedSingleImageDeletionListener = () => {
           }
         });
 
-        // Remove IP Adapter Set Image if image is deleted.
-        if (
-          getState().controlNet.ipAdapterInfo.adapterImage?.image_name ===
-          imageDTO.image_name
-        ) {
-          dispatch(ipAdapterImageChanged(null));
-        }
-
         // reset nodes that use the deleted images
         getState().nodes.nodes.forEach((node) => {
           if (!isInvocationNode(node)) {
@@ -236,14 +227,6 @@ export const addRequestedMultipleImageDeletionListener = () => {
             }
           });
 
-          // Remove IP Adapter Set Image if image is deleted.
-          if (
-            getState().controlNet.ipAdapterInfo.adapterImage?.image_name ===
-            imageDTO.image_name
-          ) {
-            dispatch(ipAdapterImageChanged(null));
-          }
-
           // reset nodes that use the deleted images
           getState().nodes.nodes.forEach((node) => {
             if (!isInvocationNode(node)) {

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDropped.ts

@@ -1,11 +1,7 @@
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
-import { parseify } from 'common/util/serialize';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
-import {
-  controlNetImageChanged,
-  ipAdapterImageChanged,
-} from 'features/controlNet/store/controlNetSlice';
+import { controlNetImageChanged } from 'features/controlNet/store/controlNetSlice';
 import {
   TypesafeDraggableData,
   TypesafeDroppableData,
@@ -18,6 +14,7 @@ import {
 import { initialImageChanged } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
 import { startAppListening } from '../';
+import { parseify } from 'common/util/serialize';
 
 export const dndDropped = createAction<{
   overData: TypesafeDroppableData;
@@ -102,18 +99,6 @@ export const addImageDroppedListener = () => {
         return;
       }
 
-      /**
-       * Image dropped on IP Adapter image
-       */
-      if (
-        overData.actionType === 'SET_IP_ADAPTER_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        dispatch(ipAdapterImageChanged(activeData.payload.imageDTO));
-        return;
-      }
-
       /**
        * Image dropped on Canvas
        */

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageToDeleteSelected.ts

@@ -19,7 +19,6 @@ export const addImageToDeleteSelectedListener = () => {
         imagesUsage.some((i) => i.isCanvasImage) ||
         imagesUsage.some((i) => i.isInitialImage) ||
         imagesUsage.some((i) => i.isControlNetImage) ||
-        imagesUsage.some((i) => i.isIPAdapterImage) ||
         imagesUsage.some((i) => i.isNodesImage);
 
       if (shouldConfirmOnDelete || isImageInUse) {

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageUploaded.ts

@@ -1,18 +1,15 @@
 import { UseToastOptions } from '@chakra-ui/react';
 import { logger } from 'app/logging/logger';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
-import {
-  controlNetImageChanged,
-  ipAdapterImageChanged,
-} from 'features/controlNet/store/controlNetSlice';
+import { controlNetImageChanged } from 'features/controlNet/store/controlNetSlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { initialImageChanged } from 'features/parameters/store/generationSlice';
 import { addToast } from 'features/system/store/systemSlice';
-import { t } from 'i18next';
 import { omit } from 'lodash-es';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { startAppListening } from '..';
 import { imagesApi } from '../../../../../services/api/endpoints/images';
+import { t } from 'i18next';
 
 const DEFAULT_UPLOADED_TOAST: UseToastOptions = {
   title: t('toast.imageUploaded'),
@@ -102,17 +99,6 @@ export const addImageUploadedFulfilledListener = () => {
         return;
       }
 
-      if (postUploadAction?.type === 'SET_IP_ADAPTER_IMAGE') {
-        dispatch(ipAdapterImageChanged(imageDTO));
-        dispatch(
-          addToast({
-            ...DEFAULT_UPLOADED_TOAST,
-            description: t('toast.setIPAdapterImage'),
-          })
-        );
-        return;
-      }
-
       if (postUploadAction?.type === 'SET_INITIAL_IMAGE') {
         dispatch(initialImageChanged(imageDTO));
         dispatch(

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelSelected.ts

@@ -1,9 +1,6 @@
 import { logger } from 'app/logging/logger';
 import { setBoundingBoxDimensions } from 'features/canvas/store/canvasSlice';
-import {
-  controlNetRemoved,
-  ipAdapterStateReset,
-} from 'features/controlNet/store/controlNetSlice';
+import { controlNetRemoved } from 'features/controlNet/store/controlNetSlice';
 import { loraRemoved } from 'features/lora/store/loraSlice';
 import { modelSelected } from 'features/parameters/store/actions';
 import {
@@ -59,7 +56,6 @@ export const addModelSelectedListener = () => {
           modelsCleared += 1;
         }
 
-        // handle incompatible controlnets
         const { controlNets } = state.controlNet;
         forEach(controlNets, (controlNet, controlNetId) => {
           if (controlNet.model?.base_model !== base_model) {
@@ -68,16 +64,6 @@ export const addModelSelectedListener = () => {
           }
         });
 
-        // handle incompatible IP-Adapter
-        const { ipAdapterInfo } = state.controlNet;
-        if (
-          ipAdapterInfo.model &&
-          ipAdapterInfo.model.base_model !== base_model
-        ) {
-          dispatch(ipAdapterStateReset());
-          modelsCleared += 1;
-        }
-
         if (modelsCleared > 0) {
           dispatch(
             addToast(

invokeai/frontend/web/src/app/store/nanostores/store.ts

@@ -0,0 +1,5 @@
+import { Store } from '@reduxjs/toolkit';
+import { atom } from 'nanostores';
+
+// eslint-disable-next-line @typescript-eslint/no-explicit-any
+export const $store = atom<Store<any> | undefined>();

invokeai/frontend/web/src/app/store/store.ts

@@ -31,6 +31,7 @@ import { actionSanitizer } from './middleware/devtools/actionSanitizer';
 import { actionsDenylist } from './middleware/devtools/actionsDenylist';
 import { stateSanitizer } from './middleware/devtools/stateSanitizer';
 import { listenerMiddleware } from './middleware/listenerMiddleware';
+import { $store } from './nanostores/store';
 
 const allReducers = {
   canvas: canvasReducer,
@@ -86,10 +87,7 @@ export const store = configureStore({
       .concat(autoBatchEnhancer());
   },
   middleware: (getDefaultMiddleware) =>
-    getDefaultMiddleware({
-      serializableCheck: false,
-      immutableCheck: false,
-    })
+    getDefaultMiddleware({ immutableCheck: false })
       .concat(api.middleware)
       .concat(dynamicMiddlewares)
       .prepend(listenerMiddleware.middleware),
@@ -124,3 +122,4 @@ export type RootState = ReturnType<typeof store.getState>;
 export type AppThunkDispatch = ThunkDispatch<RootState, any, AnyAction>;
 export type AppDispatch = typeof store.dispatch;
 export const stateSelector = (state: RootState) => state;
+$store.set(store);

invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx

@@ -18,7 +18,6 @@ import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAIIconButton from 'common/components/IAIIconButton';
 import IAISwitch from 'common/components/IAISwitch';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
-import { useTranslation } from 'react-i18next';
 import { useToggle } from 'react-use';
 import { v4 as uuidv4 } from 'uuid';
 import ControlNetImagePreview from './ControlNetImagePreview';
@@ -29,6 +28,7 @@ import ParamControlNetBeginEnd from './parameters/ParamControlNetBeginEnd';
 import ParamControlNetControlMode from './parameters/ParamControlNetControlMode';
 import ParamControlNetProcessorSelect from './parameters/ParamControlNetProcessorSelect';
 import ParamControlNetResizeMode from './parameters/ParamControlNetResizeMode';
+import { useTranslation } from 'react-i18next';
 
 type ControlNetProps = {
   controlNet: ControlNetConfig;

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/IPAdapterPanel.tsx

@@ -1,35 +0,0 @@
-import { Flex } from '@chakra-ui/react';
-import { memo } from 'react';
-import ParamIPAdapterBeginEnd from './ParamIPAdapterBeginEnd';
-import ParamIPAdapterFeatureToggle from './ParamIPAdapterFeatureToggle';
-import ParamIPAdapterImage from './ParamIPAdapterImage';
-import ParamIPAdapterModelSelect from './ParamIPAdapterModelSelect';
-import ParamIPAdapterWeight from './ParamIPAdapterWeight';
-
-const IPAdapterPanel = () => {
-  return (
-    <Flex
-      sx={{
-        flexDir: 'column',
-        gap: 3,
-        paddingInline: 3,
-        paddingBlock: 2,
-        paddingBottom: 5,
-        borderRadius: 'base',
-        position: 'relative',
-        bg: 'base.250',
-        _dark: {
-          bg: 'base.750',
-        },
-      }}
-    >
-      <ParamIPAdapterFeatureToggle />
-      <ParamIPAdapterImage />
-      <ParamIPAdapterModelSelect />
-      <ParamIPAdapterWeight />
-      <ParamIPAdapterBeginEnd />
-    </Flex>
-  );
-};
-
-export default memo(IPAdapterPanel);

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/ParamIPAdapterBeginEnd.tsx

@@ -1,100 +0,0 @@
-import {
-  FormControl,
-  FormLabel,
-  HStack,
-  RangeSlider,
-  RangeSliderFilledTrack,
-  RangeSliderMark,
-  RangeSliderThumb,
-  RangeSliderTrack,
-  Tooltip,
-} from '@chakra-ui/react';
-import { RootState } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import {
-  ipAdapterBeginStepPctChanged,
-  ipAdapterEndStepPctChanged,
-} from 'features/controlNet/store/controlNetSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-
-const formatPct = (v: number) => `${Math.round(v * 100)}%`;
-
-const ParamIPAdapterBeginEnd = () => {
-  const isEnabled = useAppSelector(
-    (state: RootState) => state.controlNet.isIPAdapterEnabled
-  );
-  const beginStepPct = useAppSelector(
-    (state: RootState) => state.controlNet.ipAdapterInfo.beginStepPct
-  );
-  const endStepPct = useAppSelector(
-    (state: RootState) => state.controlNet.ipAdapterInfo.endStepPct
-  );
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-
-  const handleStepPctChanged = useCallback(
-    (v: number[]) => {
-      dispatch(ipAdapterBeginStepPctChanged(v[0] as number));
-      dispatch(ipAdapterEndStepPctChanged(v[1] as number));
-    },
-    [dispatch]
-  );
-
-  return (
-    <FormControl isDisabled={!isEnabled}>
-      <FormLabel>{t('controlnet.beginEndStepPercent')}</FormLabel>
-      <HStack w="100%" gap={2} alignItems="center">
-        <RangeSlider
-          aria-label={['Begin Step %', 'End Step %!']}
-          value={[beginStepPct, endStepPct]}
-          onChange={handleStepPctChanged}
-          min={0}
-          max={1}
-          step={0.01}
-          minStepsBetweenThumbs={5}
-          isDisabled={!isEnabled}
-        >
-          <RangeSliderTrack>
-            <RangeSliderFilledTrack />
-          </RangeSliderTrack>
-          <Tooltip label={formatPct(beginStepPct)} placement="top" hasArrow>
-            <RangeSliderThumb index={0} />
-          </Tooltip>
-          <Tooltip label={formatPct(endStepPct)} placement="top" hasArrow>
-            <RangeSliderThumb index={1} />
-          </Tooltip>
-          <RangeSliderMark
-            value={0}
-            sx={{
-              insetInlineStart: '0 !important',
-              insetInlineEnd: 'unset !important',
-            }}
-          >
-            0%
-          </RangeSliderMark>
-          <RangeSliderMark
-            value={0.5}
-            sx={{
-              insetInlineStart: '50% !important',
-              transform: 'translateX(-50%)',
-            }}
-          >
-            50%
-          </RangeSliderMark>
-          <RangeSliderMark
-            value={1}
-            sx={{
-              insetInlineStart: 'unset !important',
-              insetInlineEnd: '0 !important',
-            }}
-          >
-            100%
-          </RangeSliderMark>
-        </RangeSlider>
-      </HStack>
-    </FormControl>
-  );
-};
-
-export default memo(ParamIPAdapterBeginEnd);

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/ParamIPAdapterFeatureToggle.tsx

@@ -1,41 +0,0 @@
-import { createSelector } from '@reduxjs/toolkit';
-import { stateSelector } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import IAISwitch from 'common/components/IAISwitch';
-import { isIPAdapterEnableToggled } from 'features/controlNet/store/controlNetSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-
-const selector = createSelector(
-  stateSelector,
-  (state) => {
-    const { isIPAdapterEnabled } = state.controlNet;
-
-    return { isIPAdapterEnabled };
-  },
-  defaultSelectorOptions
-);
-
-const ParamIPAdapterFeatureToggle = () => {
-  const { isIPAdapterEnabled } = useAppSelector(selector);
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-
-  const handleChange = useCallback(() => {
-    dispatch(isIPAdapterEnableToggled());
-  }, [dispatch]);
-
-  return (
-    <IAISwitch
-      label={t('controlnet.enableIPAdapter')}
-      isChecked={isIPAdapterEnabled}
-      onChange={handleChange}
-      formControlProps={{
-        width: '100%',
-      }}
-    />
-  );
-};
-
-export default memo(ParamIPAdapterFeatureToggle);

