feat(nodes): add ResultsServiceABC & SqliteResultsService

**Doesn't actually work bc of circular imports. Can't even test it.**

- add a base class for ResultsService and SQLite implementation
- use `graph_execution_manager` `on_changed` callback to keep `results` table in sync

installer/lib/installer.py

@@ -247,8 +247,8 @@ class InvokeAiInstance:
             pip[
                 "install",
                 "--require-virtualenv",
-                "torch",
-                "torchvision",
+                "torch~=2.0.0",
+                "torchvision>=0.14.1",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,

invokeai/app/api/dependencies.py

@@ -18,6 +18,7 @@ from ..services.invoker import Invoker
 from ..services.processor import DefaultInvocationProcessor
 from ..services.sqlite import SqliteItemStorage
 from ..services.metadata import PngMetadataService
+from ..services.results import SqliteResultsService
 from .events import FastAPIEventService
 
 
@@ -69,6 +70,14 @@ class ApiDependencies:
         # TODO: build a file/path manager?
         db_location = os.path.join(output_folder, "invokeai.db")
 
+        results = SqliteResultsService(filename=db_location)
+
+        graph_execution_manager = SqliteItemStorage[GraphExecutionState](
+            filename=db_location, table_name="graph_executions"
+        )
+
+        graph_execution_manager.on_changed(results.handle_graph_execution_state_change)
+
         services = InvocationServices(
             model_manager=get_model_manager(config,logger),
             events=events,
@@ -76,13 +85,12 @@ class ApiDependencies:
             latents=latents,
             images=images,
             metadata=metadata,
+            results=results,
             queue=MemoryInvocationQueue(),
             graph_library=SqliteItemStorage[LibraryGraph](
                 filename=db_location, table_name="graphs"
             ),
-            graph_execution_manager=SqliteItemStorage[GraphExecutionState](
-                filename=db_location, table_name="graph_executions"
-            ),
+            graph_execution_manager=graph_execution_manager,
             processor=DefaultInvocationProcessor(),
             restoration=RestorationServices(config,logger),
         )

invokeai/app/api/routers/images.py

@@ -83,7 +83,7 @@ async def get_thumbnail(
     status_code=201,
 )
 async def upload_image(
-    file: UploadFile, request: Request, response: Response
+    file: UploadFile, image_type: ImageType, request: Request, response: Response
 ) -> ImageResponse:
     if not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
@@ -99,21 +99,21 @@ async def upload_image(
     filename = f"{uuid.uuid4()}_{str(int(datetime.now(timezone.utc).timestamp()))}.png"
 
     saved_image = ApiDependencies.invoker.services.images.save(
-        ImageType.UPLOAD, filename, img
+        image_type, filename, img
     )
 
     invokeai_metadata = ApiDependencies.invoker.services.metadata.get_metadata(img)
 
     image_url = ApiDependencies.invoker.services.images.get_uri(
-        ImageType.UPLOAD, saved_image.image_name
+        image_type, saved_image.image_name
     )
 
     thumbnail_url = ApiDependencies.invoker.services.images.get_uri(
-        ImageType.UPLOAD, saved_image.image_name, True
+        image_type, saved_image.image_name, True
     )
 
     res = ImageResponse(
-        image_type=ImageType.UPLOAD,
+        image_type=image_type,
         image_name=saved_image.image_name,
         image_url=image_url,
         thumbnail_url=thumbnail_url,

invokeai/app/api_app.py

@@ -126,7 +126,6 @@ app.openapi = custom_openapi
 # Override API doc favicons
 app.mount("/static", StaticFiles(directory="static/dream_web"), name="static")
 
-
 @app.get("/docs", include_in_schema=False)
 def overridden_swagger():
     return get_swagger_ui_html(
@@ -144,6 +143,8 @@ def overridden_redoc():
         redoc_favicon_url="/static/favicon.ico",
     )
 
+# Must mount *after* the other routes else it borks em
+app.mount("/", StaticFiles(directory="invokeai/frontend/web/dist", html=True), name="ui")
 
 def invoke_api():
     # Start our own event loop for eventing usage

invokeai/app/invocations/collections.py

@@ -3,12 +3,12 @@
 from typing import Literal, Optional
 
 import numpy as np
-import numpy.random
 from pydantic import Field
 
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
+
 from .baseinvocation import (
     BaseInvocation,
-    InvocationConfig,
     InvocationContext,
     BaseInvocationOutput,
 )
@@ -50,11 +50,11 @@ class RandomRangeInvocation(BaseInvocation):
         default=np.iinfo(np.int32).max, description="The exclusive high value"
     )
     size: int = Field(default=1, description="The number of values to generate")
-    seed: Optional[int] = Field(
+    seed: int = Field(
         ge=0,
-        le=np.iinfo(np.int32).max,
-        description="The seed for the RNG",
-        default_factory=lambda: numpy.random.randint(0, np.iinfo(np.int32).max),
+        le=SEED_MAX,
+        description="The seed for the RNG (omit for random)",
+        default_factory=get_random_seed,
     )
 
     def invoke(self, context: InvocationContext) -> IntCollectionOutput:

invokeai/app/invocations/compel.py

@@ -100,7 +100,8 @@ class CompelInvocation(BaseInvocation):
 
         # TODO: support legacy blend?
 
-        prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(prompt_str)
+        conjunction = Compel.parse_prompt_string(prompt_str)
+        prompt: Union[FlattenedPrompt, Blend] = conjunction.prompts[0]
 
         if getattr(Globals, "log_tokenization", False):
             log_tokenization_for_prompt_object(prompt, tokenizer)

invokeai/app/invocations/generate.py

@@ -1,15 +1,17 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from functools import partial
-from typing import Literal, Optional, Union
+from typing import Literal, Optional, Union, get_args
 
 import numpy as np
 from torch import Tensor
 
 from pydantic import BaseModel, Field
 
-from invokeai.app.models.image import ImageField, ImageType
+from invokeai.app.models.image import ColorField, ImageField, ImageType
 from invokeai.app.invocations.util.choose_model import choose_model
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
+from invokeai.backend.generator.inpaint import infill_methods
 from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
 from .image import ImageOutput, build_image_output
 from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
@@ -17,7 +19,8 @@ from ...backend.stable_diffusion import PipelineIntermediateState
 from ..util.step_callback import stable_diffusion_step_callback
 
 SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
-
+INFILL_METHODS = Literal[tuple(infill_methods())]
+DEFAULT_INFILL_METHOD = 'patchmatch' if 'patchmatch' in get_args(INFILL_METHODS) else 'tile'
 
 class SDImageInvocation(BaseModel):
     """Helper class to provide all Stable Diffusion raster image invocations with additional config"""
@@ -44,15 +47,13 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
     # TODO: consider making prompt optional to enable providing prompt through a link
     # fmt: off
     prompt: Optional[str] = Field(description="The prompt to generate an image from")
-    seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
-    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
+    seed:        int = Field(ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed)
+    steps:       int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
     width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
     height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
-    cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
-    seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
+    cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="lms", description="The scheduler to use" )
     model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
     # fmt: on
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
@@ -148,7 +149,6 @@ class ImageToImageInvocation(TextToImageInvocation):
                 self.image.image_type, self.image.image_name
             )
         )
-        mask = None
 
         if self.fit:
             image = image.resize((self.width, self.height))
@@ -165,7 +165,6 @@ class ImageToImageInvocation(TextToImageInvocation):
         outputs = Img2Img(model).generate(
             prompt=self.prompt,
             init_image=image,
-            init_mask=mask,
             step_callback=partial(self.dispatch_progress, context, source_node_id),
             **self.dict(
                 exclude={"prompt", "image", "mask"}
@@ -197,7 +196,6 @@ class ImageToImageInvocation(TextToImageInvocation):
             image=result_image,
         )
 
-
 class InpaintInvocation(ImageToImageInvocation):
     """Generates an image using inpaint."""
 
@@ -205,6 +203,17 @@ class InpaintInvocation(ImageToImageInvocation):
 
     # Inputs
     mask: Union[ImageField, None] = Field(description="The mask")
+    seam_size: int = Field(default=96, ge=1, description="The seam inpaint size (px)")
+    seam_blur: int = Field(default=16, ge=0, description="The seam inpaint blur radius (px)")
+    seam_strength: float = Field(
+        default=0.75, gt=0, le=1, description="The seam inpaint strength"
+    )
+    seam_steps: int = Field(default=30, ge=1, description="The number of steps to use for seam inpaint")
+    tile_size: int = Field(default=32, ge=1, description="The tile infill method size (px)")
+    infill_method: INFILL_METHODS = Field(default=DEFAULT_INFILL_METHOD, description="The method used to infill empty regions (px)")
+    inpaint_width:       Optional[int] = Field(default=None, multiple_of=8, gt=0, description="The width of the inpaint region (px)")
+    inpaint_height:      Optional[int] = Field(default=None, multiple_of=8, gt=0, description="The height of the inpaint region (px)")
+    inpaint_fill: Optional[ColorField] = Field(default=ColorField(r=127, g=127, b=127, a=255), description="The solid infill method color")
     inpaint_replace: float = Field(
         default=0.0,
         ge=0.0,

invokeai/app/invocations/image.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+import io
 from typing import Literal, Optional
 
 import numpy
@@ -30,16 +31,14 @@ class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
 
     # fmt: off
-    type: Literal["image"] = "image"
+    type: Literal["image_output"] = "image_output"
     image:      ImageField = Field(default=None, description="The output image")
-    width:   Optional[int] = Field(default=None, description="The width of the image in pixels")
-    height:  Optional[int] = Field(default=None, description="The height of the image in pixels")
+    width:             int = Field(description="The width of the image in pixels")
+    height:            int = Field(description="The height of the image in pixels")
     # fmt: on
 
     class Config:
-        schema_extra = {
-            "required": ["type", "image", "width", "height", "mode"]
-        }
+        schema_extra = {"required": ["type", "image", "width", "height"]}
 
 
 def build_image_output(
@@ -54,7 +53,6 @@ def build_image_output(
         image=image_field,
         width=image.width,
         height=image.height,
-        mode=image.mode,
     )
 
 
@@ -151,7 +149,7 @@ class CropImageInvocation(BaseInvocation, PILInvocationConfig):
         metadata = context.services.metadata.build_metadata(
             session_id=context.graph_execution_state_id, node=self
         )
-        
+
         context.services.images.save(image_type, image_name, image_crop, metadata)
         return build_image_output(
             image_type=image_type,
@@ -209,7 +207,7 @@ class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
         metadata = context.services.metadata.build_metadata(
             session_id=context.graph_execution_state_id, node=self
         )
-        
+
         context.services.images.save(image_type, image_name, new_image, metadata)
         return build_image_output(
             image_type=image_type,

invokeai/app/invocations/infill.py

@@ -0,0 +1,233 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from typing import Literal, Optional, Union, get_args
+
+import numpy as np
+import math
+from PIL import Image, ImageOps
+from pydantic import Field
+
+from invokeai.app.invocations.image import ImageOutput, build_image_output
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
+from invokeai.backend.image_util.patchmatch import PatchMatch
+
+from ..models.image import ColorField, ImageField, ImageType
+from .baseinvocation import (
+    BaseInvocation,
+    InvocationContext,
+)
+
+
+def infill_methods() -> list[str]:
+    methods = [
+        "tile",
+        "solid",
+    ]
+    if PatchMatch.patchmatch_available():
+        methods.insert(0, "patchmatch")
+    return methods
+
+
+INFILL_METHODS = Literal[tuple(infill_methods())]
+DEFAULT_INFILL_METHOD = (
+    "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
+)
+
+
+def infill_patchmatch(im: Image.Image) -> Image.Image:
+    if im.mode != "RGBA":
+        return im
+
+    # Skip patchmatch if patchmatch isn't available
+    if not PatchMatch.patchmatch_available():
+        return im
+
+    # Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
+    im_patched_np = PatchMatch.inpaint(
+        im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3
+    )
+    im_patched = Image.fromarray(im_patched_np, mode="RGB")
+    return im_patched
+
+
+def get_tile_images(image: np.ndarray, width=8, height=8):
+    _nrows, _ncols, depth = image.shape
+    _strides = image.strides
+
+    nrows, _m = divmod(_nrows, height)
+    ncols, _n = divmod(_ncols, width)
+    if _m != 0 or _n != 0:
+        return None
+
+    return np.lib.stride_tricks.as_strided(
+        np.ravel(image),
+        shape=(nrows, ncols, height, width, depth),
+        strides=(height * _strides[0], width * _strides[1], *_strides),
+        writeable=False,
+    )
+
+
+def tile_fill_missing(
+    im: Image.Image, tile_size: int = 16, seed: Union[int, None] = None
+) -> Image.Image:
+    # Only fill if there's an alpha layer
+    if im.mode != "RGBA":
+        return im
+
+    a = np.asarray(im, dtype=np.uint8)
+
+    tile_size_tuple = (tile_size, tile_size)
+
+    # Get the image as tiles of a specified size
+    tiles = get_tile_images(a, *tile_size_tuple).copy()
+
+    # Get the mask as tiles
+    tiles_mask = tiles[:, :, :, :, 3]
+
+    # Find any mask tiles with any fully transparent pixels (we will be replacing these later)
+    tmask_shape = tiles_mask.shape
+    tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
+    n, ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
+    tiles_mask = tiles_mask > 0
+    tiles_mask = tiles_mask.reshape((n, ny)).all(axis=1)
+
+    # Get RGB tiles in single array and filter by the mask
+    tshape = tiles.shape
+    tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), *tiles.shape[2:]))
+    filtered_tiles = tiles_all[tiles_mask]
+
+    if len(filtered_tiles) == 0:
+        return im
+
+    # Find all invalid tiles and replace with a random valid tile
+    replace_count = (tiles_mask == False).sum()
+    rng = np.random.default_rng(seed=seed)
+    tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[
+        rng.choice(filtered_tiles.shape[0], replace_count), :, :, :
+    ]
+
+    # Convert back to an image
+    tiles_all = tiles_all.reshape(tshape)
+    tiles_all = tiles_all.swapaxes(1, 2)
+    st = tiles_all.reshape(
+        (
+            math.prod(tiles_all.shape[0:2]),
+            math.prod(tiles_all.shape[2:4]),
+            tiles_all.shape[4],
+        )
+    )
+    si = Image.fromarray(st, mode="RGBA")
+
+    return si
+
+
+class InfillColorInvocation(BaseInvocation):
+    """Infills transparent areas of an image with a solid color"""
+
+    type: Literal["infill_rgba"] = "infill_rgba"
+    image: Optional[ImageField] = Field(default=None, description="The image to infill")
+    color: Optional[ColorField] = Field(
+        default=ColorField(r=127, g=127, b=127, a=255),
+        description="The color to use to infill",
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+
+        solid_bg = Image.new("RGBA", image.size, self.color.tuple())
+        infilled = Image.alpha_composite(solid_bg, image)
+
+        infilled.paste(image, (0, 0), image.split()[-1])
+
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
+
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(image_type, image_name, infilled, metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=image,
+        )
+
+
+class InfillTileInvocation(BaseInvocation):
+    """Infills transparent areas of an image with tiles of the image"""
+
+    type: Literal["infill_tile"] = "infill_tile"
+
+    image: Optional[ImageField] = Field(default=None, description="The image to infill")
+    tile_size: int = Field(default=32, ge=1, description="The tile size (px)")
+    seed: int = Field(
+        ge=0,
+        le=SEED_MAX,
+        description="The seed to use for tile generation (omit for random)",
+        default_factory=get_random_seed,
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+
+        infilled = tile_fill_missing(
+            image.copy(), seed=self.seed, tile_size=self.tile_size
+        )
+        infilled.paste(image, (0, 0), image.split()[-1])
+
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
+
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(image_type, image_name, infilled, metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=image,
+        )
+
+
+class InfillPatchMatchInvocation(BaseInvocation):
+    """Infills transparent areas of an image using the PatchMatch algorithm"""
+
+    type: Literal["infill_patchmatch"] = "infill_patchmatch"
+
+    image: Optional[ImageField] = Field(default=None, description="The image to infill")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+
+        if PatchMatch.patchmatch_available():
+            infilled = infill_patchmatch(image.copy())
+        else:
+            raise ValueError("PatchMatch is not available on this system")
+
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
+
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(image_type, image_name, infilled, metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=image,
+        )

invokeai/app/invocations/latent.py

@@ -1,11 +1,13 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
 import random
-from typing import Literal, Optional
+from typing import Literal, Optional, Union
+import einops
 from pydantic import BaseModel, Field
 import torch
 
 from invokeai.app.invocations.util.choose_model import choose_model
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 
@@ -13,7 +15,9 @@ from ...backend.model_management.model_manager import ModelManager
 from ...backend.util.devices import choose_torch_device, torch_dtype
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.image_util.seamless import configure_model_padding
-from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline
+from ...backend.prompting.conditioning import get_uc_and_c_and_ec
+from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline, image_resized_to_grid_as_tensor
+from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
 import numpy as np
 from ..services.image_storage import ImageType
@@ -37,41 +41,55 @@ class LatentsField(BaseModel):
 class LatentsOutput(BaseInvocationOutput):
     """Base class for invocations that output latents"""
     #fmt: off
-    type: Literal["latent_output"] = "latent_output"
-    latents: LatentsField            = Field(default=None, description="The output latents")
+    type: Literal["latents_output"] = "latents_output"
+
+    # Inputs
+    latents: LatentsField          = Field(default=None, description="The output latents")
+    width:                     int = Field(description="The width of the latents in pixels")
+    height:                    int = Field(description="The height of the latents in pixels")
     #fmt: on
 
+
+def build_latents_output(latents_name: str, latents: torch.Tensor):
+      return LatentsOutput(
+          latents=LatentsField(latents_name=latents_name),
+          width=latents.size()[3] * 8,
+          height=latents.size()[2] * 8,
+      )
+
 class NoiseOutput(BaseInvocationOutput):
     """Invocation noise output"""
     #fmt: off
-    type: Literal["noise_output"] = "noise_output"
+    type:  Literal["noise_output"] = "noise_output"
+
+    # Inputs
     noise: LatentsField            = Field(default=None, description="The output noise")
+    width:                     int = Field(description="The width of the noise in pixels")
+    height:                    int = Field(description="The height of the noise in pixels")
     #fmt: on
 
-
-# TODO: this seems like a hack
-scheduler_map = dict(
-    ddim=diffusers.DDIMScheduler,
-    dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-    k_dpm_2=diffusers.KDPM2DiscreteScheduler,
-    k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
-    k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-    k_euler=diffusers.EulerDiscreteScheduler,
-    k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
-    k_heun=diffusers.HeunDiscreteScheduler,
-    k_lms=diffusers.LMSDiscreteScheduler,
-    plms=diffusers.PNDMScheduler,
-)
+def build_noise_output(latents_name: str, latents: torch.Tensor):
+      return NoiseOutput(
+          noise=LatentsField(latents_name=latents_name),
+          width=latents.size()[3] * 8,
+          height=latents.size()[2] * 8,
+      )
 
 
 SAMPLER_NAME_VALUES = Literal[
-    tuple(list(scheduler_map.keys()))
+    tuple(list(SCHEDULER_MAP.keys()))
 ]
 
 
 def get_scheduler(scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-    scheduler_class = scheduler_map.get(scheduler_name,'ddim')
-    scheduler = scheduler_class.from_config(model.scheduler.config)
+    scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
+    
+    scheduler_config = model.scheduler.config
+    if "_backup" in scheduler_config:
+        scheduler_config = scheduler_config["_backup"]
+    scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
+    scheduler = scheduler_class.from_config(scheduler_config)
+    
     # hack copied over from generate.py
     if not hasattr(scheduler, 'uses_inpainting_model'):
         scheduler.uses_inpainting_model = lambda: False
@@ -102,17 +120,13 @@ def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_c
     return x
 
 
-def random_seed():
-    return random.randint(0, np.iinfo(np.uint32).max)
-
-
 class NoiseInvocation(BaseInvocation):
     """Generates latent noise."""
 
     type: Literal["noise"] = "noise"
 
     # Inputs
-    seed:        int = Field(ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", default_factory=random_seed)
+    seed:       int = Field(ge=0, le=SEED_MAX, description="The seed to use", default_factory=get_random_seed)
     width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting noise", )
     height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting noise", )
 
@@ -131,9 +145,7 @@ class NoiseInvocation(BaseInvocation):
 
         name = f'{context.graph_execution_state_id}__{self.id}'
         context.services.latents.set(name, noise)
-        return NoiseOutput(
-            noise=LatentsField(latents_name=name)
-        )
+        return build_noise_output(latents_name=name, latents=noise)
 
 
 # Text to image
@@ -149,11 +161,10 @@ class TextToLatentsInvocation(BaseInvocation):
     noise: Optional[LatentsField] = Field(description="The noise to use")
     steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
     cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="lms", description="The scheduler to use" )
+    model:       str = Field(default="", description="The model to use (currently ignored)")
     seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
     seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
-    model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
     # fmt: on
 
     # Schema customisation
@@ -218,7 +229,7 @@ class TextToLatentsInvocation(BaseInvocation):
                 h_symmetry_time_pct=None,#h_symmetry_time_pct,
                 v_symmetry_time_pct=None#v_symmetry_time_pct,
             ),
-        ).add_scheduler_args_if_applicable(model.scheduler, eta=None)#ddim_eta)
+        ).add_scheduler_args_if_applicable(model.scheduler, eta=0.0)#ddim_eta)
         return conditioning_data
 
 
@@ -250,9 +261,7 @@ class TextToLatentsInvocation(BaseInvocation):
 
         name = f'{context.graph_execution_state_id}__{self.id}'
         context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
+        return build_latents_output(latents_name=name, latents=result_latents)
 
 
 class LatentsToLatentsInvocation(TextToLatentsInvocation):
@@ -260,6 +269,10 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
     type: Literal["l2l"] = "l2l"
 
+    # Inputs
+    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
+    strength: float = Field(default=0.5, description="The strength of the latents to use")
+
     # Schema customisation
     class Config(InvocationConfig):
         schema_extra = {
@@ -271,10 +284,6 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             },
         }
 
-    # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
-    strength: float = Field(default=0.5, description="The strength of the latents to use")
-
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         noise = context.services.latents.get(self.noise.latents_name)
         latent = context.services.latents.get(self.latents.latents_name)
@@ -287,7 +296,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             self.dispatch_progress(context, source_node_id, state)
 
         model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(model)
+        conditioning_data = self.get_conditioning_data(context, model)
 
         # TODO: Verify the noise is the right size
 
@@ -295,11 +304,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             latent, device=model.device, dtype=latent.dtype
         )
 
-        timesteps, _ = model.get_img2img_timesteps(
-            self.steps,
-            self.strength,
-            device=model.device,
-        )
+        timesteps, _ = model.get_img2img_timesteps(self.steps, self.strength)
 
         result_latents, result_attention_map_saver = model.latents_from_embeddings(
             latents=initial_latents,
@@ -315,9 +320,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
         name = f'{context.graph_execution_state_id}__{self.id}'
         context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
+        return build_latents_output(latents_name=name, latents=result_latents)
 
 
 # Latent to image
@@ -381,11 +384,11 @@ class ResizeLatentsInvocation(BaseInvocation):
     type: Literal["lresize"] = "lresize"
 
     # Inputs
-    latents: Optional[LatentsField]             = Field(description="The latents to resize")
-    width: int                                  = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
-    height: int                                 = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
-    mode: Optional[LATENTS_INTERPOLATION_MODE]  = Field(default="bilinear", description="The interpolation mode")
-    antialias: Optional[bool]                   = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+    latents:    Optional[LatentsField] = Field(description="The latents to resize")
+    width:                         int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
+    height:                        int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
+    mode:   LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
+    antialias:                    bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.services.latents.get(self.latents.latents_name)
@@ -402,7 +405,7 @@ class ResizeLatentsInvocation(BaseInvocation):
 
         name = f"{context.graph_execution_state_id}__{self.id}"
         context.services.latents.set(name, resized_latents)
-        return LatentsOutput(latents=LatentsField(latents_name=name))
+        return build_latents_output(latents_name=name, latents=resized_latents)
 
 
 class ScaleLatentsInvocation(BaseInvocation):
@@ -411,10 +414,10 @@ class ScaleLatentsInvocation(BaseInvocation):
     type: Literal["lscale"] = "lscale"
 
