fix(upscale_sdx4): upgrade for v3.1 nodes API

Fixed upstream by https://github.com/huggingface/diffusers/pull/4278 before I even got to reporting it — thank you!

invokeai/app/api/dependencies.py

@@ -9,7 +9,6 @@ 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
@@ -127,7 +126,6 @@ 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

@@ -1,7 +1,5 @@
 # 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
@@ -311,7 +309,6 @@ 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

@@ -568,24 +568,7 @@ class BaseInvocation(ABC, BaseModel):
                     raise RequiredConnectionException(self.__fields__["type"].default, field_name)
                 elif _input == Input.Any:
                     raise MissingInputException(self.__fields__["type"].default, field_name)
-        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
+        return self.invoke(context)
 
     id: str = Field(
         description="The id of this instance of an invocation. Must be unique among all instances of invocations."
@@ -598,7 +581,6 @@ 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):
@@ -622,7 +604,6 @@ 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.
@@ -655,8 +636,6 @@ 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

@@ -56,7 +56,6 @@ 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/image.py

@@ -965,42 +965,3 @@ 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/math.py

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

invokeai/app/invocations/prompt.py

@@ -10,14 +10,7 @@ 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",
-    use_cache=False,
-)
+@invocation("dynamic_prompt", title="Dynamic Prompt", tags=["prompt", "collection"], category="prompt", version="1.0.0")
 class DynamicPromptInvocation(BaseInvocation):
     """Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator"""
 

invokeai/app/invocations/upscale_sdx4.py

@@ -0,0 +1,173 @@
+from typing import List, Union
+
+import torch
+from diffusers import StableDiffusionUpscalePipeline
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    UIType,
+    invocation,
+)
+from invokeai.app.invocations.image import ImageOutput
+from invokeai.app.invocations.latent import SAMPLER_NAME_VALUES, get_scheduler
+from invokeai.app.invocations.metadata import CoreMetadata
+from invokeai.app.invocations.model import UNetField, VaeField
+from invokeai.app.invocations.primitives import ConditioningField, ImageField
+from invokeai.app.models.image import ImageCategory, ResourceOrigin
+from invokeai.app.util.step_callback import stable_diffusion_step_callback
+from invokeai.backend import BaseModelType
+from invokeai.backend.stable_diffusion import ConditioningData, PipelineIntermediateState, PostprocessingSettings
+
+
+@invocation("upscale_sdx4", title="Upscale (Stable Diffusion x4)", tags=["upscale"], version="0.1.0")
+class UpscaleLatentsInvocation(BaseInvocation):
+    """Upscales an image using an upscaling diffusion model.
+
+    https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler
+
+    The upscaling model is its own thing, independent of other Stable Diffusion text-to-image
+    models. We don't have ControlNet or LoRA support for it. It has its own VAE.
+    """
+
+    # Inputs
+    image: ImageField = InputField(description="The image to upscale")
+
+    positive_conditioning: ConditioningField = InputField(
+        description=FieldDescriptions.positive_cond, input=Input.Connection
+    )
+    negative_conditioning: ConditioningField = InputField(
+        description=FieldDescriptions.negative_cond, input=Input.Connection
+    )
+    steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
+    cfg_scale: Union[float, List[float]] = InputField(
+        default=7.5, ge=1, description=FieldDescriptions.cfg_scale, ui_type=UIType.Float
+    )
+    scheduler: SAMPLER_NAME_VALUES = InputField(default="euler", description=FieldDescriptions.scheduler)
+    seed: int = InputField(default=0, description=FieldDescriptions.seed)
+
+    unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection)
+    vae: VaeField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
+    metadata: CoreMetadata = InputField(default=None, description=FieldDescriptions.core_metadata)
+    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
+
+    @torch.inference_mode()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+
+        model_manager = context.services.model_manager
+        unet_info = model_manager.get_model(**self.unet.unet.dict(), context=context)
+        vae_info = model_manager.get_model(**self.vae.vae.dict(), context=context)
+
+        with unet_info as unet, vae_info as vae:
+            # don't re-use the same scheduler instance for both fields
+            low_res_scheduler = get_scheduler(context, self.unet.scheduler, self.scheduler, self.seed ^ 0xFFFFFFFF)
+            scheduler = get_scheduler(context, self.unet.scheduler, self.scheduler, self.seed ^ 0xF7F7F7F7)
+
+            conditioning_data = self.get_conditioning_data(context, scheduler, unet, self.seed)
+
+            pipeline = StableDiffusionUpscalePipeline(
+                vae=vae,
+                text_encoder=None,
+                tokenizer=None,
+                unet=unet,
+                low_res_scheduler=low_res_scheduler,
+                scheduler=scheduler,
+            )
+
+            if self.tiled or context.services.configuration.tiled_decode:
+                vae.enable_tiling()
+            else:
+                vae.disable_tiling()
+
+            generator = torch.Generator().manual_seed(self.seed)
+
+            output = pipeline(
+                image=image,
+                # latents=noise,
+                num_inference_steps=self.steps,
+                guidance_scale=self.cfg_scale,
+                # noise_level =
+                generator=generator,
+                prompt_embeds=conditioning_data.text_embeddings.embeds.data,
+                negative_prompt_embeds=conditioning_data.unconditioned_embeddings.embeds.data,
+                output_type="pil",
+                callback=lambda *args: self.dispatch_upscale_progress(context, *args),
+            )
+            result_image = output.images[0]
+
+        image_dto = context.services.images.create(
+            image=result_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,
+        )
+
+    def get_conditioning_data(
+        self,
+        context: InvocationContext,
+        scheduler,
+        unet,
+        seed,
+    ) -> ConditioningData:
+        # FIXME: duplicated from DenoiseLatentsInvocation.get_conditoning_data
+        positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
+        extra_conditioning_info = c.extra_conditioning
+
+        negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
+        uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
+
+        conditioning_data = ConditioningData(
+            unconditioned_embeddings=uc,
+            text_embeddings=c,
+            guidance_scale=self.cfg_scale,
+            extra=extra_conditioning_info,
+            postprocessing_settings=PostprocessingSettings(
+                threshold=0.0,  # threshold,
+                warmup=0.2,  # warmup,
+                h_symmetry_time_pct=None,  # h_symmetry_time_pct,
+                v_symmetry_time_pct=None,  # v_symmetry_time_pct,
+            ),
+        )
+
+        conditioning_data = conditioning_data.add_scheduler_args_if_applicable(
+            scheduler,
+            # for ddim scheduler
+            eta=0.0,  # ddim_eta
+            # for ancestral and sde schedulers
+            # FIXME: why do we need both a generator here and a seed argument to get_scheduler?
+            generator=torch.Generator(device=unet.device).manual_seed(seed ^ 0xFFFFFFFF),
+        )
+        return conditioning_data
+
+    def dispatch_upscale_progress(self, context, step, timestep, latents):
+        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+        intermediate_state = PipelineIntermediateState(
+            step=step,
+            order=1,  # FIXME: fudging this, but why does it need both order and total-steps anyway?
+            total_steps=self.steps,
+            timestep=timestep,
+            latents=latents,
+        )
+        stable_diffusion_step_callback(
+            context=context,
+            intermediate_state=intermediate_state,
+            node=self.dict(),
+            source_node_id=source_node_id,
+            base_model=BaseModelType.StableDiffusionXLRefiner,  # FIXME: this upscaler needs its own model type
+        )

invokeai/app/services/config/invokeai_config.py

@@ -253,7 +253,6 @@ 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

@@ -1,29 +0,0 @@
-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

@@ -1,34 +0,0 @@
-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

@@ -12,7 +12,6 @@ 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
@@ -38,7 +37,6 @@ class InvocationServices:
     processor: "InvocationProcessorABC"
     performance_statistics: "InvocationStatsServiceBase"
     queue: "InvocationQueueABC"
-    invocation_cache: "InvocationCacheBase"
 
     def __init__(
         self,
@@ -55,7 +53,6 @@ class InvocationServices:
         processor: "InvocationProcessorABC",
         performance_statistics: "InvocationStatsServiceBase",
         queue: "InvocationQueueABC",
-        invocation_cache: "InvocationCacheBase",
     ):
         self.board_images = board_images
         self.boards = boards
@@ -71,4 +68,3 @@ class InvocationServices:
         self.processor = processor
         self.performance_statistics = performance_statistics
         self.queue = queue
-        self.invocation_cache = invocation_cache

invokeai/backend/model_management/model_probe.py

@@ -47,6 +47,7 @@ class ModelProbe(object):
     CLASS2TYPE = {
         "StableDiffusionPipeline": ModelType.Main,
         "StableDiffusionInpaintPipeline": ModelType.Main,
+        "StableDiffusionUpscalePipeline": ModelType.Main,
         "StableDiffusionXLPipeline": ModelType.Main,
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
         "StableDiffusionXLInpaintPipeline": ModelType.Main,

invokeai/backend/model_management/models/stable_diffusion.py

@@ -194,6 +194,8 @@ class StableDiffusion2Model(DiffusersModel):
             variant = ModelVariantType.Depth
         elif in_channels == 4:
             variant = ModelVariantType.Normal
+        elif in_channels == 7:
+            variant = ModelVariantType.Normal  # FIXME: temp kludge for 4x upscaler
         else:
             raise Exception("Unkown stable diffusion 2.* model format")
 