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/ParamIPAdapterImage.tsx

@@ -1,93 +0,0 @@
-import { Flex } from '@chakra-ui/react';
-import { skipToken } from '@reduxjs/toolkit/dist/query';
-import { RootState } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIDndImage from 'common/components/IAIDndImage';
-import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
-import { IAINoContentFallback } from 'common/components/IAIImageFallback';
-import { ipAdapterImageChanged } from 'features/controlNet/store/controlNetSlice';
-import {
-  TypesafeDraggableData,
-  TypesafeDroppableData,
-} from 'features/dnd/types';
-import { memo, useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { FaUndo } from 'react-icons/fa';
-import { useGetImageDTOQuery } from 'services/api/endpoints/images';
-import { PostUploadAction } from 'services/api/types';
-
-const ParamIPAdapterImage = () => {
-  const ipAdapterInfo = useAppSelector(
-    (state: RootState) => state.controlNet.ipAdapterInfo
-  );
-
-  const isIPAdapterEnabled = useAppSelector(
-    (state: RootState) => state.controlNet.isIPAdapterEnabled
-  );
-
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-
-  const { currentData: imageDTO } = useGetImageDTOQuery(
-    ipAdapterInfo.adapterImage?.image_name ?? skipToken
-  );
-
-  const draggableData = useMemo<TypesafeDraggableData | undefined>(() => {
-    if (imageDTO) {
-      return {
-        id: 'ip-adapter-image',
-        payloadType: 'IMAGE_DTO',
-        payload: { imageDTO },
-      };
-    }
-  }, [imageDTO]);
-
-  const droppableData = useMemo<TypesafeDroppableData | undefined>(
-    () => ({
-      id: 'ip-adapter-image',
-      actionType: 'SET_IP_ADAPTER_IMAGE',
-    }),
-    []
-  );
-
-  const postUploadAction = useMemo<PostUploadAction>(
-    () => ({
-      type: 'SET_IP_ADAPTER_IMAGE',
-    }),
-    []
-  );
-
-  return (
-    <Flex
-      sx={{
-        position: 'relative',
-        w: 'full',
-        alignItems: 'center',
-        justifyContent: 'center',
-      }}
-    >
-      <IAIDndImage
-        imageDTO={imageDTO}
-        droppableData={droppableData}
-        draggableData={draggableData}
-        postUploadAction={postUploadAction}
-        isUploadDisabled={!isIPAdapterEnabled}
-        isDropDisabled={!isIPAdapterEnabled}
-        dropLabel={t('toast.setIPAdapterImage')}
-        noContentFallback={
-          <IAINoContentFallback
-            label={t('controlnet.ipAdapterImageFallback')}
-          />
-        }
-      />
-
-      <IAIDndImageIcon
-        onClick={() => dispatch(ipAdapterImageChanged(null))}
-        icon={ipAdapterInfo.adapterImage ? <FaUndo /> : undefined}
-        tooltip={t('controlnet.resetIPAdapterImage')}
-      />
-    </Flex>
-  );
-};
-
-export default memo(ParamIPAdapterImage);

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/ParamIPAdapterModelSelect.tsx

@@ -1,97 +0,0 @@
-import { SelectItem } from '@mantine/core';
-import { RootState } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIMantineSelect from 'common/components/IAIMantineSelect';
-import { ipAdapterModelChanged } from 'features/controlNet/store/controlNetSlice';
-import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
-import { modelIdToIPAdapterModelParam } from 'features/parameters/util/modelIdToIPAdapterModelParams';
-import { forEach } from 'lodash-es';
-import { memo, useCallback, useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { useGetIPAdapterModelsQuery } from 'services/api/endpoints/models';
-
-const ParamIPAdapterModelSelect = () => {
-  const ipAdapterModel = useAppSelector(
-    (state: RootState) => state.controlNet.ipAdapterInfo.model
-  );
-  const model = useAppSelector((state: RootState) => state.generation.model);
-
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-
-  const { data: ipAdapterModels } = useGetIPAdapterModelsQuery();
-
-  // grab the full model entity from the RTK Query cache
-  const selectedModel = useMemo(
-    () =>
-      ipAdapterModels?.entities[
-        `${ipAdapterModel?.base_model}/ip_adapter/${ipAdapterModel?.model_name}`
-      ] ?? null,
-    [
-      ipAdapterModel?.base_model,
-      ipAdapterModel?.model_name,
-      ipAdapterModels?.entities,
-    ]
-  );
-
-  const data = useMemo(() => {
-    if (!ipAdapterModels) {
-      return [];
-    }
-
-    const data: SelectItem[] = [];
-
-    forEach(ipAdapterModels.entities, (ipAdapterModel, id) => {
-      if (!ipAdapterModel) {
-        return;
-      }
-
-      const disabled = model?.base_model !== ipAdapterModel.base_model;
-
-      data.push({
-        value: id,
-        label: ipAdapterModel.model_name,
-        group: MODEL_TYPE_MAP[ipAdapterModel.base_model],
-        disabled,
-        tooltip: disabled
-          ? `Incompatible base model: ${ipAdapterModel.base_model}`
-          : undefined,
-      });
-    });
-
-    return data.sort((a, b) => (a.disabled && !b.disabled ? 1 : -1));
-  }, [ipAdapterModels, model?.base_model]);
-
-  const handleValueChanged = useCallback(
-    (v: string | null) => {
-      if (!v) {
-        return;
-      }
-
-      const newIPAdapterModel = modelIdToIPAdapterModelParam(v);
-
-      if (!newIPAdapterModel) {
-        return;
-      }
-
-      dispatch(ipAdapterModelChanged(newIPAdapterModel));
-    },
-    [dispatch]
-  );
-
-  return (
-    <IAIMantineSelect
-      label={t('controlnet.ipAdapterModel')}
-      className="nowheel nodrag"
-      tooltip={selectedModel?.description}
-      value={selectedModel?.id ?? null}
-      placeholder="Pick one"
-      error={!selectedModel}
-      data={data}
-      onChange={handleValueChanged}
-      sx={{ width: '100%' }}
-    />
-  );
-};
-
-export default memo(ParamIPAdapterModelSelect);

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/ParamIPAdapterWeight.tsx

@@ -1,46 +0,0 @@
-import { RootState } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAISlider from 'common/components/IAISlider';
-import { ipAdapterWeightChanged } from 'features/controlNet/store/controlNetSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-
-const ParamIPAdapterWeight = () => {
-  const isIpAdapterEnabled = useAppSelector(
-    (state: RootState) => state.controlNet.isIPAdapterEnabled
-  );
-  const ipAdapterWeight = useAppSelector(
-    (state: RootState) => state.controlNet.ipAdapterInfo.weight
-  );
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-
-  const handleWeightChanged = useCallback(
-    (weight: number) => {
-      dispatch(ipAdapterWeightChanged(weight));
-    },
-    [dispatch]
-  );
-
-  const handleWeightReset = useCallback(() => {
-    dispatch(ipAdapterWeightChanged(1));
-  }, [dispatch]);
-
-  return (
-    <IAISlider
-      isDisabled={!isIpAdapterEnabled}
-      label={t('controlnet.weight')}
-      value={ipAdapterWeight}
-      onChange={handleWeightChanged}
-      min={0}
-      max={2}
-      step={0.01}
-      withSliderMarks
-      sliderMarks={[0, 1, 2]}
-      withReset
-      handleReset={handleWeightReset}
-    />
-  );
-};
-
-export default memo(ParamIPAdapterWeight);

invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts

@@ -1,13 +1,9 @@
 import { PayloadAction, createSlice } from '@reduxjs/toolkit';
-import {
-  ControlNetModelParam,
-  IPAdapterModelParam,
-} from 'features/parameters/types/parameterSchemas';
+import { ControlNetModelParam } from 'features/parameters/types/parameterSchemas';
 import { cloneDeep, forEach } from 'lodash-es';
 import { imagesApi } from 'services/api/endpoints/images';
 import { components } from 'services/api/schema';
 import { isAnySessionRejected } from 'services/api/thunks/session';
-import { ImageDTO } from 'services/api/types';
 import { appSocketInvocationError } from 'services/events/actions';
 import { controlNetImageProcessed } from './actions';
 import {
@@ -60,36 +56,16 @@ export type ControlNetConfig = {
   shouldAutoConfig: boolean;
 };
 
-export type IPAdapterConfig = {
-  adapterImage: ImageDTO | null;
-  model: IPAdapterModelParam | null;
-  weight: number;
-  beginStepPct: number;
-  endStepPct: number;
-};
-
 export type ControlNetState = {
   controlNets: Record<string, ControlNetConfig>;
   isEnabled: boolean;
   pendingControlImages: string[];
-  isIPAdapterEnabled: boolean;
-  ipAdapterInfo: IPAdapterConfig;
-};
-
-export const initialIPAdapterState: IPAdapterConfig = {
-  adapterImage: null,
-  model: null,
-  weight: 1,
-  beginStepPct: 0,
-  endStepPct: 1,
 };
 
 export const initialControlNetState: ControlNetState = {
   controlNets: {},
   isEnabled: false,
   pendingControlImages: [],
-  isIPAdapterEnabled: false,
-  ipAdapterInfo: { ...initialIPAdapterState },
 };
 
 export const controlNetSlice = createSlice({
@@ -377,31 +353,6 @@ export const controlNetSlice = createSlice({
     controlNetReset: () => {
       return { ...initialControlNetState };
     },
-    isIPAdapterEnableToggled: (state) => {
-      state.isIPAdapterEnabled = !state.isIPAdapterEnabled;
-    },
-    ipAdapterImageChanged: (state, action: PayloadAction<ImageDTO | null>) => {
-      state.ipAdapterInfo.adapterImage = action.payload;
-    },
-    ipAdapterWeightChanged: (state, action: PayloadAction<number>) => {
-      state.ipAdapterInfo.weight = action.payload;
-    },
-    ipAdapterModelChanged: (
-      state,
-      action: PayloadAction<IPAdapterModelParam | null>
-    ) => {
-      state.ipAdapterInfo.model = action.payload;
-    },
-    ipAdapterBeginStepPctChanged: (state, action: PayloadAction<number>) => {
-      state.ipAdapterInfo.beginStepPct = action.payload;
-    },
-    ipAdapterEndStepPctChanged: (state, action: PayloadAction<number>) => {
-      state.ipAdapterInfo.endStepPct = action.payload;
-    },
-    ipAdapterStateReset: (state) => {
-      state.isIPAdapterEnabled = false;
-      state.ipAdapterInfo = { ...initialIPAdapterState };
-    },
   },
   extraReducers: (builder) => {
     builder.addCase(controlNetImageProcessed, (state, action) => {
@@ -461,13 +412,6 @@ export const {
   controlNetProcessorTypeChanged,
   controlNetReset,
   controlNetAutoConfigToggled,
-  isIPAdapterEnableToggled,
-  ipAdapterImageChanged,
-  ipAdapterWeightChanged,
-  ipAdapterModelChanged,
-  ipAdapterBeginStepPctChanged,
-  ipAdapterEndStepPctChanged,
-  ipAdapterStateReset,
 } = controlNetSlice.actions;
 
 export default controlNetSlice.reducer;

invokeai/frontend/web/src/features/deleteImageModal/components/DeleteImageModal.tsx

@@ -10,20 +10,20 @@ import {
   Text,
 } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAIButton from 'common/components/IAIButton';
 import IAISwitch from 'common/components/IAISwitch';
 import { setShouldConfirmOnDelete } from 'features/system/store/systemSlice';
+import { stateSelector } from 'app/store/store';
 import { some } from 'lodash-es';
 import { ChangeEvent, memo, useCallback, useRef } from 'react';
 import { useTranslation } from 'react-i18next';
 import { imageDeletionConfirmed } from '../store/actions';
 import { getImageUsage, selectImageUsage } from '../store/selectors';
 import { imageDeletionCanceled, isModalOpenChanged } from '../store/slice';
-import { ImageUsage } from '../store/types';
 import ImageUsageMessage from './ImageUsageMessage';
+import { ImageUsage } from '../store/types';
 
 const selector = createSelector(
   [stateSelector, selectImageUsage],
@@ -42,7 +42,6 @@ const selector = createSelector(
       isCanvasImage: some(allImageUsage, (i) => i.isCanvasImage),
       isNodesImage: some(allImageUsage, (i) => i.isNodesImage),
       isControlNetImage: some(allImageUsage, (i) => i.isControlNetImage),
-      isIPAdapterImage: some(allImageUsage, (i) => i.isIPAdapterImage),
     };
 
     return {

invokeai/frontend/web/src/features/deleteImageModal/components/ImageUsageMessage.tsx