     # Inputs
-    latents: Optional[LatentsField]             = Field(description="The latents to scale")
-    scale_factor: float                         = Field(gt=0, description="The factor by which to scale the latents")
-    mode: Optional[LATENTS_INTERPOLATION_MODE]  = Field(default="bilinear", description="The interpolation mode")
-    antialias: Optional[bool]                   = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+    latents:   Optional[LatentsField] = Field(description="The latents to scale")
+    scale_factor:               float = Field(gt=0, description="The factor by which to scale the latents")
+    mode:  LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
+    antialias:                   bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.services.latents.get(self.latents.latents_name)
@@ -432,4 +435,48 @@ class ScaleLatentsInvocation(BaseInvocation):
 
         name = f"{context.graph_execution_state_id}__{self.id}"
         context.services.latents.set(name, resized_latents)
-        return LatentsOutput(latents=LatentsField(latents_name=name))
+        return build_latents_output(latents_name=name, latents=resized_latents)
+
+
+class ImageToLatentsInvocation(BaseInvocation):
+    """Encodes an image into latents."""
+
+    type: Literal["i2l"] = "i2l"
+
+    # Inputs
+    image: Union[ImageField, None] = Field(description="The image to encode")
+    model: str = Field(default="", description="The model to use")
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents", "image"],
+                "type_hints": {"model": "model"},
+            },
+        }
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+
+        # TODO: this only really needs the vae
+        model_info = choose_model(context.services.model_manager, self.model)
+        model: StableDiffusionGeneratorPipeline = model_info["model"]
+
+        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+
+        if image_tensor.dim() == 3:
+            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
+
+        latents = model.non_noised_latents_from_image(
+            image_tensor,
+            device=model._model_group.device_for(model.unet),
+            dtype=model.unet.dtype,
+        )
+
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.set(name, latents)
+        return build_latents_output(latents_name=name, latents=latents)

invokeai/app/invocations/math.py

@@ -3,8 +3,14 @@
 from typing import Literal
 
 from pydantic import BaseModel, Field
+import numpy as np
 
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    InvocationContext,
+    InvocationConfig,
+)
 
 
 class MathInvocationConfig(BaseModel):
@@ -21,19 +27,21 @@ class MathInvocationConfig(BaseModel):
 
 class IntOutput(BaseInvocationOutput):
     """An integer output"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["int_output"] = "int_output"
     a: int = Field(default=None, description="The output integer")
-    #fmt: on
+    # fmt: on
 
 
 class AddInvocation(BaseInvocation, MathInvocationConfig):
     """Adds two numbers"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["add"] = "add"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=self.a + self.b)
@@ -41,11 +49,12 @@ class AddInvocation(BaseInvocation, MathInvocationConfig):
 
 class SubtractInvocation(BaseInvocation, MathInvocationConfig):
     """Subtracts two numbers"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["sub"] = "sub"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=self.a - self.b)
@@ -53,11 +62,12 @@ class SubtractInvocation(BaseInvocation, MathInvocationConfig):
 
 class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
     """Multiplies two numbers"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["mul"] = "mul"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=self.a * self.b)
@@ -65,11 +75,26 @@ class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
 
 class DivideInvocation(BaseInvocation, MathInvocationConfig):
     """Divides two numbers"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["div"] = "div"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=int(self.a / self.b))
+
+
+class RandomIntInvocation(BaseInvocation):
+    """Outputs a single random integer."""
+
+    # fmt: off
+    type: Literal["rand_int"] = "rand_int"
+    low: int = Field(default=0, description="The inclusive low value")
+    high: int = Field(
+        default=np.iinfo(np.int32).max, description="The exclusive high value"
+    )
+    # fmt: on
+    def invoke(self, context: InvocationContext) -> IntOutput:
+        return IntOutput(a=np.random.randint(self.low, self.high))

invokeai/app/invocations/util/choose_model.py

@@ -4,10 +4,11 @@ from invokeai.backend.model_management.model_manager import ModelManager
 def choose_model(model_manager: ModelManager, model_name: str):
     """Returns the default model if the `model_name` not a valid model, else returns the selected model."""
     logger = model_manager.logger
-    if model_manager.valid_model(model_name):
-        model = model_manager.get_model(model_name)
-    else:
+    if model_name and not model_manager.valid_model(model_name):
+        default_model_name = model_manager.default_model()
+        logger.warning(f"\'{model_name}\' is not a valid model name. Using default model \'{default_model_name}\' instead.")
         model = model_manager.get_model()
-        logger.warning(f"{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead.")
+    else:
+        model = model_manager.get_model(model_name)
 
     return model

invokeai/app/models/image.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Optional
+from typing import Optional, Tuple
 from pydantic import BaseModel, Field
 
 
@@ -27,3 +27,13 @@ class ImageField(BaseModel):
 
     class Config:
         schema_extra = {"required": ["image_type", "image_name"]}
+
+
+class ColorField(BaseModel):
+    r: int = Field(ge=0, le=255, description="The red component")
+    g: int = Field(ge=0, le=255, description="The green component")
+    b: int = Field(ge=0, le=255, description="The blue component")
+    a: int = Field(ge=0, le=255, description="The alpha component")
+
+    def tuple(self) -> Tuple[int, int, int, int]:
+        return (self.r, self.g, self.b, self.a)

invokeai/app/services/default_graphs.py

@@ -48,13 +48,14 @@ def create_text_to_image() -> LibraryGraph:
 
 def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[LibraryGraph]:
     """Creates the default system graphs, or adds new versions if the old ones don't match"""
-
+    
+    # TODO: Uncomment this when we are ready to fix this up to prevent breaking changes
     graphs: list[LibraryGraph] = list()
 
-    text_to_image = graph_library.get(default_text_to_image_graph_id)
+    # text_to_image = graph_library.get(default_text_to_image_graph_id)
     
-    # TODO: Check if the graph is the same as the default one, and if not, update it
-    #if text_to_image is None:
+    # # TODO: Check if the graph is the same as the default one, and if not, update it
+    # #if text_to_image is None:
     text_to_image = create_text_to_image()
     graph_library.set(text_to_image)
 

invokeai/app/services/image_storage.py

@@ -270,4 +270,5 @@ class DiskImageStorage(ImageStorageBase):
             )  # TODO: this should refresh position for LRU cache
             if len(self.__cache) > self.__max_cache_size:
                 cache_id = self.__cache_ids.get()
-                del self.__cache[cache_id]
+                if cache_id in self.__cache:
+                    del self.__cache[cache_id]

invokeai/app/services/invocation_services.py

@@ -10,6 +10,7 @@ from .image_storage import ImageStorageBase
 from .restoration_services import RestorationServices
 from .invocation_queue import InvocationQueueABC
 from .item_storage import ItemStorageABC
+from invokeai.app.services.results import ResultsServiceABC
 
 class InvocationServices:
     """Services that can be used by invocations"""
@@ -21,6 +22,7 @@ class InvocationServices:
     queue: InvocationQueueABC
     model_manager: ModelManager
     restoration: RestorationServices
+    results: ResultsServiceABC
 
     # NOTE: we must forward-declare any types that include invocations, since invocations can use services
     graph_library: ItemStorageABC["LibraryGraph"]
@@ -36,6 +38,7 @@ class InvocationServices:
             images: ImageStorageBase,
             metadata: MetadataServiceBase,
             queue: InvocationQueueABC,
+            results: ResultsServiceABC,
             graph_library: ItemStorageABC["LibraryGraph"],
             graph_execution_manager: ItemStorageABC["GraphExecutionState"],
             processor: "InvocationProcessorABC",
@@ -48,6 +51,7 @@ class InvocationServices:
         self.images = images
         self.metadata = metadata
         self.queue = queue
+        self.results = results
         self.graph_library = graph_library
         self.graph_execution_manager = graph_execution_manager
         self.processor = processor

invokeai/app/services/metadata.py

@@ -20,9 +20,18 @@ class MetadataLatentsField(TypedDict):
     latents_name: str
 
 
+class MetadataColorField(TypedDict):
+    """Pydantic-less ColorField, used for metadata parsing"""
+    r: int
+    g: int
+    b: int
+    a: int
+
+
+
 # TODO: This is a placeholder for `InvocationsUnion` pending resolution of circular imports
 NodeMetadata = Dict[
-    str, str | int | float | bool | MetadataImageField | MetadataLatentsField
+    str, None | str | int | float | bool | MetadataImageField | MetadataLatentsField | MetadataColorField
 ]
 
 

invokeai/app/services/processor.py

@@ -1,3 +1,4 @@
+import time
 import traceback
 from threading import Event, Thread, BoundedSemaphore
 
@@ -6,6 +7,7 @@ from .invocation_queue import InvocationQueueItem
 from .invoker import InvocationProcessorABC, Invoker
 from ..models.exceptions import CanceledException
 
+import invokeai.backend.util.logging as logger
 class DefaultInvocationProcessor(InvocationProcessorABC):
     __invoker_thread: Thread
     __stop_event: Event
@@ -34,8 +36,14 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
         try:
             self.__threadLimit.acquire()
             while not stop_event.is_set():
-                queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
+                try:
+                    queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
+                except Exception as e:
+                    logger.debug("Exception while getting from queue: %s" % e)
+
                 if not queue_item:  # Probably stopping
+                    # do not hammer the queue
+                    time.sleep(0.5)
                     continue
 
                 graph_execution_state = (
@@ -124,7 +132,16 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                 # Queue any further commands if invoking all
                 is_complete = graph_execution_state.is_complete()
                 if queue_item.invoke_all and not is_complete:
-                    self.__invoker.invoke(graph_execution_state, invoke_all=True)
+                    try:
+                        self.__invoker.invoke(graph_execution_state, invoke_all=True)
+                    except Exception as e:
+                        logger.error("Error while invoking: %s" % e)
+                        self.__invoker.services.events.emit_invocation_error(
+                            graph_execution_state_id=graph_execution_state.id,
+                            node=invocation.dict(),
+                            source_node_id=source_node_id,
+                            error=traceback.format_exc()
+                        )
                 elif is_complete:
                     self.__invoker.services.events.emit_graph_execution_complete(
                         graph_execution_state.id

invokeai/app/services/results.py

@@ -0,0 +1,221 @@
+from __future__ import annotations
+from abc import ABC, abstractmethod
+import json
+import sqlite3
+from threading import Lock
+from typing import Union
+
+from pydantic import BaseModel, Field, parse_raw_as
+
+from invokeai.app.models.image import ImageField
+from invokeai.app.invocations.latent import LatentsField
+from invokeai.app.services.graph import GraphExecutionState
+from invokeai.app.services.item_storage import PaginatedResults
+
+
+class Result(BaseModel):
+    id: str = Field(description="Result ID")
+    session_id: str = Field(description="Session ID")
+    node_id: str = Field(description="Node ID")
+    data: Union[LatentsField, ImageField] = Field(description="The result data")
+
+
+class ResultWithSession(BaseModel):
+    result: Result = Field(description="The result")
+    session: GraphExecutionState = Field(description="The session")
+
+
+class ResultsServiceABC(ABC):
+    @abstractmethod
+    def get(self, output_id: str) -> str:
+        pass
+
+    @abstractmethod
+    def list(
+        self, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[ResultWithSession]:
+        pass
+
+    @abstractmethod
+    def search(
+        self, query: str, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[ResultWithSession]:
+        pass
+
+    @abstractmethod
+    def handle_graph_execution_state_change(self, session: GraphExecutionState) -> None:
+        pass
+
+
+class SqliteResultsService(ResultsServiceABC):
+    _filename: str
+    _conn: sqlite3.Connection
+    _cursor: sqlite3.Cursor
+    _lock: Lock
+
+    def __init__(self, filename: str):
+        super().__init__()
+
+        self._filename = filename
+        self._lock = Lock()
+
+        self._conn = sqlite3.connect(
+            self._filename, check_same_thread=False
+        )  # TODO: figure out a better threading solution
+        self._cursor = self._conn.cursor()
+
+        self._create_table()
+
+    def _create_table(self):
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                """
+                CREATE TABLE IF NOT EXISTS results (
+                  id TEXT PRIMARY KEY,
+                  node_id TEXT,
+                  session_id TEXT,
+                  data TEXT
+                );
+                """
+            )
+            self._cursor.execute(
+                """CREATE UNIQUE INDEX IF NOT EXISTS result_id ON result(id);"""
+            )
+        finally:
+            self._lock.release()
+
+    def get(self, id: str) -> Union[ResultWithSession, None]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                """
+                SELECT results.data, graph_executions.state
+                FROM results
+                JOIN graph_executions ON results.session_id = graph_executions.id
+                WHERE results.id = ?
+                """,
+                (id,),
+            )
+
+            result_row = self._cursor.fetchone()
+
+            if result_row is None:
+                return None
+
+            result_raw, graph_execution_state_raw = result_row
+            result = parse_raw_as(Result, result_raw)
+            graph_execution_state = parse_raw_as(
+                GraphExecutionState, graph_execution_state_raw
+            )
+        finally:
+            self._lock.release()
+
+        if not result:
+            return None
+
+        return ResultWithSession(result=result, session=graph_execution_state)
+
+    def list(
+        self, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[ResultWithSession]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                """
+                SELECT results.data, graph_executions.state
+                FROM results
+                JOIN graph_executions ON results.session_id = graph_executions.id
+                LIMIT ? OFFSET ?;
+                """,
+                (per_page, page * per_page),
+            )
+
+            result_rows = self._cursor.fetchall()
+
+            items = list(
+                map(
+                    lambda r: ResultWithSession(
+                        result=parse_raw_as(Result, r[0]),
+                        session=parse_raw_as(GraphExecutionState, r[1]),
+                    ),
+                    result_rows,
+                )
+            )
+
+            self._cursor.execute("""SELECT count(*) FROM results;""")
+            count = self._cursor.fetchone()[0]
+        finally:
+            self._lock.release()
+
+        pageCount = int(count / per_page) + 1
+
+        return PaginatedResults[ResultWithSession](
+            items=items, page=page, pages=pageCount, per_page=per_page, total=count
+        )
+
+    def search(
+        self, query: str, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[ResultWithSession]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                """
+                SELECT results.data, graph_executions.state
+                FROM results
+                JOIN graph_executions ON results.session_id = graph_executions.id
+                WHERE item LIKE ?
+                LIMIT ? OFFSET ?;
+                """,
+                (f"%{query}%", per_page, page * per_page),
+            )
+
+            result_rows = self._cursor.fetchall()
+
+            items = list(
+                map(
+                    lambda r: ResultWithSession(
+                        result=parse_raw_as(Result, r[0]),
+                        session=parse_raw_as(GraphExecutionState, r[1]),
+                    ),
+                    result_rows,
+                )
+            )
+            self._cursor.execute(
+                f"""SELECT count(*) FROM results WHERE item LIKE ?;""",
+                (f"%{query}%",),
+            )
+            count = self._cursor.fetchone()[0]
+        finally:
+            self._lock.release()
+
+        pageCount = int(count / per_page) + 1
+
+        return PaginatedResults[ResultWithSession](
+            items=items, page=page, pages=pageCount, per_page=per_page, total=count
+        )
+
+    def handle_graph_execution_state_change(self, session: GraphExecutionState) -> None:
+        with self._conn as conn:
+            for node_id, result in session.results.items():
+                # We'll only process 'image_output' or 'latents_output'
+                if result["type"] not in ["image_output", "latents_output"]:
+                    continue
+
+                # The id depends on the result type
+                if result["type"] == "image_output":
+                    id = result["image"]["image_name"]
+                else:  # 'latents_output'
+                    id = result["latents"]["latents_name"]
+
+                # Stringify the entire result object for the data column
+                data = json.dumps(result)
+
+                # Insert the result into the results table, ignoring if it already exists
+                conn.execute(
+                    """
+                    INSERT OR IGNORE INTO results (id, node_id, session_id, data)
+                    VALUES (?, ?, ?, ?)
+                """,
+                    (id, node_id, session.id, data),
+                )

invokeai/app/util/misc.py

@@ -1,5 +1,13 @@
 import datetime
+import numpy as np
 
 
 def get_timestamp():
     return int(datetime.datetime.now(datetime.timezone.utc).timestamp())
+
+
+SEED_MAX = np.iinfo(np.int32).max
+
+
+def get_random_seed():
+    return np.random.randint(0, SEED_MAX)

invokeai/backend/args.py

@@ -108,17 +108,21 @@ APP_VERSION = invokeai.version.__version__
 
 SAMPLER_CHOICES = [
     "ddim",
-    "k_dpm_2_a",
-    "k_dpm_2",
-    "k_dpmpp_2_a",
-    "k_dpmpp_2",
-    "k_euler_a",
-    "k_euler",
-    "k_heun",
-    "k_lms",
-    "plms",
-    # diffusers:
+    "ddpm",
+    "deis",
+    "lms",
     "pndm",
+    "heun",
+    "heun_k",
+    "euler",
+    "euler_k",
+    "euler_a",
+    "kdpm_2",
+    "kdpm_2_a",
+    "dpmpp_2s",
+    "dpmpp_2m",
+    "dpmpp_2m_k",
+    "unipc",
 ]
 
 PRECISION_CHOICES = [
@@ -631,7 +635,7 @@ class Args(object):
             choices=SAMPLER_CHOICES,
             metavar="SAMPLER_NAME",
             help=f'Set the default sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
-            default="k_lms",
+            default="lms",
         )
         render_group.add_argument(
             "--log_tokenization",

invokeai/backend/generate.py

@@ -37,6 +37,7 @@ from .safety_checker import SafetyChecker
 from .prompting import get_uc_and_c_and_ec
 from .prompting.conditioning import log_tokenization
 from .stable_diffusion import HuggingFaceConceptsLibrary
+from .stable_diffusion.schedulers import SCHEDULER_MAP
 from .util import choose_precision, choose_torch_device
 
 def fix_func(orig):
@@ -141,7 +142,7 @@ class Generate:
         model=None,
         conf="configs/models.yaml",
         embedding_path=None,
-        sampler_name="k_lms",
+        sampler_name="lms",
         ddim_eta=0.0,  # deterministic
         full_precision=False,
         precision="auto",
@@ -1047,29 +1048,12 @@ class Generate:
     def _set_scheduler(self):
         default = self.model.scheduler
 
-        # See https://github.com/huggingface/diffusers/issues/277#issuecomment-1371428672
-        scheduler_map = dict(
-            ddim=diffusers.DDIMScheduler,
-            dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-            k_dpm_2=diffusers.KDPM2DiscreteScheduler,
-            k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
-            # DPMSolverMultistepScheduler is technically not `k_` anything, as it is neither
-            # the k-diffusers implementation nor included in EDM (Karras 2022), but we can
-            # provide an alias for compatibility.
-            k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-            k_euler=diffusers.EulerDiscreteScheduler,
-            k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
-            k_heun=diffusers.HeunDiscreteScheduler,
-            k_lms=diffusers.LMSDiscreteScheduler,
-            plms=diffusers.PNDMScheduler,
-        )
-
-        if self.sampler_name in scheduler_map:
-            sampler_class = scheduler_map[self.sampler_name]
+        if self.sampler_name in SCHEDULER_MAP:
+            sampler_class, sampler_extra_config = SCHEDULER_MAP[self.sampler_name]
             msg = (
                 f"Setting Sampler to {self.sampler_name} ({sampler_class.__name__})"
             )
-            self.sampler = sampler_class.from_config(self.model.scheduler.config)
+            self.sampler = sampler_class.from_config({**self.model.scheduler.config, **sampler_extra_config})
         else:
             msg = (
                 f" Unsupported Sampler: {self.sampler_name} "+

invokeai/backend/generator/base.py

@@ -31,6 +31,7 @@ from ..util.util import rand_perlin_2d
 from ..safety_checker import SafetyChecker
 from ..prompting.conditioning import get_uc_and_c_and_ec
 from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
+from ..stable_diffusion.schedulers import SCHEDULER_MAP
 
 downsampling = 8
 
@@ -71,19 +72,6 @@ class InvokeAIGeneratorOutput:
 # we are interposing a wrapper around the original Generator classes so that
 # old code that calls Generate will continue to work.
 class InvokeAIGenerator(metaclass=ABCMeta):
-    scheduler_map = dict(
-        ddim=diffusers.DDIMScheduler,
-        dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-        k_dpm_2=diffusers.KDPM2DiscreteScheduler,
-        k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
-        k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-        k_euler=diffusers.EulerDiscreteScheduler,
-        k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
-        k_heun=diffusers.HeunDiscreteScheduler,
-        k_lms=diffusers.LMSDiscreteScheduler,
-        plms=diffusers.PNDMScheduler,
-    )
-
     def __init__(self,
                  model_info: dict,
                  params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
@@ -175,14 +163,20 @@ class InvokeAIGenerator(metaclass=ABCMeta):
         '''
         Return list of all the schedulers that we currently handle.
         '''
-        return list(self.scheduler_map.keys())
+        return list(SCHEDULER_MAP.keys())
 
     def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]):
         return generator_class(model, self.params.precision)
 
     def get_scheduler(self, scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-        scheduler_class = self.scheduler_map.get(scheduler_name,'ddim')
-        scheduler = scheduler_class.from_config(model.scheduler.config)
+        scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
+        
+        scheduler_config = model.scheduler.config
+        if "_backup" in scheduler_config:
+            scheduler_config = scheduler_config["_backup"]
+        scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
+        scheduler = scheduler_class.from_config(scheduler_config)
+        
         # hack copied over from generate.py
         if not hasattr(scheduler, 'uses_inpainting_model'):
             scheduler.uses_inpainting_model = lambda: False
@@ -226,10 +220,10 @@ class Inpaint(Img2Img):
     def generate(self,
                  mask_image: Image.Image | torch.FloatTensor,
                  # Seam settings - when 0, doesn't fill seam
-                 seam_size: int = 0,
-                 seam_blur: int = 0,
+                 seam_size: int = 96,
+                 seam_blur: int = 16,
                  seam_strength: float = 0.7,
-                 seam_steps: int = 10,
+                 seam_steps: int = 30,
                  tile_size: int = 32,
                  inpaint_replace=False,
                  infill_method=None,

invokeai/backend/generator/inpaint.py

@@ -4,6 +4,7 @@ invokeai.backend.generator.inpaint descends from .generator
 from __future__ import annotations
 
 import math
+from typing import Tuple, Union
 
 import cv2
 import numpy as np
@@ -59,7 +60,7 @@ class Inpaint(Img2Img):
             writeable=False,
         )
 
-    def infill_patchmatch(self, im: Image.Image) -> Image:
+    def infill_patchmatch(self, im: Image.Image) -> Image.Image:
         if im.mode != "RGBA":
             return im
 
@@ -75,18 +76,18 @@ class Inpaint(Img2Img):
         return im_patched
 
     def tile_fill_missing(
-        self, im: Image.Image, tile_size: int = 16, seed: int = None
-    ) -> Image:
+        self, im: Image.Image, tile_size: int = 16, seed: Union[int, None] = None
+    ) -> Image.Image:
         # Only fill if there's an alpha layer
         if im.mode != "RGBA":
             return im
 
         a = np.asarray(im, dtype=np.uint8)
 
-        tile_size = (tile_size, tile_size)
+        tile_size_tuple = (tile_size, tile_size)
 
         # Get the image as tiles of a specified size
-        tiles = self.get_tile_images(a, *tile_size).copy()
+        tiles = self.get_tile_images(a, *tile_size_tuple).copy()
 
         # Get the mask as tiles
         tiles_mask = tiles[:, :, :, :, 3]
@@ -127,7 +128,9 @@ class Inpaint(Img2Img):
 
         return si
 
-    def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
+    def mask_edge(
+        self, mask: Image.Image, edge_size: int, edge_blur: int
+    ) -> Image.Image:
         npimg = np.asarray(mask, dtype=np.uint8)
 
         # Detect any partially transparent regions
@@ -206,15 +209,15 @@ class Inpaint(Img2Img):
         cfg_scale,
         ddim_eta,
         conditioning,
-        init_image: PIL.Image.Image | torch.FloatTensor,
-        mask_image: PIL.Image.Image | torch.FloatTensor,
+        init_image: Image.Image | torch.FloatTensor,
+        mask_image: Image.Image | torch.FloatTensor,
         strength: float,
         mask_blur_radius: int = 8,
         # Seam settings - when 0, doesn't fill seam
-        seam_size: int = 0,
-        seam_blur: int = 0,
+        seam_size: int = 96,
+        seam_blur: int = 16,
         seam_strength: float = 0.7,
-        seam_steps: int = 10,
+        seam_steps: int = 30,
         tile_size: int = 32,
         step_callback=None,
         inpaint_replace=False,
@@ -222,7 +225,7 @@ class Inpaint(Img2Img):
         infill_method=None,
         inpaint_width=None,
         inpaint_height=None,
-        inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
+        inpaint_fill: Tuple[int, int, int, int] = (0x7F, 0x7F, 0x7F, 0xFF),
         attention_maps_callback=None,
         **kwargs,
     ):
@@ -239,7 +242,7 @@ class Inpaint(Img2Img):
         self.inpaint_width = inpaint_width
         self.inpaint_height = inpaint_height
 
-        if isinstance(init_image, PIL.Image.Image):
+        if isinstance(init_image, Image.Image):
             self.pil_image = init_image.copy()
 