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

@@ -1,5 +0,0 @@
-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

@@ -31,7 +31,6 @@ 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,
@@ -122,4 +121,3 @@ 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/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx

@@ -1,9 +1,8 @@
-import { CoreMetadata, LoRAMetadataItem } from 'features/nodes/types/types';
+import { CoreMetadata } from 'features/nodes/types/types';
 import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters';
 import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { isValidLoRAModel } from '../../../parameters/types/parameterSchemas';
 import ImageMetadataItem from './ImageMetadataItem';
+import { useTranslation } from 'react-i18next';
 
 type Props = {
   metadata?: CoreMetadata;
@@ -25,7 +24,6 @@ const ImageMetadataActions = (props: Props) => {
     recallWidth,
     recallHeight,
     recallStrength,
-    recallLoRA,
   } = useRecallParameters();
 
   const handleRecallPositivePrompt = useCallback(() => {
@@ -68,13 +66,6 @@ const ImageMetadataActions = (props: Props) => {
     recallStrength(metadata?.strength);
   }, [metadata?.strength, recallStrength]);
 
-  const handleRecallLoRA = useCallback(
-    (lora: LoRAMetadataItem) => {
-      recallLoRA(lora);
-    },
-    [recallLoRA]
-  );
-
   if (!metadata || Object.keys(metadata).length === 0) {
     return null;
   }
@@ -139,6 +130,20 @@ const ImageMetadataActions = (props: Props) => {
           onClick={handleRecallHeight}
         />
       )}
+      {/* {metadata.threshold !== undefined && (
+          <MetadataItem
+            label={t('metadata.threshold')}
+            value={metadata.threshold}
+            onClick={() => dispatch(setThreshold(Number(metadata.threshold)))}
+          />
+        )}
+        {metadata.perlin !== undefined && (
+          <MetadataItem
+            label={t('metadata.perlin')}
+            value={metadata.perlin}
+            onClick={() => dispatch(setPerlin(Number(metadata.perlin)))}
+          />
+        )} */}
       {metadata.scheduler && (
         <ImageMetadataItem
           label={t('metadata.scheduler')}
@@ -160,6 +165,40 @@ const ImageMetadataActions = (props: Props) => {
           onClick={handleRecallCfgScale}
         />
       )}
+      {/* {metadata.variations && metadata.variations.length > 0 && (
+          <MetadataItem
+            label="{t('metadata.variations')}
+            value={seedWeightsToString(metadata.variations)}
+            onClick={() =>
+              dispatch(
+                setSeedWeights(seedWeightsToString(metadata.variations))
+              )
+            }
+          />
+        )}
+        {metadata.seamless && (
+          <MetadataItem
+            label={t('metadata.seamless')}
+            value={metadata.seamless}
+            onClick={() => dispatch(setSeamless(metadata.seamless))}
+          />
+        )}
+        {metadata.hires_fix && (
+          <MetadataItem
+            label={t('metadata.hiresFix')}
+            value={metadata.hires_fix}
+            onClick={() => dispatch(setHiresFix(metadata.hires_fix))}
+          />
+        )} */}
+
+      {/* {init_image_path && (
+          <MetadataItem
+            label={t('metadata.initImage')}
+            value={init_image_path}
+            isLink
+            onClick={() => dispatch(setInitialImage(init_image_path))}
+          />
+        )} */}
       {metadata.strength && (
         <ImageMetadataItem
           label={t('metadata.strength')}
@@ -167,19 +206,13 @@ const ImageMetadataActions = (props: Props) => {
           onClick={handleRecallStrength}
         />
       )}
-      {metadata.loras &&
-        metadata.loras.map((lora, index) => {
-          if (isValidLoRAModel(lora.lora)) {
-            return (
-              <ImageMetadataItem
-                key={index}
-                label="LoRA"
-                value={`${lora.lora.model_name} - ${lora.weight}`}
-                onClick={() => handleRecallLoRA(lora)}
-              />
-            );
-          }
-        })}
+      {/* {metadata.fit && (
+          <MetadataItem
+            label={t('metadata.fit')}
+            value={metadata.fit}
+            onClick={() => dispatch(setShouldFitToWidthHeight(metadata.fit))}
+          />
+        )} */}
     </>
   );
 };

invokeai/frontend/web/src/features/lora/store/loraSlice.ts

@@ -27,13 +27,6 @@ export const loraSlice = createSlice({
       const { model_name, id, base_model } = action.payload;
       state.loras[id] = { id, model_name, base_model, ...defaultLoRAConfig };
     },
-    loraRecalled: (
-      state,
-      action: PayloadAction<LoRAModelConfigEntity & { weight: number }>
-    ) => {
-      const { model_name, id, base_model, weight } = action.payload;
-      state.loras[id] = { id, model_name, base_model, weight };
-    },
     loraRemoved: (state, action: PayloadAction<string>) => {
       const id = action.payload;
       delete state.loras[id];
@@ -69,7 +62,6 @@ export const {
   loraWeightChanged,
   loraWeightReset,
   lorasCleared,
-  loraRecalled,
 } = loraSlice.actions;
 
 export default loraSlice.reducer;

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' }}>Workflow</FormLabel>
+      <FormLabel sx={{ fontSize: 'xs', mb: '1px' }}>Embed Workflow</FormLabel>
       <Checkbox
         className="nopan"
         size="sm"

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

@@ -1,13 +1,14 @@
 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 InputField from './fields/InputField';
+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';
 
 type Props = {
   nodeId: string;
@@ -21,6 +22,7 @@ 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}>
@@ -41,7 +43,7 @@ const InvocationNode = ({ nodeId, isOpen, label, type, selected }: Props) => {
               h: 'full',
               py: 2,
               gap: 1,
-              borderBottomRadius: 0,
+              borderBottomRadius: withFooter ? 0 : 'base',
             }}
           >
             <Flex sx={{ flexDir: 'column', px: 2, w: 'full', h: 'full' }}>
@@ -74,7 +76,7 @@ const InvocationNode = ({ nodeId, isOpen, label, type, selected }: Props) => {
               ))}
             </Flex>
           </Flex>
-          <InvocationNodeFooter nodeId={nodeId} />
+          {withFooter && <InvocationNodeFooter nodeId={nodeId} />}
         </>
       )}
     </NodeWrapper>

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

@@ -3,15 +3,12 @@ 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}
@@ -25,9 +22,8 @@ const InvocationNodeFooter = ({ nodeId }: Props) => {
         justifyContent: 'space-between',
       }}
     >
-      {hasImageOutput && <EmbedWorkflowCheckbox nodeId={nodeId} />}
-      <UseCacheCheckbox nodeId={nodeId} />
-      {hasImageOutput && <SaveToGalleryCheckbox nodeId={nodeId} />}
+      <EmbedWorkflowCheckbox nodeId={nodeId} />
+      <SaveToGalleryCheckbox nodeId={nodeId} />
     </Flex>
   );
 };

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

@@ -1,35 +0,0 @@
-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/hooks/useBuildNodeData.ts

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

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

@@ -1,29 +0,0 @@
-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 useHasImageOutputs = (nodeId: string) => {
+export const useWithFooter = (nodeId: string) => {
   const selector = useMemo(
     () =>
       createSelector(

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

@@ -260,20 +260,6 @@ 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 }>
@@ -918,7 +904,6 @@ export const {
   nodeIsIntermediateChanged,
   mouseOverNodeChanged,
   nodeExclusivelySelected,
-  nodeUseCacheChanged,
 } = nodesSlice.actions;
 
 export default nodesSlice.reducer;