@@ -1,8 +1,8 @@
 import { ListItem, Text, UnorderedList } from '@chakra-ui/react';
 import { some } from 'lodash-es';
 import { memo } from 'react';
-import { useTranslation } from 'react-i18next';
 import { ImageUsage } from '../store/types';
+import { useTranslation } from 'react-i18next';
 
 type Props = {
   imageUsage?: ImageUsage;
@@ -38,9 +38,6 @@ const ImageUsageMessage = (props: Props) => {
         {imageUsage.isControlNetImage && (
           <ListItem>{t('common.controlNet')}</ListItem>
         )}
-        {imageUsage.isIPAdapterImage && (
-          <ListItem>{t('common.ipAdapter')}</ListItem>
-        )}
         {imageUsage.isNodesImage && (
           <ListItem>{t('common.nodeEditor')}</ListItem>
         )}

invokeai/frontend/web/src/features/deleteImageModal/store/selectors.ts

@@ -1,9 +1,9 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { RootState } from 'app/store/store';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import { isInvocationNode } from 'features/nodes/types/types';
 import { some } from 'lodash-es';
 import { ImageUsage } from './types';
+import { isInvocationNode } from 'features/nodes/types/types';
 
 export const getImageUsage = (state: RootState, image_name: string) => {
   const { generation, canvas, nodes, controlNet } = state;
@@ -27,15 +27,11 @@ export const getImageUsage = (state: RootState, image_name: string) => {
       c.controlImage === image_name || c.processedControlImage === image_name
   );
 
-  const isIPAdapterImage =
-    controlNet.ipAdapterInfo.adapterImage?.image_name === image_name;
-
   const imageUsage: ImageUsage = {
     isInitialImage,
     isCanvasImage,
     isNodesImage,
     isControlNetImage,
-    isIPAdapterImage,
   };
 
   return imageUsage;

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

@@ -10,5 +10,4 @@ export type ImageUsage = {
   isCanvasImage: boolean;
   isNodesImage: boolean;
   isControlNetImage: boolean;
-  isIPAdapterImage: boolean;
 };

invokeai/frontend/web/src/features/dnd/types/index.ts

@@ -35,10 +35,6 @@ export type ControlNetDropData = BaseDropData & {
   };
 };
 
-export type IPAdapterImageDropData = BaseDropData & {
-  actionType: 'SET_IP_ADAPTER_IMAGE';
-};
-
 export type CanvasInitialImageDropData = BaseDropData & {
   actionType: 'SET_CANVAS_INITIAL_IMAGE';
 };
@@ -77,7 +73,6 @@ export type TypesafeDroppableData =
   | CurrentImageDropData
   | InitialImageDropData
   | ControlNetDropData
-  | IPAdapterImageDropData
   | CanvasInitialImageDropData
   | NodesImageDropData
   | AddToBatchDropData

invokeai/frontend/web/src/features/dnd/util/isValidDrop.ts

@@ -24,8 +24,6 @@ export const isValidDrop = (
       return payloadType === 'IMAGE_DTO';
     case 'SET_CONTROLNET_IMAGE':
       return payloadType === 'IMAGE_DTO';
-    case 'SET_IP_ADAPTER_IMAGE':
-      return payloadType === 'IMAGE_DTO';
     case 'SET_CANVAS_INITIAL_IMAGE':
       return payloadType === 'IMAGE_DTO';
     case 'SET_NODES_IMAGE':

invokeai/frontend/web/src/features/gallery/components/Boards/DeleteBoardModal.tsx

@@ -53,7 +53,6 @@ const DeleteBoardModal = (props: Props) => {
           isCanvasImage: some(allImageUsage, (i) => i.isCanvasImage),
           isNodesImage: some(allImageUsage, (i) => i.isNodesImage),
           isControlNetImage: some(allImageUsage, (i) => i.isControlNetImage),
-          isIPAdapterImage: some(allImageUsage, (i) => i.isIPAdapterImage),
         };
         return { imageUsageSummary };
       }),

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/EmbedWorkflowCheckbox.tsx

@@ -27,7 +27,7 @@ const EmbedWorkflowCheckbox = ({ nodeId }: { nodeId: string }) => {
 
   return (
     <FormControl as={Flex} sx={{ alignItems: 'center', gap: 2, w: 'auto' }}>
-      <FormLabel sx={{ fontSize: 'xs', mb: '1px' }}>Embed Workflow</FormLabel>
+      <FormLabel sx={{ fontSize: 'xs', mb: '1px' }}>Workflow</FormLabel>
       <Checkbox
         className="nopan"
         size="sm"

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNode.tsx

@@ -1,14 +1,13 @@
 import { Flex, Grid, GridItem } from '@chakra-ui/react';
+import { useAnyOrDirectInputFieldNames } from 'features/nodes/hooks/useAnyOrDirectInputFieldNames';
+import { useConnectionInputFieldNames } from 'features/nodes/hooks/useConnectionInputFieldNames';
+import { useOutputFieldNames } from 'features/nodes/hooks/useOutputFieldNames';
 import { memo } from 'react';
+import NodeWrapper from '../common/NodeWrapper';
 import InvocationNodeFooter from './InvocationNodeFooter';
 import InvocationNodeHeader from './InvocationNodeHeader';
-import NodeWrapper from '../common/NodeWrapper';
-import OutputField from './fields/OutputField';
 import InputField from './fields/InputField';
-import { useOutputFieldNames } from 'features/nodes/hooks/useOutputFieldNames';
-import { useWithFooter } from 'features/nodes/hooks/useWithFooter';
-import { useConnectionInputFieldNames } from 'features/nodes/hooks/useConnectionInputFieldNames';
-import { useAnyOrDirectInputFieldNames } from 'features/nodes/hooks/useAnyOrDirectInputFieldNames';
+import OutputField from './fields/OutputField';
 
 type Props = {
   nodeId: string;
@@ -22,7 +21,6 @@ const InvocationNode = ({ nodeId, isOpen, label, type, selected }: Props) => {
   const inputConnectionFieldNames = useConnectionInputFieldNames(nodeId);
   const inputAnyOrDirectFieldNames = useAnyOrDirectInputFieldNames(nodeId);
   const outputFieldNames = useOutputFieldNames(nodeId);
-  const withFooter = useWithFooter(nodeId);
 
   return (
     <NodeWrapper nodeId={nodeId} selected={selected}>
@@ -43,7 +41,7 @@ const InvocationNode = ({ nodeId, isOpen, label, type, selected }: Props) => {
               h: 'full',
               py: 2,
               gap: 1,
-              borderBottomRadius: withFooter ? 0 : 'base',
+              borderBottomRadius: 0,
             }}
           >
             <Flex sx={{ flexDir: 'column', px: 2, w: 'full', h: 'full' }}>
@@ -76,7 +74,7 @@ const InvocationNode = ({ nodeId, isOpen, label, type, selected }: Props) => {
               ))}
             </Flex>
           </Flex>
-          {withFooter && <InvocationNodeFooter nodeId={nodeId} />}
+          <InvocationNodeFooter nodeId={nodeId} />
         </>
       )}
     </NodeWrapper>

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeFooter.tsx

@@ -3,12 +3,15 @@ import { DRAG_HANDLE_CLASSNAME } from 'features/nodes/types/constants';
 import { memo } from 'react';
 import EmbedWorkflowCheckbox from './EmbedWorkflowCheckbox';
 import SaveToGalleryCheckbox from './SaveToGalleryCheckbox';
+import UseCacheCheckbox from './UseCacheCheckbox';
+import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput';
 
 type Props = {
   nodeId: string;
 };
 
 const InvocationNodeFooter = ({ nodeId }: Props) => {
+  const hasImageOutput = useHasImageOutput(nodeId);
   return (
     <Flex
       className={DRAG_HANDLE_CLASSNAME}
@@ -22,8 +25,9 @@ const InvocationNodeFooter = ({ nodeId }: Props) => {
         justifyContent: 'space-between',
       }}
     >
-      <EmbedWorkflowCheckbox nodeId={nodeId} />
-      <SaveToGalleryCheckbox nodeId={nodeId} />
+      {hasImageOutput && <EmbedWorkflowCheckbox nodeId={nodeId} />}
+      <UseCacheCheckbox nodeId={nodeId} />
+      {hasImageOutput && <SaveToGalleryCheckbox nodeId={nodeId} />}
     </Flex>
   );
 };

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/UseCacheCheckbox.tsx

@@ -0,0 +1,35 @@
+import { Checkbox, Flex, FormControl, FormLabel } from '@chakra-ui/react';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { useUseCache } from 'features/nodes/hooks/useUseCache';
+import { nodeUseCacheChanged } from 'features/nodes/store/nodesSlice';
+import { ChangeEvent, memo, useCallback } from 'react';
+
+const UseCacheCheckbox = ({ nodeId }: { nodeId: string }) => {
+  const dispatch = useAppDispatch();
+  const useCache = useUseCache(nodeId);
+  const handleChange = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      dispatch(
+        nodeUseCacheChanged({
+          nodeId,
+          useCache: e.target.checked,
+        })
+      );
+    },
+    [dispatch, nodeId]
+  );
+
+  return (
+    <FormControl as={Flex} sx={{ alignItems: 'center', gap: 2, w: 'auto' }}>
+      <FormLabel sx={{ fontSize: 'xs', mb: '1px' }}>Use Cache</FormLabel>
+      <Checkbox
+        className="nopan"
+        size="sm"
+        onChange={handleChange}
+        isChecked={useCache}
+      />
+    </FormControl>
+  );
+};
+
+export default memo(UseCacheCheckbox);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/InputFieldRenderer.tsx

@@ -15,7 +15,6 @@ import SDXLMainModelInputField from './inputs/SDXLMainModelInputField';
 import SchedulerInputField from './inputs/SchedulerInputField';
 import StringInputField from './inputs/StringInputField';
 import VaeModelInputField from './inputs/VaeModelInputField';
-import IPAdapterModelInputField from './inputs/IPAdapterModelInputField';
 
 type InputFieldProps = {
   nodeId: string;
@@ -148,19 +147,6 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
     );
   }
 
-  if (
-    field?.type === 'IPAdapterModelField' &&
-    fieldTemplate?.type === 'IPAdapterModelField'
-  ) {
-    return (
-      <IPAdapterModelInputField
-        nodeId={nodeId}
-        field={field}
-        fieldTemplate={fieldTemplate}
-      />
-    );
-  }
-
   if (field?.type === 'ColorField' && fieldTemplate?.type === 'ColorField') {
     return (
       <ColorInputField

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/IPAdapterInputField.tsx

@@ -1,17 +0,0 @@
-import {
-  IPAdapterInputFieldTemplate,
-  IPAdapterInputFieldValue,
-  FieldComponentProps,
-} from 'features/nodes/types/types';
-import { memo } from 'react';
-
-const IPAdapterInputFieldComponent = (
-  _props: FieldComponentProps<
-    IPAdapterInputFieldValue,
-    IPAdapterInputFieldTemplate
-  >
-) => {
-  return null;
-};
-
-export default memo(IPAdapterInputFieldComponent);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/IPAdapterModelInputField.tsx