             # Do infill
@@ -250,8 +253,8 @@ class Inpaint(Img2Img):
                     self.pil_image.copy(), seed=self.seed, tile_size=tile_size
                 )
             elif infill_method == "solid":
-                solid_bg = PIL.Image.new("RGBA", init_image.size, inpaint_fill)
-                init_filled = PIL.Image.alpha_composite(solid_bg, init_image)
+                solid_bg = Image.new("RGBA", init_image.size, inpaint_fill)
+                init_filled = Image.alpha_composite(solid_bg, init_image)
             else:
                 raise ValueError(
                     f"Non-supported infill type {infill_method}", infill_method
@@ -269,7 +272,7 @@ class Inpaint(Img2Img):
             # Create init tensor
             init_image = image_resized_to_grid_as_tensor(init_filled.convert("RGB"))
 
-        if isinstance(mask_image, PIL.Image.Image):
+        if isinstance(mask_image, Image.Image):
             self.pil_mask = mask_image.copy()
             debug_image(
                 mask_image,

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -47,6 +47,7 @@ from diffusers import (
     LDMTextToImagePipeline,
     LMSDiscreteScheduler,
     PNDMScheduler,
+    UniPCMultistepScheduler,
     StableDiffusionPipeline,
     UNet2DConditionModel,
 )
@@ -1209,6 +1210,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
             scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
         elif scheduler_type == "dpm":
             scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
+        elif scheduler_type == 'unipc':
+            scheduler = UniPCMultistepScheduler.from_config(scheduler.config)
         elif scheduler_type == "ddim":
             scheduler = scheduler
         else:

invokeai/backend/model_management/model_manager.py

@@ -30,7 +30,7 @@ from diffusers import (
     UNet2DConditionModel,
     SchedulerMixin,
     logging as dlogging,
-)    
+)
 from huggingface_hub import scan_cache_dir
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
@@ -68,7 +68,7 @@ class SDModelComponent(Enum):
     scheduler="scheduler"
     safety_checker="safety_checker"
     feature_extractor="feature_extractor"
-    
+
 DEFAULT_MAX_MODELS = 2
 
 class ModelManager(object):
@@ -182,7 +182,7 @@ class ModelManager(object):
         vae from the model currently in the GPU.
         """
         return self._get_sub_model(model_name, SDModelComponent.vae)
-                                   
+
     def get_model_tokenizer(self, model_name: str=None)->CLIPTokenizer:
         """Given a model name identified in models.yaml, load the model into
         GPU if necessary and return its assigned CLIPTokenizer. If no
@@ -190,12 +190,12 @@ class ModelManager(object):
         currently in the GPU.
         """
         return self._get_sub_model(model_name, SDModelComponent.tokenizer)
-                                   
+
     def get_model_unet(self, model_name: str=None)->UNet2DConditionModel:
         """Given a model name identified in models.yaml, load the model into
         GPU if necessary and return its assigned UNet2DConditionModel. If no model
         name is provided, return the UNet from the model
-        currently in the GPU.  
+        currently in the GPU.
         """
         return self._get_sub_model(model_name, SDModelComponent.unet)
 
@@ -222,7 +222,7 @@ class ModelManager(object):
         currently in the GPU.
         """
         return self._get_sub_model(model_name, SDModelComponent.scheduler)
-    
+
     def _get_sub_model(
             self,
             model_name: str=None,
@@ -1228,7 +1228,7 @@ class ModelManager(object):
                         sha.update(chunk)
         hash = sha.hexdigest()
         toc = time.time()
-        self.logger.debug(f"sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
+        self.logger.debug(f"sha256 = {hash} ({count} files hashed in {toc - tic:4.2f}s)")
         with open(hashpath, "w") as f:
             f.write(hash)
         return hash

invokeai/backend/prompting/conditioning.py

@@ -16,6 +16,7 @@ from compel.prompt_parser import (
     FlattenedPrompt,
     Fragment,
     PromptParser,
+    Conjunction,
 )
 
 import invokeai.backend.util.logging as logger
@@ -25,58 +26,48 @@ from ..stable_diffusion import InvokeAIDiffuserComponent
 from ..util import torch_dtype
 
 
-def get_uc_and_c_and_ec(
-    prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False
-):
+def get_uc_and_c_and_ec(prompt_string,
+                        model: InvokeAIDiffuserComponent,
+                        log_tokens=False, skip_normalize_legacy_blend=False):
     # lazy-load any deferred textual inversions.
     # this might take a couple of seconds the first time a textual inversion is used.
-    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
-        prompt_string
-    )
+    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(prompt_string)
 
-    tokenizer = model.tokenizer
-    compel = Compel(
-        tokenizer=tokenizer,
-        text_encoder=model.text_encoder,
-        textual_inversion_manager=model.textual_inversion_manager,
-        dtype_for_device_getter=torch_dtype,
-        truncate_long_prompts=False
-    )
+    compel = Compel(tokenizer=model.tokenizer,
+                    text_encoder=model.text_encoder,
+                    textual_inversion_manager=model.textual_inversion_manager,
+                    dtype_for_device_getter=torch_dtype,
+                    truncate_long_prompts=False,
+                    )
 
     # get rid of any newline characters
     prompt_string = prompt_string.replace("\n", " ")
-    (
-        positive_prompt_string,
-        negative_prompt_string,
-    ) = split_prompt_to_positive_and_negative(prompt_string)
-    legacy_blend = try_parse_legacy_blend(
-        positive_prompt_string, skip_normalize_legacy_blend
-    )
-    positive_prompt: Union[FlattenedPrompt, Blend]
-    if legacy_blend is not None:
-        positive_prompt = legacy_blend
-    else:
-        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
-        negative_prompt_string
-    )
+    positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
 
+    legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
+    positive_conjunction: Conjunction
+    if legacy_blend is not None:
+        positive_conjunction = legacy_blend
+    else:
+        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
+    positive_prompt = positive_conjunction.prompts[0]
+
+    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
+    negative_prompt: FlattenedPrompt | Blend = negative_conjunction.prompts[0]
+
+    tokens_count = get_max_token_count(model.tokenizer, positive_prompt)
     if log_tokens or getattr(Globals, "log_tokenization", False):
-        log_tokenization(positive_prompt, negative_prompt, tokenizer=tokenizer)
+        log_tokenization(positive_prompt, negative_prompt, tokenizer=model.tokenizer)
 
     c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
     uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
     [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
 
-    tokens_count = get_max_token_count(tokenizer, positive_prompt)
-
-    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
-        tokens_count_including_eos_bos=tokens_count,
-        cross_attention_control_args=options.get("cross_attention_control", None),
-    )
+    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,
+                                                         cross_attention_control_args=options.get(
+                                                             'cross_attention_control', None))
     return uc, c, ec
 
-
 def get_prompt_structure(
     prompt_string, skip_normalize_legacy_blend: bool = False
 ) -> (Union[FlattenedPrompt, Blend], FlattenedPrompt):
@@ -87,18 +78,17 @@ def get_prompt_structure(
     legacy_blend = try_parse_legacy_blend(
         positive_prompt_string, skip_normalize_legacy_blend
     )
-    positive_prompt: Union[FlattenedPrompt, Blend]
+    positive_prompt: Conjunction
     if legacy_blend is not None:
-        positive_prompt = legacy_blend
+        positive_conjunction = legacy_blend
     else:
-        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
-        negative_prompt_string
-    )
+        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
+    positive_prompt = positive_conjunction.prompts[0]
+    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
+    negative_prompt: FlattenedPrompt|Blend = negative_conjunction.prompts[0]
 
     return positive_prompt, negative_prompt
 
-
 def get_max_token_count(
     tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
 ) -> int:
@@ -245,22 +235,21 @@ def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_t
         logger.info(f"[TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
         logger.debug(f"{discarded}\x1b[0m")
 
-
-def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Blend]:
+def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Conjunction]:
     weighted_subprompts = split_weighted_subprompts(text, skip_normalize=skip_normalize)
     if len(weighted_subprompts) <= 1:
         return None
     strings = [x[0] for x in weighted_subprompts]
-    weights = [x[1] for x in weighted_subprompts]
 
     pp = PromptParser()
     parsed_conjunctions = [pp.parse_conjunction(x) for x in strings]
-    flattened_prompts = [x.prompts[0] for x in parsed_conjunctions]
-
-    return Blend(
-        prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize
-    )
-
+    flattened_prompts = []
+    weights = []
+    for i, x in enumerate(parsed_conjunctions):
+        if len(x.prompts)>0:
+            flattened_prompts.append(x.prompts[0])
+            weights.append(weighted_subprompts[i][1])
+    return Conjunction([Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)])
 
 def split_weighted_subprompts(text, skip_normalize=False) -> list:
     """

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -509,10 +509,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         run_id=None,
         callback: Callable[[PipelineIntermediateState], None] = None,
     ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
+        if self.scheduler.config.get("cpu_only", False):
+            scheduler_device = torch.device('cpu')
+        else:
+            scheduler_device = self._model_group.device_for(self.unet)
+
         if timesteps is None:
-            self.scheduler.set_timesteps(
-                num_inference_steps, device=self._model_group.device_for(self.unet)
-            )
+            self.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
             timesteps = self.scheduler.timesteps
         infer_latents_from_embeddings = GeneratorToCallbackinator(
             self.generate_latents_from_embeddings, PipelineIntermediateState
@@ -545,8 +548,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             additional_guidance = []
         extra_conditioning_info = conditioning_data.extra
         with self.invokeai_diffuser.custom_attention_context(
-            extra_conditioning_info=extra_conditioning_info,
-            step_count=len(self.scheduler.timesteps),
+                self.invokeai_diffuser.model,
+                extra_conditioning_info=extra_conditioning_info,
+                step_count=len(self.scheduler.timesteps),
         ):
             yield PipelineIntermediateState(
                 run_id=run_id,
@@ -725,12 +729,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         noise: torch.Tensor,
         run_id=None,
         callback=None,
-    ) -> InvokeAIStableDiffusionPipelineOutput:
-        timesteps, _ = self.get_img2img_timesteps(
-            num_inference_steps,
-            strength,
-            device=self._model_group.device_for(self.unet),
-        )
+    ) -> InvokeAIStableDiffusionPipelineOutput:        
+        timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
         result_latents, result_attention_maps = self.latents_from_embeddings(
             latents=initial_latents if strength < 1.0 else torch.zeros_like(
                 initial_latents, device=initial_latents.device, dtype=initial_latents.dtype
@@ -756,13 +756,19 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
     def get_img2img_timesteps(
-        self, num_inference_steps: int, strength: float, device
+        self, num_inference_steps: int, strength: float, device=None
     ) -> (torch.Tensor, int):
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         assert img2img_pipeline.scheduler is self.scheduler
-        img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
+
+        if self.scheduler.config.get("cpu_only", False):
+            scheduler_device = torch.device('cpu')
+        else:
+            scheduler_device = self._model_group.device_for(self.unet)
+
+        img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
         timesteps, adjusted_steps = img2img_pipeline.get_timesteps(
-            num_inference_steps, strength, device=device
+            num_inference_steps, strength, device=scheduler_device
         )
         # Workaround for low strength resulting in zero timesteps.
         # TODO: submit upstream fix for zero-step img2img
@@ -796,9 +802,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if init_image.dim() == 3:
             init_image = init_image.unsqueeze(0)
 
-        timesteps, _ = self.get_img2img_timesteps(
-            num_inference_steps, strength, device=device
-        )
+        timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
 
         # 6. Prepare latent variables
         # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -10,6 +10,7 @@ import diffusers
 import psutil
 import torch
 from compel.cross_attention_control import Arguments
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from diffusers.models.attention_processor import AttentionProcessor
 from torch import nn
 
@@ -352,8 +353,7 @@ def restore_default_cross_attention(
     else:
         remove_attention_function(model)
 
-
-def override_cross_attention(model, context: Context, is_running_diffusers=False):
+def setup_cross_attention_control_attention_processors(unet: UNet2DConditionModel, context: Context):
     """
     Inject attention parameters and functions into the passed in model to enable cross attention editing.
 
@@ -372,37 +372,22 @@ def override_cross_attention(model, context: Context, is_running_diffusers=False
     indices = torch.arange(max_length, dtype=torch.long)
     for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
         if b0 < max_length:
-            if name == "equal":  # or (name == "replace" and a1 - a0 == b1 - b0):
+            if name == "equal":# or (name == "replace" and a1 - a0 == b1 - b0):
                 # these tokens have not been edited
                 indices[b0:b1] = indices_target[a0:a1]
                 mask[b0:b1] = 1
 
     context.cross_attention_mask = mask.to(device)
     context.cross_attention_index_map = indices.to(device)
-    if is_running_diffusers:
-        unet = model
-        old_attn_processors = unet.attn_processors
-        if torch.backends.mps.is_available():
-            # see note in StableDiffusionGeneratorPipeline.__init__ about borked slicing on MPS
-            unet.set_attn_processor(SwapCrossAttnProcessor())
-        else:
-            # try to re-use an existing slice size
-            default_slice_size = 4
-            slice_size = next(
-                (
-                    p.slice_size
-                    for p in old_attn_processors.values()
-                    if type(p) is SlicedAttnProcessor
-                ),
-                default_slice_size,
-            )
-            unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
-        return old_attn_processors
+    old_attn_processors = unet.attn_processors
+    if torch.backends.mps.is_available():
+        # see note in StableDiffusionGeneratorPipeline.__init__ about borked slicing on MPS
+        unet.set_attn_processor(SwapCrossAttnProcessor())
     else:
-        context.register_cross_attention_modules(model)
-        inject_attention_function(model, context)
-        return None
-
+        # try to re-use an existing slice size
+        default_slice_size = 4
+        slice_size = next((p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size)
+        unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
 
 def get_cross_attention_modules(
     model, which: CrossAttentionType

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -5,6 +5,7 @@ from typing import Any, Callable, Dict, Optional, Union
 
 import numpy as np
 import torch
+from diffusers import UNet2DConditionModel
 from diffusers.models.attention_processor import AttentionProcessor
 from typing_extensions import TypeAlias
 
@@ -17,8 +18,8 @@ from .cross_attention_control import (
     CrossAttentionType,
     SwapCrossAttnContext,
     get_cross_attention_modules,
-    override_cross_attention,
     restore_default_cross_attention,
+    setup_cross_attention_control_attention_processors,
 )
 from .cross_attention_map_saving import AttentionMapSaver
 
@@ -79,24 +80,35 @@ class InvokeAIDiffuserComponent:
         self.cross_attention_control_context = None
         self.sequential_guidance = Globals.sequential_guidance
 
+    @classmethod
     @contextmanager
     def custom_attention_context(
-        self, extra_conditioning_info: Optional[ExtraConditioningInfo], step_count: int
+        cls,
+        unet: UNet2DConditionModel, # note: also may futz with the text encoder depending on requested LoRAs
+        extra_conditioning_info: Optional[ExtraConditioningInfo],
+        step_count: int
     ):
-        do_swap = (
-            extra_conditioning_info is not None
-            and extra_conditioning_info.wants_cross_attention_control
-        )
-        old_attn_processor = None
-        if do_swap:
-            old_attn_processor = self.override_cross_attention(
-                extra_conditioning_info, step_count=step_count
-            )
+        old_attn_processors = None
+        if extra_conditioning_info and (
+            extra_conditioning_info.wants_cross_attention_control
+        ):
+            old_attn_processors = unet.attn_processors
+            # Load lora conditions into the model
+            if extra_conditioning_info.wants_cross_attention_control:
+                cross_attention_control_context = Context(
+                    arguments=extra_conditioning_info.cross_attention_control_args,
+                    step_count=step_count,
+                )
+                setup_cross_attention_control_attention_processors(
+                    unet,
+                    cross_attention_control_context,
+                )
+
         try:
             yield None
         finally:
-            if old_attn_processor is not None:
-                self.restore_default_cross_attention(old_attn_processor)
+            if old_attn_processors is not None:
+                unet.set_attn_processor(old_attn_processors)
             # TODO resuscitate attention map saving
             # self.remove_attention_map_saving()
 

invokeai/backend/stable_diffusion/schedulers/__init__.py

@@ -0,0 +1 @@
+from .schedulers import SCHEDULER_MAP

invokeai/backend/stable_diffusion/schedulers/schedulers.py

@@ -0,0 +1,23 @@
+from diffusers import DDIMScheduler, DPMSolverMultistepScheduler, KDPM2DiscreteScheduler, \
+    KDPM2AncestralDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, \
+    HeunDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, UniPCMultistepScheduler, \
+    DPMSolverSinglestepScheduler, DEISMultistepScheduler, DDPMScheduler
+
+SCHEDULER_MAP = dict(
+    ddim=(DDIMScheduler, dict()),
+    ddpm=(DDPMScheduler, dict()),
+    deis=(DEISMultistepScheduler, dict()),
+    lms=(LMSDiscreteScheduler, dict()),
+    pndm=(PNDMScheduler, dict()),
+    heun=(HeunDiscreteScheduler, dict(use_karras_sigmas=False)),
+    heun_k=(HeunDiscreteScheduler, dict(use_karras_sigmas=True)),
+    euler=(EulerDiscreteScheduler, dict(use_karras_sigmas=False)),
+    euler_k=(EulerDiscreteScheduler, dict(use_karras_sigmas=True)),
+    euler_a=(EulerAncestralDiscreteScheduler, dict()),
+    kdpm_2=(KDPM2DiscreteScheduler, dict()),
+    kdpm_2_a=(KDPM2AncestralDiscreteScheduler, dict()),
+    dpmpp_2s=(DPMSolverSinglestepScheduler, dict()),
+    dpmpp_2m=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=False)),
+    dpmpp_2m_k=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=True)),
+    unipc=(UniPCMultistepScheduler, dict(cpu_only=True))
+)

invokeai/backend/util/logging.py

@@ -2,34 +2,37 @@
 
 """invokeai.util.logging
 
-Logging class for InvokeAI that produces console messages that follow
-the conventions established in InvokeAI 1.X through 2.X.
+Logging class for InvokeAI that produces console messages
 
-
-One way to use it:
+Usage:
 
 from invokeai.backend.util.logging import InvokeAILogger
 
-logger = InvokeAILogger.getLogger(__name__)
-logger.critical('this is critical')
-logger.error('this is an error')
-logger.warning('this is a warning')
-logger.info('this is info')
-logger.debug('this is debugging')
+logger = InvokeAILogger.getLogger(name='InvokeAI') // Initialization
+(or)
+logger = InvokeAILogger.getLogger(__name__) // To use the filename
+
+logger.critical('this is critical') // Critical Message
+logger.error('this is an error') // Error Message
+logger.warning('this is a warning') // Warning Message
+logger.info('this is info') // Info Message
+logger.debug('this is debugging') // Debug Message
 
 Console messages:
-     ### this is critical
-     *** this is an error ***
-     ** this is a warning
-     >> this is info
-        | this is debugging
+    [12-05-2023 20]::[InvokeAI]::CRITICAL --> This is an info message [In Bold Red]
+    [12-05-2023 20]::[InvokeAI]::ERROR --> This is an info message [In Red]
+    [12-05-2023 20]::[InvokeAI]::WARNING --> This is an info message [In Yellow]
+    [12-05-2023 20]::[InvokeAI]::INFO --> This is an info message [In Grey]
+    [12-05-2023 20]::[InvokeAI]::DEBUG --> This is an info message [In Grey]
 
-Another way:
-import invokeai.backend.util.logging as ialog
-ialogger.debug('this is a debugging message')
+Alternate Method (in this case the logger name will be set to InvokeAI):
+import invokeai.backend.util.logging as IAILogger
+IAILogger.debug('this is a debugging message')
 """
+
 import logging
 
+
 # module level functions
 def debug(msg, *args, **kwargs):
     InvokeAILogger.getLogger().debug(msg, *args, **kwargs)
@@ -42,7 +45,7 @@ def warning(msg, *args, **kwargs):
 
 def error(msg, *args, **kwargs):
     InvokeAILogger.getLogger().error(msg, *args, **kwargs)
-    
+
 def critical(msg, *args, **kwargs):
     InvokeAILogger.getLogger().critical(msg, *args, **kwargs)
 
@@ -55,49 +58,47 @@ def disable(level=logging.CRITICAL):
 def basicConfig(**kwargs):
     InvokeAILogger.getLogger().basicConfig(**kwargs)
 
-def getLogger(name: str=None)->logging.Logger:
+def getLogger(name: str = None) -> logging.Logger:
     return InvokeAILogger.getLogger(name)
 
+
 class InvokeAILogFormatter(logging.Formatter):
     '''
-    Repurposed from:
-    https://stackoverflow.com/questions/14844970/modifying-logging-message-format-based-on-message-logging-level-in-python3
+    Custom Formatting for the InvokeAI Logger
     '''
-    crit_fmt = "### %(msg)s"
-    err_fmt = "*** %(msg)s"
-    warn_fmt = "** %(msg)s"
-    info_fmt = ">> %(msg)s"
-    dbg_fmt = "   | %(msg)s"
 
-    def __init__(self):
-        super().__init__(fmt="%(levelno)d: %(msg)s", datefmt=None, style='%')
+    # Color Codes
+    grey = "\x1b[38;20m"
+    yellow = "\x1b[33;20m"
+    red = "\x1b[31;20m"
+    cyan = "\x1b[36;20m"
+    bold_red = "\x1b[31;1m"
+    reset = "\x1b[0m"
+
+    # Log Format
+    format = "[%(asctime)s]::[%(name)s]::%(levelname)s --> %(message)s"
+    ## More Formatting Options: %(pathname)s, %(filename)s, %(module)s, %(lineno)d
+
+    # Format Map
+    FORMATS = {
+        logging.DEBUG: cyan + format + reset,
+        logging.INFO: grey + format + reset,
+        logging.WARNING: yellow + format + reset,
+        logging.ERROR: red + format + reset,
+        logging.CRITICAL: bold_red + format + reset
+    }
 
     def format(self, record):
-        # Remember the format used when the logging module
-        # was installed (in the event that this formatter is
-        # used with the vanilla logging module.
-        format_orig = self._style._fmt
-        if record.levelno == logging.DEBUG:
-            self._style._fmt = InvokeAILogFormatter.dbg_fmt
-        if record.levelno == logging.INFO:
-            self._style._fmt = InvokeAILogFormatter.info_fmt
-        if record.levelno == logging.WARNING:
-            self._style._fmt = InvokeAILogFormatter.warn_fmt
-        if record.levelno == logging.ERROR:
-            self._style._fmt = InvokeAILogFormatter.err_fmt
-        if record.levelno == logging.CRITICAL:
-            self._style._fmt = InvokeAILogFormatter.crit_fmt
+        log_fmt = self.FORMATS.get(record.levelno)
+        formatter = logging.Formatter(log_fmt, datefmt="%d-%m-%Y %H:%M:%S")
+        return formatter.format(record)
 
-        # parent class does the work
-        result = super().format(record)
-        self._style._fmt = format_orig
-        return result
 
 class InvokeAILogger(object):
     loggers = dict()
-    
+
     @classmethod
-    def getLogger(self, name:str='invokeai')->logging.Logger:
+    def getLogger(self, name: str = 'InvokeAI') -> logging.Logger:
         if name not in self.loggers:
             logger = logging.getLogger(name)
             logger.setLevel(logging.DEBUG)

invokeai/backend/web/modules/parameters.py

@@ -4,17 +4,21 @@ from .parse_seed_weights import parse_seed_weights
 
 SAMPLER_CHOICES = [
     "ddim",
-    "k_dpm_2_a",
-    "k_dpm_2",
-    "k_dpmpp_2_a",
-    "k_dpmpp_2",
-    "k_euler_a",
-    "k_euler",
-    "k_heun",
-    "k_lms",
-    "plms",
-    # diffusers:
+    "ddpm",
+    "deis",
+    "lms",
     "pndm",
+    "heun",
+    'heun_k',
+    "euler",
+    "euler_k",
+    "euler_a",
+    "kdpm_2",
+    "kdpm_2_a",
+    "dpmpp_2s",
+    "dpmpp_2m",
+    "dpmpp_2m_k",
+    "unipc",
 ]
 