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

@@ -1,4 +1,3 @@
-import { $store } from 'app/store/nanostores/store';
 import {
   SchedulerParam,
   zBaseModel,
@@ -8,8 +7,7 @@ import {
   zSDXLRefinerModel,
   zScheduler,
 } from 'features/parameters/types/parameterSchemas';
-import i18n from 'i18next';
-import { has, keyBy } from 'lodash-es';
+import { keyBy } from 'lodash-es';
 import { OpenAPIV3 } from 'openapi-types';
 import { RgbaColor } from 'react-colorful';
 import { Node } from 'reactflow';
@@ -22,6 +20,7 @@ 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'>;
 
@@ -58,10 +57,6 @@ export type InvocationTemplate = {
    * The invocation's version.
    */
   version?: string;
-  /**
-   * Whether or not this node should use the cache
-   */
-  useCache: boolean;
 };
 
 export type FieldUIConfig = {
@@ -981,9 +976,6 @@ export type InvocationSchemaExtra = {
     type: Omit<OpenAPIV3.SchemaObject, 'default'> & {
       default: AnyInvocationType;
     };
-    use_cache: Omit<OpenAPIV3.SchemaObject, 'default'> & {
-      default: boolean;
-    };
   };
 };
 
@@ -1065,13 +1057,6 @@ export const isInvocationFieldSchema = (
 
 export type InvocationEdgeExtra = { type: 'default' | 'collapsed' };
 
-const zLoRAMetadataItem = z.object({
-  lora: zLoRAModelField.deepPartial(),
-  weight: z.number(),
-});
-
-export type LoRAMetadataItem = z.infer<typeof zLoRAMetadataItem>;
-
 export const zCoreMetadata = z
   .object({
     app_version: z.string().nullish(),
@@ -1091,7 +1076,14 @@ export const zCoreMetadata = z
       .union([zMainModel.deepPartial(), zOnnxModel.deepPartial()])
       .nullish(),
     controlnets: z.array(zControlField.deepPartial()).nullish(),
-    loras: z.array(zLoRAMetadataItem).nullish(),
+    loras: z
+      .array(
+        z.object({
+          lora: zLoRAModelField.deepPartial(),
+          weight: z.number(),
+        })
+      )
+      .nullish(),
     vae: zVaeModelField.nullish(),
     strength: z.number().nullish(),
     init_image: z.string().nullish(),
@@ -1147,37 +1139,9 @@ 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 zInvocationNodeDataV2>,
+  z.infer<typeof zInvocationNodeData>,
   'type'
 > & {
   type: AnyInvocationType;
@@ -1205,7 +1169,7 @@ const zDimension = z.number().gt(0).nullish();
 export const zWorkflowInvocationNode = z.object({
   id: z.string().trim().min(1),
   type: z.literal('invocation'),
-  data: zInvocationNodeDataV2,
+  data: zInvocationNodeData,
   width: zDimension,
   height: zDimension,
   position: zPosition,
@@ -1267,8 +1231,6 @@ 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(''),
@@ -1284,7 +1246,7 @@ export const zWorkflow = z.object({
     .object({
       version: zSemVer,
     })
-    .default({ version: CURRENT_WORKFLOW_VERSION }),
+    .default({ version: '1.0.0' }),
 });
 
 export const zValidatedWorkflow = zWorkflow.transform((workflow) => {

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

@@ -1,32 +1,46 @@
 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, NSFW_CHECKER } from './constants';
+import {
+  LATENTS_TO_IMAGE,
+  METADATA_ACCUMULATOR,
+  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: true,
+    is_intermediate,
   };
 
   graph.nodes[NSFW_CHECKER] = nsfwCheckerNode as ImageNSFWBlurInvocation;
@@ -40,4 +54,17 @@ 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

@@ -1,92 +0,0 @@
-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

@@ -51,7 +51,6 @@ 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

@@ -25,7 +25,6 @@ import {
   POSITIVE_CONDITIONING,
   SEAMLESS,
 } from './constants';
-import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Image to Image graph.
@@ -54,10 +53,14 @@ 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 } = state.canvas;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-  const is_intermediate = true;
+
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -89,31 +92,31 @@ export const buildCanvasImageToImageGraph = (
       [modelLoaderNodeId]: {
         type: 'main_model_loader',
         id: modelLoaderNodeId,
-        is_intermediate,
+        is_intermediate: true,
         model,
       },
       [CLIP_SKIP]: {
         type: 'clip_skip',
         id: CLIP_SKIP,
-        is_intermediate,
+        is_intermediate: true,
         skipped_layers: clipSkip,
       },
       [POSITIVE_CONDITIONING]: {
         type: 'compel',
         id: POSITIVE_CONDITIONING,
-        is_intermediate,
+        is_intermediate: true,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: 'compel',
         id: NEGATIVE_CONDITIONING,
-        is_intermediate,
+        is_intermediate: true,
         prompt: negativePrompt,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
-        is_intermediate,
+        is_intermediate: true,
         use_cpu,
         width: !isUsingScaledDimensions
           ? width
@@ -125,12 +128,12 @@ export const buildCanvasImageToImageGraph = (
       [IMAGE_TO_LATENTS]: {
         type: 'i2l',
         id: IMAGE_TO_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
       },
       [DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: DENOISE_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
         cfg_scale,
         scheduler,
         steps,
@@ -140,7 +143,7 @@ export const buildCanvasImageToImageGraph = (
       [CANVAS_OUTPUT]: {
         type: 'l2i',
         id: CANVAS_OUTPUT,
-        is_intermediate,
+        is_intermediate: !shouldAutoSave,
       },
     },
     edges: [
@@ -235,7 +238,7 @@ export const buildCanvasImageToImageGraph = (
     graph.nodes[IMG2IMG_RESIZE] = {
       id: IMG2IMG_RESIZE,
       type: 'img_resize',
-      is_intermediate,
+      is_intermediate: true,
       image: initialImage,
       width: scaledBoundingBoxDimensions.width,
       height: scaledBoundingBoxDimensions.height,
@@ -243,13 +246,13 @@ export const buildCanvasImageToImageGraph = (
     graph.nodes[LATENTS_TO_IMAGE] = {
       id: LATENTS_TO_IMAGE,
       type: 'l2i',
-      is_intermediate,
+      is_intermediate: true,
       fp32,
     };
     graph.nodes[CANVAS_OUTPUT] = {
       id: CANVAS_OUTPUT,
       type: 'img_resize',
-      is_intermediate,
+      is_intermediate: !shouldAutoSave,
       width: width,
       height: height,
     };
@@ -290,7 +293,7 @@ export const buildCanvasImageToImageGraph = (
     graph.nodes[CANVAS_OUTPUT] = {
       type: 'l2i',
       id: CANVAS_OUTPUT,
-      is_intermediate,
+      is_intermediate: !shouldAutoSave,
       fp32,
     };
 
@@ -334,6 +337,17 @@ 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);
@@ -363,7 +377,5 @@ export const buildCanvasImageToImageGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
-  addSaveImageNode(state, graph);
-
   return graph;
 };

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

@@ -44,7 +44,6 @@ import {
   RANGE_OF_SIZE,
   SEAMLESS,
 } from './constants';
-import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Inpaint graph.
@@ -88,8 +87,12 @@ 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 } = state.canvas;
-  const is_intermediate = true;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
+
   const fp32 = vaePrecision === 'fp32';
 