@@ -1,100 +0,0 @@
-import { SelectItem } from '@mantine/core';
-import { useAppDispatch } from 'app/store/storeHooks';
-import IAIMantineSelect from 'common/components/IAIMantineSelect';
-import { fieldIPAdapterModelValueChanged } from 'features/nodes/store/nodesSlice';
-import {
-  IPAdapterModelInputFieldTemplate,
-  IPAdapterModelInputFieldValue,
-  FieldComponentProps,
-} from 'features/nodes/types/types';
-import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
-import { modelIdToIPAdapterModelParam } from 'features/parameters/util/modelIdToIPAdapterModelParams';
-import { forEach } from 'lodash-es';
-import { memo, useCallback, useMemo } from 'react';
-import { useGetIPAdapterModelsQuery } from 'services/api/endpoints/models';
-
-const IPAdapterModelInputFieldComponent = (
-  props: FieldComponentProps<
-    IPAdapterModelInputFieldValue,
-    IPAdapterModelInputFieldTemplate
-  >
-) => {
-  const { nodeId, field } = props;
-  const ipAdapterModel = field.value;
-  const dispatch = useAppDispatch();
-
-  const { data: ipAdapterModels } = useGetIPAdapterModelsQuery();
-
-  // grab the full model entity from the RTK Query cache
-  const selectedModel = useMemo(
-    () =>
-      ipAdapterModels?.entities[
-        `${ipAdapterModel?.base_model}/ip_adapter/${ipAdapterModel?.model_name}`
-      ] ?? null,
-    [
-      ipAdapterModel?.base_model,
-      ipAdapterModel?.model_name,
-      ipAdapterModels?.entities,
-    ]
-  );
-
-  const data = useMemo(() => {
-    if (!ipAdapterModels) {
-      return [];
-    }
-
-    const data: SelectItem[] = [];
-
-    forEach(ipAdapterModels.entities, (model, id) => {
-      if (!model) {
-        return;
-      }
-
-      data.push({
-        value: id,
-        label: model.model_name,
-        group: MODEL_TYPE_MAP[model.base_model],
-      });
-    });
-
-    return data;
-  }, [ipAdapterModels]);
-
-  const handleValueChanged = useCallback(
-    (v: string | null) => {
-      if (!v) {
-        return;
-      }
-
-      const newIPAdapterModel = modelIdToIPAdapterModelParam(v);
-
-      if (!newIPAdapterModel) {
-        return;
-      }
-
-      dispatch(
-        fieldIPAdapterModelValueChanged({
-          nodeId,
-          fieldName: field.name,
-          value: newIPAdapterModel,
-        })
-      );
-    },
-    [dispatch, field.name, nodeId]
-  );
-
-  return (
-    <IAIMantineSelect
-      className="nowheel nodrag"
-      tooltip={selectedModel?.description}
-      value={selectedModel?.id ?? null}
-      placeholder="Pick one"
-      error={!selectedModel}
-      data={data}
-      onChange={handleValueChanged}
-      sx={{ width: '100%' }}
-    />
-  );
-};
-
-export default memo(IPAdapterModelInputFieldComponent);

invokeai/frontend/web/src/features/nodes/hooks/useBuildNodeData.ts

@@ -146,6 +146,7 @@ export const useBuildNodeData = () => {
           isIntermediate: true,
           inputs,
           outputs,
+          useCache: template.useCache,
         },
       };
 

invokeai/frontend/web/src/features/nodes/hooks/useUseCache.ts

@@ -0,0 +1,29 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import { useMemo } from 'react';
+import { isInvocationNode } from '../types/types';
+
+export const useUseCache = (nodeId: string) => {
+  const selector = useMemo(
+    () =>
+      createSelector(
+        stateSelector,
+        ({ nodes }) => {
+          const node = nodes.nodes.find((node) => node.id === nodeId);
+          if (!isInvocationNode(node)) {
+            return false;
+          }
+          // cast to boolean to support older workflows that didn't have useCache
+          // TODO: handle this better somehow
+          return node.data.useCache;
+        },
+        defaultSelectorOptions
+      ),
+    [nodeId]
+  );
+
+  const useCache = useAppSelector(selector);
+  return useCache;
+};

invokeai/frontend/web/src/features/nodes/hooks/useWithFooter.ts

@@ -7,7 +7,7 @@ import { useMemo } from 'react';
 import { FOOTER_FIELDS } from '../types/constants';
 import { isInvocationNode } from '../types/types';
 
-export const useWithFooter = (nodeId: string) => {
+export const useHasImageOutputs = (nodeId: string) => {
   const selector = useMemo(
     () =>
       createSelector(

invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts

@@ -41,7 +41,6 @@ import {
   IntegerInputFieldValue,
   InvocationNodeData,
   InvocationTemplate,
-  IPAdapterModelInputFieldValue,
   isInvocationNode,
   isNotesNode,
   LoRAModelInputFieldValue,
@@ -261,6 +260,20 @@ const nodesSlice = createSlice({
       }
       node.data.embedWorkflow = embedWorkflow;
     },
+    nodeUseCacheChanged: (
+      state,
+      action: PayloadAction<{ nodeId: string; useCache: boolean }>
+    ) => {
+      const { nodeId, useCache } = action.payload;
+      const nodeIndex = state.nodes.findIndex((n) => n.id === nodeId);
+
+      const node = state.nodes?.[nodeIndex];
+
+      if (!isInvocationNode(node)) {
+        return;
+      }
+      node.data.useCache = useCache;
+    },
     nodeIsIntermediateChanged: (
       state,
       action: PayloadAction<{ nodeId: string; isIntermediate: boolean }>
@@ -521,12 +534,6 @@ const nodesSlice = createSlice({
     ) => {
       fieldValueReducer(state, action);
     },
-    fieldIPAdapterModelValueChanged: (
-      state,
-      action: FieldValueAction<IPAdapterModelInputFieldValue>
-    ) => {
-      fieldValueReducer(state, action);
-    },
     fieldEnumModelValueChanged: (
       state,
       action: FieldValueAction<EnumInputFieldValue>
@@ -873,7 +880,6 @@ export const {
   fieldLoRAModelValueChanged,
   fieldEnumModelValueChanged,
   fieldControlNetModelValueChanged,
-  fieldIPAdapterModelValueChanged,
   fieldRefinerModelValueChanged,
   fieldSchedulerValueChanged,
   nodeIsOpenChanged,
@@ -912,6 +918,7 @@ export const {
   nodeIsIntermediateChanged,
   mouseOverNodeChanged,
   nodeExclusivelySelected,
+  nodeUseCacheChanged,
 } = nodesSlice.actions;
 
 export default nodesSlice.reducer;

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

@@ -41,7 +41,6 @@ export const POLYMORPHIC_TYPES = [
 ];
 
 export const MODEL_TYPES = [
-  'IPAdapterModelField',
   'ControlNetModelField',
   'LoRAModelField',
   'MainModelField',
@@ -237,16 +236,6 @@ export const FIELDS: Record<FieldType, FieldUIConfig> = {
     description: t('nodes.integerPolymorphicDescription'),
     title: t('nodes.integerPolymorphic'),
   },
-  IPAdapterField: {
-    color: 'green.300',
-    description: 'IP-Adapter info passed between nodes.',
-    title: 'IP-Adapter',
-  },
-  IPAdapterModelField: {
-    color: 'teal.500',
-    description: 'IP-Adapter model',
-    title: 'IP-Adapter Model',
-  },
   LatentsCollection: {
     color: 'pink.500',
     description: t('nodes.latentsCollectionDescription'),

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

@@ -1,3 +1,4 @@
+import { $store } from 'app/store/nanostores/store';
 import {
   SchedulerParam,
   zBaseModel,
@@ -7,7 +8,8 @@ import {
   zSDXLRefinerModel,
   zScheduler,
 } from 'features/parameters/types/parameterSchemas';
-import { keyBy } from 'lodash-es';
+import i18n from 'i18next';
+import { has, keyBy } from 'lodash-es';
 import { OpenAPIV3 } from 'openapi-types';
 import { RgbaColor } from 'react-colorful';
 import { Node } from 'reactflow';
@@ -20,7 +22,6 @@ import {
 import { O } from 'ts-toolbelt';
 import { JsonObject } from 'type-fest';
 import { z } from 'zod';
-import i18n from 'i18next';
 
 export type NonNullableGraph = O.Required<Graph, 'nodes' | 'edges'>;
 
@@ -57,6 +58,10 @@ export type InvocationTemplate = {
    * The invocation's version.
    */
   version?: string;
+  /**
+   * Whether or not this node should use the cache
+   */
+  useCache: boolean;
 };
 
 export type FieldUIConfig = {
@@ -94,8 +99,6 @@ export const zFieldType = z.enum([
   'integer',
   'IntegerCollection',
   'IntegerPolymorphic',
-  'IPAdapterField',
-  'IPAdapterModelField',
   'LatentsCollection',
   'LatentsField',
   'LatentsPolymorphic',
@@ -391,25 +394,6 @@ export type ControlCollectionInputFieldValue = z.infer<
   typeof zControlCollectionInputFieldValue
 >;
 
-export const zIPAdapterModel = zModelIdentifier;
-export type IPAdapterModel = z.infer<typeof zIPAdapterModel>;
-
-export const zIPAdapterField = z.object({
-  image: zImageField,
-  ip_adapter_model: zIPAdapterModel,
-  image_encoder_model: z.string().trim().min(1),
-  weight: z.number(),
-});
-export type IPAdapterField = z.infer<typeof zIPAdapterField>;
-
-export const zIPAdapterInputFieldValue = zInputFieldValueBase.extend({
-  type: z.literal('IPAdapterField'),
-  value: zIPAdapterField.optional(),
-});
-export type IPAdapterInputFieldValue = z.infer<
-  typeof zIPAdapterInputFieldValue
->;
-
 export const zModelType = z.enum([
   'onnx',
   'main',
@@ -559,17 +543,6 @@ export type ControlNetModelInputFieldValue = z.infer<
   typeof zControlNetModelInputFieldValue
 >;
 
-export const zIPAdapterModelField = zModelIdentifier;
-export type IPAdapterModelField = z.infer<typeof zIPAdapterModelField>;
-
-export const zIPAdapterModelInputFieldValue = zInputFieldValueBase.extend({
-  type: z.literal('IPAdapterModelField'),
-  value: zIPAdapterModelField.optional(),
-});
-export type IPAdapterModelInputFieldValue = z.infer<
-  typeof zIPAdapterModelInputFieldValue
->;
-
 export const zCollectionInputFieldValue = zInputFieldValueBase.extend({
   type: z.literal('Collection'),
   value: z.array(z.any()).optional(), // TODO: should this field ever have a value?
@@ -652,8 +625,6 @@ export const zInputFieldValue = z.discriminatedUnion('type', [
   zIntegerCollectionInputFieldValue,
   zIntegerPolymorphicInputFieldValue,
   zIntegerInputFieldValue,
-  zIPAdapterInputFieldValue,
-  zIPAdapterModelInputFieldValue,
   zLatentsInputFieldValue,
   zLatentsCollectionInputFieldValue,
   zLatentsPolymorphicInputFieldValue,
@@ -856,11 +827,6 @@ export type ControlPolymorphicInputFieldTemplate = Omit<
   type: 'ControlPolymorphic';
 };
 
-export type IPAdapterInputFieldTemplate = InputFieldTemplateBase & {
-  default: undefined;
-  type: 'IPAdapterField';
-};
-
 export type EnumInputFieldTemplate = InputFieldTemplateBase & {
   default: string;
   type: 'enum';
@@ -898,11 +864,6 @@ export type ControlNetModelInputFieldTemplate = InputFieldTemplateBase & {
   type: 'ControlNetModelField';
 };
 
-export type IPAdapterModelInputFieldTemplate = InputFieldTemplateBase & {
-  default: string;
-  type: 'IPAdapterModelField';
-};
-
 export type CollectionInputFieldTemplate = InputFieldTemplateBase & {
   default: [];
   type: 'Collection';
@@ -974,8 +935,6 @@ export type InputFieldTemplate =
   | IntegerCollectionInputFieldTemplate
   | IntegerPolymorphicInputFieldTemplate
   | IntegerInputFieldTemplate
-  | IPAdapterInputFieldTemplate
-  | IPAdapterModelInputFieldTemplate
   | LatentsInputFieldTemplate
   | LatentsCollectionInputFieldTemplate
   | LatentsPolymorphicInputFieldTemplate
@@ -1022,6 +981,9 @@ export type InvocationSchemaExtra = {
     type: Omit<OpenAPIV3.SchemaObject, 'default'> & {
       default: AnyInvocationType;
     };
+    use_cache: Omit<OpenAPIV3.SchemaObject, 'default'> & {
+      default: boolean;
+    };
   };
 };
 
@@ -1185,9 +1147,37 @@ export const zInvocationNodeData = z.object({
   version: zSemVer.optional(),
 });
 
+export const zInvocationNodeDataV2 = z.preprocess(
+  (arg) => {
+    try {
+      const data = zInvocationNodeData.parse(arg);
+      if (!has(data, 'useCache')) {
+        const nodeTemplates = $store.get()?.getState().nodes.nodeTemplates as
+          | Record<string, InvocationTemplate>
+          | undefined;
+
+        const template = nodeTemplates?.[data.type];
+
+        let useCache = true;
+        if (template) {
+          useCache = template.useCache;
+        }
+
+        Object.assign(data, { useCache });
+      }
+      return data;
+    } catch {
+      return arg;
+    }
+  },
+  zInvocationNodeData.extend({
+    useCache: z.boolean(),
+  })
+);
+
 // Massage this to get better type safety while developing
 export type InvocationNodeData = Omit<
-  z.infer<typeof zInvocationNodeData>,
+  z.infer<typeof zInvocationNodeDataV2>,
   'type'
 > & {
   type: AnyInvocationType;
@@ -1215,7 +1205,7 @@ const zDimension = z.number().gt(0).nullish();
 export const zWorkflowInvocationNode = z.object({
   id: z.string().trim().min(1),
   type: z.literal('invocation'),
-  data: zInvocationNodeData,
+  data: zInvocationNodeDataV2,
   width: zDimension,
   height: zDimension,
   position: zPosition,
@@ -1277,6 +1267,8 @@ export type WorkflowWarning = {
   data: JsonObject;
 };
 