 

invokeai/frontend/web/.babelrc

@@ -1,13 +0,0 @@
-{
-  "plugins": [
-    [
-      "transform-imports",
-      {
-        "lodash": {
-          "transform": "lodash/${member}",
-          "preventFullImport": true
-        }
-      }
-    ]
-  ]
-}

invokeai/frontend/web/.gitignore

@@ -34,4 +34,8 @@ stats.html
 !.yarn/plugins
 !.yarn/releases
 !.yarn/sdks
-!.yarn/versions
+!.yarn/versions
+
+# Yalc
+.yalc
+yalc.lock

invokeai/frontend/web/config/vite.package.config.ts

@@ -5,6 +5,7 @@ import { PluginOption, UserConfig } from 'vite';
 import dts from 'vite-plugin-dts';
 import eslint from 'vite-plugin-eslint';
 import tsconfigPaths from 'vite-tsconfig-paths';
+import cssInjectedByJsPlugin from 'vite-plugin-css-injected-by-js';
 
 export const packageConfig: UserConfig = {
   base: './',
@@ -16,9 +17,10 @@ export const packageConfig: UserConfig = {
     dts({
       insertTypesEntry: true,
     }),
+    cssInjectedByJsPlugin(),
   ],
   build: {
-    chunkSizeWarningLimit: 1500,
+    cssCodeSplit: true,
     lib: {
       entry: path.resolve(__dirname, '../src/index.ts'),
       name: 'InvokeAIUI',
@@ -30,6 +32,7 @@ export const packageConfig: UserConfig = {
         globals: {
           react: 'React',
           'react-dom': 'ReactDOM',
+          '@emotion/react': 'EmotionReact',
         },
       },
     },

invokeai/frontend/web/docs/PACKAGE_SCRIPTS.md

@@ -15,15 +15,3 @@ The `postinstall` script patches a few packages and runs the Chakra CLI to gener
 ### Patch `@chakra-ui/cli`
 
 See: <https://github.com/chakra-ui/chakra-ui/issues/7394>
-
-### Patch `redux-persist`
-
-We want to persist the canvas state to `localStorage` but many canvas operations change data very quickly, so we need to debounce the writes to `localStorage`.
-
-`redux-persist` is unfortunately unmaintained. The repo's current code is nonfunctional, but the last release's code depends on a package that was removed from `npm` for being malware, so we cannot just fork it.
-
-So, we have to patch it directly. Perhaps a better way would be to write a debounced storage adapter, but I couldn't figure out how to do that.
-
-### Patch `redux-deep-persist`
-
-This package makes blacklisting and whitelisting persist configs very simple, but we have to patch it to match `redux-persist` for the types to work.

invokeai/frontend/web/docs/README.md

@@ -37,7 +37,7 @@ From `invokeai/frontend/web/` run `yarn install` to get everything set up.
 Start everything in dev mode:
 
 1. Start the dev server: `yarn dev`
-2. Start the InvokeAI UI per usual: `invokeai --web`
+2. Start the InvokeAI Nodes backend: `python scripts/invokeai-new.py --web # run from the repo root`
 3. Point your browser to the dev server address e.g. <http://localhost:5173/>
 
 ### Production builds

invokeai/frontend/web/package.json

@@ -21,7 +21,6 @@
   "scripts": {
     "prepare": "cd ../../../ && husky install invokeai/frontend/web/.husky",
     "dev": "concurrently \"vite dev\" \"yarn run theme:watch\"",
-    "dev:nodes": "concurrently \"vite dev --mode nodes\" \"yarn run theme:watch\"",
     "dev:host": "concurrently \"vite dev --host\" \"yarn run theme:watch\"",
     "build": "yarn run lint && vite build",
     "api:web": "openapi -i http://localhost:9090/openapi.json -o src/services/api --client axios --useOptions --useUnionTypes --exportSchemas true --indent 2 --request src/services/fixtures/request.ts",
@@ -63,11 +62,13 @@
     "@dagrejs/graphlib": "^2.1.12",
     "@emotion/react": "^11.10.6",
     "@emotion/styled": "^11.10.6",
+    "@floating-ui/react-dom": "^2.0.0",
     "@fontsource/inter": "^4.5.15",
     "@reduxjs/toolkit": "^1.9.5",
     "@roarr/browser-log-writer": "^1.1.5",
     "chakra-ui-contextmenu": "^1.0.5",
     "dateformat": "^5.0.3",
+    "downshift": "^7.6.0",
     "formik": "^2.2.9",
     "framer-motion": "^10.12.4",
     "fuse.js": "^6.6.2",
@@ -88,17 +89,14 @@
     "react-i18next": "^12.2.2",
     "react-icons": "^4.7.1",
     "react-konva": "^18.2.7",
-    "react-konva-utils": "^1.0.4",
     "react-redux": "^8.0.5",
-    "react-rnd": "^10.4.1",
-    "react-transition-group": "^4.4.5",
+    "react-resizable-panels": "^0.0.42",
     "react-use": "^17.4.0",
     "react-virtuoso": "^4.3.5",
     "react-zoom-pan-pinch": "^3.0.7",
     "reactflow": "^11.7.0",
-    "redux-deep-persist": "^1.0.7",
     "redux-dynamic-middlewares": "^2.2.0",
-    "redux-persist": "^6.0.0",
+    "redux-remember": "^3.3.1",
     "roarr": "^7.15.0",
     "serialize-error": "^11.0.0",
     "socket.io-client": "^4.6.0",
@@ -118,6 +116,7 @@
     "@types/node": "^18.16.2",
     "@types/react": "^18.2.0",
     "@types/react-dom": "^18.2.1",
+    "@types/react-redux": "^7.1.25",
     "@types/react-transition-group": "^4.4.5",
     "@types/uuid": "^9.0.0",
     "@typescript-eslint/eslint-plugin": "^5.59.1",
@@ -143,6 +142,7 @@
     "terser": "^5.17.1",
     "ts-toolbelt": "^9.6.0",
     "vite": "^4.3.3",
+    "vite-plugin-css-injected-by-js": "^3.1.1",
     "vite-plugin-dts": "^2.3.0",
     "vite-plugin-eslint": "^1.8.1",
     "vite-tsconfig-paths": "^4.2.0",

invokeai/frontend/web/patches/redux-deep-persist+1.0.7.patch

@@ -1,24 +0,0 @@
-diff --git a/node_modules/redux-deep-persist/lib/types.d.ts b/node_modules/redux-deep-persist/lib/types.d.ts
-index b67b8c2..7fc0fa1 100644
---- a/node_modules/redux-deep-persist/lib/types.d.ts
-+++ b/node_modules/redux-deep-persist/lib/types.d.ts
-@@ -35,6 +35,7 @@ export interface PersistConfig<S, RS = any, HSS = any, ESS = any> {
-     whitelist?: Array<string>;
-     transforms?: Array<Transform<HSS, ESS, S, RS>>;
-     throttle?: number;
-+    debounce?: number;
-     migrate?: PersistMigrate;
-     stateReconciler?: false | StateReconciler<S>;
-     getStoredState?: (config: PersistConfig<S, RS, HSS, ESS>) => Promise<PersistedState>;
-diff --git a/node_modules/redux-deep-persist/src/types.ts b/node_modules/redux-deep-persist/src/types.ts
-index 398ac19..cbc5663 100644
---- a/node_modules/redux-deep-persist/src/types.ts
-+++ b/node_modules/redux-deep-persist/src/types.ts
-@@ -91,6 +91,7 @@ export interface PersistConfig<S, RS = any, HSS = any, ESS = any> {
-     whitelist?: Array<string>;
-     transforms?: Array<Transform<HSS, ESS, S, RS>>;
-     throttle?: number;
-+    debounce?: number;
-     migrate?: PersistMigrate;
-     stateReconciler?: false | StateReconciler<S>;
-     /**

invokeai/frontend/web/patches/redux-persist+6.0.0.patch

@@ -1,116 +0,0 @@
-diff --git a/node_modules/redux-persist/es/createPersistoid.js b/node_modules/redux-persist/es/createPersistoid.js
-index 8b43b9a..184faab 100644
---- a/node_modules/redux-persist/es/createPersistoid.js
-+++ b/node_modules/redux-persist/es/createPersistoid.js
-@@ -6,6 +6,7 @@ export default function createPersistoid(config) {
-   var whitelist = config.whitelist || null;
-   var transforms = config.transforms || [];
-   var throttle = config.throttle || 0;
-+  var debounce = config.debounce || 0;
-   var storageKey = "".concat(config.keyPrefix !== undefined ? config.keyPrefix : KEY_PREFIX).concat(config.key);
-   var storage = config.storage;
-   var serialize;
-@@ -28,30 +29,37 @@ export default function createPersistoid(config) {
-   var timeIterator = null;
-   var writePromise = null;
- 
--  var update = function update(state) {
--    // add any changed keys to the queue
--    Object.keys(state).forEach(function (key) {
--      if (!passWhitelistBlacklist(key)) return; // is keyspace ignored? noop
-+  // Timer for debounced `update()`
-+  let timer = 0;
- 
--      if (lastState[key] === state[key]) return; // value unchanged? noop
-+  function update(state) {
-+    // Debounce the update
-+    clearTimeout(timer);
-+    timer = setTimeout(() => {
-+      // add any changed keys to the queue
-+      Object.keys(state).forEach(function (key) {
-+        if (!passWhitelistBlacklist(key)) return; // is keyspace ignored? noop
- 
--      if (keysToProcess.indexOf(key) !== -1) return; // is key already queued? noop
-+        if (lastState[key] === state[key]) return; // value unchanged? noop
- 
--      keysToProcess.push(key); // add key to queue
--    }); //if any key is missing in the new state which was present in the lastState,
--    //add it for processing too
-+        if (keysToProcess.indexOf(key) !== -1) return; // is key already queued? noop
- 
--    Object.keys(lastState).forEach(function (key) {
--      if (state[key] === undefined && passWhitelistBlacklist(key) && keysToProcess.indexOf(key) === -1 && lastState[key] !== undefined) {
--        keysToProcess.push(key);
--      }
--    }); // start the time iterator if not running (read: throttle)
-+        keysToProcess.push(key); // add key to queue
-+      }); //if any key is missing in the new state which was present in the lastState,
-+      //add it for processing too
- 
--    if (timeIterator === null) {
--      timeIterator = setInterval(processNextKey, throttle);
--    }
-+      Object.keys(lastState).forEach(function (key) {
-+        if (state[key] === undefined && passWhitelistBlacklist(key) && keysToProcess.indexOf(key) === -1 && lastState[key] !== undefined) {
-+          keysToProcess.push(key);
-+        }
-+      }); // start the time iterator if not running (read: throttle)
-+
-+      if (timeIterator === null) {
-+        timeIterator = setInterval(processNextKey, throttle);
-+      }
- 
--    lastState = state;
-+      lastState = state;
-+    }, debounce)
-   };
- 
-   function processNextKey() {
-diff --git a/node_modules/redux-persist/es/types.js.flow b/node_modules/redux-persist/es/types.js.flow
-index c50d3cd..39d8be2 100644
---- a/node_modules/redux-persist/es/types.js.flow
-+++ b/node_modules/redux-persist/es/types.js.flow
-@@ -19,6 +19,7 @@ export type PersistConfig = {
-   whitelist?: Array<string>,
-   transforms?: Array<Transform>,
-   throttle?: number,
-+  debounce?: number,
-   migrate?: (PersistedState, number) => Promise<PersistedState>,
-   stateReconciler?: false | Function,
-   getStoredState?: PersistConfig => Promise<PersistedState>, // used for migrations
-diff --git a/node_modules/redux-persist/lib/types.js.flow b/node_modules/redux-persist/lib/types.js.flow
-index c50d3cd..39d8be2 100644
---- a/node_modules/redux-persist/lib/types.js.flow
-+++ b/node_modules/redux-persist/lib/types.js.flow
-@@ -19,6 +19,7 @@ export type PersistConfig = {
-   whitelist?: Array<string>,
-   transforms?: Array<Transform>,
-   throttle?: number,
-+  debounce?: number,
-   migrate?: (PersistedState, number) => Promise<PersistedState>,
-   stateReconciler?: false | Function,
-   getStoredState?: PersistConfig => Promise<PersistedState>, // used for migrations
-diff --git a/node_modules/redux-persist/src/types.js b/node_modules/redux-persist/src/types.js
-index c50d3cd..39d8be2 100644
---- a/node_modules/redux-persist/src/types.js
-+++ b/node_modules/redux-persist/src/types.js
-@@ -19,6 +19,7 @@ export type PersistConfig = {
-   whitelist?: Array<string>,
-   transforms?: Array<Transform>,
-   throttle?: number,
-+  debounce?: number,
-   migrate?: (PersistedState, number) => Promise<PersistedState>,
-   stateReconciler?: false | Function,
-   getStoredState?: PersistConfig => Promise<PersistedState>, // used for migrations
-diff --git a/node_modules/redux-persist/types/types.d.ts b/node_modules/redux-persist/types/types.d.ts
-index b3733bc..2a1696c 100644
---- a/node_modules/redux-persist/types/types.d.ts
-+++ b/node_modules/redux-persist/types/types.d.ts
-@@ -35,6 +35,7 @@ declare module "redux-persist/es/types" {
-     whitelist?: Array<string>;
-     transforms?: Array<Transform<HSS, ESS, S, RS>>;
-     throttle?: number;
-+    debounce?: number;
-     migrate?: PersistMigrate;
-     stateReconciler?: false | StateReconciler<S>;
-     /**

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

@@ -25,7 +25,7 @@
     "common": {
         "hotkeysLabel": "Hotkeys",
         "themeLabel": "Theme",
-        "languagePickerLabel": "Language Picker",
+        "languagePickerLabel": "Language",
         "reportBugLabel": "Report Bug",
         "githubLabel": "Github",
         "discordLabel": "Discord",
@@ -54,7 +54,7 @@
         "img2img": "Image To Image",
         "unifiedCanvas": "Unified Canvas",
         "linear": "Linear",
-        "nodes": "Nodes",
+        "nodes": "Node Editor",
         "postprocessing": "Post Processing",
         "nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
         "postProcessing": "Post Processing",
@@ -102,7 +102,8 @@
         "generate": "Generate",
         "openInNewTab": "Open in New Tab",
         "dontAskMeAgain": "Don't ask me again",
-        "areYouSure": "Are you sure?"
+        "areYouSure": "Are you sure?",
+        "imagePrompt": "Image Prompt"
     },
     "gallery": {
         "generations": "Generations",
@@ -449,13 +450,14 @@
         "cfgScale": "CFG Scale",
         "width": "Width",
         "height": "Height",
-        "sampler": "Sampler",
+        "scheduler": "Scheduler",
         "seed": "Seed",
         "imageToImage": "Image to Image",
         "randomizeSeed": "Randomize Seed",
-        "shuffle": "Shuffle",
+        "shuffle": "Shuffle Seed",
         "noiseThreshold": "Noise Threshold",
         "perlinNoise": "Perlin Noise",
+        "noiseSettings": "Noise",
         "variations": "Variations",
         "variationAmount": "Variation Amount",
         "seedWeights": "Seed Weights",
@@ -470,6 +472,8 @@
         "scale": "Scale",
         "otherOptions": "Other Options",
         "seamlessTiling": "Seamless Tiling",
+        "seamlessXAxis": "X Axis",
+        "seamlessYAxis": "Y Axis",
         "hiresOptim": "High Res Optimization",
         "hiresStrength": "High Res Strength",
         "imageFit": "Fit Initial Image To Output Size",
@@ -527,7 +531,8 @@
         "useCanvasBeta": "Use Canvas Beta Layout",
         "enableImageDebugging": "Enable Image Debugging",
         "useSlidersForAll": "Use Sliders For All Options",
-        "autoShowProgress": "Auto Show Progress Images",
+        "showProgressInViewer": "Show Progress Images in Viewer",
+        "antialiasProgressImages": "Antialias Progress Images",
         "resetWebUI": "Reset Web UI",
         "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
         "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",
@@ -535,7 +540,10 @@
         "consoleLogLevel": "Log Level",
         "shouldLogToConsole": "Console Logging",
         "developer": "Developer",
-        "general": "General"
+        "general": "General",
+        "generation": "Generation",
+        "ui": "User Interface",
+        "availableSchedulers": "Available Schedulers"
     },
     "toast": {
         "serverError": "Server Error",
@@ -544,13 +552,14 @@
         "canceled": "Processing Canceled",
         "tempFoldersEmptied": "Temp Folder Emptied",
         "uploadFailed": "Upload failed",
-        "uploadFailedMultipleImagesDesc": "Multiple images pasted, may only upload one image at a time",
         "uploadFailedUnableToLoadDesc": "Unable to load file",
+        "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
         "downloadImageStarted": "Image Download Started",
         "imageCopied": "Image Copied",
         "imageLinkCopied": "Image Link Copied",
+        "problemCopyingImageLink": "Unable to Copy Image Link",
         "imageNotLoaded": "No Image Loaded",
-        "imageNotLoadedDesc": "No image found to send to image to image module",
+        "imageNotLoadedDesc": "Could not find image",
         "imageSavedToGallery": "Image Saved to Gallery",
         "canvasMerged": "Canvas Merged",
         "sentToImageToImage": "Sent To Image To Image",
@@ -645,7 +654,8 @@
         "betaClear": "Clear",
         "betaDarkenOutside": "Darken Outside",
         "betaLimitToBox": "Limit To Box",
-        "betaPreserveMasked": "Preserve Masked"
+        "betaPreserveMasked": "Preserve Masked",
+        "antialiasing": "Antialiasing"
     },
     "ui": {
         "showProgressImages": "Show Progress Images",

invokeai/frontend/web/src/app/components/App.tsx

@@ -1,46 +1,44 @@
 import ImageUploader from 'common/components/ImageUploader';
-import ProgressBar from 'features/system/components/ProgressBar';
 import SiteHeader from 'features/system/components/SiteHeader';
+import ProgressBar from 'features/system/components/ProgressBar';
 import InvokeTabs from 'features/ui/components/InvokeTabs';
-
-import useToastWatcher from 'features/system/hooks/useToastWatcher';
-
 import FloatingGalleryButton from 'features/ui/components/FloatingGalleryButton';
 import FloatingParametersPanelButtons from 'features/ui/components/FloatingParametersPanelButtons';
-import { Box, Flex, Grid, Portal, useColorMode } from '@chakra-ui/react';
+import { Box, Flex, Grid, Portal } from '@chakra-ui/react';
 import { APP_HEIGHT, APP_WIDTH } from 'theme/util/constants';
-import ImageGalleryPanel from 'features/gallery/components/ImageGalleryPanel';
+import GalleryDrawer from 'features/gallery/components/GalleryPanel';
 import Lightbox from 'features/lightbox/components/Lightbox';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import {
-  memo,
-  PropsWithChildren,
-  useCallback,
-  useEffect,
-  useState,
-} from 'react';
+import { memo, ReactNode, useCallback, useEffect, useState } from 'react';
 import { motion, AnimatePresence } from 'framer-motion';
 import Loading from 'common/components/Loading/Loading';
 import { useIsApplicationReady } from 'features/system/hooks/useIsApplicationReady';
 import { PartialAppConfig } from 'app/types/invokeai';
-import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
 import { configChanged } from 'features/system/store/configSlice';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
 import { useLogger } from 'app/logging/useLogger';
-import ProgressImagePreview from 'features/parameters/components/ProgressImagePreview';
+import ParametersDrawer from 'features/ui/components/ParametersDrawer';
+import { languageSelector } from 'features/system/store/systemSelectors';
+import i18n from 'i18n';
+import Toaster from './Toaster';
+import GlobalHotkeys from './GlobalHotkeys';
 
 const DEFAULT_CONFIG = {};
 
-interface Props extends PropsWithChildren {
+interface Props {
   config?: PartialAppConfig;
+  headerComponent?: ReactNode;
+  setIsReady?: (isReady: boolean) => void;
 }
 
-const App = ({ config = DEFAULT_CONFIG, children }: Props) => {
-  useToastWatcher();
-  useGlobalHotkeys();
-  const log = useLogger();
+const App = ({
+  config = DEFAULT_CONFIG,
+  headerComponent,
+  setIsReady,
+}: Props) => {
+  const language = useAppSelector(languageSelector);
 
-  const currentTheme = useAppSelector((state) => state.ui.currentTheme);
+  const log = useLogger();
 
   const isLightboxEnabled = useFeatureStatus('lightbox').isFeatureEnabled;
 
@@ -48,81 +46,95 @@ const App = ({ config = DEFAULT_CONFIG, children }: Props) => {
 
   const [loadingOverridden, setLoadingOverridden] = useState(false);
 
-  const { setColorMode } = useColorMode();
   const dispatch = useAppDispatch();
 
+  useEffect(() => {
+    i18n.changeLanguage(language);
+  }, [language]);
+
   useEffect(() => {
     log.info({ namespace: 'App', data: config }, 'Received config');
     dispatch(configChanged(config));
   }, [dispatch, config, log]);
 
-  useEffect(() => {
-    setColorMode(['light'].includes(currentTheme) ? 'light' : 'dark');
-  }, [setColorMode, currentTheme]);
-
   const handleOverrideClicked = useCallback(() => {
     setLoadingOverridden(true);
   }, []);
 
+  useEffect(() => {
+    if (isApplicationReady && setIsReady) {
+      setIsReady(true);
+    }
+
+    return () => {
+      setIsReady && setIsReady(false);
+    };
+  }, [isApplicationReady, setIsReady]);
+
   return (
-    <Grid w="100vw" h="100vh" position="relative" overflow="hidden">
-      {isLightboxEnabled && <Lightbox />}
-      <ImageUploader>
-        <ProgressBar />
-        <Grid
-          gap={4}
-          p={4}
-          gridAutoRows="min-content auto"
-          w={APP_WIDTH}
-          h={APP_HEIGHT}
-        >
-          {children || <SiteHeader />}
-          <Flex
+    <>
+      <Grid w="100vw" h="100vh" position="relative" overflow="hidden">
+        {isLightboxEnabled && <Lightbox />}
+        <ImageUploader>
+          <ProgressBar />
+          <Grid
             gap={4}
-            w={{ base: '100vw', xl: 'full' }}
-            h="full"
-            flexDir={{ base: 'column', xl: 'row' }}
+            p={4}
+            gridAutoRows="min-content auto"
+            w={APP_WIDTH}
+            h={APP_HEIGHT}
           >
-            <InvokeTabs />
-            <ImageGalleryPanel />
-          </Flex>
-        </Grid>
-      </ImageUploader>
+            {headerComponent || <SiteHeader />}
+            <Flex
+              gap={4}
+              w={{ base: '100vw', xl: 'full' }}
+              h="full"
+              flexDir={{ base: 'column', xl: 'row' }}
+            >
+              <InvokeTabs />
+            </Flex>
+          </Grid>
+        </ImageUploader>
 
-      <AnimatePresence>
-        {!isApplicationReady && !loadingOverridden && (
-          <motion.div
-            key="loading"
-            initial={{ opacity: 1 }}
-            animate={{ opacity: 1 }}
-            exit={{ opacity: 0 }}
-            transition={{ duration: 0.3 }}
-            style={{ zIndex: 3 }}
-          >
-            <Box position="absolute" top={0} left={0} w="100vw" h="100vh">
-              <Loading />
-            </Box>
-            <Box
-              onClick={handleOverrideClicked}
-              position="absolute"
-              top={0}
-              right={0}
-              cursor="pointer"
-              w="2rem"
-              h="2rem"
-            />
-          </motion.div>
-        )}
-      </AnimatePresence>
+        <GalleryDrawer />
+        <ParametersDrawer />
 
-      <Portal>
-        <FloatingParametersPanelButtons />
-      </Portal>
-      <Portal>
-        <FloatingGalleryButton />
-      </Portal>
-      <ProgressImagePreview />
-    </Grid>
+        <AnimatePresence>
+          {!isApplicationReady && !loadingOverridden && (
+            <motion.div
+              key="loading"
+              initial={{ opacity: 1 }}
+              animate={{ opacity: 1 }}
+              exit={{ opacity: 0 }}
+              transition={{ duration: 0.3 }}
+              style={{ zIndex: 3 }}
+            >
+              <Box position="absolute" top={0} left={0} w="100vw" h="100vh">
+                <Loading />
+              </Box>
+              <Box
+                onClick={handleOverrideClicked}
+                position="absolute"
+                top={0}
+                right={0}
+                cursor="pointer"
+                w="2rem"
+                h="2rem"
+              />
+            </motion.div>
+          )}
+        </AnimatePresence>
+
+        <Portal>
+          <FloatingParametersPanelButtons />
+        </Portal>
+        <Portal>
+          <FloatingGalleryButton />
+        </Portal>
+      </Grid>
+      <Toaster />
+      <GlobalHotkeys />
+    </>
   );
 };
 

invokeai/frontend/web/src/app/components/AuxiliaryProgressIndicator.tsx

@@ -0,0 +1,44 @@
+import { Flex, Spinner, Tooltip } from '@chakra-ui/react';
+import { createSelector } from '@reduxjs/toolkit';
+import { useAppSelector } from 'app/store/storeHooks';
+import { systemSelector } from 'features/system/store/systemSelectors';
+import { memo } from 'react';
+
+const selector = createSelector(systemSelector, (system) => {
+  const { isUploading } = system;
+
+  let tooltip = '';
+
+  if (isUploading) {
+    tooltip = 'Uploading...';
+  }
+
+  return {
+    tooltip,
+    shouldShow: isUploading,
+  };
+});
+
+export const AuxiliaryProgressIndicator = () => {
+  const { shouldShow, tooltip } = useAppSelector(selector);
+
+  if (!shouldShow) {
+    return null;
+  }
+
+  return (
+    <Flex
+      sx={{
+        alignItems: 'center',
+        justifyContent: 'center',
+        color: 'base.600',
+      }}
+    >
+      <Tooltip label={tooltip} placement="right" hasArrow>
+        <Spinner />
+      </Tooltip>
+    </Flex>
+  );
+};
+
+export default memo(AuxiliaryProgressIndicator);

invokeai/frontend/web/src/app/components/GlobalHotkeys.ts

@@ -2,7 +2,15 @@ import { createSelector } from '@reduxjs/toolkit';
 import { RootState } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { shiftKeyPressed } from 'features/ui/store/hotkeysSlice';
+import {
+  setActiveTab,
+  toggleGalleryPanel,
+  toggleParametersPanel,
+  togglePinGalleryPanel,
+  togglePinParametersPanel,
+} from 'features/ui/store/uiSlice';
 import { isEqual } from 'lodash-es';
+import React, { memo } from 'react';
 import { isHotkeyPressed, useHotkeys } from 'react-hotkeys-hook';
 
 const globalHotkeysSelector = createSelector(
@@ -20,7 +28,11 @@ const globalHotkeysSelector = createSelector(
 
 // TODO: Does not catch keypresses while focused in an input. Maybe there is a way?
 