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
@@ -108,56 +111,56 @@ export const buildCanvasInpaintGraph = (
       [modelLoaderNodeId]: {
         type: 'main_model_loader',
         id: modelLoaderNodeId,
-        is_intermediate,
+        is_intermediate: true,
         model,
       },
       [CLIP_SKIP]: {
         type: 'clip_skip',
         id: CLIP_SKIP,
-        is_intermediate,
+        is_intermediate: true,
         skipped_layers: clipSkip,
       },
       [POSITIVE_CONDITIONING]: {
         type: 'compel',
         id: POSITIVE_CONDITIONING,
-        is_intermediate,
+        is_intermediate: true,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: 'compel',
         id: NEGATIVE_CONDITIONING,
-        is_intermediate,
+        is_intermediate: true,
         prompt: negativePrompt,
       },
       [MASK_BLUR]: {
         type: 'img_blur',
         id: MASK_BLUR,
-        is_intermediate,
+        is_intermediate: true,
         radius: maskBlur,
         blur_type: maskBlurMethod,
       },
       [INPAINT_IMAGE]: {
         type: 'i2l',
         id: INPAINT_IMAGE,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate,
+        is_intermediate: true,
       },
       [INPAINT_CREATE_MASK]: {
         type: 'create_denoise_mask',
         id: INPAINT_CREATE_MASK,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: DENOISE_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
         steps: steps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -168,18 +171,18 @@ export const buildCanvasInpaintGraph = (
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate,
+        is_intermediate: true,
       },
       [CANVAS_COHERENCE_NOISE_INCREMENT]: {
         type: 'add',
         id: CANVAS_COHERENCE_NOISE_INCREMENT,
         b: 1,
-        is_intermediate,
+        is_intermediate: true,
       },
       [CANVAS_COHERENCE_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: CANVAS_COHERENCE_DENOISE_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -189,19 +192,19 @@ export const buildCanvasInpaintGraph = (
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [CANVAS_OUTPUT]: {
         type: 'color_correct',
         id: CANVAS_OUTPUT,
-        is_intermediate,
+        is_intermediate: !shouldAutoSave,
         reference: canvasInitImage,
       },
       [RANGE_OF_SIZE]: {
         type: 'range_of_size',
         id: RANGE_OF_SIZE,
-        is_intermediate,
+        is_intermediate: true,
         // seed - must be connected manually
         // start: 0,
         size: iterations,
@@ -210,7 +213,7 @@ export const buildCanvasInpaintGraph = (
       [ITERATE]: {
         type: 'iterate',
         id: ITERATE,
-        is_intermediate,
+        is_intermediate: true,
       },
     },
     edges: [
@@ -433,7 +436,7 @@ export const buildCanvasInpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_UP,
-      is_intermediate,
+      is_intermediate: true,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasInitImage,
@@ -441,7 +444,7 @@ export const buildCanvasInpaintGraph = (
     graph.nodes[MASK_RESIZE_UP] = {
       type: 'img_resize',
       id: MASK_RESIZE_UP,
-      is_intermediate,
+      is_intermediate: true,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasMaskImage,
@@ -449,14 +452,14 @@ export const buildCanvasInpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_DOWN,
-      is_intermediate,
+      is_intermediate: true,
       width: width,
       height: height,
     };
     graph.nodes[MASK_RESIZE_DOWN] = {
       type: 'img_resize',
       id: MASK_RESIZE_DOWN,
-      is_intermediate,
+      is_intermediate: true,
       width: width,
       height: height,
     };
@@ -594,7 +597,7 @@ export const buildCanvasInpaintGraph = (
     graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
       type: 'create_denoise_mask',
       id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
-      is_intermediate,
+      is_intermediate: true,
       fp32,
     };
 
@@ -647,7 +650,7 @@ export const buildCanvasInpaintGraph = (
       graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
         type: 'mask_edge',
         id: CANVAS_COHERENCE_MASK_EDGE,
-        is_intermediate,
+        is_intermediate: true,
         edge_blur: maskBlur,
         edge_size: maskBlur * 2,
         low_threshold: 100,
@@ -744,7 +747,5 @@ export const buildCanvasInpaintGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
-  addSaveImageNode(state, graph);
-
   return graph;
 };

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

@@ -46,7 +46,6 @@ import {
   RANGE_OF_SIZE,
   SEAMLESS,
 } from './constants';
-import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Outpaint graph.
@@ -92,10 +91,14 @@ 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 } = state.canvas;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-  const is_intermediate = true;
+
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -112,61 +115,61 @@ export const buildCanvasOutpaintGraph = (
       [modelLoaderNodeId]: {
         type: 'main_model_loader',
         id: modelLoaderNodeId,
-        is_intermediate,
+        is_intermediate: true,
         model,
       },
       [CLIP_SKIP]: {
         type: 'clip_skip',
         id: CLIP_SKIP,
-        is_intermediate,
+        is_intermediate: true,
         skipped_layers: clipSkip,
       },
       [POSITIVE_CONDITIONING]: {
         type: 'compel',
         id: POSITIVE_CONDITIONING,
-        is_intermediate,
+        is_intermediate: true,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: 'compel',
         id: NEGATIVE_CONDITIONING,
-        is_intermediate,
+        is_intermediate: true,
         prompt: negativePrompt,
       },
       [MASK_FROM_ALPHA]: {
         type: 'tomask',
         id: MASK_FROM_ALPHA,
-        is_intermediate,
+        is_intermediate: true,
         image: canvasInitImage,
       },
       [MASK_COMBINE]: {
         type: 'mask_combine',
         id: MASK_COMBINE,
-        is_intermediate,
+        is_intermediate: true,
         mask2: canvasMaskImage,
       },
       [INPAINT_IMAGE]: {
         type: 'i2l',
         id: INPAINT_IMAGE,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate,
+        is_intermediate: true,
       },
       [INPAINT_CREATE_MASK]: {
         type: 'create_denoise_mask',
         id: INPAINT_CREATE_MASK,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: DENOISE_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
         steps: steps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -177,18 +180,18 @@ export const buildCanvasOutpaintGraph = (
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate,
+        is_intermediate: true,
       },
       [CANVAS_COHERENCE_NOISE_INCREMENT]: {
         type: 'add',
         id: CANVAS_COHERENCE_NOISE_INCREMENT,
         b: 1,
-        is_intermediate,
+        is_intermediate: true,
       },
       [CANVAS_COHERENCE_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: CANVAS_COHERENCE_DENOISE_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -198,18 +201,18 @@ export const buildCanvasOutpaintGraph = (
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [CANVAS_OUTPUT]: {
         type: 'color_correct',
         id: CANVAS_OUTPUT,
-        is_intermediate,
+        is_intermediate: !shouldAutoSave,
       },
       [RANGE_OF_SIZE]: {
         type: 'range_of_size',
         id: RANGE_OF_SIZE,
-        is_intermediate,
+        is_intermediate: true,
         // seed - must be connected manually
         // start: 0,
         size: iterations,
@@ -218,7 +221,7 @@ export const buildCanvasOutpaintGraph = (
       [ITERATE]: {
         type: 'iterate',
         id: ITERATE,
-        is_intermediate,
+        is_intermediate: true,
       },
     },
     edges: [
@@ -469,7 +472,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_patchmatch',
       id: INPAINT_INFILL,
-      is_intermediate,
+      is_intermediate: true,
       downscale: infillPatchmatchDownscaleSize,
     };
   }
@@ -478,7 +481,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_lama',
       id: INPAINT_INFILL,
-      is_intermediate,
+      is_intermediate: true,
     };
   }
 
@@ -486,7 +489,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_cv2',
       id: INPAINT_INFILL,
-      is_intermediate,
+      is_intermediate: true,
     };
   }
 
@@ -494,7 +497,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_tile',
       id: INPAINT_INFILL,
-      is_intermediate,
+      is_intermediate: true,
       tile_size: infillTileSize,
     };
   }
@@ -508,7 +511,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_UP,
-      is_intermediate,
+      is_intermediate: true,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasInitImage,
@@ -516,28 +519,28 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[MASK_RESIZE_UP] = {
       type: 'img_resize',
       id: MASK_RESIZE_UP,
-      is_intermediate,
+      is_intermediate: true,
       width: scaledWidth,
       height: scaledHeight,
     };
     graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_DOWN,
-      is_intermediate,
+      is_intermediate: true,
       width: width,
       height: height,
     };
     graph.nodes[INPAINT_INFILL_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_INFILL_RESIZE_DOWN,
-      is_intermediate,
+      is_intermediate: true,
       width: width,
       height: height,
     };
     graph.nodes[MASK_RESIZE_DOWN] = {
       type: 'img_resize',
       id: MASK_RESIZE_DOWN,
-      is_intermediate,
+      is_intermediate: true,
       width: width,
       height: height,
     };
@@ -696,7 +699,7 @@ export const buildCanvasOutpaintGraph = (
     graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
       type: 'create_denoise_mask',
       id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
-      is_intermediate,
+      is_intermediate: true,
       fp32,
     };
 
@@ -743,7 +746,7 @@ export const buildCanvasOutpaintGraph = (
       graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
         type: 'mask_edge',
         id: CANVAS_COHERENCE_MASK_EDGE,
-        is_intermediate,
+        is_intermediate: true,
         edge_blur: maskBlur,
         edge_size: maskBlur * 2,
         low_threshold: 100,
@@ -846,7 +849,5 @@ export const buildCanvasOutpaintGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
-  addSaveImageNode(state, graph);
-
   return graph;
 };