+const CURRENT_WORKFLOW_VERSION = '1.0.0';
+
 export const zWorkflow = z.object({
   name: z.string().default(''),
   author: z.string().default(''),
@@ -1292,7 +1284,7 @@ export const zWorkflow = z.object({
     .object({
       version: zSemVer,
     })
-    .default({ version: '1.0.0' }),
+    .default({ version: CURRENT_WORKFLOW_VERSION }),
 });
 
 export const zValidatedWorkflow = zWorkflow.transform((workflow) => {

invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts

@@ -60,8 +60,6 @@ import {
   ImageField,
   LatentsField,
   ConditioningField,
-  IPAdapterInputFieldTemplate,
-  IPAdapterModelInputFieldTemplate,
 } from '../types/types';
 import { ControlField } from 'services/api/types';
 
@@ -437,19 +435,6 @@ const buildControlNetModelInputFieldTemplate = ({
   return template;
 };
 
-const buildIPAdapterModelInputFieldTemplate = ({
-  schemaObject,
-  baseField,
-}: BuildInputFieldArg): IPAdapterModelInputFieldTemplate => {
-  const template: IPAdapterModelInputFieldTemplate = {
-    ...baseField,
-    type: 'IPAdapterModelField',
-    default: schemaObject.default ?? undefined,
-  };
-
-  return template;
-};
-
 const buildImageInputFieldTemplate = ({
   schemaObject,
   baseField,
@@ -663,19 +648,6 @@ const buildControlCollectionInputFieldTemplate = ({
   return template;
 };
 
-const buildIPAdapterInputFieldTemplate = ({
-  schemaObject,
-  baseField,
-}: BuildInputFieldArg): IPAdapterInputFieldTemplate => {
-  const template: IPAdapterInputFieldTemplate = {
-    ...baseField,
-    type: 'IPAdapterField',
-    default: schemaObject.default ?? undefined,
-  };
-
-  return template;
-};
-
 const buildEnumInputFieldTemplate = ({
   schemaObject,
   baseField,
@@ -879,8 +851,6 @@ const TEMPLATE_BUILDER_MAP = {
   integer: buildIntegerInputFieldTemplate,
   IntegerCollection: buildIntegerCollectionInputFieldTemplate,
   IntegerPolymorphic: buildIntegerPolymorphicInputFieldTemplate,
-  IPAdapterField: buildIPAdapterInputFieldTemplate,
-  IPAdapterModelField: buildIPAdapterModelInputFieldTemplate,
   LatentsCollection: buildLatentsCollectionInputFieldTemplate,
   LatentsField: buildLatentsInputFieldTemplate,
   LatentsPolymorphic: buildLatentsPolymorphicInputFieldTemplate,

invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts

@@ -28,8 +28,6 @@ const FIELD_VALUE_FALLBACK_MAP = {
   integer: 0,
   IntegerCollection: [],
   IntegerPolymorphic: 0,
-  IPAdapterField: undefined,
-  IPAdapterModelField: undefined,
   LatentsCollection: [],
   LatentsField: undefined,
   LatentsPolymorphic: undefined,

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addIPAdapterToLinearGraph.ts

@@ -1,59 +0,0 @@
-import { RootState } from 'app/store/store';
-import { IPAdapterInvocation } from 'services/api/types';
-import { NonNullableGraph } from '../../types/types';
-import { IP_ADAPTER } from './constants';
-
-export const addIPAdapterToLinearGraph = (
-  state: RootState,
-  graph: NonNullableGraph,
-  baseNodeId: string
-): void => {
-  const { isIPAdapterEnabled, ipAdapterInfo } = state.controlNet;
-
-  // const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-  //   | MetadataAccumulatorInvocation
-  //   | undefined;
-
-  if (isIPAdapterEnabled && ipAdapterInfo.model) {
-    const ipAdapterNode: IPAdapterInvocation = {
-      id: IP_ADAPTER,
-      type: 'ip_adapter',
-      is_intermediate: true,
-      weight: ipAdapterInfo.weight,
-      ip_adapter_model: {
-        base_model: ipAdapterInfo.model?.base_model,
-        model_name: ipAdapterInfo.model?.model_name,
-      },
-      begin_step_percent: ipAdapterInfo.beginStepPct,
-      end_step_percent: ipAdapterInfo.endStepPct,
-    };
-
-    if (ipAdapterInfo.adapterImage) {
-      ipAdapterNode.image = {
-        image_name: ipAdapterInfo.adapterImage.image_name,
-      };
-    } else {
-      return;
-    }
-
-    graph.nodes[ipAdapterNode.id] = ipAdapterNode as IPAdapterInvocation;
-
-    // if (metadataAccumulator?.ip_adapters) {
-    //   // metadata accumulator only needs the ip_adapter field - not the whole node
-    //   // extract what we need and add to the accumulator
-    //   const ipAdapterField = omit(ipAdapterNode, [
-    //     'id',
-    //     'type',
-    //   ]) as IPAdapterField;
-    //   metadataAccumulator.ip_adapters.push(ipAdapterField);
-    // }
-
-    graph.edges.push({
-      source: { node_id: ipAdapterNode.id, field: 'ip_adapter' },
-      destination: {
-        node_id: baseNodeId,
-        field: 'ip_adapter',
-      },
-    });
-  }
-};

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addNSFWCheckerToGraph.ts

@@ -1,46 +1,32 @@
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
-import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import {
   ImageNSFWBlurInvocation,
   LatentsToImageInvocation,
-  MetadataAccumulatorInvocation,
 } from 'services/api/types';
-import {
-  LATENTS_TO_IMAGE,
-  METADATA_ACCUMULATOR,
-  NSFW_CHECKER,
-} from './constants';
+import { LATENTS_TO_IMAGE, NSFW_CHECKER } from './constants';
 
 export const addNSFWCheckerToGraph = (
   state: RootState,
   graph: NonNullableGraph,
   nodeIdToAddTo = LATENTS_TO_IMAGE
 ): void => {
-  const activeTabName = activeTabNameSelector(state);
-
-  const is_intermediate =
-    activeTabName === 'unifiedCanvas' ? !state.canvas.shouldAutoSave : false;
-
   const nodeToAddTo = graph.nodes[nodeIdToAddTo] as
     | LatentsToImageInvocation
     | undefined;
 
-  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    | MetadataAccumulatorInvocation
-    | undefined;
-
   if (!nodeToAddTo) {
     // something has gone terribly awry
     return;
   }
 
   nodeToAddTo.is_intermediate = true;
+  nodeToAddTo.use_cache = true;
 
   const nsfwCheckerNode: ImageNSFWBlurInvocation = {
     id: NSFW_CHECKER,
     type: 'img_nsfw',
-    is_intermediate,
+    is_intermediate: true,
   };
 
   graph.nodes[NSFW_CHECKER] = nsfwCheckerNode as ImageNSFWBlurInvocation;
@@ -54,17 +40,4 @@ export const addNSFWCheckerToGraph = (
       field: 'image',
     },
   });
-
-  if (metadataAccumulator) {
-    graph.edges.push({
-      source: {
-        node_id: METADATA_ACCUMULATOR,
-        field: 'metadata',
-      },
-      destination: {
-        node_id: NSFW_CHECKER,
-        field: 'metadata',
-      },
-    });
-  }
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSaveImageNode.ts

@@ -0,0 +1,92 @@
+import { NonNullableGraph } from 'features/nodes/types/types';
+import {
+  CANVAS_OUTPUT,
+  LATENTS_TO_IMAGE,
+  METADATA_ACCUMULATOR,
+  NSFW_CHECKER,
+  SAVE_IMAGE,
+  WATERMARKER,
+} from './constants';
+import {
+  MetadataAccumulatorInvocation,
+  SaveImageInvocation,
+} from 'services/api/types';
+import { RootState } from 'app/store/store';
+import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
+
+/**
+ * Set the `use_cache` field on the linear/canvas graph's final image output node to False.
+ */
+export const addSaveImageNode = (
+  state: RootState,
+  graph: NonNullableGraph
+): void => {
+  const activeTabName = activeTabNameSelector(state);
+  const is_intermediate =
+    activeTabName === 'unifiedCanvas' ? !state.canvas.shouldAutoSave : false;
+
+  const saveImageNode: SaveImageInvocation = {
+    id: SAVE_IMAGE,
+    type: 'save_image',
+    is_intermediate,
+    use_cache: false,
+  };
+
+  graph.nodes[SAVE_IMAGE] = saveImageNode;
+
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
+    | MetadataAccumulatorInvocation
+    | undefined;
+
+  if (metadataAccumulator) {
+    graph.edges.push({
+      source: {
+        node_id: METADATA_ACCUMULATOR,
+        field: 'metadata',
+      },
+      destination: {
+        node_id: SAVE_IMAGE,
+        field: 'metadata',
+      },
+    });
+  }
+
+  const destination = {
+    node_id: SAVE_IMAGE,
+    field: 'image',
+  };
+
+  if (WATERMARKER in graph.nodes) {
+    graph.edges.push({
+      source: {
+        node_id: WATERMARKER,
+        field: 'image',
+      },
+      destination,
+    });
+  } else if (NSFW_CHECKER in graph.nodes) {
+    graph.edges.push({
+      source: {
+        node_id: NSFW_CHECKER,
+        field: 'image',
+      },
+      destination,
+    });
+  } else if (CANVAS_OUTPUT in graph.nodes) {
+    graph.edges.push({
+      source: {
+        node_id: CANVAS_OUTPUT,
+        field: 'image',
+      },
+      destination,
+    });
+  } else if (LATENTS_TO_IMAGE in graph.nodes) {
+    graph.edges.push({
+      source: {
+        node_id: LATENTS_TO_IMAGE,
+        field: 'image',
+      },
+      destination,
+    });
+  }
+};

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts

@@ -51,6 +51,7 @@ export const addWatermarkerToGraph = (
 
   // no matter the situation, we want the l2i node to be intermediate
   nodeToAddTo.is_intermediate = true;
+  nodeToAddTo.use_cache = true;
 
   if (nsfwCheckerNode) {
     // if we are using NSFW checker, we need to "disable" it output by marking it intermediate,

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts

@@ -5,7 +5,6 @@ import { initialGenerationState } from 'features/parameters/store/generationSlic
 import { ImageDTO, ImageToLatentsInvocation } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
@@ -26,6 +25,7 @@ import {
   POSITIVE_CONDITIONING,
   SEAMLESS,
 } from './constants';
+import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Image to Image graph.
@@ -54,14 +54,10 @@ export const buildCanvasImageToImageGraph = (
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
-  const {
-    scaledBoundingBoxDimensions,
-    boundingBoxScaleMethod,
-    shouldAutoSave,
-  } = state.canvas;
+  const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-
+  const is_intermediate = true;
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -93,31 +89,31 @@ export const buildCanvasImageToImageGraph = (
       [modelLoaderNodeId]: {
         type: 'main_model_loader',
         id: modelLoaderNodeId,
-        is_intermediate: true,
+        is_intermediate,
         model,
       },
       [CLIP_SKIP]: {
         type: 'clip_skip',
         id: CLIP_SKIP,
-        is_intermediate: true,
+        is_intermediate,
         skipped_layers: clipSkip,
       },
       [POSITIVE_CONDITIONING]: {
         type: 'compel',
         id: POSITIVE_CONDITIONING,
-        is_intermediate: true,
+        is_intermediate,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: 'compel',
         id: NEGATIVE_CONDITIONING,
-        is_intermediate: true,
+        is_intermediate,
         prompt: negativePrompt,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
-        is_intermediate: true,
+        is_intermediate,
         use_cpu,
         width: !isUsingScaledDimensions
           ? width
@@ -129,12 +125,12 @@ export const buildCanvasImageToImageGraph = (
       [IMAGE_TO_LATENTS]: {
         type: 'i2l',
         id: IMAGE_TO_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
       },
       [DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: DENOISE_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
         cfg_scale,
         scheduler,
         steps,
@@ -144,7 +140,7 @@ export const buildCanvasImageToImageGraph = (
       [CANVAS_OUTPUT]: {
         type: 'l2i',
         id: CANVAS_OUTPUT,
-        is_intermediate: !shouldAutoSave,
+        is_intermediate,
       },
     },
     edges: [
@@ -239,7 +235,7 @@ export const buildCanvasImageToImageGraph = (
     graph.nodes[IMG2IMG_RESIZE] = {
       id: IMG2IMG_RESIZE,
       type: 'img_resize',
-      is_intermediate: true,
+      is_intermediate,
       image: initialImage,
       width: scaledBoundingBoxDimensions.width,
       height: scaledBoundingBoxDimensions.height,
@@ -247,13 +243,13 @@ export const buildCanvasImageToImageGraph = (
     graph.nodes[LATENTS_TO_IMAGE] = {
       id: LATENTS_TO_IMAGE,
       type: 'l2i',
-      is_intermediate: true,
+      is_intermediate,
       fp32,
     };
     graph.nodes[CANVAS_OUTPUT] = {
       id: CANVAS_OUTPUT,
       type: 'img_resize',
-      is_intermediate: !shouldAutoSave,
+      is_intermediate,
       width: width,
       height: height,
     };
@@ -294,7 +290,7 @@ export const buildCanvasImageToImageGraph = (
     graph.nodes[CANVAS_OUTPUT] = {
       type: 'l2i',
       id: CANVAS_OUTPUT,
-      is_intermediate: !shouldAutoSave,
+      is_intermediate,
       fp32,
     };
 