-export const useGlobalHotkeys = () => {
+/**
+ * Logical component. Handles app-level global hotkeys.
+ * @returns null
+ */
+const GlobalHotkeys: React.FC = () => {
   const dispatch = useAppDispatch();
   const { shift } = useAppSelector(globalHotkeysSelector);
 
@@ -36,4 +48,40 @@ export const useGlobalHotkeys = () => {
     { keyup: true, keydown: true },
     [shift]
   );
+
+  useHotkeys('o', () => {
+    dispatch(toggleParametersPanel());
+  });
+
+  useHotkeys(['shift+o'], () => {
+    dispatch(togglePinParametersPanel());
+  });
+
+  useHotkeys('g', () => {
+    dispatch(toggleGalleryPanel());
+  });
+
+  useHotkeys(['shift+g'], () => {
+    dispatch(togglePinGalleryPanel());
+  });
+
+  useHotkeys('1', () => {
+    dispatch(setActiveTab('txt2img'));
+  });
+
+  useHotkeys('2', () => {
+    dispatch(setActiveTab('img2img'));
+  });
+
+  useHotkeys('3', () => {
+    dispatch(setActiveTab('unifiedCanvas'));
+  });
+
+  useHotkeys('4', () => {
+    dispatch(setActiveTab('nodes'));
+  });
+
+  return null;
 };
+
+export default memo(GlobalHotkeys);

invokeai/frontend/web/src/app/components/InvokeAIUI.tsx

@@ -1,18 +1,13 @@
-import React, { lazy, memo, PropsWithChildren, useEffect } from 'react';
+import React, {
+  lazy,
+  memo,
+  PropsWithChildren,
+  ReactNode,
+  useEffect,
+} from 'react';
 import { Provider } from 'react-redux';
-import { PersistGate } from 'redux-persist/integration/react';
 import { store } from 'app/store/store';
-import { persistor } from '../store/persistor';
 import { OpenAPI } from 'services/api';
-import '@fontsource/inter/100.css';
-import '@fontsource/inter/200.css';
-import '@fontsource/inter/300.css';
-import '@fontsource/inter/400.css';
-import '@fontsource/inter/500.css';
-import '@fontsource/inter/600.css';
-import '@fontsource/inter/700.css';
-import '@fontsource/inter/800.css';
-import '@fontsource/inter/900.css';
 
 import Loading from '../../common/components/Loading/Loading';
 import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares';
@@ -28,9 +23,17 @@ interface Props extends PropsWithChildren {
   apiUrl?: string;
   token?: string;
   config?: PartialAppConfig;
+  headerComponent?: ReactNode;
+  setIsReady?: (isReady: boolean) => void;
 }
 
-const InvokeAIUI = ({ apiUrl, token, config, children }: Props) => {
+const InvokeAIUI = ({
+  apiUrl,
+  token,
+  config,
+  headerComponent,
+  setIsReady,
+}: Props) => {
   useEffect(() => {
     // configure API client token
     if (token) {
@@ -57,13 +60,15 @@ const InvokeAIUI = ({ apiUrl, token, config, children }: Props) => {
   return (
     <React.StrictMode>
       <Provider store={store}>
-        <PersistGate loading={<Loading />} persistor={persistor}>
-          <React.Suspense fallback={<Loading />}>
-            <ThemeLocaleProvider>
-              <App config={config}>{children}</App>
-            </ThemeLocaleProvider>
-          </React.Suspense>
-        </PersistGate>
+        <React.Suspense fallback={<Loading />}>
+          <ThemeLocaleProvider>
+            <App
+              config={config}
+              headerComponent={headerComponent}
+              setIsReady={setIsReady}
+            />
+          </ThemeLocaleProvider>
+        </React.Suspense>
       </Provider>
     </React.StrictMode>
   );

invokeai/frontend/web/src/app/components/ThemeLocaleProvider.tsx

@@ -1,4 +1,8 @@
-import { ChakraProvider, extendTheme } from '@chakra-ui/react';
+import {
+  ChakraProvider,
+  createLocalStorageManager,
+  extendTheme,
+} from '@chakra-ui/react';
 import { ReactNode, useEffect } from 'react';
 import { useTranslation } from 'react-i18next';
 import { theme as invokeAITheme } from 'theme/theme';
@@ -9,15 +13,8 @@ import { greenTeaThemeColors } from 'theme/colors/greenTea';
 import { invokeAIThemeColors } from 'theme/colors/invokeAI';
 import { lightThemeColors } from 'theme/colors/lightTheme';
 import { oceanBlueColors } from 'theme/colors/oceanBlue';
-import '@fontsource/inter/100.css';
-import '@fontsource/inter/200.css';
-import '@fontsource/inter/300.css';
-import '@fontsource/inter/400.css';
-import '@fontsource/inter/500.css';
-import '@fontsource/inter/600.css';
-import '@fontsource/inter/700.css';
-import '@fontsource/inter/800.css';
-import '@fontsource/inter/900.css';
+
+import '@fontsource/inter/variable.css';
 import 'overlayscrollbars/overlayscrollbars.css';
 import 'theme/css/overlayscrollbars.css';
 
@@ -32,6 +29,8 @@ const THEMES = {
   ocean: oceanBlueColors,
 };
 
+const manager = createLocalStorageManager('@@invokeai-color-mode');
+
 function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
   const { i18n } = useTranslation();
 
@@ -51,7 +50,11 @@ function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
     document.body.dir = direction;
   }, [direction]);
 
-  return <ChakraProvider theme={theme}>{children}</ChakraProvider>;
+  return (
+    <ChakraProvider theme={theme} colorModeManager={manager}>
+      {children}
+    </ChakraProvider>
+  );
 }
 
 export default ThemeLocaleProvider;

invokeai/frontend/web/src/app/components/Toaster.ts

@@ -0,0 +1,65 @@
+import { useToast, UseToastOptions } from '@chakra-ui/react';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { toastQueueSelector } from 'features/system/store/systemSelectors';
+import { addToast, clearToastQueue } from 'features/system/store/systemSlice';
+import { useCallback, useEffect } from 'react';
+
+export type MakeToastArg = string | UseToastOptions;
+
+/**
+ * Makes a toast from a string or a UseToastOptions object.
+ * If a string is passed, the toast will have the status 'info' and will be closable with a duration of 2500ms.
+ */
+export const makeToast = (arg: MakeToastArg): UseToastOptions => {
+  if (typeof arg === 'string') {
+    return {
+      title: arg,
+      status: 'info',
+      isClosable: true,
+      duration: 2500,
+    };
+  }
+
+  return { status: 'info', isClosable: true, duration: 2500, ...arg };
+};
+
+/**
+ * Logical component. Watches the toast queue and makes toasts when the queue is not empty.
+ * @returns null
+ */
+const Toaster = () => {
+  const dispatch = useAppDispatch();
+  const toastQueue = useAppSelector(toastQueueSelector);
+  const toast = useToast();
+  useEffect(() => {
+    toastQueue.forEach((t) => {
+      toast(t);
+    });
+    toastQueue.length > 0 && dispatch(clearToastQueue());
+  }, [dispatch, toast, toastQueue]);
+
+  return null;
+};
+
+/**
+ * Returns a function that can be used to make a toast.
+ * @example
+ * const toaster = useAppToaster();
+ * toaster('Hello world!');
+ * toaster({ title: 'Hello world!', status: 'success' });
+ * @returns A function that can be used to make a toast.
+ * @see makeToast
+ * @see MakeToastArg
+ * @see UseToastOptions
+ */
+export const useAppToaster = () => {
+  const dispatch = useAppDispatch();
+  const toaster = useCallback(
+    (arg: MakeToastArg) => dispatch(addToast(makeToast(arg))),
+    [dispatch]
+  );
+
+  return toaster;
+};
+
+export default Toaster;

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

@@ -1,17 +1,28 @@
 // TODO: use Enums?
 
-export const DIFFUSERS_SCHEDULERS: Array<string> = [
+export const SCHEDULERS = [
   'ddim',
-  'plms',
-  'k_lms',
-  'dpmpp_2',
-  'k_dpm_2',
-  'k_dpm_2_a',
-  'k_dpmpp_2',
-  'k_euler',
-  'k_euler_a',
-  'k_heun',
-];
+  'lms',
+  'euler',
+  'euler_k',
+  'euler_a',
+  'dpmpp_2s',
+  'dpmpp_2m',
+  'dpmpp_2m_k',
+  'kdpm_2',
+  'kdpm_2_a',
+  'deis',
+  'ddpm',
+  'pndm',
+  'heun',
+  'heun_k',
+  'unipc',
+] as const;
+
+export type Scheduler = (typeof SCHEDULERS)[number];
+
+export const isScheduler = (x: string): x is Scheduler =>
+  SCHEDULERS.includes(x as Scheduler);
 
 // Valid image widths
 export const WIDTHS: Array<number> = Array.from(Array(64)).map(

invokeai/frontend/web/src/app/selectors/readinessSelector.ts

@@ -1,26 +1,20 @@
 import { createSelector } from '@reduxjs/toolkit';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import { validateSeedWeights } from 'common/util/seedWeightPairs';
-import { initialCanvasImageSelector } from 'features/canvas/store/canvasSelectors';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
 import { systemSelector } from 'features/system/store/systemSelectors';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { isEqual } from 'lodash-es';
 
 export const readinessSelector = createSelector(
-  [
-    generationSelector,
-    systemSelector,
-    initialCanvasImageSelector,
-    activeTabNameSelector,
-  ],
-  (generation, system, initialCanvasImage, activeTabName) => {
+  [generationSelector, systemSelector, activeTabNameSelector],
+  (generation, system, activeTabName) => {
     const {
       prompt,
       shouldGenerateVariations,
       seedWeights,
       initialImage,
       seed,
-      isImageToImageEnabled,
     } = generation;
 
     const { isProcessing, isConnected } = system;
@@ -34,7 +28,7 @@ export const readinessSelector = createSelector(
       reasonsWhyNotReady.push('Missing prompt');
     }
 
-    if (isImageToImageEnabled && !initialImage) {
+    if (activeTabName === 'img2img' && !initialImage) {
       isReady = false;
       reasonsWhyNotReady.push('No initial image selected');
     }
@@ -64,10 +58,5 @@ export const readinessSelector = createSelector(
     // All good
     return { isReady, reasonsWhyNotReady };
   },
-  {
-    memoizeOptions: {
-      equalityCheck: isEqual,
-      resultEqualityCheck: isEqual,
-    },
-  }
+  defaultSelectorOptions
 );

invokeai/frontend/web/src/app/socketio/emitters.ts

@@ -1,209 +1,209 @@
-// import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
-// import * as InvokeAI from 'app/types/invokeai';
-// import type { RootState } from 'app/store/store';
-// import {
-//   frontendToBackendParameters,
-//   FrontendToBackendParametersConfig,
-// } from 'common/util/parameterTranslation';
-// import dateFormat from 'dateformat';
-// import {
-//   GalleryCategory,
-//   GalleryState,
-//   removeImage,
-// } from 'features/gallery/store/gallerySlice';
-// import {
-//   generationRequested,
-//   modelChangeRequested,
-//   modelConvertRequested,
-//   modelMergingRequested,
-//   setIsProcessing,
-// } from 'features/system/store/systemSlice';
-// import { InvokeTabName } from 'features/ui/store/tabMap';
-// import { Socket } from 'socket.io-client';
+import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
+import * as InvokeAI from 'app/types/invokeai';
+import type { RootState } from 'app/store/store';
+import {
+  frontendToBackendParameters,
+  FrontendToBackendParametersConfig,
+} from 'common/util/parameterTranslation';
+import dateFormat from 'dateformat';
+import {
+  GalleryCategory,
+  GalleryState,
+  removeImage,
+} from 'features/gallery/store/gallerySlice';
+import {
+  generationRequested,
+  modelChangeRequested,
+  modelConvertRequested,
+  modelMergingRequested,
+  setIsProcessing,
+} from 'features/system/store/systemSlice';
+import { InvokeTabName } from 'features/ui/store/tabMap';
+import { Socket } from 'socket.io-client';
 
-// /**
-//  * Returns an object containing all functions which use `socketio.emit()`.
-//  * i.e. those which make server requests.
-//  */
-// const makeSocketIOEmitters = (
-//   store: MiddlewareAPI<Dispatch<AnyAction>, RootState>,
-//   socketio: Socket
-// ) => {
-//   // We need to dispatch actions to redux and get pieces of state from the store.
-//   const { dispatch, getState } = store;
+/**
+ * Returns an object containing all functions which use `socketio.emit()`.
+ * i.e. those which make server requests.
+ */
+const makeSocketIOEmitters = (
+  store: MiddlewareAPI<Dispatch<AnyAction>, RootState>,
+  socketio: Socket
+) => {
+  // We need to dispatch actions to redux and get pieces of state from the store.
+  const { dispatch, getState } = store;
 
-//   return {
-//     emitGenerateImage: (generationMode: InvokeTabName) => {
-//       dispatch(setIsProcessing(true));
+  return {
+    emitGenerateImage: (generationMode: InvokeTabName) => {
+      dispatch(setIsProcessing(true));
 
-//       const state: RootState = getState();
+      const state: RootState = getState();
 
-//       const {
-//         generation: generationState,
-//         postprocessing: postprocessingState,
-//         system: systemState,
-//         canvas: canvasState,
-//       } = state;
+      const {
+        generation: generationState,
+        postprocessing: postprocessingState,
+        system: systemState,
+        canvas: canvasState,
+      } = state;
 
-//       const frontendToBackendParametersConfig: FrontendToBackendParametersConfig =
-//         {
-//           generationMode,
-//           generationState,
-//           postprocessingState,
-//           canvasState,
-//           systemState,
-//         };
+      const frontendToBackendParametersConfig: FrontendToBackendParametersConfig =
+        {
+          generationMode,
+          generationState,
+          postprocessingState,
+          canvasState,
+          systemState,
+        };
 
-//       dispatch(generationRequested());
+      dispatch(generationRequested());
 
-//       const { generationParameters, esrganParameters, facetoolParameters } =
-//         frontendToBackendParameters(frontendToBackendParametersConfig);
+      const { generationParameters, esrganParameters, facetoolParameters } =
+        frontendToBackendParameters(frontendToBackendParametersConfig);
 
-//       socketio.emit(
-//         'generateImage',
-//         generationParameters,
-//         esrganParameters,
-//         facetoolParameters
-//       );
+      socketio.emit(
+        'generateImage',
+        generationParameters,
+        esrganParameters,
+        facetoolParameters
+      );
 
-//       // we need to truncate the init_mask base64 else it takes up the whole log
-//       // TODO: handle maintaining masks for reproducibility in future
-//       if (generationParameters.init_mask) {
-//         generationParameters.init_mask = generationParameters.init_mask
-//           .substr(0, 64)
-//           .concat('...');
-//       }
-//       if (generationParameters.init_img) {
-//         generationParameters.init_img = generationParameters.init_img
-//           .substr(0, 64)
-//           .concat('...');
-//       }
+      // we need to truncate the init_mask base64 else it takes up the whole log
+      // TODO: handle maintaining masks for reproducibility in future
+      if (generationParameters.init_mask) {
+        generationParameters.init_mask = generationParameters.init_mask
+          .substr(0, 64)
+          .concat('...');
+      }
+      if (generationParameters.init_img) {
+        generationParameters.init_img = generationParameters.init_img
+          .substr(0, 64)
+          .concat('...');
+      }
 
-//       dispatch(
-//         addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Image generation requested: ${JSON.stringify({
-//             ...generationParameters,
-//             ...esrganParameters,
-//             ...facetoolParameters,
-//           })}`,
-//         })
-//       );
-//     },
-//     emitRunESRGAN: (imageToProcess: InvokeAI._Image) => {
-//       dispatch(setIsProcessing(true));
+      dispatch(
+        addLogEntry({
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
+          message: `Image generation requested: ${JSON.stringify({
+            ...generationParameters,
+            ...esrganParameters,
+            ...facetoolParameters,
+          })}`,
+        })
+      );
+    },
+    emitRunESRGAN: (imageToProcess: InvokeAI._Image) => {
+      dispatch(setIsProcessing(true));
 
-//       const {
-//         postprocessing: {
-//           upscalingLevel,
-//           upscalingDenoising,
-//           upscalingStrength,
-//         },
-//       } = getState();
+      const {
+        postprocessing: {
+          upscalingLevel,
+          upscalingDenoising,
+          upscalingStrength,
+        },
+      } = getState();
 
-//       const esrganParameters = {
-//         upscale: [upscalingLevel, upscalingDenoising, upscalingStrength],
-//       };
-//       socketio.emit('runPostprocessing', imageToProcess, {
-//         type: 'esrgan',
-//         ...esrganParameters,
-//       });
-//       dispatch(
-//         addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `ESRGAN upscale requested: ${JSON.stringify({
-//             file: imageToProcess.url,
-//             ...esrganParameters,
-//           })}`,
-//         })
-//       );
-//     },
-//     emitRunFacetool: (imageToProcess: InvokeAI._Image) => {
-//       dispatch(setIsProcessing(true));
+      const esrganParameters = {
+        upscale: [upscalingLevel, upscalingDenoising, upscalingStrength],
+      };
+      socketio.emit('runPostprocessing', imageToProcess, {
+        type: 'esrgan',
+        ...esrganParameters,
+      });
+      dispatch(
+        addLogEntry({
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
+          message: `ESRGAN upscale requested: ${JSON.stringify({
+            file: imageToProcess.url,
+            ...esrganParameters,
+          })}`,
+        })
+      );
+    },
+    emitRunFacetool: (imageToProcess: InvokeAI._Image) => {
+      dispatch(setIsProcessing(true));
 
-//       const {
-//         postprocessing: { facetoolType, facetoolStrength, codeformerFidelity },
-//       } = getState();
+      const {
+        postprocessing: { facetoolType, facetoolStrength, codeformerFidelity },
+      } = getState();
 
-//       const facetoolParameters: Record<string, unknown> = {
-//         facetool_strength: facetoolStrength,
-//       };
+      const facetoolParameters: Record<string, unknown> = {
+        facetool_strength: facetoolStrength,
+      };
 
-//       if (facetoolType === 'codeformer') {
-//         facetoolParameters.codeformer_fidelity = codeformerFidelity;
-//       }
+      if (facetoolType === 'codeformer') {
+        facetoolParameters.codeformer_fidelity = codeformerFidelity;
+      }
 
-//       socketio.emit('runPostprocessing', imageToProcess, {
-//         type: facetoolType,
-//         ...facetoolParameters,
-//       });
-//       dispatch(
-//         addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Face restoration (${facetoolType}) requested: ${JSON.stringify(
-//             {
-//               file: imageToProcess.url,
-//               ...facetoolParameters,
-//             }
-//           )}`,
-//         })
-//       );
-//     },
-//     emitDeleteImage: (imageToDelete: InvokeAI._Image) => {
-//       const { url, uuid, category, thumbnail } = imageToDelete;
-//       dispatch(removeImage(imageToDelete));
-//       socketio.emit('deleteImage', url, thumbnail, uuid, category);
-//     },
-//     emitRequestImages: (category: GalleryCategory) => {
-//       const gallery: GalleryState = getState().gallery;
-//       const { earliest_mtime } = gallery.categories[category];
-//       socketio.emit('requestImages', category, earliest_mtime);
-//     },
-//     emitRequestNewImages: (category: GalleryCategory) => {
-//       const gallery: GalleryState = getState().gallery;
-//       const { latest_mtime } = gallery.categories[category];
-//       socketio.emit('requestLatestImages', category, latest_mtime);
-//     },
-//     emitCancelProcessing: () => {
-//       socketio.emit('cancel');
-//     },
-//     emitRequestSystemConfig: () => {
-//       socketio.emit('requestSystemConfig');
-//     },
-//     emitSearchForModels: (modelFolder: string) => {
-//       socketio.emit('searchForModels', modelFolder);
-//     },
-//     emitAddNewModel: (modelConfig: InvokeAI.InvokeModelConfigProps) => {
-//       socketio.emit('addNewModel', modelConfig);
-//     },
-//     emitDeleteModel: (modelName: string) => {
-//       socketio.emit('deleteModel', modelName);
-//     },
-//     emitConvertToDiffusers: (
-//       modelToConvert: InvokeAI.InvokeModelConversionProps
-//     ) => {
-//       dispatch(modelConvertRequested());
-//       socketio.emit('convertToDiffusers', modelToConvert);
-//     },
-//     emitMergeDiffusersModels: (
-//       modelMergeInfo: InvokeAI.InvokeModelMergingProps
-//     ) => {
-//       dispatch(modelMergingRequested());
-//       socketio.emit('mergeDiffusersModels', modelMergeInfo);
-//     },
-//     emitRequestModelChange: (modelName: string) => {
-//       dispatch(modelChangeRequested());
-//       socketio.emit('requestModelChange', modelName);
-//     },
-//     emitSaveStagingAreaImageToGallery: (url: string) => {
-//       socketio.emit('requestSaveStagingAreaImageToGallery', url);
-//     },
-//     emitRequestEmptyTempFolder: () => {
-//       socketio.emit('requestEmptyTempFolder');
-//     },
-//   };
-// };
+      socketio.emit('runPostprocessing', imageToProcess, {
+        type: facetoolType,
+        ...facetoolParameters,
+      });
+      dispatch(
+        addLogEntry({
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
+          message: `Face restoration (${facetoolType}) requested: ${JSON.stringify(
+            {
+              file: imageToProcess.url,
+              ...facetoolParameters,
+            }
+          )}`,
+        })
+      );
+    },
+    emitDeleteImage: (imageToDelete: InvokeAI._Image) => {
+      const { url, uuid, category, thumbnail } = imageToDelete;
+      dispatch(removeImage(imageToDelete));
+      socketio.emit('deleteImage', url, thumbnail, uuid, category);
+    },
+    emitRequestImages: (category: GalleryCategory) => {
+      const gallery: GalleryState = getState().gallery;
+      const { earliest_mtime } = gallery.categories[category];
+      socketio.emit('requestImages', category, earliest_mtime);
+    },
+    emitRequestNewImages: (category: GalleryCategory) => {
+      const gallery: GalleryState = getState().gallery;
+      const { latest_mtime } = gallery.categories[category];
+      socketio.emit('requestLatestImages', category, latest_mtime);
+    },
+    emitCancelProcessing: () => {
+      socketio.emit('cancel');
+    },
+    emitRequestSystemConfig: () => {
+      socketio.emit('requestSystemConfig');
+    },
+    emitSearchForModels: (modelFolder: string) => {
+      socketio.emit('searchForModels', modelFolder);
+    },
+    emitAddNewModel: (modelConfig: InvokeAI.InvokeModelConfigProps) => {
+      socketio.emit('addNewModel', modelConfig);
+    },
+    emitDeleteModel: (modelName: string) => {
+      socketio.emit('deleteModel', modelName);
+    },
+    emitConvertToDiffusers: (
+      modelToConvert: InvokeAI.InvokeModelConversionProps
+    ) => {
+      dispatch(modelConvertRequested());
+      socketio.emit('convertToDiffusers', modelToConvert);
+    },
+    emitMergeDiffusersModels: (
+      modelMergeInfo: InvokeAI.InvokeModelMergingProps
+    ) => {
+      dispatch(modelMergingRequested());
+      socketio.emit('mergeDiffusersModels', modelMergeInfo);
+    },
+    emitRequestModelChange: (modelName: string) => {
+      dispatch(modelChangeRequested());
+      socketio.emit('requestModelChange', modelName);
+    },
+    emitSaveStagingAreaImageToGallery: (url: string) => {
+      socketio.emit('requestSaveStagingAreaImageToGallery', url);
+    },
+    emitRequestEmptyTempFolder: () => {
+      socketio.emit('requestEmptyTempFolder');
+    },
+  };
+};
 