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

@@ -27,7 +27,6 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
-import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Image to Image graph.
@@ -62,10 +61,14 @@ 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 } = state.canvas;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-  const is_intermediate = true;
+
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -119,7 +122,7 @@ export const buildCanvasSDXLImageToImageGraph = (
       [NOISE]: {
         type: 'noise',
         id: NOISE,
-        is_intermediate,
+        is_intermediate: true,
         use_cpu,
         width: !isUsingScaledDimensions
           ? width
@@ -131,13 +134,13 @@ export const buildCanvasSDXLImageToImageGraph = (
       [IMAGE_TO_LATENTS]: {
         type: 'i2l',
         id: IMAGE_TO_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: SDXL_DENOISE_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
         cfg_scale,
         scheduler,
         steps,
@@ -248,7 +251,7 @@ export const buildCanvasSDXLImageToImageGraph = (
     graph.nodes[IMG2IMG_RESIZE] = {
       id: IMG2IMG_RESIZE,
       type: 'img_resize',
-      is_intermediate,
+      is_intermediate: true,
       image: initialImage,
       width: scaledBoundingBoxDimensions.width,
       height: scaledBoundingBoxDimensions.height,
@@ -256,13 +259,13 @@ export const buildCanvasSDXLImageToImageGraph = (
     graph.nodes[LATENTS_TO_IMAGE] = {
       id: LATENTS_TO_IMAGE,
       type: 'l2i',
-      is_intermediate,
+      is_intermediate: true,
       fp32,
     };
     graph.nodes[CANVAS_OUTPUT] = {
       id: CANVAS_OUTPUT,
       type: 'img_resize',
-      is_intermediate,
+      is_intermediate: !shouldAutoSave,
       width: width,
       height: height,
     };
@@ -303,7 +306,7 @@ export const buildCanvasSDXLImageToImageGraph = (
     graph.nodes[CANVAS_OUTPUT] = {
       type: 'l2i',
       id: CANVAS_OUTPUT,
-      is_intermediate,
+      is_intermediate: !shouldAutoSave,
       fp32,
     };
 
@@ -400,7 +403,5 @@ 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 +46,6 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
-import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Inpaint graph.
@@ -95,10 +94,14 @@ 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 } = state.canvas;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-  const is_intermediate = true;
+
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -136,32 +139,32 @@ export const buildCanvasSDXLInpaintGraph = (
       [MASK_BLUR]: {
         type: 'img_blur',
         id: MASK_BLUR,
-        is_intermediate,
+        is_intermediate: true,
         radius: maskBlur,
         blur_type: maskBlurMethod,
       },
       [INPAINT_IMAGE]: {
         type: 'i2l',
         id: INPAINT_IMAGE,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate,
+        is_intermediate: true,
       },
       [INPAINT_CREATE_MASK]: {
         type: 'create_denoise_mask',
         id: INPAINT_CREATE_MASK,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: SDXL_DENOISE_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
         steps: steps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -174,18 +177,18 @@ export const buildCanvasSDXLInpaintGraph = (
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate,
+        is_intermediate: true,
       },
       [CANVAS_COHERENCE_NOISE_INCREMENT]: {
         type: 'add',
         id: CANVAS_COHERENCE_NOISE_INCREMENT,
         b: 1,
-        is_intermediate,
+        is_intermediate: true,
       },
       [CANVAS_COHERENCE_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: CANVAS_COHERENCE_DENOISE_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -195,19 +198,19 @@ export const buildCanvasSDXLInpaintGraph = (
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [CANVAS_OUTPUT]: {
         type: 'color_correct',
         id: CANVAS_OUTPUT,
-        is_intermediate,
+        is_intermediate: !shouldAutoSave,
         reference: canvasInitImage,
       },
       [RANGE_OF_SIZE]: {
         type: 'range_of_size',
         id: RANGE_OF_SIZE,
-        is_intermediate,
+        is_intermediate: true,
         // seed - must be connected manually
         // start: 0,
         size: iterations,
@@ -216,7 +219,7 @@ export const buildCanvasSDXLInpaintGraph = (
       [ITERATE]: {
         type: 'iterate',
         id: ITERATE,
-        is_intermediate,
+        is_intermediate: true,
       },
     },
     edges: [
@@ -448,7 +451,7 @@ export const buildCanvasSDXLInpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_UP,
-      is_intermediate,
+      is_intermediate: true,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasInitImage,
@@ -456,7 +459,7 @@ export const buildCanvasSDXLInpaintGraph = (
     graph.nodes[MASK_RESIZE_UP] = {
       type: 'img_resize',
       id: MASK_RESIZE_UP,
-      is_intermediate,
+      is_intermediate: true,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasMaskImage,
@@ -464,14 +467,14 @@ export const buildCanvasSDXLInpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_DOWN,
-      is_intermediate,
+      is_intermediate: true,
       width: width,
       height: height,
     };
     graph.nodes[MASK_RESIZE_DOWN] = {
       type: 'img_resize',
       id: MASK_RESIZE_DOWN,
-      is_intermediate,
+      is_intermediate: true,
       width: width,
       height: height,
     };
@@ -609,7 +612,7 @@ export const buildCanvasSDXLInpaintGraph = (
     graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
       type: 'create_denoise_mask',
       id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
-      is_intermediate,
+      is_intermediate: true,
       fp32,
     };
 
@@ -662,7 +665,7 @@ export const buildCanvasSDXLInpaintGraph = (
       graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
         type: 'mask_edge',
         id: CANVAS_COHERENCE_MASK_EDGE,
-        is_intermediate,
+        is_intermediate: true,
         edge_blur: maskBlur,
         edge_size: maskBlur * 2,
         low_threshold: 100,
@@ -773,7 +776,5 @@ export const buildCanvasSDXLInpaintGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
-  addSaveImageNode(state, graph);
-
   return graph;
 };

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

@@ -48,7 +48,6 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
-import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Outpaint graph.
@@ -99,10 +98,14 @@ 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 } = state.canvas;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-  const is_intermediate = true;
+
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -140,37 +143,37 @@ export const buildCanvasSDXLOutpaintGraph = (
       [MASK_FROM_ALPHA]: {
         type: 'tomask',
         id: MASK_FROM_ALPHA,
-        is_intermediate,
+        is_intermediate: true,
         image: canvasInitImage,
       },
       [MASK_COMBINE]: {
         type: 'mask_combine',
         id: MASK_COMBINE,
-        is_intermediate,
+        is_intermediate: true,
         mask2: canvasMaskImage,
       },
       [INPAINT_IMAGE]: {
         type: 'i2l',
         id: INPAINT_IMAGE,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate,
+        is_intermediate: true,
       },
       [INPAINT_CREATE_MASK]: {
         type: 'create_denoise_mask',
         id: INPAINT_CREATE_MASK,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: SDXL_DENOISE_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
         steps: steps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -183,18 +186,18 @@ export const buildCanvasSDXLOutpaintGraph = (
         type: 'noise',
         id: NOISE,
         use_cpu,
-        is_intermediate,
+        is_intermediate: true,
       },
       [CANVAS_COHERENCE_NOISE_INCREMENT]: {
         type: 'add',
         id: CANVAS_COHERENCE_NOISE_INCREMENT,
         b: 1,
-        is_intermediate,
+        is_intermediate: true,
       },
       [CANVAS_COHERENCE_DENOISE_LATENTS]: {
         type: 'denoise_latents',
         id: CANVAS_COHERENCE_DENOISE_LATENTS,
-        is_intermediate,
+        is_intermediate: true,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
         scheduler: scheduler,
@@ -204,18 +207,18 @@ export const buildCanvasSDXLOutpaintGraph = (
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
-        is_intermediate,
+        is_intermediate: true,
         fp32,
       },
       [CANVAS_OUTPUT]: {
         type: 'color_correct',
         id: CANVAS_OUTPUT,
-        is_intermediate,
+        is_intermediate: !shouldAutoSave,
       },
       [RANGE_OF_SIZE]: {
         type: 'range_of_size',
         id: RANGE_OF_SIZE,
-        is_intermediate,
+        is_intermediate: true,
         // seed - must be connected manually
         // start: 0,
         size: iterations,
@@ -224,7 +227,7 @@ export const buildCanvasSDXLOutpaintGraph = (
       [ITERATE]: {
         type: 'iterate',
         id: ITERATE,
-        is_intermediate,
+        is_intermediate: true,
       },
     },
     edges: [
@@ -484,7 +487,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_patchmatch',
       id: INPAINT_INFILL,
-      is_intermediate,
+      is_intermediate: true,
       downscale: infillPatchmatchDownscaleSize,
     };
   }
@@ -493,7 +496,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_lama',
       id: INPAINT_INFILL,
-      is_intermediate,
+      is_intermediate: true,
     };
   }
 