@@ -338,17 +334,6 @@ export const buildCanvasImageToImageGraph = (
     init_image: initialImage.image_name,
   };
 
-  graph.edges.push({
-    source: {
-      node_id: METADATA_ACCUMULATOR,
-      field: 'metadata',
-    },
-    destination: {
-      node_id: CANVAS_OUTPUT,
-      field: 'metadata',
-    },
-  });
-
   // Add Seamless To Graph
   if (seamlessXAxis || seamlessYAxis) {
     addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
@@ -367,9 +352,6 @@ export const buildCanvasImageToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
-  // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!
@@ -381,5 +363,7 @@ export const buildCanvasImageToImageGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts

@@ -12,7 +12,6 @@ import {
   RangeOfSizeInvocation,
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
@@ -45,6 +44,7 @@ import {
   RANGE_OF_SIZE,
   SEAMLESS,
 } from './constants';
+import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Inpaint graph.
@@ -88,12 +88,8 @@ export const buildCanvasInpaintGraph = (
   const { width, height } = state.canvas.boundingBoxDimensions;
 
   // We may need to set the inpaint width and height to scale the image
-  const {
-    scaledBoundingBoxDimensions,
-    boundingBoxScaleMethod,
-    shouldAutoSave,
-  } = state.canvas;
-
+  const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
+  const is_intermediate = true;
   const fp32 = vaePrecision === 'fp32';
 
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
@@ -112,56 +108,56 @@ export const buildCanvasInpaintGraph = (
       [modelLoaderNodeId]: {
         type: 'main_model_loader',
         id: modelLoaderNodeId,
-        is_intermediate: true,
+        is_intermediate,
         model,
       },
       [CLIP_SKIP]: {
         type: 'clip_skip',
         id: CLIP_SKIP,
-        is_intermediate: true,
+        is_intermediate,
         skipped_layers: clipSkip,
       },
       [POSITIVE_CONDITIONING]: {
         type: 'compel',
         id: POSITIVE_CONDITIONING,
-        is_intermediate: true,
+        is_intermediate,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: 'compel',
         id: NEGATIVE_CONDITIONING,
-        is_intermediate: true,
+        is_intermediate,
         prompt: negativePrompt,
       },
       [MASK_BLUR]: {
         type: 'img_blur',
         id: MASK_BLUR,
-        is_intermediate: true,
+        is_intermediate,
         radius: maskBlur,
         blur_type: maskBlurMethod,
       },
       [INPAINT_IMAGE]: {
         type: 'i2l',
         id: INPAINT_IMAGE,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate: true,
+        is_intermediate,
       },
       [INPAINT_CREATE_MASK]: {
         type: 'create_denoise_mask',
         id: INPAINT_CREATE_MASK,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: DENOISE_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -172,18 +168,18 @@ export const buildCanvasInpaintGraph = (
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate: true,
+        is_intermediate,
       },
       [CANVAS_COHERENCE_NOISE_INCREMENT]: {
         type: 'add',
         id: CANVAS_COHERENCE_NOISE_INCREMENT,
         b: 1,
-        is_intermediate: true,
+        is_intermediate,
       },
       [CANVAS_COHERENCE_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: CANVAS_COHERENCE_DENOISE_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -193,19 +189,19 @@ export const buildCanvasInpaintGraph = (
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [CANVAS_OUTPUT]: {
         type: 'color_correct',
         id: CANVAS_OUTPUT,
-        is_intermediate: !shouldAutoSave,
+        is_intermediate,
         reference: canvasInitImage,
       },
       [RANGE_OF_SIZE]: {
         type: 'range_of_size',
         id: RANGE_OF_SIZE,
-        is_intermediate: true,
+        is_intermediate,
         // seed - must be connected manually
         // start: 0,
         size: iterations,
@@ -214,7 +210,7 @@ export const buildCanvasInpaintGraph = (
       [ITERATE]: {
         type: 'iterate',
         id: ITERATE,
-        is_intermediate: true,
+        is_intermediate,
       },
     },
     edges: [
@@ -437,7 +433,7 @@ export const buildCanvasInpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_UP,
-      is_intermediate: true,
+      is_intermediate,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasInitImage,
@@ -445,7 +441,7 @@ export const buildCanvasInpaintGraph = (
     graph.nodes[MASK_RESIZE_UP] = {
       type: 'img_resize',
       id: MASK_RESIZE_UP,
-      is_intermediate: true,
+      is_intermediate,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasMaskImage,
@@ -453,14 +449,14 @@ export const buildCanvasInpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_DOWN,
-      is_intermediate: true,
+      is_intermediate,
       width: width,
       height: height,
     };
     graph.nodes[MASK_RESIZE_DOWN] = {
       type: 'img_resize',
       id: MASK_RESIZE_DOWN,
-      is_intermediate: true,
+      is_intermediate,
       width: width,
       height: height,
     };
@@ -598,7 +594,7 @@ export const buildCanvasInpaintGraph = (
     graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
       type: 'create_denoise_mask',
       id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
-      is_intermediate: true,
+      is_intermediate,
       fp32,
     };
 
@@ -651,7 +647,7 @@ export const buildCanvasInpaintGraph = (
       graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
         type: 'mask_edge',
         id: CANVAS_COHERENCE_MASK_EDGE,
-        is_intermediate: true,
+        is_intermediate,
         edge_blur: maskBlur,
         edge_size: maskBlur * 2,
         low_threshold: 100,
@@ -737,9 +733,6 @@ export const buildCanvasInpaintGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
-  // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!
@@ -751,5 +744,7 @@ export const buildCanvasInpaintGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasOutpaintGraph.ts

@@ -11,7 +11,6 @@ import {
   RangeOfSizeInvocation,
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
@@ -47,6 +46,7 @@ import {
   RANGE_OF_SIZE,
   SEAMLESS,
 } from './constants';
+import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Outpaint graph.
@@ -92,14 +92,10 @@ export const buildCanvasOutpaintGraph = (
   const { width, height } = state.canvas.boundingBoxDimensions;
 
   // We may need to set the inpaint width and height to scale the image
-  const {
-    scaledBoundingBoxDimensions,
-    boundingBoxScaleMethod,
-    shouldAutoSave,
-  } = state.canvas;
+  const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-
+  const is_intermediate = true;
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -116,61 +112,61 @@ export const buildCanvasOutpaintGraph = (
       [modelLoaderNodeId]: {
         type: 'main_model_loader',
         id: modelLoaderNodeId,
-        is_intermediate: true,
+        is_intermediate,
         model,
       },
       [CLIP_SKIP]: {
         type: 'clip_skip',
         id: CLIP_SKIP,
-        is_intermediate: true,
+        is_intermediate,
         skipped_layers: clipSkip,
       },
       [POSITIVE_CONDITIONING]: {
         type: 'compel',
         id: POSITIVE_CONDITIONING,
-        is_intermediate: true,
+        is_intermediate,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: 'compel',
         id: NEGATIVE_CONDITIONING,
-        is_intermediate: true,
+        is_intermediate,
         prompt: negativePrompt,
       },
       [MASK_FROM_ALPHA]: {
         type: 'tomask',
         id: MASK_FROM_ALPHA,
-        is_intermediate: true,
+        is_intermediate,
         image: canvasInitImage,
       },
       [MASK_COMBINE]: {
         type: 'mask_combine',
         id: MASK_COMBINE,
-        is_intermediate: true,
+        is_intermediate,
         mask2: canvasMaskImage,
       },
       [INPAINT_IMAGE]: {
         type: 'i2l',
         id: INPAINT_IMAGE,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate: true,
+        is_intermediate,
       },
       [INPAINT_CREATE_MASK]: {
         type: 'create_denoise_mask',
         id: INPAINT_CREATE_MASK,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: DENOISE_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -181,18 +177,18 @@ export const buildCanvasOutpaintGraph = (
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate: true,
+        is_intermediate,
       },
       [CANVAS_COHERENCE_NOISE_INCREMENT]: {
         type: 'add',
         id: CANVAS_COHERENCE_NOISE_INCREMENT,
         b: 1,
-        is_intermediate: true,
+        is_intermediate,
       },
       [CANVAS_COHERENCE_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: CANVAS_COHERENCE_DENOISE_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -202,18 +198,18 @@ export const buildCanvasOutpaintGraph = (
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [CANVAS_OUTPUT]: {
         type: 'color_correct',
         id: CANVAS_OUTPUT,
-        is_intermediate: !shouldAutoSave,
+        is_intermediate,
       },
       [RANGE_OF_SIZE]: {
         type: 'range_of_size',
         id: RANGE_OF_SIZE,
-        is_intermediate: true,
+        is_intermediate,
         // seed - must be connected manually
         // start: 0,
         size: iterations,
@@ -222,7 +218,7 @@ export const buildCanvasOutpaintGraph = (
       [ITERATE]: {
         type: 'iterate',
         id: ITERATE,
-        is_intermediate: true,
+        is_intermediate,
       },
     },
     edges: [
@@ -473,7 +469,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_patchmatch',
       id: INPAINT_INFILL,
-      is_intermediate: true,
+      is_intermediate,
       downscale: infillPatchmatchDownscaleSize,
     };
   }
@@ -482,7 +478,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_lama',
       id: INPAINT_INFILL,
-      is_intermediate: true,
+      is_intermediate,
     };
   }
 
@@ -490,7 +486,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_cv2',
       id: INPAINT_INFILL,
-      is_intermediate: true,
+      is_intermediate,
     };
   }
 
@@ -498,7 +494,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_tile',
       id: INPAINT_INFILL,
-      is_intermediate: true,
+      is_intermediate,
       tile_size: infillTileSize,
     };
   }
@@ -512,7 +508,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_UP,
-      is_intermediate: true,
+      is_intermediate,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasInitImage,
@@ -520,28 +516,28 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[MASK_RESIZE_UP] = {
       type: 'img_resize',
       id: MASK_RESIZE_UP,
-      is_intermediate: true,
+      is_intermediate,
       width: scaledWidth,
       height: scaledHeight,
     };
     graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_DOWN,
-      is_intermediate: true,
+      is_intermediate,
       width: width,
       height: height,
     };
     graph.nodes[INPAINT_INFILL_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_INFILL_RESIZE_DOWN,
-      is_intermediate: true,
+      is_intermediate,
       width: width,
       height: height,
     };
     graph.nodes[MASK_RESIZE_DOWN] = {
       type: 'img_resize',
       id: MASK_RESIZE_DOWN,
-      is_intermediate: true,
+      is_intermediate,
       width: width,
       height: height,
     };
@@ -700,7 +696,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
       type: 'create_denoise_mask',
       id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
-      is_intermediate: true,
+      is_intermediate,
       fp32,
     };
 
@@ -747,7 +743,7 @@ export const buildCanvasOutpaintGraph = (
       graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
         type: 'mask_edge',
         id: CANVAS_COHERENCE_MASK_EDGE,
-        is_intermediate: true,
+        is_intermediate,
         edge_blur: maskBlur,
         edge_size: maskBlur * 2,
         low_threshold: 100,
@@ -839,9 +835,6 @@ export const buildCanvasOutpaintGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
-  // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!
@@ -853,5 +846,7 @@ export const buildCanvasOutpaintGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts

@@ -5,7 +5,6 @@ import { initialGenerationState } from 'features/parameters/store/generationSlic
 import { ImageDTO, ImageToLatentsInvocation } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
@@ -28,6 +27,7 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
+import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Image to Image graph.
@@ -62,14 +62,10 @@ export const buildCanvasSDXLImageToImageGraph = (
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
-  const {
-    scaledBoundingBoxDimensions,
-    boundingBoxScaleMethod,
-    shouldAutoSave,
-  } = state.canvas;
+  const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-
+  const is_intermediate = true;
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -123,7 +119,7 @@ export const buildCanvasSDXLImageToImageGraph = (
       [NOISE]: {
         type: 'noise',
         id: NOISE,
-        is_intermediate: true,
+        is_intermediate,
         use_cpu,
         width: !isUsingScaledDimensions
           ? width
@@ -135,13 +131,13 @@ export const buildCanvasSDXLImageToImageGraph = (
       [IMAGE_TO_LATENTS]: {
         type: 'i2l',
         id: IMAGE_TO_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: SDXL_DENOISE_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
         cfg_scale,
         scheduler,
         steps,
@@ -252,7 +248,7 @@ export const buildCanvasSDXLImageToImageGraph = (
     graph.nodes[IMG2IMG_RESIZE] = {
       id: IMG2IMG_RESIZE,
       type: 'img_resize',
-      is_intermediate: true,
+      is_intermediate,
       image: initialImage,
       width: scaledBoundingBoxDimensions.width,
       height: scaledBoundingBoxDimensions.height,
@@ -260,13 +256,13 @@ export const buildCanvasSDXLImageToImageGraph = (
     graph.nodes[LATENTS_TO_IMAGE] = {
       id: LATENTS_TO_IMAGE,
       type: 'l2i',
-      is_intermediate: true,
+      is_intermediate,
       fp32,
     };
     graph.nodes[CANVAS_OUTPUT] = {
       id: CANVAS_OUTPUT,
       type: 'img_resize',
-      is_intermediate: !shouldAutoSave,
+      is_intermediate,
       width: width,
       height: height,
     };
@@ -307,7 +303,7 @@ export const buildCanvasSDXLImageToImageGraph = (
     graph.nodes[CANVAS_OUTPUT] = {
       type: 'l2i',
       id: CANVAS_OUTPUT,
-      is_intermediate: !shouldAutoSave,
+      is_intermediate,
       fp32,
     };
 
@@ -393,9 +389,6 @@ export const buildCanvasSDXLImageToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
-  // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!
@@ -407,5 +400,7 @@ export const buildCanvasSDXLImageToImageGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLInpaintGraph.ts

@@ -46,7 +46,7 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
+import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Inpaint graph.
@@ -95,14 +95,10 @@ export const buildCanvasSDXLInpaintGraph = (
   const { width, height } = state.canvas.boundingBoxDimensions;
 
   // We may need to set the inpaint width and height to scale the image
-  const {
-    scaledBoundingBoxDimensions,
-    boundingBoxScaleMethod,
-    shouldAutoSave,
-  } = state.canvas;
+  const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-
+  const is_intermediate = true;
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -140,32 +136,32 @@ export const buildCanvasSDXLInpaintGraph = (
       [MASK_BLUR]: {
         type: 'img_blur',
         id: MASK_BLUR,
-        is_intermediate: true,
+        is_intermediate,
         radius: maskBlur,
         blur_type: maskBlurMethod,
       },
       [INPAINT_IMAGE]: {
         type: 'i2l',
         id: INPAINT_IMAGE,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate: true,
+        is_intermediate,
       },
       [INPAINT_CREATE_MASK]: {
         type: 'create_denoise_mask',
         id: INPAINT_CREATE_MASK,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: SDXL_DENOISE_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -178,18 +174,18 @@ export const buildCanvasSDXLInpaintGraph = (
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate: true,
+        is_intermediate,
       },
       [CANVAS_COHERENCE_NOISE_INCREMENT]: {
         type: 'add',
         id: CANVAS_COHERENCE_NOISE_INCREMENT,
         b: 1,
-        is_intermediate: true,
+        is_intermediate,
       },
       [CANVAS_COHERENCE_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: CANVAS_COHERENCE_DENOISE_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -199,19 +195,19 @@ export const buildCanvasSDXLInpaintGraph = (
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [CANVAS_OUTPUT]: {
         type: 'color_correct',
         id: CANVAS_OUTPUT,
-        is_intermediate: !shouldAutoSave,
+        is_intermediate,
         reference: canvasInitImage,
       },
       [RANGE_OF_SIZE]: {
         type: 'range_of_size',
         id: RANGE_OF_SIZE,
-        is_intermediate: true,
+        is_intermediate,
         // seed - must be connected manually
         // start: 0,
         size: iterations,
@@ -220,7 +216,7 @@ export const buildCanvasSDXLInpaintGraph = (
       [ITERATE]: {
         type: 'iterate',
         id: ITERATE,
-        is_intermediate: true,
+        is_intermediate,
       },
     },
     edges: [
@@ -452,7 +448,7 @@ export const buildCanvasSDXLInpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_UP,
-      is_intermediate: true,
+      is_intermediate,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasInitImage,
@@ -460,7 +456,7 @@ export const buildCanvasSDXLInpaintGraph = (
     graph.nodes[MASK_RESIZE_UP] = {
       type: 'img_resize',
       id: MASK_RESIZE_UP,
-      is_intermediate: true,
+      is_intermediate,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasMaskImage,
@@ -468,14 +464,14 @@ export const buildCanvasSDXLInpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_DOWN,
-      is_intermediate: true,
+      is_intermediate,
       width: width,
       height: height,
     };
     graph.nodes[MASK_RESIZE_DOWN] = {
       type: 'img_resize',
       id: MASK_RESIZE_DOWN,
-      is_intermediate: true,
+      is_intermediate,
       width: width,
       height: height,
     };
@@ -613,7 +609,7 @@ export const buildCanvasSDXLInpaintGraph = (
     graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
       type: 'create_denoise_mask',
       id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
-      is_intermediate: true,
+      is_intermediate,
       fp32,
     };
 
@@ -666,7 +662,7 @@ export const buildCanvasSDXLInpaintGraph = (
       graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
         type: 'mask_edge',
         id: CANVAS_COHERENCE_MASK_EDGE,
-        is_intermediate: true,
+        is_intermediate,
         edge_blur: maskBlur,
         edge_size: maskBlur * 2,
         low_threshold: 100,
@@ -766,9 +762,6 @@ export const buildCanvasSDXLInpaintGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
-  // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!
@@ -780,5 +773,7 @@ export const buildCanvasSDXLInpaintGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLOutpaintGraph.ts

@@ -11,7 +11,6 @@ import {
   RangeOfSizeInvocation,
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
@@ -49,6 +48,7 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
+import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Outpaint graph.
@@ -99,14 +99,10 @@ export const buildCanvasSDXLOutpaintGraph = (
   const { width, height } = state.canvas.boundingBoxDimensions;
 
   // We may need to set the inpaint width and height to scale the image
-  const {
-    scaledBoundingBoxDimensions,
-    boundingBoxScaleMethod,
-    shouldAutoSave,
-  } = state.canvas;
+  const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-
+  const is_intermediate = true;
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -144,37 +140,37 @@ export const buildCanvasSDXLOutpaintGraph = (
       [MASK_FROM_ALPHA]: {
         type: 'tomask',
         id: MASK_FROM_ALPHA,
-        is_intermediate: true,
+        is_intermediate,
         image: canvasInitImage,
       },
       [MASK_COMBINE]: {
         type: 'mask_combine',
         id: MASK_COMBINE,
-        is_intermediate: true,
+        is_intermediate,
         mask2: canvasMaskImage,
       },
       [INPAINT_IMAGE]: {
         type: 'i2l',
         id: INPAINT_IMAGE,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate: true,
+        is_intermediate,
       },
       [INPAINT_CREATE_MASK]: {
         type: 'create_denoise_mask',
         id: INPAINT_CREATE_MASK,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: SDXL_DENOISE_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -187,18 +183,18 @@ export const buildCanvasSDXLOutpaintGraph = (
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate: true,
+        is_intermediate,
       },
       [CANVAS_COHERENCE_NOISE_INCREMENT]: {
         type: 'add',
         id: CANVAS_COHERENCE_NOISE_INCREMENT,
         b: 1,
-        is_intermediate: true,
+        is_intermediate,
       },
       [CANVAS_COHERENCE_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: CANVAS_COHERENCE_DENOISE_LATENTS,
-        is_intermediate: true,
+        is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -208,18 +204,18 @@ export const buildCanvasSDXLOutpaintGraph = (
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
-        is_intermediate: true,
+        is_intermediate,
         fp32,
       },
       [CANVAS_OUTPUT]: {
         type: 'color_correct',
         id: CANVAS_OUTPUT,
-        is_intermediate: !shouldAutoSave,
+        is_intermediate,
       },
       [RANGE_OF_SIZE]: {
         type: 'range_of_size',
         id: RANGE_OF_SIZE,
-        is_intermediate: true,
+        is_intermediate,
         // seed - must be connected manually
         // start: 0,
         size: iterations,
@@ -228,7 +224,7 @@ export const buildCanvasSDXLOutpaintGraph = (
       [ITERATE]: {
         type: 'iterate',
         id: ITERATE,
-        is_intermediate: true,
+        is_intermediate,
       },
     },
     edges: [
@@ -488,7 +484,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_patchmatch',
       id: INPAINT_INFILL,
-      is_intermediate: true,
+      is_intermediate,
       downscale: infillPatchmatchDownscaleSize,
     };
   }
@@ -497,7 +493,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_lama',
       id: INPAINT_INFILL,
-      is_intermediate: true,
+      is_intermediate,
     };
   }
 
@@ -505,7 +501,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_cv2',
       id: INPAINT_INFILL,
-      is_intermediate: true,
+      is_intermediate,
     };
   }
 
@@ -513,7 +509,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_tile',
       id: INPAINT_INFILL,
-      is_intermediate: true,
+      is_intermediate,
       tile_size: infillTileSize,
     };
   }
@@ -527,7 +523,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_UP,
-      is_intermediate: true,
+      is_intermediate,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasInitImage,
@@ -535,28 +531,28 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[MASK_RESIZE_UP] = {
       type: 'img_resize',
       id: MASK_RESIZE_UP,
-      is_intermediate: true,
+      is_intermediate,
       width: scaledWidth,
       height: scaledHeight,
     };
     graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_DOWN,
-      is_intermediate: true,
+      is_intermediate,
       width: width,
       height: height,
     };
     graph.nodes[INPAINT_INFILL_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_INFILL_RESIZE_DOWN,
-      is_intermediate: true,
+      is_intermediate,
       width: width,
       height: height,
     };
     graph.nodes[MASK_RESIZE_DOWN] = {
       type: 'img_resize',
       id: MASK_RESIZE_DOWN,
-      is_intermediate: true,
+      is_intermediate,
       width: width,
       height: height,
     };
@@ -716,7 +712,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
       type: 'create_denoise_mask',
       id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
-      is_intermediate: true,
+      is_intermediate,
       fp32,
     };
 
@@ -763,7 +759,7 @@ export const buildCanvasSDXLOutpaintGraph = (
       graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
         type: 'mask_edge',
         id: CANVAS_COHERENCE_MASK_EDGE,
-        is_intermediate: true,
+        is_intermediate,
         edge_blur: maskBlur,
         edge_size: maskBlur * 2,
         low_threshold: 100,
@@ -869,9 +865,6 @@ export const buildCanvasSDXLOutpaintGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
-  // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!
@@ -883,5 +876,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts

@@ -8,7 +8,6 @@ import {
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
@@ -30,6 +29,7 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
+import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Text to Image graph.
@@ -56,14 +56,10 @@ export const buildCanvasSDXLTextToImageGraph = (
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
-  const {
-    scaledBoundingBoxDimensions,
-    boundingBoxScaleMethod,
-    shouldAutoSave,
-  } = state.canvas;
+  const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-
+  const is_intermediate = true;
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -95,7 +91,7 @@ export const buildCanvasSDXLTextToImageGraph = (
       ? {
           type: 't2l_onnx',
           id: SDXL_DENOISE_LATENTS,
-          is_intermediate: true,
+          is_intermediate,
           cfg_scale,
           scheduler,
           steps,
@@ -103,7 +99,7 @@ export const buildCanvasSDXLTextToImageGraph = (
       : {
           type: 'denoise_latents',
           id: SDXL_DENOISE_LATENTS,
-          is_intermediate: true,
+          is_intermediate,
           cfg_scale,
           scheduler,
           steps,
@@ -132,27 +128,27 @@ export const buildCanvasSDXLTextToImageGraph = (
       [modelLoaderNodeId]: {
         type: modelLoaderNodeType,
         id: modelLoaderNodeId,
-        is_intermediate: true,
+        is_intermediate,
         model,
       },
       [POSITIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'sdxl_compel_prompt',
         id: POSITIVE_CONDITIONING,
-        is_intermediate: true,
+        is_intermediate,
         prompt: positivePrompt,
         style: craftedPositiveStylePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'sdxl_compel_prompt',
         id: NEGATIVE_CONDITIONING,
-        is_intermediate: true,
+        is_intermediate,
         prompt: negativePrompt,
         style: craftedNegativeStylePrompt,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
-        is_intermediate: true,
+        is_intermediate,
         width: !isUsingScaledDimensions
           ? width
           : scaledBoundingBoxDimensions.width,
@@ -254,14 +250,14 @@ export const buildCanvasSDXLTextToImageGraph = (
     graph.nodes[LATENTS_TO_IMAGE] = {
       id: LATENTS_TO_IMAGE,
       type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
-      is_intermediate: true,
+      is_intermediate,
       fp32,
     };
 
     graph.nodes[CANVAS_OUTPUT] = {
       id: CANVAS_OUTPUT,
       type: 'img_resize',
-      is_intermediate: !shouldAutoSave,
+      is_intermediate,
       width: width,
       height: height,
     };
@@ -292,7 +288,7 @@ export const buildCanvasSDXLTextToImageGraph = (
     graph.nodes[CANVAS_OUTPUT] = {
       type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
       id: CANVAS_OUTPUT,
-      is_intermediate: !shouldAutoSave,
+      is_intermediate,
       fp32,
     };
 