-// export default makeSocketIOEmitters;
+export default makeSocketIOEmitters;
 
 export default {};

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

@@ -0,0 +1,4 @@
+import { createAction } from '@reduxjs/toolkit';
+import { InvokeTabName } from 'features/ui/store/tabMap';
+
+export const userInvoked = createAction<InvokeTabName>('app/userInvoked');

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

@@ -0,0 +1,8 @@
+export const LOCALSTORAGE_KEYS = [
+  'chakra-ui-color-mode',
+  'i18nextLng',
+  'ROARR_FILTER',
+  'ROARR_LOG',
+];
+
+export const LOCALSTORAGE_PREFIX = '@@invokeai-';

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/serialize.ts

@@ -0,0 +1,36 @@
+import { canvasPersistDenylist } from 'features/canvas/store/canvasPersistDenylist';
+import { galleryPersistDenylist } from 'features/gallery/store/galleryPersistDenylist';
+import { resultsPersistDenylist } from 'features/gallery/store/resultsPersistDenylist';
+import { uploadsPersistDenylist } from 'features/gallery/store/uploadsPersistDenylist';
+import { lightboxPersistDenylist } from 'features/lightbox/store/lightboxPersistDenylist';
+import { nodesPersistDenylist } from 'features/nodes/store/nodesPersistDenylist';
+import { generationPersistDenylist } from 'features/parameters/store/generationPersistDenylist';
+import { postprocessingPersistDenylist } from 'features/parameters/store/postprocessingPersistDenylist';
+import { modelsPersistDenylist } from 'features/system/store/modelsPersistDenylist';
+import { systemPersistDenylist } from 'features/system/store/systemPersistDenylist';
+import { uiPersistDenylist } from 'features/ui/store/uiPersistDenylist';
+import { omit } from 'lodash-es';
+import { SerializeFunction } from 'redux-remember';
+
+const serializationDenylist: {
+  [key: string]: string[];
+} = {
+  canvas: canvasPersistDenylist,
+  gallery: galleryPersistDenylist,
+  generation: generationPersistDenylist,
+  lightbox: lightboxPersistDenylist,
+  models: modelsPersistDenylist,
+  nodes: nodesPersistDenylist,
+  postprocessing: postprocessingPersistDenylist,
+  results: resultsPersistDenylist,
+  system: systemPersistDenylist,
+  // config: configPersistDenyList,
+  ui: uiPersistDenylist,
+  uploads: uploadsPersistDenylist,
+  // hotkeys: hotkeysPersistDenylist,
+};
+
+export const serialize: SerializeFunction = (data, key) => {
+  const result = omit(data, serializationDenylist[key]);
+  return JSON.stringify(result);
+};

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/unserialize.ts

@@ -0,0 +1,38 @@
+import { initialCanvasState } from 'features/canvas/store/canvasSlice';
+import { initialGalleryState } from 'features/gallery/store/gallerySlice';
+import { initialResultsState } from 'features/gallery/store/resultsSlice';
+import { initialUploadsState } from 'features/gallery/store/uploadsSlice';
+import { initialLightboxState } from 'features/lightbox/store/lightboxSlice';
+import { initialNodesState } from 'features/nodes/store/nodesSlice';
+import { initialGenerationState } from 'features/parameters/store/generationSlice';
+import { initialPostprocessingState } from 'features/parameters/store/postprocessingSlice';
+import { initialConfigState } from 'features/system/store/configSlice';
+import { initialModelsState } from 'features/system/store/modelSlice';
+import { initialSystemState } from 'features/system/store/systemSlice';
+import { initialHotkeysState } from 'features/ui/store/hotkeysSlice';
+import { initialUIState } from 'features/ui/store/uiSlice';
+import { defaultsDeep } from 'lodash-es';
+import { UnserializeFunction } from 'redux-remember';
+
+const initialStates: {
+  [key: string]: any;
+} = {
+  canvas: initialCanvasState,
+  gallery: initialGalleryState,
+  generation: initialGenerationState,
+  lightbox: initialLightboxState,
+  models: initialModelsState,
+  nodes: initialNodesState,
+  postprocessing: initialPostprocessingState,
+  results: initialResultsState,
+  system: initialSystemState,
+  config: initialConfigState,
+  ui: initialUIState,
+  uploads: initialUploadsState,
+  hotkeys: initialHotkeysState,
+};
+
+export const unserialize: UnserializeFunction = (data, key) => {
+  const result = defaultsDeep(JSON.parse(data), initialStates[key]);
+  return result;
+};

invokeai/frontend/web/src/app/store/middleware/devtools/actionSanitizer.ts

@@ -0,0 +1,30 @@
+import { AnyAction } from '@reduxjs/toolkit';
+import { isAnyGraphBuilt } from 'features/nodes/store/actions';
+import { forEach } from 'lodash-es';
+import { Graph } from 'services/api';
+
+export const actionSanitizer = <A extends AnyAction>(action: A): A => {
+  if (isAnyGraphBuilt(action)) {
+    if (action.payload.nodes) {
+      const sanitizedNodes: Graph['nodes'] = {};
+
+      // Sanitize nodes as needed
+      forEach(action.payload.nodes, (node, key) => {
+        // Don't log the whole freaking dataURL
+        if (node.type === 'dataURL_image') {
+          const { dataURL, ...rest } = node;
+          sanitizedNodes[key] = { ...rest, dataURL: '<dataURL>' };
+        } else {
+          sanitizedNodes[key] = { ...node };
+        }
+      });
+
+      return {
+        ...action,
+        payload: { ...action.payload, nodes: sanitizedNodes },
+      };
+    }
+  }
+
+  return action;
+};

invokeai/frontend/web/src/app/store/middleware/devtools/actionsDenylist.ts

@@ -0,0 +1,11 @@
+export const actionsDenylist = [
+  'canvas/setCursorPosition',
+  'canvas/setStageCoordinates',
+  'canvas/setStageScale',
+  'canvas/setIsDrawing',
+  'canvas/setBoundingBoxCoordinates',
+  'canvas/setBoundingBoxDimensions',
+  'canvas/setIsDrawing',
+  'canvas/addPointToCurrentLine',
+  'socket/generatorProgress',
+];

invokeai/frontend/web/src/app/store/middleware/devtools/stateSanitizer.ts

@@ -0,0 +1,3 @@
+export const stateSanitizer = <S>(state: S): S => {
+  return state;
+};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -0,0 +1,54 @@
+import {
+  createListenerMiddleware,
+  addListener,
+  ListenerEffect,
+  AnyAction,
+} from '@reduxjs/toolkit';
+import type { TypedStartListening, TypedAddListener } from '@reduxjs/toolkit';
+
+import type { RootState, AppDispatch } from '../../store';
+import { addInitialImageSelectedListener } from './listeners/initialImageSelected';
+import { addImageResultReceivedListener } from './listeners/invocationComplete';
+import { addImageUploadedListener } from './listeners/imageUploaded';
+import { addRequestedImageDeletionListener } from './listeners/imageDeleted';
+import { addUserInvokedCanvasListener } from './listeners/userInvokedCanvas';
+import { addUserInvokedNodesListener } from './listeners/userInvokedNodes';
+import { addUserInvokedTextToImageListener } from './listeners/userInvokedTextToImage';
+import { addUserInvokedImageToImageListener } from './listeners/userInvokedImageToImage';
+import { addCanvasSavedToGalleryListener } from './listeners/canvasSavedToGallery';
+import { addCanvasDownloadedAsImageListener } from './listeners/canvasDownloadedAsImage';
+import { addCanvasCopiedToClipboardListener } from './listeners/canvasCopiedToClipboard';
+import { addCanvasMergedListener } from './listeners/canvasMerged';
+
+export const listenerMiddleware = createListenerMiddleware();
+
+export type AppStartListening = TypedStartListening<RootState, AppDispatch>;
+
+export const startAppListening =
+  listenerMiddleware.startListening as AppStartListening;
+
+export const addAppListener = addListener as TypedAddListener<
+  RootState,
+  AppDispatch
+>;
+
+export type AppListenerEffect = ListenerEffect<
+  AnyAction,
+  RootState,
+  AppDispatch
+>;
+
+addImageUploadedListener();
+addInitialImageSelectedListener();
+addImageResultReceivedListener();
+addRequestedImageDeletionListener();
+
+addUserInvokedCanvasListener();
+addUserInvokedNodesListener();
+addUserInvokedTextToImageListener();
+addUserInvokedImageToImageListener();
+
+addCanvasSavedToGalleryListener();
+addCanvasDownloadedAsImageListener();
+addCanvasCopiedToClipboardListener();
+addCanvasMergedListener();

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

@@ -0,0 +1,33 @@
+import { canvasCopiedToClipboard } from 'features/canvas/store/actions';
+import { startAppListening } from '..';
+import { log } from 'app/logging/useLogger';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { addToast } from 'features/system/store/systemSlice';
+import { copyBlobToClipboard } from 'features/canvas/util/copyBlobToClipboard';
+
+const moduleLog = log.child({ namespace: 'canvasCopiedToClipboardListener' });
+
+export const addCanvasCopiedToClipboardListener = () => {
+  startAppListening({
+    actionCreator: canvasCopiedToClipboard,
+    effect: async (action, { dispatch, getState }) => {
+      const state = getState();
+
+      const blob = await getBaseLayerBlob(state);
+
+      if (!blob) {
+        moduleLog.error('Problem getting base layer blob');
+        dispatch(
+          addToast({
+            title: 'Problem Copying Canvas',
+            description: 'Unable to export base layer',
+            status: 'error',
+          })
+        );
+        return;
+      }
+
+      copyBlobToClipboard(blob);
+    },
+  });
+};

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

@@ -0,0 +1,33 @@
+import { canvasDownloadedAsImage } from 'features/canvas/store/actions';
+import { startAppListening } from '..';
+import { log } from 'app/logging/useLogger';
+import { downloadBlob } from 'features/canvas/util/downloadBlob';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { addToast } from 'features/system/store/systemSlice';
+
+const moduleLog = log.child({ namespace: 'canvasSavedToGalleryListener' });
+
+export const addCanvasDownloadedAsImageListener = () => {
+  startAppListening({
+    actionCreator: canvasDownloadedAsImage,
+    effect: async (action, { dispatch, getState }) => {
+      const state = getState();
+
+      const blob = await getBaseLayerBlob(state);
+
+      if (!blob) {
+        moduleLog.error('Problem getting base layer blob');
+        dispatch(
+          addToast({
+            title: 'Problem Downloading Canvas',
+            description: 'Unable to export base layer',
+            status: 'error',
+          })
+        );
+        return;
+      }
+
+      downloadBlob(blob, 'mergedCanvas.png');
+    },
+  });
+};

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

@@ -0,0 +1,88 @@
+import { canvasMerged } from 'features/canvas/store/actions';
+import { startAppListening } from '..';
+import { log } from 'app/logging/useLogger';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { addToast } from 'features/system/store/systemSlice';
+import { imageUploaded } from 'services/thunks/image';
+import { v4 as uuidv4 } from 'uuid';
+import { deserializeImageResponse } from 'services/util/deserializeImageResponse';
+import { setMergedCanvas } from 'features/canvas/store/canvasSlice';
+import { getCanvasBaseLayer } from 'features/canvas/util/konvaInstanceProvider';
+
+const moduleLog = log.child({ namespace: 'canvasCopiedToClipboardListener' });
+
+export const addCanvasMergedListener = () => {
+  startAppListening({
+    actionCreator: canvasMerged,
+    effect: async (action, { dispatch, getState, take }) => {
+      const state = getState();
+
+      const blob = await getBaseLayerBlob(state, true);
+
+      if (!blob) {
+        moduleLog.error('Problem getting base layer blob');
+        dispatch(
+          addToast({
+            title: 'Problem Merging Canvas',
+            description: 'Unable to export base layer',
+            status: 'error',
+          })
+        );
+        return;
+      }
+
+      const canvasBaseLayer = getCanvasBaseLayer();
+
+      if (!canvasBaseLayer) {
+        moduleLog.error('Problem getting canvas base layer');
+        dispatch(
+          addToast({
+            title: 'Problem Merging Canvas',
+            description: 'Unable to export base layer',
+            status: 'error',
+          })
+        );
+        return;
+      }
+
+      const baseLayerRect = canvasBaseLayer.getClientRect({
+        relativeTo: canvasBaseLayer.getParent(),
+      });
+
+      const filename = `mergedCanvas_${uuidv4()}.png`;
+
+      dispatch(
+        imageUploaded({
+          imageType: 'intermediates',
+          formData: {
+            file: new File([blob], filename, { type: 'image/png' }),
+          },
+        })
+      );
+
+      const [{ payload }] = await take(
+        (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
+          imageUploaded.fulfilled.match(action) &&
+          action.meta.arg.formData.file.name === filename
+      );
+
+      const mergedCanvasImage = deserializeImageResponse(payload.response);
+
+      dispatch(
+        setMergedCanvas({
+          kind: 'image',
+          layer: 'base',
+          image: mergedCanvasImage,
+          ...baseLayerRect,
+        })
+      );
+
+      dispatch(
+        addToast({
+          title: 'Canvas Merged',
+          status: 'success',
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,40 @@
+import { canvasSavedToGallery } from 'features/canvas/store/actions';
+import { startAppListening } from '..';
+import { log } from 'app/logging/useLogger';
+import { imageUploaded } from 'services/thunks/image';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { addToast } from 'features/system/store/systemSlice';
+
+const moduleLog = log.child({ namespace: 'canvasSavedToGalleryListener' });
+
+export const addCanvasSavedToGalleryListener = () => {
+  startAppListening({
+    actionCreator: canvasSavedToGallery,
+    effect: async (action, { dispatch, getState }) => {
+      const state = getState();
+
+      const blob = await getBaseLayerBlob(state);
+
+      if (!blob) {
+        moduleLog.error('Problem getting base layer blob');
+        dispatch(
+          addToast({
+            title: 'Problem Saving Canvas',
+            description: 'Unable to export base layer',
+            status: 'error',
+          })
+        );
+        return;
+      }
+
+      dispatch(
+        imageUploaded({
+          imageType: 'results',
+          formData: {
+            file: new File([blob], 'mergedCanvas.png', { type: 'image/png' }),
+          },
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,59 @@
+import { requestedImageDeletion } from 'features/gallery/store/actions';
+import { startAppListening } from '..';
+import { imageDeleted } from 'services/thunks/image';
+import { log } from 'app/logging/useLogger';
+import { clamp } from 'lodash-es';
+import { imageSelected } from 'features/gallery/store/gallerySlice';
+
+const moduleLog = log.child({ namespace: 'addRequestedImageDeletionListener' });
+
+export const addRequestedImageDeletionListener = () => {
+  startAppListening({
+    actionCreator: requestedImageDeletion,
+    effect: (action, { dispatch, getState }) => {
+      const image = action.payload;
+      if (!image) {
+        moduleLog.warn('No image provided');
+        return;
+      }
+
+      const { name, type } = image;
+
+      if (type !== 'uploads' && type !== 'results') {
+        moduleLog.warn({ data: image }, `Invalid image type ${type}`);
+        return;
+      }
+
+      const selectedImageName = getState().gallery.selectedImage?.name;
+
+      if (selectedImageName === name) {
+        const allIds = getState()[type].ids;
+        const allEntities = getState()[type].entities;
+
+        const deletedImageIndex = allIds.findIndex(
+          (result) => result.toString() === name
+        );
+
+        const filteredIds = allIds.filter((id) => id.toString() !== name);
+
+        const newSelectedImageIndex = clamp(
+          deletedImageIndex,
+          0,
+          filteredIds.length - 1
+        );
+
+        const newSelectedImageId = filteredIds[newSelectedImageIndex];
+
+        const newSelectedImage = allEntities[newSelectedImageId];
+
+        if (newSelectedImageId) {
+          dispatch(imageSelected(newSelectedImage));
+        } else {
+          dispatch(imageSelected());
+        }
+      }
+
+      dispatch(imageDeleted({ imageName: name, imageType: type }));
+    },
+  });
+};

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

@@ -0,0 +1,46 @@
+import { deserializeImageResponse } from 'services/util/deserializeImageResponse';
+import { startAppListening } from '..';
+import { uploadAdded } from 'features/gallery/store/uploadsSlice';
+import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { imageUploaded } from 'services/thunks/image';
+import { addToast } from 'features/system/store/systemSlice';
+import { initialImageSelected } from 'features/parameters/store/actions';
+import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
+import { resultAdded } from 'features/gallery/store/resultsSlice';
+
+export const addImageUploadedListener = () => {
+  startAppListening({
+    predicate: (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
+      imageUploaded.fulfilled.match(action) &&
+      action.payload.response.image_type !== 'intermediates',
+    effect: (action, { dispatch, getState }) => {
+      const { response } = action.payload;
+      const { imageType } = action.meta.arg;
+
+      const state = getState();
+      const image = deserializeImageResponse(response);
+
+      if (imageType === 'uploads') {
+        dispatch(uploadAdded(image));
+
+        dispatch(addToast({ title: 'Image Uploaded', status: 'success' }));
+
+        if (state.gallery.shouldAutoSwitchToNewImages) {
+          dispatch(imageSelected(image));
+        }
+
+        if (action.meta.arg.activeTabName === 'img2img') {
+          dispatch(initialImageSelected(image));
+        }
+
+        if (action.meta.arg.activeTabName === 'unifiedCanvas') {
+          dispatch(setInitialCanvasImage(image));
+        }
+      }
+
+      if (imageType === 'results') {
+        dispatch(resultAdded(image));
+      }
+    },
+  });
+};

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

@@ -0,0 +1,54 @@
+import { initialImageChanged } from 'features/parameters/store/generationSlice';
+import { Image, isInvokeAIImage } from 'app/types/invokeai';
+import { selectResultsById } from 'features/gallery/store/resultsSlice';
+import { selectUploadsById } from 'features/gallery/store/uploadsSlice';
+import { t } from 'i18next';
+import { addToast } from 'features/system/store/systemSlice';
+import { startAppListening } from '..';
+import { initialImageSelected } from 'features/parameters/store/actions';
+import { makeToast } from 'app/components/Toaster';
+
+export const addInitialImageSelectedListener = () => {
+  startAppListening({
+    actionCreator: initialImageSelected,
+    effect: (action, { getState, dispatch }) => {
+      if (!action.payload) {
+        dispatch(
+          addToast(
+            makeToast({ title: t('toast.imageNotLoadedDesc'), status: 'error' })
+          )
+        );
+        return;
+      }
+
+      if (isInvokeAIImage(action.payload)) {
+        dispatch(initialImageChanged(action.payload));
+        dispatch(addToast(makeToast(t('toast.sentToImageToImage'))));
+        return;
+      }
+
+      const { name, type } = action.payload;
+
+      let image: Image | undefined;
+      const state = getState();
+
+      if (type === 'results') {
+        image = selectResultsById(state, name);
+      } else if (type === 'uploads') {
+        image = selectUploadsById(state, name);
+      }
+
+      if (!image) {
+        dispatch(
+          addToast(
+            makeToast({ title: t('toast.imageNotLoadedDesc'), status: 'error' })
+          )
+        );
+        return;
+      }
+
+      dispatch(initialImageChanged(image));
+      dispatch(addToast(makeToast(t('toast.sentToImageToImage'))));
+    },
+  });
+};

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

@@ -0,0 +1,88 @@
+import { invocationComplete } from 'services/events/actions';
+import { isImageOutput } from 'services/types/guards';
+import {
+  buildImageUrls,
+  extractTimestampFromImageName,
+} from 'services/util/deserializeImageField';
+import { Image } from 'app/types/invokeai';
+import { resultAdded } from 'features/gallery/store/resultsSlice';
+import { imageReceived, thumbnailReceived } from 'services/thunks/image';
+import { startAppListening } from '..';
+import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
+
+const nodeDenylist = ['dataURL_image'];
+
+export const addImageResultReceivedListener = () => {
+  startAppListening({
+    predicate: (action) => {
+      if (
+        invocationComplete.match(action) &&
+        isImageOutput(action.payload.data.result)
+      ) {
+        return true;
+      }
+      return false;
+    },
+    effect: (action, { getState, dispatch }) => {
+      if (!invocationComplete.match(action)) {
+        return;
+      }
+
+      const { data, shouldFetchImages } = action.payload;
+      const { result, node, graph_execution_state_id } = data;
+
+      if (isImageOutput(result) && !nodeDenylist.includes(node.type)) {
+        const name = result.image.image_name;
+        const type = result.image.image_type;
+        const state = getState();
+
+        // if we need to refetch, set URLs to placeholder for now
+        const { url, thumbnail } = shouldFetchImages
+          ? { url: '', thumbnail: '' }
+          : buildImageUrls(type, name);
+
+        const timestamp = extractTimestampFromImageName(name);
+
+        const image: Image = {
+          name,
+          type,
+          url,
+          thumbnail,
+          metadata: {
+            created: timestamp,
+            width: result.width,
+            height: result.height,
+            invokeai: {
+              session_id: graph_execution_state_id,
+              ...(node ? { node } : {}),
+            },
+          },
+        };
+
+        dispatch(resultAdded(image));
+
+        if (state.gallery.shouldAutoSwitchToNewImages) {
+          dispatch(imageSelected(image));
+        }
+
+        if (state.config.shouldFetchImages) {
+          dispatch(imageReceived({ imageName: name, imageType: type }));
+          dispatch(
+            thumbnailReceived({
+              thumbnailName: name,
+              thumbnailType: type,
+            })
+          );
+        }
+
+        if (
+          graph_execution_state_id ===
+          state.canvas.layerState.stagingArea.sessionId
+        ) {
+          dispatch(addImageToStagingArea(image));
+        }
+      }
+    },
+  });
+};

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

@@ -0,0 +1,164 @@
+import { startAppListening } from '..';
+import { sessionCreated, sessionInvoked } from 'services/thunks/session';
+import { buildCanvasGraphComponents } from 'features/nodes/util/graphBuilders/buildCanvasGraph';
+import { log } from 'app/logging/useLogger';
+import { canvasGraphBuilt } from 'features/nodes/store/actions';
+import { imageUploaded } from 'services/thunks/image';
+import { v4 as uuidv4 } from 'uuid';
+import { Graph } from 'services/api';
+import {
+  canvasSessionIdChanged,
+  stagingAreaInitialized,
+} from 'features/canvas/store/canvasSlice';
+import { userInvoked } from 'app/store/actions';
+import { getCanvasData } from 'features/canvas/util/getCanvasData';
+import { getCanvasGenerationMode } from 'features/canvas/util/getCanvasGenerationMode';
+import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
+import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
+
+const moduleLog = log.child({ namespace: 'invoke' });
+
+/**
+ * This listener is responsible for building the canvas graph and blobs when the user invokes the canvas.
+ * It is also responsible for uploading the base and mask layers to the server.
+ */
+export const addUserInvokedCanvasListener = () => {
+  startAppListening({
+    predicate: (action): action is ReturnType<typeof userInvoked> =>
+      userInvoked.match(action) && action.payload === 'unifiedCanvas',
+    effect: async (action, { getState, dispatch, take }) => {
+      const state = getState();
+
+      // Build canvas blobs
+      const canvasBlobsAndImageData = await getCanvasData(state);
+
+      if (!canvasBlobsAndImageData) {
+        moduleLog.error('Unable to create canvas data');
+        return;
+      }
+
+      const { baseBlob, baseImageData, maskBlob, maskImageData } =
+        canvasBlobsAndImageData;
+
+      // Determine the generation mode
+      const generationMode = getCanvasGenerationMode(
+        baseImageData,
+        maskImageData
+      );
+
+      if (state.system.enableImageDebugging) {
+        const baseDataURL = await blobToDataURL(baseBlob);
+        const maskDataURL = await blobToDataURL(maskBlob);
+        openBase64ImageInTab([
+          { base64: maskDataURL, caption: 'mask b64' },
+          { base64: baseDataURL, caption: 'image b64' },
+        ]);
+      }
+
+      moduleLog.debug(`Generation mode: ${generationMode}`);
+
+      // Build the canvas graph
+      const graphComponents = await buildCanvasGraphComponents(
+        state,
+        generationMode
+      );
+
+      if (!graphComponents) {
+        moduleLog.error('Problem building graph');
+        return;
+      }
+
+      const { rangeNode, iterateNode, baseNode, edges } = graphComponents;
+
+      // Upload the base layer, to be used as init image
+      const baseFilename = `${uuidv4()}.png`;
+
+      dispatch(
+        imageUploaded({
+          imageType: 'intermediates',
+          formData: {
+            file: new File([baseBlob], baseFilename, { type: 'image/png' }),
+          },
+        })
+      );
+
+      if (baseNode.type === 'img2img' || baseNode.type === 'inpaint') {
+        const [{ payload: basePayload }] = await take(
+          (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
+            imageUploaded.fulfilled.match(action) &&
+            action.meta.arg.formData.file.name === baseFilename
+        );
+
+        const { image_name: baseName, image_type: baseType } =
+          basePayload.response;
+
+        baseNode.image = {
+          image_name: baseName,
+          image_type: baseType,
+        };
+      }
+
+      // Upload the mask layer image
+      const maskFilename = `${uuidv4()}.png`;
+
+      if (baseNode.type === 'inpaint') {
+        dispatch(
+          imageUploaded({
+            imageType: 'intermediates',
+            formData: {
+              file: new File([maskBlob], maskFilename, { type: 'image/png' }),
+            },
+          })
+        );
+
+        const [{ payload: maskPayload }] = await take(
+          (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
+            imageUploaded.fulfilled.match(action) &&
+            action.meta.arg.formData.file.name === maskFilename
+        );
+
+        const { image_name: maskName, image_type: maskType } =
+          maskPayload.response;
+
+        baseNode.mask = {
+          image_name: maskName,
+          image_type: maskType,
+        };
+      }
+
+      // Assemble!
+      const nodes: Graph['nodes'] = {
+        [rangeNode.id]: rangeNode,
+        [iterateNode.id]: iterateNode,
+        [baseNode.id]: baseNode,
+      };
+
+      const graph = { nodes, edges };
+
+      dispatch(canvasGraphBuilt(graph));
+      moduleLog({ data: graph }, 'Canvas graph built');
+
+      // Actually create the session
+      dispatch(sessionCreated({ graph }));
+
+      // Wait for the session to be invoked (this is just the HTTP request to start processing)
+      const [{ meta }] = await take(sessionInvoked.fulfilled.match);
+
+      const { sessionId } = meta.arg;
+
+      if (!state.canvas.layerState.stagingArea.boundingBox) {
+        dispatch(
+          stagingAreaInitialized({
+            sessionId,
+            boundingBox: {
+              ...state.canvas.boundingBoxCoordinates,
+              ...state.canvas.boundingBoxDimensions,
+            },
+          })
+        );
+      }
+
+      dispatch(canvasSessionIdChanged(sessionId));
+    },
+  });
+};