@@ -501,7 +504,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_cv2',
       id: INPAINT_INFILL,
-      is_intermediate,
+      is_intermediate: true,
     };
   }
 
@@ -509,7 +512,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[INPAINT_INFILL] = {
       type: 'infill_tile',
       id: INPAINT_INFILL,
-      is_intermediate,
+      is_intermediate: true,
       tile_size: infillTileSize,
     };
   }
@@ -523,7 +526,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_UP,
-      is_intermediate,
+      is_intermediate: true,
       width: scaledWidth,
       height: scaledHeight,
       image: canvasInitImage,
@@ -531,28 +534,28 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[MASK_RESIZE_UP] = {
       type: 'img_resize',
       id: MASK_RESIZE_UP,
-      is_intermediate,
+      is_intermediate: true,
       width: scaledWidth,
       height: scaledHeight,
     };
     graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_IMAGE_RESIZE_DOWN,
-      is_intermediate,
+      is_intermediate: true,
       width: width,
       height: height,
     };
     graph.nodes[INPAINT_INFILL_RESIZE_DOWN] = {
       type: 'img_resize',
       id: INPAINT_INFILL_RESIZE_DOWN,
-      is_intermediate,
+      is_intermediate: true,
       width: width,
       height: height,
     };
     graph.nodes[MASK_RESIZE_DOWN] = {
       type: 'img_resize',
       id: MASK_RESIZE_DOWN,
-      is_intermediate,
+      is_intermediate: true,
       width: width,
       height: height,
     };
@@ -712,7 +715,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
       type: 'create_denoise_mask',
       id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
-      is_intermediate,
+      is_intermediate: true,
       fp32,
     };
 
@@ -759,7 +762,7 @@ export const buildCanvasSDXLOutpaintGraph = (
       graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
         type: 'mask_edge',
         id: CANVAS_COHERENCE_MASK_EDGE,
-        is_intermediate,
+        is_intermediate: true,
         edge_blur: maskBlur,
         edge_size: maskBlur * 2,
         low_threshold: 100,
@@ -876,7 +879,5 @@ export const buildCanvasSDXLOutpaintGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
-  addSaveImageNode(state, graph);
-
   return graph;
 };

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

@@ -29,7 +29,6 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
-import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Canvas tab's Text to Image graph.
@@ -56,10 +55,14 @@ 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 } = state.canvas;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-  const is_intermediate = true;
+
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -91,7 +94,7 @@ export const buildCanvasSDXLTextToImageGraph = (
       ? {
           type: 't2l_onnx',
           id: SDXL_DENOISE_LATENTS,
-          is_intermediate,
+          is_intermediate: true,
           cfg_scale,
           scheduler,
           steps,
@@ -99,7 +102,7 @@ export const buildCanvasSDXLTextToImageGraph = (
       : {
           type: 'denoise_latents',
           id: SDXL_DENOISE_LATENTS,
-          is_intermediate,
+          is_intermediate: true,
           cfg_scale,
           scheduler,
           steps,
@@ -128,27 +131,27 @@ export const buildCanvasSDXLTextToImageGraph = (
       [modelLoaderNodeId]: {
         type: modelLoaderNodeType,
         id: modelLoaderNodeId,
-        is_intermediate,
+        is_intermediate: true,
         model,
       },
       [POSITIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'sdxl_compel_prompt',
         id: POSITIVE_CONDITIONING,
-        is_intermediate,
+        is_intermediate: true,
         prompt: positivePrompt,
         style: craftedPositiveStylePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'sdxl_compel_prompt',
         id: NEGATIVE_CONDITIONING,
-        is_intermediate,
+        is_intermediate: true,
         prompt: negativePrompt,
         style: craftedNegativeStylePrompt,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
-        is_intermediate,
+        is_intermediate: true,
         width: !isUsingScaledDimensions
           ? width
           : scaledBoundingBoxDimensions.width,
@@ -250,14 +253,14 @@ export const buildCanvasSDXLTextToImageGraph = (
     graph.nodes[LATENTS_TO_IMAGE] = {
       id: LATENTS_TO_IMAGE,
       type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
-      is_intermediate,
+      is_intermediate: true,
       fp32,
     };
 
     graph.nodes[CANVAS_OUTPUT] = {
       id: CANVAS_OUTPUT,
       type: 'img_resize',
-      is_intermediate,
+      is_intermediate: !shouldAutoSave,
       width: width,
       height: height,
     };
@@ -288,7 +291,7 @@ export const buildCanvasSDXLTextToImageGraph = (
     graph.nodes[CANVAS_OUTPUT] = {
       type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
       id: CANVAS_OUTPUT,
-      is_intermediate,
+      is_intermediate: !shouldAutoSave,
       fp32,
     };
 
@@ -380,7 +383,5 @@ export const buildCanvasSDXLTextToImageGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
-  addSaveImageNode(state, graph);
-
   return graph;
 };

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

@@ -10,7 +10,6 @@ import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
-import { addSaveImageNode } from './addSaveImageNode';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
@@ -54,10 +53,14 @@ 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 } = state.canvas;
+  const {
+    scaledBoundingBoxDimensions,
+    boundingBoxScaleMethod,
+    shouldAutoSave,
+  } = state.canvas;
 
   const fp32 = vaePrecision === 'fp32';
-  const is_intermediate = true;
+
   const isUsingScaledDimensions = ['auto', 'manual'].includes(
     boundingBoxScaleMethod
   );
@@ -86,7 +89,7 @@ export const buildCanvasTextToImageGraph = (
       ? {
           type: 't2l_onnx',
           id: DENOISE_LATENTS,
-          is_intermediate,
+          is_intermediate: true,
           cfg_scale,
           scheduler,
           steps,
@@ -94,7 +97,7 @@ export const buildCanvasTextToImageGraph = (
       : {
           type: 'denoise_latents',
           id: DENOISE_LATENTS,
-          is_intermediate,
+          is_intermediate: true,
           cfg_scale,
           scheduler,
           steps,
@@ -119,31 +122,31 @@ export const buildCanvasTextToImageGraph = (
       [modelLoaderNodeId]: {
         type: modelLoaderNodeType,
         id: modelLoaderNodeId,
-        is_intermediate,
+        is_intermediate: true,
         model,
       },
       [CLIP_SKIP]: {
         type: 'clip_skip',
         id: CLIP_SKIP,
-        is_intermediate,
+        is_intermediate: true,
         skipped_layers: clipSkip,
       },
       [POSITIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'compel',
         id: POSITIVE_CONDITIONING,
-        is_intermediate,
+        is_intermediate: true,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'compel',
         id: NEGATIVE_CONDITIONING,
-        is_intermediate,
+        is_intermediate: true,
         prompt: negativePrompt,
       },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
-        is_intermediate,
+        is_intermediate: true,
         width: !isUsingScaledDimensions
           ? width
           : scaledBoundingBoxDimensions.width,
@@ -236,14 +239,14 @@ export const buildCanvasTextToImageGraph = (
     graph.nodes[LATENTS_TO_IMAGE] = {
       id: LATENTS_TO_IMAGE,
       type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
-      is_intermediate,
+      is_intermediate: true,
       fp32,
     };
 
     graph.nodes[CANVAS_OUTPUT] = {
       id: CANVAS_OUTPUT,
       type: 'img_resize',
-      is_intermediate,
+      is_intermediate: !shouldAutoSave,
       width: width,
       height: height,
     };
@@ -274,7 +277,7 @@ export const buildCanvasTextToImageGraph = (
     graph.nodes[CANVAS_OUTPUT] = {
       type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
       id: CANVAS_OUTPUT,
-      is_intermediate,
+      is_intermediate: !shouldAutoSave,
       fp32,
     };
 
@@ -353,7 +356,5 @@ export const buildCanvasTextToImageGraph = (
     addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
   }
 