@@ -373,9 +369,6 @@ export const buildCanvasSDXLTextToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
-  // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!
@@ -387,5 +380,7 @@ export const buildCanvasSDXLTextToImageGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts

@@ -8,9 +8,9 @@ import {
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
+import { addSaveImageNode } from './addSaveImageNode';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
@@ -54,14 +54,10 @@ export const buildCanvasTextToImageGraph = (
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
-  const {
-    scaledBoundingBoxDimensions,
-    boundingBoxScaleMethod,
-    shouldAutoSave,
-  } = state.canvas;
+  const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-
+  const is_intermediate = true;
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -90,7 +86,7 @@ export const buildCanvasTextToImageGraph = (
       ? {
           type: 't2l_onnx',
           id: DENOISE_LATENTS,
-          is_intermediate: true,
+          is_intermediate,
           cfg_scale,
           scheduler,
           steps,
@@ -98,7 +94,7 @@ export const buildCanvasTextToImageGraph = (
       : {
           type: 'denoise_latents',
           id: DENOISE_LATENTS,
-          is_intermediate: true,
+          is_intermediate,
           cfg_scale,
           scheduler,
           steps,
@@ -123,31 +119,31 @@ export const buildCanvasTextToImageGraph = (
       [modelLoaderNodeId]: {
         type: modelLoaderNodeType,
         id: modelLoaderNodeId,
-        is_intermediate: true,
+        is_intermediate,
         model,
       },
       [CLIP_SKIP]: {
         type: 'clip_skip',
         id: CLIP_SKIP,
-        is_intermediate: true,
+        is_intermediate,
         skipped_layers: clipSkip,
       },
       [POSITIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'compel',
         id: POSITIVE_CONDITIONING,
-        is_intermediate: true,
+        is_intermediate,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'compel',
         id: NEGATIVE_CONDITIONING,
-        is_intermediate: true,
+        is_intermediate,
         prompt: negativePrompt,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
-        is_intermediate: true,
+        is_intermediate,
         width: !isUsingScaledDimensions
           ? width
           : scaledBoundingBoxDimensions.width,
@@ -240,14 +236,14 @@ export const buildCanvasTextToImageGraph = (
     graph.nodes[LATENTS_TO_IMAGE] = {
       id: LATENTS_TO_IMAGE,
       type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
-      is_intermediate: true,
+      is_intermediate,
       fp32,
     };
 
     graph.nodes[CANVAS_OUTPUT] = {
       id: CANVAS_OUTPUT,
       type: 'img_resize',
-      is_intermediate: !shouldAutoSave,
+      is_intermediate,
       width: width,
       height: height,
     };
@@ -278,7 +274,7 @@ export const buildCanvasTextToImageGraph = (
     graph.nodes[CANVAS_OUTPUT] = {
       type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
       id: CANVAS_OUTPUT,
-      is_intermediate: !shouldAutoSave,
+      is_intermediate,
       fp32,
     };
 
@@ -346,9 +342,6 @@ export const buildCanvasTextToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
-  // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!
@@ -360,5 +353,7 @@ export const buildCanvasTextToImageGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts

@@ -8,7 +8,6 @@ import {
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
@@ -28,6 +27,7 @@ import {
   RESIZE,
   SEAMLESS,
 } from './constants';
+import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Image to Image tab graph.
@@ -86,6 +86,7 @@ export const buildLinearImageToImageGraph = (
   }
 
   const fp32 = vaePrecision === 'fp32';
+  const is_intermediate = true;
 
   let modelLoaderNodeId = MAIN_MODEL_LOADER;
 
@@ -101,31 +102,37 @@ export const buildLinearImageToImageGraph = (
         type: 'main_model_loader',
         id: modelLoaderNodeId,
         model,
+        is_intermediate,
       },
       [CLIP_SKIP]: {
         type: 'clip_skip',
         id: CLIP_SKIP,
         skipped_layers: clipSkip,
+        is_intermediate,
       },
       [POSITIVE_CONDITIONING]: {
         type: 'compel',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
+        is_intermediate,
       },
       [NEGATIVE_CONDITIONING]: {
         type: 'compel',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
+        is_intermediate,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
         use_cpu,
+        is_intermediate,
       },
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
         fp32,
+        is_intermediate,
       },
       [DENOISE_LATENTS]: {
         type: 'denoise_latents',
@@ -135,6 +142,7 @@ export const buildLinearImageToImageGraph = (
         steps,
         denoising_start: 1 - strength,
         denoising_end: 1,
+        is_intermediate,
       },
       [IMAGE_TO_LATENTS]: {
         type: 'i2l',
@@ -144,6 +152,7 @@ export const buildLinearImageToImageGraph = (
         //   image_name: initialImage.image_name,
         // },
         fp32,
+        is_intermediate,
       },
     },
     edges: [
@@ -365,9 +374,6 @@ export const buildLinearImageToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
-  // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!
@@ -379,5 +385,7 @@ export const buildLinearImageToImageGraph = (
     addWatermarkerToGraph(state, graph);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts

@@ -8,7 +8,6 @@ import {
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
@@ -30,6 +29,7 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
+import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Image to Image tab graph.
@@ -86,6 +86,7 @@ export const buildLinearSDXLImageToImageGraph = (
   }
 
   const fp32 = vaePrecision === 'fp32';
+  const is_intermediate = true;
 
   // Model Loader ID
   let modelLoaderNodeId = SDXL_MODEL_LOADER;
@@ -106,28 +107,33 @@ export const buildLinearSDXLImageToImageGraph = (
         type: 'sdxl_model_loader',
         id: modelLoaderNodeId,
         model,
+        is_intermediate,
       },
       [POSITIVE_CONDITIONING]: {
         type: 'sdxl_compel_prompt',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
         style: craftedPositiveStylePrompt,
+        is_intermediate,
       },
       [NEGATIVE_CONDITIONING]: {
         type: 'sdxl_compel_prompt',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
         style: craftedNegativeStylePrompt,
+        is_intermediate,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
         use_cpu,
+        is_intermediate,
       },
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
         fp32,
+        is_intermediate,
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
@@ -139,6 +145,7 @@ export const buildLinearSDXLImageToImageGraph = (
           ? Math.min(refinerStart, 1 - strength)
           : 1 - strength,
         denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
+        is_intermediate,
       },
       [IMAGE_TO_LATENTS]: {
         type: 'i2l',
@@ -148,6 +155,7 @@ export const buildLinearSDXLImageToImageGraph = (
         //   image_name: initialImage.image_name,
         // },
         fp32,
+        is_intermediate,
       },
     },
     edges: [
@@ -385,9 +393,6 @@ export const buildLinearSDXLImageToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
-  // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);
 
@@ -402,5 +407,7 @@ export const buildLinearSDXLImageToImageGraph = (
     addWatermarkerToGraph(state, graph);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts

@@ -4,7 +4,6 @@ import { NonNullableGraph } from 'features/nodes/types/types';
 import { initialGenerationState } from 'features/parameters/store/generationSlice';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
@@ -24,6 +23,7 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
+import { addSaveImageNode } from './addSaveImageNode';
 
 export const buildLinearSDXLTextToImageGraph = (
   state: RootState
@@ -57,13 +57,13 @@ export const buildLinearSDXLTextToImageGraph = (
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
-
   if (!model) {
     log.error('No model found in state');
     throw new Error('No model found in state');
   }
 
   const fp32 = vaePrecision === 'fp32';
+  const is_intermediate = true;
 
   // Construct Style Prompt
   const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } =
@@ -89,18 +89,21 @@ export const buildLinearSDXLTextToImageGraph = (
         type: 'sdxl_model_loader',
         id: modelLoaderNodeId,
         model,
+        is_intermediate,
       },
       [POSITIVE_CONDITIONING]: {
         type: 'sdxl_compel_prompt',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
         style: craftedPositiveStylePrompt,
+        is_intermediate,
       },
       [NEGATIVE_CONDITIONING]: {
         type: 'sdxl_compel_prompt',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
         style: craftedNegativeStylePrompt,
+        is_intermediate,
       },
       [NOISE]: {
         type: 'noise',
@@ -108,6 +111,7 @@ export const buildLinearSDXLTextToImageGraph = (
         width,
         height,
         use_cpu,
+        is_intermediate,
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
@@ -117,11 +121,13 @@ export const buildLinearSDXLTextToImageGraph = (
         steps,
         denoising_start: 0,
         denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
+        is_intermediate,
       },
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
         fp32,
+        is_intermediate,
       },
     },
     edges: [
@@ -278,9 +284,6 @@ export const buildLinearSDXLTextToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
-  // add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);
 
@@ -295,5 +298,7 @@ export const buildLinearSDXLTextToImageGraph = (
     addWatermarkerToGraph(state, graph);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts

@@ -8,7 +8,6 @@ import {
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
-import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
@@ -27,6 +26,7 @@ import {
   SEAMLESS,
   TEXT_TO_IMAGE_GRAPH,
 } from './constants';
+import { addSaveImageNode } from './addSaveImageNode';
 
 export const buildLinearTextToImageGraph = (
   state: RootState
@@ -59,7 +59,7 @@ export const buildLinearTextToImageGraph = (
   }
 
   const fp32 = vaePrecision === 'fp32';
-
+  const is_intermediate = true;
   const isUsingOnnxModel = model.model_type === 'onnx';
 
   let modelLoaderNodeId = isUsingOnnxModel
@@ -75,7 +75,7 @@ export const buildLinearTextToImageGraph = (
       ? {
           type: 't2l_onnx',
           id: DENOISE_LATENTS,
-          is_intermediate: true,
+          is_intermediate,
           cfg_scale,
           scheduler,
           steps,
@@ -83,7 +83,7 @@ export const buildLinearTextToImageGraph = (
       : {
           type: 'denoise_latents',
           id: DENOISE_LATENTS,
-          is_intermediate: true,
+          is_intermediate,
           cfg_scale,
           scheduler,
           steps,
@@ -109,26 +109,26 @@ export const buildLinearTextToImageGraph = (
       [modelLoaderNodeId]: {
         type: modelLoaderNodeType,
         id: modelLoaderNodeId,
-        is_intermediate: true,
+        is_intermediate,
         model,
       },
       [CLIP_SKIP]: {
         type: 'clip_skip',
         id: CLIP_SKIP,
         skipped_layers: clipSkip,
-        is_intermediate: true,
+        is_intermediate,
       },
       [POSITIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'compel',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
-        is_intermediate: true,
+        is_intermediate,
       },
       [NEGATIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'compel',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
-        is_intermediate: true,
+        is_intermediate,
       },
       [NOISE]: {
         type: 'noise',
@@ -136,13 +136,14 @@ export const buildLinearTextToImageGraph = (
         width,
         height,
         use_cpu,
-        is_intermediate: true,
+        is_intermediate,
       },
       [t2lNode.id]: t2lNode,
       [LATENTS_TO_IMAGE]: {
         type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
         id: LATENTS_TO_IMAGE,
         fp32,
+        is_intermediate,
       },
     },
     edges: [
@@ -283,9 +284,6 @@ export const buildLinearTextToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
-  // add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!
@@ -297,5 +295,7 @@ export const buildLinearTextToImageGraph = (
     addWatermarkerToGraph(state, graph);
   }
 
+  addSaveImageNode(state, graph);
+
   return graph;
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildNodesGraph.ts

@@ -55,6 +55,9 @@ export const buildNodesGraph = (nodesState: NodesState): Graph => {
         {} as Record<Exclude<string, 'id' | 'type'>, unknown>
       );
 
+      // add reserved use_cache
+      transformedInputs['use_cache'] = node.data.useCache;
+
       // Build this specific node
       const graphNode = {
         type,