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

@@ -0,0 +1,24 @@
+import { startAppListening } from '..';
+import { buildImageToImageGraph } from 'features/nodes/util/graphBuilders/buildImageToImageGraph';
+import { sessionCreated } from 'services/thunks/session';
+import { log } from 'app/logging/useLogger';
+import { imageToImageGraphBuilt } from 'features/nodes/store/actions';
+import { userInvoked } from 'app/store/actions';
+
+const moduleLog = log.child({ namespace: 'invoke' });
+
+export const addUserInvokedImageToImageListener = () => {
+  startAppListening({
+    predicate: (action): action is ReturnType<typeof userInvoked> =>
+      userInvoked.match(action) && action.payload === 'img2img',
+    effect: (action, { getState, dispatch }) => {
+      const state = getState();
+
+      const graph = buildImageToImageGraph(state);
+      dispatch(imageToImageGraphBuilt(graph));
+      moduleLog({ data: graph }, 'Image to Image graph built');
+
+      dispatch(sessionCreated({ graph }));
+    },
+  });
+};

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

@@ -0,0 +1,24 @@
+import { startAppListening } from '..';
+import { sessionCreated } from 'services/thunks/session';
+import { buildNodesGraph } from 'features/nodes/util/graphBuilders/buildNodesGraph';
+import { log } from 'app/logging/useLogger';
+import { nodesGraphBuilt } from 'features/nodes/store/actions';
+import { userInvoked } from 'app/store/actions';
+
+const moduleLog = log.child({ namespace: 'invoke' });
+
+export const addUserInvokedNodesListener = () => {
+  startAppListening({
+    predicate: (action): action is ReturnType<typeof userInvoked> =>
+      userInvoked.match(action) && action.payload === 'nodes',
+    effect: (action, { getState, dispatch }) => {
+      const state = getState();
+
+      const graph = buildNodesGraph(state);
+      dispatch(nodesGraphBuilt(graph));
+      moduleLog({ data: graph }, 'Nodes graph built');
+
+      dispatch(sessionCreated({ graph }));
+    },
+  });
+};

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

@@ -0,0 +1,24 @@
+import { startAppListening } from '..';
+import { buildTextToImageGraph } from 'features/nodes/util/graphBuilders/buildTextToImageGraph';
+import { sessionCreated } from 'services/thunks/session';
+import { log } from 'app/logging/useLogger';
+import { textToImageGraphBuilt } from 'features/nodes/store/actions';
+import { userInvoked } from 'app/store/actions';
+
+const moduleLog = log.child({ namespace: 'invoke' });
+
+export const addUserInvokedTextToImageListener = () => {
+  startAppListening({
+    predicate: (action): action is ReturnType<typeof userInvoked> =>
+      userInvoked.match(action) && action.payload === 'txt2img',
+    effect: (action, { getState, dispatch }) => {
+      const state = getState();
+
+      const graph = buildTextToImageGraph(state);
+      dispatch(textToImageGraphBuilt(graph));
+      moduleLog({ data: graph }, 'Text to Image graph built');
+
+      dispatch(sessionCreated({ graph }));
+    },
+  });
+};

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

@@ -1,4 +0,0 @@
-import { store } from 'app/store/store';
-import { persistStore } from 'redux-persist';
-
-export const persistor = persistStore(store);

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

@@ -1,9 +1,12 @@
-import { combineReducers, configureStore } from '@reduxjs/toolkit';
+import {
+  AnyAction,
+  ThunkDispatch,
+  combineReducers,
+  configureStore,
+} from '@reduxjs/toolkit';
 
-import { persistReducer } from 'redux-persist';
-import storage from 'redux-persist/lib/storage'; // defaults to localStorage for web
+import { rememberReducer, rememberEnhancer } from 'redux-remember';
 import dynamicMiddlewares from 'redux-dynamic-middlewares';
-import { getPersistConfig } from 'redux-deep-persist';
 
 import canvasReducer from 'features/canvas/store/canvasSlice';
 import galleryReducer from 'features/gallery/store/gallerySlice';
@@ -19,33 +22,17 @@ import hotkeysReducer from 'features/ui/store/hotkeysSlice';
 import modelsReducer from 'features/system/store/modelSlice';
 import nodesReducer from 'features/nodes/store/nodesSlice';
 
-import { canvasDenylist } from 'features/canvas/store/canvasPersistDenylist';
-import { galleryDenylist } from 'features/gallery/store/galleryPersistDenylist';
-import { generationDenylist } from 'features/parameters/store/generationPersistDenylist';
-import { lightboxDenylist } from 'features/lightbox/store/lightboxPersistDenylist';
-import { modelsDenylist } from 'features/system/store/modelsPersistDenylist';
-import { nodesDenylist } from 'features/nodes/store/nodesPersistDenylist';
-import { postprocessingDenylist } from 'features/parameters/store/postprocessingPersistDenylist';
-import { systemDenylist } from 'features/system/store/systemPersistDenylist';
-import { uiDenylist } from 'features/ui/store/uiPersistDenylist';
-import { resultsDenylist } from 'features/gallery/store/resultsPersistDenylist';
-import { uploadsDenylist } from 'features/gallery/store/uploadsPersistDenylist';
+import { listenerMiddleware } from './middleware/listenerMiddleware';
 
-/**
- * redux-persist provides an easy and reliable way to persist state across reloads.
- *
- * While we definitely want generation parameters to be persisted, there are a number
- * of things we do *not* want to be persisted across reloads:
- *   - Gallery/selected image (user may add/delete images from disk between page loads)
- *   - Connection/processing status
- *   - Availability of external libraries like ESRGAN/GFPGAN
- *
- * These can be denylisted in redux-persist.
- *
- * The necesssary nested persistors with denylists are configured below.
- */
+import { actionSanitizer } from './middleware/devtools/actionSanitizer';
+import { stateSanitizer } from './middleware/devtools/stateSanitizer';
+import { actionsDenylist } from './middleware/devtools/actionsDenylist';
 
-const rootReducer = combineReducers({
+import { serialize } from './enhancers/reduxRemember/serialize';
+import { unserialize } from './enhancers/reduxRemember/unserialize';
+import { LOCALSTORAGE_PREFIX } from './constants';
+
+const allReducers = {
   canvas: canvasReducer,
   gallery: galleryReducer,
   generation: generationReducer,
@@ -59,65 +46,54 @@ const rootReducer = combineReducers({
   ui: uiReducer,
   uploads: uploadsReducer,
   hotkeys: hotkeysReducer,
-});
+};
 
-const rootPersistConfig = getPersistConfig({
-  key: 'root',
-  storage,
-  rootReducer,
-  blacklist: [
-    ...canvasDenylist,
-    ...galleryDenylist,
-    ...generationDenylist,
-    ...lightboxDenylist,
-    ...modelsDenylist,
-    ...nodesDenylist,
-    ...postprocessingDenylist,
-    // ...resultsDenylist,
-    'results',
-    ...systemDenylist,
-    ...uiDenylist,
-    // ...uploadsDenylist,
-    'uploads',
-    'hotkeys',
-    'config',
-  ],
-});
+const rootReducer = combineReducers(allReducers);
 
-const persistedReducer = persistReducer(rootPersistConfig, rootReducer);
+const rememberedRootReducer = rememberReducer(rootReducer);
 
-// TODO: rip the old middleware out when nodes is complete
-// export function buildMiddleware() {
-//   if (import.meta.env.MODE === 'nodes' || import.meta.env.MODE === 'package') {
-//     return socketMiddleware();
-//   } else {
-//     return socketioMiddleware();
-//   }
-// }
+const rememberedKeys: (keyof typeof allReducers)[] = [
+  'canvas',
+  'gallery',
+  'generation',
+  'lightbox',
+  // 'models',
+  'nodes',
+  'postprocessing',
+  'system',
+  'ui',
+  // 'hotkeys',
+  // 'results',
+  // 'uploads',
+  // 'config',
+];
 
 export const store = configureStore({
-  reducer: persistedReducer,
+  reducer: rememberedRootReducer,
+  enhancers: [
+    rememberEnhancer(window.localStorage, rememberedKeys, {
+      persistDebounce: 300,
+      serialize,
+      unserialize,
+      prefix: LOCALSTORAGE_PREFIX,
+    }),
+  ],
   middleware: (getDefaultMiddleware) =>
     getDefaultMiddleware({
       immutableCheck: false,
       serializableCheck: false,
-    }).concat(dynamicMiddlewares),
+    })
+      .concat(dynamicMiddlewares)
+      .prepend(listenerMiddleware.middleware),
   devTools: {
-    // Uncommenting these very rapidly called actions makes the redux dev tools output much more readable
-    actionsDenylist: [
-      'canvas/setCursorPosition',
-      'canvas/setStageCoordinates',
-      'canvas/setStageScale',
-      'canvas/setIsDrawing',
-      'canvas/setBoundingBoxCoordinates',
-      'canvas/setBoundingBoxDimensions',
-      'canvas/setIsDrawing',
-      'canvas/addPointToCurrentLine',
-      'socket/generatorProgress',
-    ],
+    actionsDenylist,
+    actionSanitizer,
+    stateSanitizer,
+    trace: true,
   },
 });
 
 export type AppGetState = typeof store.getState;
 export type RootState = ReturnType<typeof store.getState>;
+export type AppThunkDispatch = ThunkDispatch<RootState, any, AnyAction>;
 export type AppDispatch = typeof store.dispatch;

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

@@ -1,6 +1,6 @@
 import { TypedUseSelectorHook, useDispatch, useSelector } from 'react-redux';
-import { AppDispatch, RootState } from 'app/store/store';
+import { AppThunkDispatch, RootState } from 'app/store/store';
 
 // Use throughout your app instead of plain `useDispatch` and `useSelector`
-export const useAppDispatch: () => AppDispatch = useDispatch;
+export const useAppDispatch = () => useDispatch<AppThunkDispatch>();
 export const useAppSelector: TypedUseSelectorHook<RootState> = useSelector;

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

@@ -0,0 +1,7 @@
+import { isEqual } from 'lodash-es';
+
+export const defaultSelectorOptions = {
+  memoizeOptions: {
+    resultEqualityCheck: isEqual,
+  },
+};

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

@@ -12,12 +12,10 @@
  * 'gfpgan'.
  */
 
-import { GalleryCategory } from 'features/gallery/store/gallerySlice';
-import { FacetoolType } from 'features/parameters/store/postprocessingSlice';
+import { SelectedImage } from 'features/parameters/store/actions';
 import { InvokeTabName } from 'features/ui/store/tabMap';
 import { IRect } from 'konva/lib/types';
 import { ImageResponseMetadata, ImageType } from 'services/api';
-import { AnyInvocation } from 'services/events/types';
 import { O } from 'ts-toolbelt';
 
 /**
@@ -49,15 +47,21 @@ export type CommonGeneratedImageMetadata = {
   postprocessing: null | Array<ESRGANMetadata | FacetoolMetadata>;
   sampler:
     | 'ddim'
-    | 'k_dpm_2_a'
-    | 'k_dpm_2'
-    | 'k_dpmpp_2_a'
-    | 'k_dpmpp_2'
-    | 'k_euler_a'
-    | 'k_euler'
-    | 'k_heun'
-    | 'k_lms'
-    | 'plms';
+    | 'ddpm'
+    | 'deis'
+    | 'lms'
+    | 'pndm'
+    | 'heun'
+    | 'heun_k'
+    | 'euler'
+    | 'euler_k'
+    | 'euler_a'
+    | 'kdpm_2'
+    | 'kdpm_2_a'
+    | 'dpmpp_2s'
+    | 'dpmpp_2m'
+    | 'dpmpp_2m_k'
+    | 'unipc';
   prompt: Prompt;
   seed: number;
   variations: SeedWeights;
@@ -126,6 +130,14 @@ export type Image = {
   metadata: ImageResponseMetadata;
 };
 
+export const isInvokeAIImage = (obj: Image | SelectedImage): obj is Image => {
+  if ('url' in obj && 'thumbnail' in obj) {
+    return true;
+  }
+
+  return false;
+};
+
 /**
  * Types related to the system status.
  */
@@ -270,7 +282,7 @@ export type FoundModelResponse = {
 
 // export type SystemConfigResponse = SystemConfig;
 
-export type ImageResultResponse = Omit<_Image, 'uuid'> & {
+export type ImageResultResponse = Omit<Image, 'uuid'> & {
   boundingBox?: IRect;
   generationMode: InvokeTabName;
 };
@@ -315,11 +327,11 @@ export type AppFeature =
 /**
  * A disable-able Stable Diffusion feature
  */
-export type StableDiffusionFeature =
-  | 'noiseConfig'
-  | 'variations'
+export type SDFeature =
+  | 'noise'
+  | 'variation'
   | 'symmetry'
-  | 'tiling'
+  | 'seamless'
   | 'hires';
 
 /**
@@ -337,6 +349,7 @@ export type AppConfig = {
   shouldFetchImages: boolean;
   disabledTabs: InvokeTabName[];
   disabledFeatures: AppFeature[];
+  disabledSDFeatures: SDFeature[];
   canRestoreDeletedImagesFromBin: boolean;
   sd: {
     iterations: {

invokeai/frontend/web/src/common/components/IAICollapse.tsx

@@ -0,0 +1,61 @@
+import { ChevronUpIcon } from '@chakra-ui/icons';
+import { Box, Collapse, Flex, Spacer, Switch } from '@chakra-ui/react';
+import { PropsWithChildren, memo } from 'react';
+
+export type IAIToggleCollapseProps = PropsWithChildren & {
+  label: string;
+  isOpen: boolean;
+  onToggle: () => void;
+  withSwitch?: boolean;
+};
+
+const IAICollapse = (props: IAIToggleCollapseProps) => {
+  const { label, isOpen, onToggle, children, withSwitch = false } = props;
+  return (
+    <Box>
+      <Flex
+        onClick={onToggle}
+        sx={{
+          alignItems: 'center',
+          p: 2,
+          px: 4,
+          borderTopRadius: 'base',
+          borderBottomRadius: isOpen ? 0 : 'base',
+          bg: isOpen ? 'base.750' : 'base.800',
+          color: 'base.100',
+          _hover: {
+            bg: isOpen ? 'base.700' : 'base.750',
+          },
+          fontSize: 'sm',
+          fontWeight: 600,
+          cursor: 'pointer',
+          transitionProperty: 'common',
+          transitionDuration: 'normal',
+          userSelect: 'none',
+        }}
+      >
+        {label}
+        <Spacer />
+        {withSwitch && <Switch isChecked={isOpen} pointerEvents="none" />}
+        {!withSwitch && (
+          <ChevronUpIcon
+            sx={{
+              w: '1rem',
+              h: '1rem',
+              transform: isOpen ? 'rotate(0deg)' : 'rotate(180deg)',
+              transitionProperty: 'common',
+              transitionDuration: 'normal',
+            }}
+          />
+        )}
+      </Flex>
+      <Collapse in={isOpen} animateOpacity>
+        <Box sx={{ p: 4, borderBottomRadius: 'base', bg: 'base.800' }}>
+          {children}
+        </Box>
+      </Collapse>
+    </Box>
+  );
+};
+
+export default memo(IAICollapse);

invokeai/frontend/web/src/common/components/IAICustomSelect.tsx

@@ -0,0 +1,172 @@
+import { CheckIcon } from '@chakra-ui/icons';
+import {
+  Box,
+  Flex,
+  FlexProps,
+  FormControl,
+  FormControlProps,
+  FormLabel,
+  Grid,
+  GridItem,
+  List,
+  ListItem,
+  Select,
+  Text,
+  Tooltip,
+  TooltipProps,
+} from '@chakra-ui/react';
+import { autoUpdate, offset, shift, useFloating } from '@floating-ui/react-dom';
+import { useSelect } from 'downshift';
+import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';
+
+import { memo } from 'react';
+
+type IAICustomSelectProps = {
+  label?: string;
+  items: string[];
+  selectedItem: string;
+  setSelectedItem: (v: string | null | undefined) => void;
+  withCheckIcon?: boolean;
+  formControlProps?: FormControlProps;
+  buttonProps?: FlexProps;
+  tooltip?: string;
+  tooltipProps?: Omit<TooltipProps, 'children'>;
+};
+
+const IAICustomSelect = (props: IAICustomSelectProps) => {
+  const {
+    label,
+    items,
+    setSelectedItem,
+    selectedItem,
+    withCheckIcon,
+    formControlProps,
+    tooltip,
+    buttonProps,
+    tooltipProps,
+  } = props;
+
+  const {
+    isOpen,
+    getToggleButtonProps,
+    getLabelProps,
+    getMenuProps,
+    highlightedIndex,
+    getItemProps,
+  } = useSelect({
+    items,
+    selectedItem,
+    onSelectedItemChange: ({ selectedItem: newSelectedItem }) =>
+      setSelectedItem(newSelectedItem),
+  });
+
+  const { refs, floatingStyles } = useFloating<HTMLButtonElement>({
+    whileElementsMounted: autoUpdate,
+    middleware: [offset(4), shift({ crossAxis: true, padding: 8 })],
+  });
+
+  return (
+    <FormControl sx={{ w: 'full' }} {...formControlProps}>
+      {label && (
+        <FormLabel
+          {...getLabelProps()}
+          onClick={() => {
+            refs.floating.current && refs.floating.current.focus();
+          }}
+        >
+          {label}
+        </FormLabel>
+      )}
+      <Tooltip label={tooltip} {...tooltipProps}>
+        <Select
+          {...getToggleButtonProps({ ref: refs.setReference })}
+          {...buttonProps}
+          as={Flex}
+          sx={{
+            alignItems: 'center',
+            userSelect: 'none',
+            cursor: 'pointer',
+          }}
+        >
+          <Text sx={{ fontSize: 'sm', fontWeight: 500, color: 'base.100' }}>
+            {selectedItem}
+          </Text>
+        </Select>
+      </Tooltip>
+      <Box {...getMenuProps()}>
+        {isOpen && (
+          <List
+            as={Flex}
+            ref={refs.setFloating}
+            sx={{
+              ...floatingStyles,
+              width: 'max-content',
+              top: 0,
+              left: 0,
+              flexDirection: 'column',
+              zIndex: 1,
+              bg: 'base.800',
+              borderRadius: 'base',
+              border: '1px',
+              borderColor: 'base.700',
+              shadow: 'dark-lg',
+              py: 2,
+              px: 0,
+              h: 'fit-content',
+              maxH: 64,
+            }}
+          >
+            <OverlayScrollbarsComponent>
+              {items.map((item, index) => (
+                <ListItem
+                  sx={{
+                    bg: highlightedIndex === index ? 'base.700' : undefined,
+                    py: 1,
+                    paddingInlineStart: 3,
+                    paddingInlineEnd: 6,
+                    cursor: 'pointer',
+                    transitionProperty: 'common',
+                    transitionDuration: '0.15s',
+                  }}
+                  key={`${item}${index}`}
+                  {...getItemProps({ item, index })}
+                >
+                  {withCheckIcon ? (
+                    <Grid gridTemplateColumns="1.25rem auto">
+                      <GridItem>
+                        {selectedItem === item && <CheckIcon boxSize={2} />}
+                      </GridItem>
+                      <GridItem>
+                        <Text
+                          sx={{
+                            fontSize: 'sm',
+                            color: 'base.100',
+                            fontWeight: 500,
+                          }}
+                        >
+                          {item}
+                        </Text>
+                      </GridItem>
+                    </Grid>
+                  ) : (
+                    <Text
+                      sx={{
+                        fontSize: 'sm',
+                        color: 'base.100',
+                        fontWeight: 500,
+                      }}
+                    >
+                      {item}
+                    </Text>
+                  )}
+                </ListItem>
+              ))}
+            </OverlayScrollbarsComponent>
+          </List>
+        )}
+      </Box>
+    </FormControl>
+  );
+};
+
+export default memo(IAICustomSelect);

invokeai/frontend/web/src/common/components/IAIInput.tsx

@@ -5,6 +5,7 @@ import {
   Input,
   InputProps,
 } from '@chakra-ui/react';
+import { stopPastePropagation } from 'common/util/stopPastePropagation';
 import { ChangeEvent, memo } from 'react';
 
 interface IAIInputProps extends InputProps {
@@ -31,7 +32,7 @@ const IAIInput = (props: IAIInputProps) => {
       {...formControlProps}
     >
       {label !== '' && <FormLabel>{label}</FormLabel>}
-      <Input {...rest} />
+      <Input {...rest} onPaste={stopPastePropagation} />
     </FormControl>
   );
 };

invokeai/frontend/web/src/common/components/IAINumberInput.tsx

@@ -14,6 +14,7 @@ import {
   Tooltip,
   TooltipProps,
 } from '@chakra-ui/react';
+import { stopPastePropagation } from 'common/util/stopPastePropagation';
 import { clamp } from 'lodash-es';
 
 import { FocusEvent, memo, useEffect, useState } from 'react';
@@ -125,6 +126,7 @@ const IAINumberInput = (props: Props) => {
           onChange={handleOnChange}
           onBlur={handleBlur}
           {...rest}
+          onPaste={stopPastePropagation}
         >
           <NumberInputField {...numberInputFieldProps} />
           {showStepper && (

invokeai/frontend/web/src/common/components/IAIPopover.tsx

@@ -27,7 +27,7 @@ const IAIPopover = (props: IAIPopoverProps) => {
   return (
     <Popover isLazy={isLazy} {...rest}>
       <PopoverTrigger>{triggerComponent}</PopoverTrigger>
-      <PopoverContent>
+      <PopoverContent shadow="dark-lg">
         {hasArrow && <PopoverArrow />}
         {children}
       </PopoverContent>

invokeai/frontend/web/src/common/components/IAITextarea.tsx

@@ -0,0 +1,9 @@
+import { Textarea, TextareaProps, forwardRef } from '@chakra-ui/react';
+import { stopPastePropagation } from 'common/util/stopPastePropagation';
+import { memo } from 'react';
+
+const IAITextarea = forwardRef((props: TextareaProps, ref) => {
+  return <Textarea ref={ref} onPaste={stopPastePropagation} {...props} />;
+});
+
+export default memo(IAITextarea);