-  addSaveImageNode(state, graph);
-
   return graph;
 };

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

@@ -27,7 +27,6 @@ import {
   RESIZE,
   SEAMLESS,
 } from './constants';
-import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Image to Image tab graph.
@@ -86,7 +85,6 @@ export const buildLinearImageToImageGraph = (
   }
 
   const fp32 = vaePrecision === 'fp32';
-  const is_intermediate = true;
 
   let modelLoaderNodeId = MAIN_MODEL_LOADER;
 
@@ -102,37 +100,31 @@ 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',
@@ -142,7 +134,6 @@ export const buildLinearImageToImageGraph = (
         steps,
         denoising_start: 1 - strength,
         denoising_end: 1,
-        is_intermediate,
       },
       [IMAGE_TO_LATENTS]: {
         type: 'i2l',
@@ -152,7 +143,6 @@ export const buildLinearImageToImageGraph = (
         //   image_name: initialImage.image_name,
         // },
         fp32,
-        is_intermediate,
       },
     },
     edges: [
@@ -385,7 +375,5 @@ export const buildLinearImageToImageGraph = (
     addWatermarkerToGraph(state, graph);
   }
 
-  addSaveImageNode(state, graph);
-
   return graph;
 };

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

@@ -29,7 +29,6 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
-import { addSaveImageNode } from './addSaveImageNode';
 
 /**
  * Builds the Image to Image tab graph.
@@ -86,7 +85,6 @@ export const buildLinearSDXLImageToImageGraph = (
   }
 
   const fp32 = vaePrecision === 'fp32';
-  const is_intermediate = true;
 
   // Model Loader ID
   let modelLoaderNodeId = SDXL_MODEL_LOADER;
@@ -107,33 +105,28 @@ 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',
@@ -145,7 +138,6 @@ export const buildLinearSDXLImageToImageGraph = (
           ? Math.min(refinerStart, 1 - strength)
           : 1 - strength,
         denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
-        is_intermediate,
       },
       [IMAGE_TO_LATENTS]: {
         type: 'i2l',
@@ -155,7 +147,6 @@ export const buildLinearSDXLImageToImageGraph = (
         //   image_name: initialImage.image_name,
         // },
         fp32,
-        is_intermediate,
       },
     },
     edges: [
@@ -407,7 +398,5 @@ export const buildLinearSDXLImageToImageGraph = (
     addWatermarkerToGraph(state, graph);
   }
 
-  addSaveImageNode(state, graph);
-
   return graph;
 };

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

@@ -23,7 +23,6 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
-import { addSaveImageNode } from './addSaveImageNode';
 
 export const buildLinearSDXLTextToImageGraph = (
   state: RootState
@@ -57,13 +56,13 @@ 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 } =
@@ -89,21 +88,18 @@ 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',
@@ -111,7 +107,6 @@ export const buildLinearSDXLTextToImageGraph = (
         width,
         height,
         use_cpu,
-        is_intermediate,
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
@@ -121,13 +116,11 @@ 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: [
@@ -298,7 +291,5 @@ export const buildLinearSDXLTextToImageGraph = (
     addWatermarkerToGraph(state, graph);
   }
 
-  addSaveImageNode(state, graph);
-
   return graph;
 };

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

@@ -26,7 +26,6 @@ import {
   SEAMLESS,
   TEXT_TO_IMAGE_GRAPH,
 } from './constants';
-import { addSaveImageNode } from './addSaveImageNode';
 
 export const buildLinearTextToImageGraph = (
   state: RootState
@@ -59,7 +58,7 @@ export const buildLinearTextToImageGraph = (
   }
 
   const fp32 = vaePrecision === 'fp32';
-  const is_intermediate = true;
+
   const isUsingOnnxModel = model.model_type === 'onnx';
 
   let modelLoaderNodeId = isUsingOnnxModel
@@ -75,7 +74,7 @@ export const buildLinearTextToImageGraph = (
       ? {
           type: 't2l_onnx',
           id: DENOISE_LATENTS,
-          is_intermediate,
+          is_intermediate: true,
           cfg_scale,
           scheduler,
           steps,
@@ -83,7 +82,7 @@ export const buildLinearTextToImageGraph = (
       : {
           type: 'denoise_latents',
           id: DENOISE_LATENTS,
-          is_intermediate,
+          is_intermediate: true,
           cfg_scale,
           scheduler,
           steps,
@@ -109,26 +108,26 @@ export const buildLinearTextToImageGraph = (
       [modelLoaderNodeId]: {
         type: modelLoaderNodeType,
         id: modelLoaderNodeId,
-        is_intermediate,
+        is_intermediate: true,
         model,
       },
       [CLIP_SKIP]: {
         type: 'clip_skip',
         id: CLIP_SKIP,
         skipped_layers: clipSkip,
-        is_intermediate,
+        is_intermediate: true,
       },
       [POSITIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'compel',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
-        is_intermediate,
+        is_intermediate: true,
       },
       [NEGATIVE_CONDITIONING]: {
         type: isUsingOnnxModel ? 'prompt_onnx' : 'compel',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
-        is_intermediate,
+        is_intermediate: true,
       },
       [NOISE]: {
         type: 'noise',
@@ -136,14 +135,13 @@ export const buildLinearTextToImageGraph = (
         width,
         height,
         use_cpu,
-        is_intermediate,
+        is_intermediate: true,
       },
       [t2lNode.id]: t2lNode,
       [LATENTS_TO_IMAGE]: {
         type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
         id: LATENTS_TO_IMAGE,
         fp32,
-        is_intermediate,
       },
     },
     edges: [
@@ -295,7 +293,5 @@ export const buildLinearTextToImageGraph = (
     addWatermarkerToGraph(state, graph);
   }
 
-  addSaveImageNode(state, graph);
-
   return graph;
 };

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

@@ -55,9 +55,6 @@ 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,

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

@@ -3,7 +3,6 @@ export const POSITIVE_CONDITIONING = 'positive_conditioning';
 export const NEGATIVE_CONDITIONING = 'negative_conditioning';
 export const DENOISE_LATENTS = 'denoise_latents';
 export const LATENTS_TO_IMAGE = 'latents_to_image';
-export const SAVE_IMAGE = 'save_image';
 export const NSFW_CHECKER = 'nsfw_checker';
 export const WATERMARKER = 'invisible_watermark';
 export const NOISE = 'noise';

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

@@ -16,7 +16,7 @@ import {
 } from '../types/types';
 import { buildInputFieldTemplate, getFieldType } from './fieldTemplateBuilders';
 
-const RESERVED_INPUT_FIELD_NAMES = ['id', 'type', 'metadata', 'use_cache'];
+const RESERVED_INPUT_FIELD_NAMES = ['id', 'type', 'metadata'];
 const RESERVED_OUTPUT_FIELD_NAMES = ['type'];
 const RESERVED_FIELD_TYPES = [
   'WorkflowField',
@@ -235,8 +235,6 @@ export const parseSchema = (
       {} as Record<string, OutputFieldTemplate>
     );
 
-    const useCache = schema.properties.use_cache.default;
-
     const invocation: InvocationTemplate = {
       title,
       type,
@@ -246,7 +244,6 @@ export const parseSchema = (
       outputType,
       inputs,
       outputs,
-      useCache,
     };
 
     Object.assign(invocationsAccumulator, { [type]: invocation });

invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts

@@ -1,8 +1,6 @@
-import { createSelector } from '@reduxjs/toolkit';
 import { useAppToaster } from 'app/components/Toaster';
-import { stateSelector } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { CoreMetadata, LoRAMetadataItem } from 'features/nodes/types/types';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { CoreMetadata } from 'features/nodes/types/types';
 import {
   refinerModelChanged,
   setNegativeStylePromptSDXL,
@@ -17,11 +15,6 @@ import {
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { ImageDTO } from 'services/api/types';
-import {
-  loraModelsAdapter,
-  useGetLoRAModelsQuery,
-} from '../../../services/api/endpoints/models';
-import { loraRecalled } from '../../lora/store/loraSlice';
 import { initialImageSelected, modelSelected } from '../store/actions';
 import {
   setCfgScale,
@@ -37,7 +30,6 @@ import {
 import {
   isValidCfgScale,
   isValidHeight,
-  isValidLoRAModel,
   isValidMainModel,
   isValidNegativePrompt,
   isValidPositivePrompt,
@@ -54,16 +46,10 @@ import {
   isValidWidth,
 } from '../types/parameterSchemas';
 
-const selector = createSelector(stateSelector, ({ generation }) => {
-  const { model } = generation;
-  return { model };
-});
-
 export const useRecallParameters = () => {
   const dispatch = useAppDispatch();
   const toaster = useAppToaster();
   const { t } = useTranslation();
-  const { model } = useAppSelector(selector);
 
   const parameterSetToast = useCallback(() => {
     toaster({
@@ -74,18 +60,14 @@ export const useRecallParameters = () => {
     });
   }, [t, toaster]);
 
-  const parameterNotSetToast = useCallback(
-    (description?: string) => {
-      toaster({
-        title: t('toast.parameterNotSet'),
-        description,
-        status: 'warning',
-        duration: 2500,
-        isClosable: true,
-      });
-    },
-    [t, toaster]
-  );
+  const parameterNotSetToast = useCallback(() => {
+    toaster({
+      title: t('toast.parameterNotSet'),
+      status: 'warning',
+      duration: 2500,
+      isClosable: true,
+    });
+  }, [t, toaster]);
 
   const allParameterSetToast = useCallback(() => {
     toaster({
@@ -96,18 +78,14 @@ export const useRecallParameters = () => {
     });
   }, [t, toaster]);
 
-  const allParameterNotSetToast = useCallback(
-    (description?: string) => {
-      toaster({
-        title: t('toast.parametersNotSet'),
-        status: 'warning',
-        description,
-        duration: 2500,
-        isClosable: true,
-      });
-    },
-    [t, toaster]
-  );
+  const allParameterNotSetToast = useCallback(() => {
+    toaster({
+      title: t('toast.parametersNotSet'),
+      status: 'warning',
+      duration: 2500,
+      isClosable: true,
+    });
+  }, [t, toaster]);
 
   /**
    * Recall both prompts with toast
@@ -329,67 +307,6 @@ export const useRecallParameters = () => {
     [dispatch, parameterSetToast, parameterNotSetToast]
   );
 
-  /**
-   * Recall LoRA with toast
-   */
-
-  const { loras } = useGetLoRAModelsQuery(undefined, {
-    selectFromResult: (result) => ({
-      loras: result.data
-        ? loraModelsAdapter.getSelectors().selectAll(result.data)
-        : [],
-    }),
-  });
-
-  const prepareLoRAMetadataItem = useCallback(
-    (loraMetadataItem: LoRAMetadataItem) => {
-      if (!isValidLoRAModel(loraMetadataItem.lora)) {
-        return { lora: null, error: 'Invalid LoRA model' };
-      }
-
-      const { base_model, model_name } = loraMetadataItem.lora;
-
-      const matchingLoRA = loras.find(
-        (l) => l.base_model === base_model && l.model_name === model_name
-      );
-
-      if (!matchingLoRA) {
-        return { lora: null, error: 'LoRA model is not installed' };
-      }
-
-      const isCompatibleBaseModel =
-        matchingLoRA?.base_model === model?.base_model;
-
-      if (!isCompatibleBaseModel) {
-        return {
-          lora: null,
-          error: 'LoRA incompatible with currently-selected model',
-        };
-      }
-
-      return { lora: matchingLoRA, error: null };
-    },
-    [loras, model?.base_model]
-  );
-
-  const recallLoRA = useCallback(
-    (loraMetadataItem: LoRAMetadataItem) => {
-      const result = prepareLoRAMetadataItem(loraMetadataItem);
-
-      if (!result.lora) {
-        parameterNotSetToast(result.error);
-        return;
-      }
-
-      dispatch(
-        loraRecalled({ ...result.lora, weight: loraMetadataItem.weight })
-      );
-
-      parameterSetToast();
-    },
-    [prepareLoRAMetadataItem, dispatch, parameterSetToast, parameterNotSetToast]
-  );
-
   /*
    * Sets image as initial image with toast
    */
@@ -427,7 +344,6 @@ export const useRecallParameters = () => {
         refiner_positive_aesthetic_score,
         refiner_negative_aesthetic_score,
         refiner_start,
-        loras,
       } = metadata;
 
       if (isValidCfgScale(cfg_scale)) {
@@ -509,21 +425,9 @@ export const useRecallParameters = () => {
         dispatch(setRefinerStart(refiner_start));
       }
 
-      loras?.forEach((lora) => {
-        const result = prepareLoRAMetadataItem(lora);
-        if (result.lora) {
-          dispatch(loraRecalled({ ...result.lora, weight: lora.weight }));
-        }
-      });
-
       allParameterSetToast();
     },
-    [
-      allParameterNotSetToast,
-      allParameterSetToast,
-      dispatch,
-      prepareLoRAMetadataItem,
-    ]
+    [allParameterNotSetToast, allParameterSetToast, dispatch]
   );
 
   return {
@@ -540,7 +444,6 @@ export const useRecallParameters = () => {
     recallWidth,
     recallHeight,
     recallStrength,
-    recallLoRA,
     recallAllParameters,
     sendToImageToImage,
   };

invokeai/frontend/web/src/services/api/endpoints/models.ts

@@ -128,7 +128,7 @@ export const mainModelsAdapter = createEntityAdapter<MainModelConfigEntity>({
 const onnxModelsAdapter = createEntityAdapter<OnnxModelConfigEntity>({
   sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
-export const loraModelsAdapter = createEntityAdapter<LoRAModelConfigEntity>({
+const loraModelsAdapter = createEntityAdapter<LoRAModelConfigEntity>({
   sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
 export const controlNetModelsAdapter =

invokeai/frontend/web/src/services/api/types.ts

@@ -132,7 +132,6 @@ export type DivideInvocation = s['DivideInvocation'];
 export type ImageNSFWBlurInvocation = s['ImageNSFWBlurInvocation'];
 export type ImageWatermarkInvocation = s['ImageWatermarkInvocation'];
 export type SeamlessModeInvocation = s['SeamlessModeInvocation'];
-export type SaveImageInvocation = s['SaveImageInvocation'];
 
 // ControlNet Nodes
 export type ControlNetInvocation = s['ControlNetInvocation'];

pyproject.toml

@@ -198,13 +198,6 @@ output = "coverage/index.xml"
 max-line-length = 120
 ignore = ["E203", "E266", "E501", "W503"]
 select = ["B", "C", "E", "F", "W", "T4"]
-exclude = [
-  ".git",
-  "__pycache__",
-  "build",
-  "dist",
-  "invokeai/frontend/web/node_modules/"
-]
 
 [tool.black]
 line-length = 120

tests/nodes/test_graph_execution_state.py

@@ -1,9 +1,5 @@
-import logging
-
 import pytest
 
-from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
-
 # This import must happen before other invoke imports or test in other files(!!) break
 from .test_nodes import (  # isort: split
     PromptCollectionTestInvocation,
@@ -46,7 +42,7 @@ def mock_services() -> InvocationServices:
     return InvocationServices(
         model_manager=None,  # type: ignore
         events=TestEventService(),
-        logger=logging,  # type: ignore
+        logger=None,  # type: ignore
         images=None,  # type: ignore
         latents=None,  # type: ignore
         boards=None,  # type: ignore
@@ -57,7 +53,6 @@ def mock_services() -> InvocationServices:
         performance_statistics=InvocationStatsService(graph_execution_manager),
         processor=DefaultInvocationProcessor(),
         configuration=None,  # type: ignore
-        invocation_cache=MemoryInvocationCache(),  # type: ignore
     )
 
 

tests/nodes/test_invoker.py

@@ -1,9 +1,6 @@
-import logging
-
 import pytest
 
 from invokeai.app.services.graph import Graph, GraphExecutionState, LibraryGraph
-from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
 from invokeai.app.services.invocation_queue import MemoryInvocationQueue
 from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.invocation_stats import InvocationStatsService
@@ -42,7 +39,7 @@ def mock_services() -> InvocationServices:
     return InvocationServices(
         model_manager=None,  # type: ignore
         events=TestEventService(),
-        logger=logging,  # type: ignore
+        logger=None,  # type: ignore
         images=None,  # type: ignore
         latents=None,  # type: ignore
         boards=None,  # type: ignore
@@ -53,7 +50,6 @@ def mock_services() -> InvocationServices:
         processor=DefaultInvocationProcessor(),
         performance_statistics=InvocationStatsService(graph_execution_manager),
         configuration=None,  # type: ignore
-        invocation_cache=MemoryInvocationCache(),
     )