invokeai/frontend/web/src/common/components/ImageMetadataOverlay.tsx

@@ -0,0 +1,54 @@
+import { Badge, Flex } from '@chakra-ui/react';
+import { Image } from 'app/types/invokeai';
+import { isNumber, isString } from 'lodash-es';
+import { useMemo } from 'react';
+
+type ImageMetadataOverlayProps = {
+  image: Image;
+};
+
+const ImageMetadataOverlay = ({ image }: ImageMetadataOverlayProps) => {
+  const dimensions = useMemo(() => {
+    if (!isNumber(image.metadata?.width) || isNumber(!image.metadata?.height)) {
+      return;
+    }
+
+    return `${image.metadata?.width} × ${image.metadata?.height}`;
+  }, [image.metadata]);
+
+  const model = useMemo(() => {
+    if (!isString(image.metadata?.invokeai?.node?.model)) {
+      return;
+    }
+
+    return image.metadata?.invokeai?.node?.model;
+  }, [image.metadata]);
+
+  return (
+    <Flex
+      sx={{
+        pointerEvents: 'none',
+        flexDirection: 'column',
+        position: 'absolute',
+        top: 0,
+        right: 0,
+        p: 2,
+        alignItems: 'flex-end',
+        gap: 2,
+      }}
+    >
+      {dimensions && (
+        <Badge variant="solid" colorScheme="base">
+          {dimensions}
+        </Badge>
+      )}
+      {model && (
+        <Badge variant="solid" colorScheme="base">
+          {model}
+        </Badge>
+      )}
+    </Flex>
+  );
+};
+
+export default ImageMetadataOverlay;

invokeai/frontend/web/src/common/components/ImageToImageOverlay.tsx

@@ -1,36 +0,0 @@
-import { Badge, Box, Flex } from '@chakra-ui/react';
-import { Image } from 'app/types/invokeai';
-
-type ImageToImageOverlayProps = {
-  image: Image;
-};
-
-const ImageToImageOverlay = ({ image }: ImageToImageOverlayProps) => {
-  return (
-    <Box
-      sx={{
-        top: 0,
-        left: 0,
-        w: 'full',
-        h: 'full',
-        position: 'absolute',
-      }}
-    >
-      <Flex
-        sx={{
-          position: 'absolute',
-          top: 0,
-          right: 0,
-          p: 2,
-          alignItems: 'flex-start',
-        }}
-      >
-        <Badge variant="solid" colorScheme="base">
-          {image.metadata?.width} × {image.metadata?.height}
-        </Badge>
-      </Flex>
-    </Box>
-  );
-};
-
-export default ImageToImageOverlay;

invokeai/frontend/web/src/common/components/ImageUploader.tsx

@@ -1,4 +1,4 @@
-import { Box, useToast } from '@chakra-ui/react';
+import { Box } from '@chakra-ui/react';
 import { ImageUploaderTriggerContext } from 'app/contexts/ImageUploaderTriggerContext';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import useImageUploader from 'common/hooks/useImageUploader';
@@ -10,12 +10,33 @@ import {
   ReactNode,
   useCallback,
   useEffect,
+  useMemo,
+  useRef,
   useState,
 } from 'react';
 import { FileRejection, useDropzone } from 'react-dropzone';
 import { useTranslation } from 'react-i18next';
 import { imageUploaded } from 'services/thunks/image';
 import ImageUploadOverlay from './ImageUploadOverlay';
+import { useAppToaster } from 'app/components/Toaster';
+import { filter, map, some } from 'lodash-es';
+import { createSelector } from '@reduxjs/toolkit';
+import { systemSelector } from 'features/system/store/systemSelectors';
+import { ErrorCode } from 'react-dropzone';
+
+const selector = createSelector(
+  [systemSelector, activeTabNameSelector],
+  (system, activeTabName) => {
+    const { isConnected, isUploading } = system;
+
+    const isUploaderDisabled = !isConnected || isUploading;
+
+    return {
+      isUploaderDisabled,
+      activeTabName,
+    };
+  }
+);
 
 type ImageUploaderProps = {
   children: ReactNode;
@@ -24,38 +45,49 @@ type ImageUploaderProps = {
 const ImageUploader = (props: ImageUploaderProps) => {
   const { children } = props;
   const dispatch = useAppDispatch();
-  const activeTabName = useAppSelector(activeTabNameSelector);
-  const toast = useToast({});
+  const { isUploaderDisabled, activeTabName } = useAppSelector(selector);
+  const toaster = useAppToaster();
   const { t } = useTranslation();
   const [isHandlingUpload, setIsHandlingUpload] = useState<boolean>(false);
-  const { setOpenUploader } = useImageUploader();
+  const { setOpenUploaderFunction } = useImageUploader();
 
   const fileRejectionCallback = useCallback(
     (rejection: FileRejection) => {
       setIsHandlingUpload(true);
-      const msg = rejection.errors.reduce(
-        (acc: string, cur: { message: string }) => `${acc}\n${cur.message}`,
-        ''
-      );
-      toast({
+
+      toaster({
         title: t('toast.uploadFailed'),
-        description: msg,
+        description: rejection.errors.map((error) => error.message).join('\n'),
         status: 'error',
-        isClosable: true,
       });
     },
-    [t, toast]
+    [t, toaster]
   );
 
   const fileAcceptedCallback = useCallback(
     async (file: File) => {
-      dispatch(imageUploaded({ formData: { file } }));
+      dispatch(
+        imageUploaded({
+          imageType: 'uploads',
+          formData: { file },
+          activeTabName,
+        })
+      );
     },
-    [dispatch]
+    [dispatch, activeTabName]
   );
 
   const onDrop = useCallback(
     (acceptedFiles: Array<File>, fileRejections: Array<FileRejection>) => {
+      if (fileRejections.length > 1) {
+        toaster({
+          title: t('toast.uploadFailed'),
+          description: t('toast.uploadFailedInvalidUploadDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
       fileRejections.forEach((rejection: FileRejection) => {
         fileRejectionCallback(rejection);
       });
@@ -64,7 +96,7 @@ const ImageUploader = (props: ImageUploaderProps) => {
         fileAcceptedCallback(file);
       });
     },
-    [fileAcceptedCallback, fileRejectionCallback]
+    [t, toaster, fileAcceptedCallback, fileRejectionCallback]
   );
 
   const {
@@ -73,92 +105,73 @@ const ImageUploader = (props: ImageUploaderProps) => {
     isDragAccept,
     isDragReject,
     isDragActive,
+    inputRef,
     open,
   } = useDropzone({
     accept: { 'image/png': ['.png'], 'image/jpeg': ['.jpg', '.jpeg', '.png'] },
     noClick: true,
     onDrop,
     onDragOver: () => setIsHandlingUpload(true),
-    maxFiles: 1,
+    disabled: isUploaderDisabled,
+    multiple: false,
   });
 
-  setOpenUploader(open);
-
   useEffect(() => {
-    const pasteImageListener = (e: ClipboardEvent) => {
-      const dataTransferItemList = e.clipboardData?.items;
-      if (!dataTransferItemList) return;
-
-      const imageItems: Array<DataTransferItem> = [];
-
-      for (const item of dataTransferItemList) {
-        if (
-          item.kind === 'file' &&
-          ['image/png', 'image/jpg'].includes(item.type)
-        ) {
-          imageItems.push(item);
-        }
-      }
-
-      if (!imageItems.length) return;
-
-      e.stopImmediatePropagation();
-
-      if (imageItems.length > 1) {
-        toast({
-          description: t('toast.uploadFailedMultipleImagesDesc'),
-          status: 'error',
-          isClosable: true,
-        });
+    // This is a hack to allow pasting images into the uploader
+    const handlePaste = async (e: ClipboardEvent) => {
+      if (!inputRef.current) {
         return;
       }
 
-      const file = imageItems[0].getAsFile();
-
-      if (!file) {
-        toast({
-          description: t('toast.uploadFailedUnableToLoadDesc'),
-          status: 'error',
-          isClosable: true,
-        });
-        return;
+      if (e.clipboardData?.files) {
+        // Set the files on the inputRef
+        inputRef.current.files = e.clipboardData.files;
+        // Dispatch the change event, dropzone catches this and we get to use its own validation
+        inputRef.current?.dispatchEvent(new Event('change', { bubbles: true }));
       }
-
-      dispatch(imageUploaded({ formData: { file } }));
     };
-    document.addEventListener('paste', pasteImageListener);
+
+    // Set the open function so we can open the uploader from anywhere
+    setOpenUploaderFunction(open);
+
+    // Add the paste event listener
+    document.addEventListener('paste', handlePaste);
+
     return () => {
-      document.removeEventListener('paste', pasteImageListener);
+      document.removeEventListener('paste', handlePaste);
+      setOpenUploaderFunction(() => {
+        return;
+      });
     };
-  }, [t, dispatch, toast, activeTabName]);
+  }, [inputRef, open, setOpenUploaderFunction]);
 
-  const overlaySecondaryText = ['img2img', 'unifiedCanvas'].includes(
-    activeTabName
-  )
-    ? ` to ${String(t(`common.${activeTabName}` as ResourceKey))}`
-    : ``;
+  const overlaySecondaryText = useMemo(() => {
+    if (['img2img', 'unifiedCanvas'].includes(activeTabName)) {
+      return ` to ${String(t(`common.${activeTabName}` as ResourceKey))}`;
+    }
+
+    return '';
+  }, [t, activeTabName]);
 
   return (
-    <ImageUploaderTriggerContext.Provider value={open}>
-      <Box
-        {...getRootProps({ style: {} })}
-        onKeyDown={(e: KeyboardEvent) => {
-          // Bail out if user hits spacebar - do not open the uploader
-          if (e.key === ' ') return;
-        }}
-      >
-        <input {...getInputProps()} />
-        {children}
-        {isDragActive && isHandlingUpload && (
-          <ImageUploadOverlay
-            isDragAccept={isDragAccept}
-            isDragReject={isDragReject}
-            overlaySecondaryText={overlaySecondaryText}
-            setIsHandlingUpload={setIsHandlingUpload}
-          />
-        )}
-      </Box>
-    </ImageUploaderTriggerContext.Provider>
+    <Box
+      {...getRootProps({ style: {} })}
+      onKeyDown={(e: KeyboardEvent) => {
+        // Bail out if user hits spacebar - do not open the uploader
+        if (e.key === ' ') return;
+      }}
+    >
+      <input {...getInputProps()} />
+      {children}
+      {isDragActive && isHandlingUpload && (
+        <ImageUploadOverlay
+          isDragAccept={isDragAccept}
+          isDragReject={isDragReject}
+          overlaySecondaryText={overlaySecondaryText}
+          setIsHandlingUpload={setIsHandlingUpload}
+        />
+      )}
+    </Box>
   );
 };
 

invokeai/frontend/web/src/common/components/ImageUploaderButton.tsx

@@ -1,6 +1,5 @@
 import { Flex, Heading, Icon } from '@chakra-ui/react';
-import { ImageUploaderTriggerContext } from 'app/contexts/ImageUploaderTriggerContext';
-import { useContext } from 'react';
+import useImageUploader from 'common/hooks/useImageUploader';
 import { FaUpload } from 'react-icons/fa';
 
 type ImageUploaderButtonProps = {
@@ -9,11 +8,7 @@ type ImageUploaderButtonProps = {
 
 const ImageUploaderButton = (props: ImageUploaderButtonProps) => {
   const { styleClass } = props;
-  const open = useContext(ImageUploaderTriggerContext);
-
-  const handleClickUpload = () => {
-    open && open();
-  };
+  const { openUploader } = useImageUploader();
 
   return (
     <Flex
@@ -26,7 +21,7 @@ const ImageUploaderButton = (props: ImageUploaderButtonProps) => {
       className={styleClass}
     >
       <Flex
-        onClick={handleClickUpload}
+        onClick={openUploader}
         sx={{
           display: 'flex',
           flexDirection: 'column',

invokeai/frontend/web/src/common/components/ImageUploaderIconButton.tsx

@@ -1,19 +1,18 @@
-import { ImageUploaderTriggerContext } from 'app/contexts/ImageUploaderTriggerContext';
-import { useContext } from 'react';
 import { useTranslation } from 'react-i18next';
 import { FaUpload } from 'react-icons/fa';
 import IAIIconButton from './IAIIconButton';
+import useImageUploader from 'common/hooks/useImageUploader';
 
 const ImageUploaderIconButton = () => {
   const { t } = useTranslation();
-  const openImageUploader = useContext(ImageUploaderTriggerContext);
+  const { openUploader } = useImageUploader();
 
   return (
     <IAIIconButton
       aria-label={t('accessibility.uploadImage')}
       tooltip="Upload Image"
       icon={<FaUpload />}
-      onClick={openImageUploader || undefined}
+      onClick={openUploader}
     />
   );
 };

invokeai/frontend/web/src/common/components/InitialImageButtons.tsx

@@ -6,10 +6,12 @@ import { FaUndo, FaUpload } from 'react-icons/fa';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { useCallback } from 'react';
 import { clearInitialImage } from 'features/parameters/store/generationSlice';
+import useImageUploader from 'common/hooks/useImageUploader';
 
-const ImageToImageSettingsHeader = () => {
+const InitialImageButtons = () => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
+  const { openUploader } = useImageUploader();
 
   const handleResetInitialImage = useCallback(() => {
     dispatch(clearInitialImage());
@@ -18,19 +20,18 @@ const ImageToImageSettingsHeader = () => {
   return (
     <Flex w="full" alignItems="center">
       <Text size="sm" fontWeight={500} color="base.300">
-        Image to Image
+        {t('parameters.initialImage')}
       </Text>
       <Spacer />
       <ButtonGroup>
         <IAIIconButton
-          size="sm"
           icon={<FaUndo />}
           aria-label={t('accessibility.reset')}
           onClick={handleResetInitialImage}
         />
         <IAIIconButton
-          size="sm"
           icon={<FaUpload />}
+          onClick={openUploader}
           aria-label={t('common.upload')}
         />
       </ButtonGroup>
@@ -38,4 +39,4 @@ const ImageToImageSettingsHeader = () => {
   );
 };
 
-export default ImageToImageSettingsHeader;
+export default InitialImageButtons;

invokeai/frontend/web/src/common/components/Loading/Loading.tsx

@@ -24,7 +24,6 @@ const Loading = () => {
         height="24px !important"
         right="1.5rem"
         bottom="1.5rem"
-        speed="1.2s"
       />
     </Flex>
   );

invokeai/frontend/web/src/common/components/SelectImagePlaceholder.tsx

@@ -7,7 +7,7 @@ const SelectImagePlaceholder = () => {
       sx={{
         w: 'full',
         h: 'full',
-        bg: 'base.800',
+        // bg: 'base.800',
         borderRadius: 'base',
         alignItems: 'center',
         justifyContent: 'center',

invokeai/frontend/web/src/common/components/WorkInProgress/PostProcessingWIP.tsx

@@ -1,29 +0,0 @@
-import { Flex, Heading, Text, VStack } from '@chakra-ui/react';
-import { useTranslation } from 'react-i18next';
-import WorkInProgress from './WorkInProgress';
-
-export const PostProcessingWIP = () => {
-  const { t } = useTranslation();
-  return (
-    <WorkInProgress>
-      <Flex
-        sx={{
-          flexDirection: 'column',
-          alignItems: 'center',
-          justifyContent: 'center',
-          w: '100%',
-          h: '100%',
-          gap: 4,
-          textAlign: 'center',
-        }}
-      >
-        <Heading>{t('common.postProcessing')}</Heading>
-        <VStack maxW="50rem" gap={4}>
-          <Text>{t('common.postProcessDesc1')}</Text>
-          <Text>{t('common.postProcessDesc2')}</Text>
-          <Text>{t('common.postProcessDesc3')}</Text>
-        </VStack>
-      </Flex>
-    </WorkInProgress>
-  );
-};

invokeai/frontend/web/src/common/components/WorkInProgress/Training.tsx

@@ -1,28 +0,0 @@
-import { Flex, Heading, Text, VStack } from '@chakra-ui/react';
-import { useTranslation } from 'react-i18next';
-import WorkInProgress from './WorkInProgress';
-
-export default function TrainingWIP() {
-  const { t } = useTranslation();
-  return (
-    <WorkInProgress>
-      <Flex
-        sx={{
-          flexDirection: 'column',
-          alignItems: 'center',
-          justifyContent: 'center',
-          w: '100%',
-          h: '100%',
-          gap: 4,
-          textAlign: 'center',
-        }}
-      >
-        <Heading>{t('common.training')}</Heading>
-        <VStack maxW="50rem" gap={4}>
-          <Text>{t('common.trainingDesc1')}</Text>
-          <Text>{t('common.trainingDesc2')}</Text>
-        </VStack>
-      </Flex>
-    </WorkInProgress>
-  );
-}

invokeai/frontend/web/src/common/components/WorkInProgress/WorkInProgress.tsx

@@ -1,26 +0,0 @@
-import { Flex } from '@chakra-ui/react';
-import { ReactNode } from 'react';
-
-type WorkInProgressProps = {
-  children: ReactNode;
-};
-
-const WorkInProgress = (props: WorkInProgressProps) => {
-  const { children } = props;
-
-  return (
-    <Flex
-      sx={{
-        width: '100%',
-        height: '100%',
-        bg: 'base.850',
-        borderRadius: 'base',
-        position: 'relative',
-      }}
-    >
-      {children}
-    </Flex>
-  );
-};
-
-export default WorkInProgress;

invokeai/frontend/web/src/common/hooks/useClickOutsideWatcher.ts

@@ -1,35 +0,0 @@
-import { RefObject, useEffect } from 'react';
-
-const watchers: {
-  ref: RefObject<HTMLElement>;
-  enable: boolean;
-  callback: () => void;
-}[] = [];
-
-const useClickOutsideWatcher = () => {
-  useEffect(() => {
-    function handleClickOutside(e: MouseEvent) {
-      watchers.forEach(({ ref, enable, callback }) => {
-        if (enable && ref.current && !ref.current.contains(e.target as Node)) {
-          callback();
-        }
-      });
-    }
-    document.addEventListener('mousedown', handleClickOutside);
-    return () => {
-      document.removeEventListener('mousedown', handleClickOutside);
-    };
-  }, []);
-
-  return {
-    addWatcher: (watcher: {
-      ref: RefObject<HTMLElement>;
-      callback: () => void;
-      enable: boolean;
-    }) => {
-      watchers.push(watcher);
-    },
-  };
-};
-
-export default useClickOutsideWatcher;

invokeai/frontend/web/src/common/hooks/useImageUploader.ts

@@ -1,13 +1,22 @@
-let openFunction: () => void;
+import { useCallback } from 'react';
+
+let openUploader = () => {
+  return;
+};
 
 const useImageUploader = () => {
-  return {
-    setOpenUploader: (open?: () => void) => {
-      if (open) {
-        openFunction = open;
+  const setOpenUploaderFunction = useCallback(
+    (openUploaderFunction?: () => void) => {
+      if (openUploaderFunction) {
+        openUploader = openUploaderFunction;
       }
     },
-    openUploader: openFunction,
+    []
+  );
+
+  return {
+    setOpenUploaderFunction,
+    openUploader,
   };
 };
 

invokeai/frontend/web/src/common/hooks/useUpdateTranslations.ts

@@ -1,17 +0,0 @@
-import React from 'react';
-import { useTranslation } from 'react-i18next';
-
-export default function useUpdateTranslations(fn: () => void) {
-  const { i18n } = useTranslation();
-  const currentLang = localStorage.getItem('i18nextLng');
-
-  React.useEffect(() => {
-    fn();
-  }, [fn]);
-
-  React.useEffect(() => {
-    i18n.on('languageChanged', () => {
-      fn();
-    });
-  }, [fn, i18n, currentLang]);
-}

invokeai/frontend/web/src/common/icons/ImageToImageIcon.tsx

@@ -1,20 +0,0 @@
-import { createIcon } from '@chakra-ui/react';
-
-const ImageToImageIcon = createIcon({
-  displayName: 'ImageToImageIcon',
-  viewBox: '0 0 3543 3543',
-  path: (
-    <g transform="matrix(1.10943,0,0,1.10943,-206.981,-213.533)">
-      <path
-        fill="currentColor"
-        fillRule="evenodd"
-        clipRule="evenodd"
-        d="M688.533,2405.95L542.987,2405.95C349.532,2405.95 192.47,2248.89 192.47,2055.44L192.47,542.987C192.47,349.532 349.532,192.47 542.987,192.47L2527.88,192.47C2721.33,192.47 2878.4,349.532 2878.4,542.987L2878.4,1172.79L3023.94,1172.79C3217.4,1172.79 3374.46,1329.85 3374.46,1523.3C3374.46,1523.3 3374.46,3035.75 3374.46,3035.75C3374.46,3229.21 3217.4,3386.27 3023.94,3386.27L1039.05,3386.27C845.595,3386.27 688.533,3229.21 688.533,3035.75L688.533,2405.95ZM3286.96,2634.37L3286.96,1523.3C3286.96,1378.14 3169.11,1260.29 3023.94,1260.29C3023.94,1260.29 1039.05,1260.29 1039.05,1260.29C893.887,1260.29 776.033,1378.14 776.033,1523.3L776.033,2489.79L1440.94,1736.22L2385.83,2775.59L2880.71,2200.41L3286.96,2634.37ZM2622.05,1405.51C2778.5,1405.51 2905.51,1532.53 2905.51,1688.98C2905.51,1845.42 2778.5,1972.44 2622.05,1972.44C2465.6,1972.44 2338.58,1845.42 2338.58,1688.98C2338.58,1532.53 2465.6,1405.51 2622.05,1405.51ZM2790.9,1172.79L1323.86,1172.79L944.882,755.906L279.97,1509.47L279.97,542.987C279.97,397.824 397.824,279.97 542.987,279.97C542.987,279.97 2527.88,279.97 2527.88,279.97C2673.04,279.97 2790.9,397.824 2790.9,542.987L2790.9,1172.79ZM2125.98,425.197C2282.43,425.197 2409.45,552.213 2409.45,708.661C2409.45,865.11 2282.43,992.126 2125.98,992.126C1969.54,992.126 1842.52,865.11 1842.52,708.661C1842.52,552.213 1969.54,425.197 2125.98,425.197Z"
-      />
-    </g>
-  ),
-  defaultProps: {
-    boxSize: '24px',
-  },
-});
-export default ImageToImageIcon;

invokeai/frontend/web/src/common/icons/NodesIcon.tsx

@@ -1,19 +0,0 @@
-import { createIcon } from '@chakra-ui/react';
-
-const NodesIcon = createIcon({
-  displayName: 'NodesIcon',
-  viewBox: '0 0 3543 3543',
-  path: (
-    <path
-      fill="currentColor"
-      fillRule="evenodd"
-      clipRule="evenodd"
-      d="M3543.31,770.787C3543.31,515.578 3336.11,308.38 3080.9,308.38L462.407,308.38C207.197,308.38 0,515.578 0,770.787L0,2766.03C0,3021.24 207.197,3228.44 462.407,3228.44L3080.9,3228.44C3336.11,3228.44 3543.31,3021.24 3543.31,2766.03C3543.31,2766.03 3543.31,770.787 3543.31,770.787ZM3427.88,770.787L3427.88,2766.03C3427.88,2957.53 3272.4,3113.01 3080.9,3113.01C3080.9,3113.01 462.407,3113.01 462.407,3113.01C270.906,3113.01 115.431,2957.53 115.431,2766.03L115.431,770.787C115.431,579.286 270.906,423.812 462.407,423.812L3080.9,423.812C3272.4,423.812 3427.88,579.286 3427.88,770.787ZM1214.23,1130.69L1321.47,1130.69C1324.01,1130.69 1326.54,1130.53 1329.05,1130.2C1329.05,1130.2 1367.3,1125.33 1397.94,1149.8C1421.63,1168.72 1437.33,1204.3 1437.33,1265.48L1437.33,2078.74L1220.99,2078.74C1146.83,2078.74 1086.61,2138.95 1086.61,2213.12L1086.61,2762.46C1086.61,2836.63 1146.83,2896.84 1220.99,2896.84L1770.34,2896.84C1844.5,2896.84 1904.71,2836.63 1904.71,2762.46L1904.71,2213.12C1904.71,2138.95 1844.5,2078.74 1770.34,2078.74L1554,2078.74L1554,1604.84C1625.84,1658.19 1703.39,1658.1 1703.39,1658.1C1703.54,1658.1 1703.69,1658.11 1703.84,1658.11L2362.2,1658.11L2362.2,1874.44C2362.2,1948.61 2422.42,2008.82 2496.58,2008.82L3045.93,2008.82C3120.09,2008.82 3180.3,1948.61 3180.3,1874.44L3180.3,1325.1C3180.3,1250.93 3120.09,1190.72 3045.93,1190.72L2496.58,1190.72C2422.42,1190.72 2362.2,1250.93 2362.2,1325.1L2362.2,1558.97L2362.2,1541.44L1704.23,1541.44C1702.2,1541.37 1650.96,1539.37 1609.51,1499.26C1577.72,1468.49 1554,1416.47 1554,1331.69L1554,1265.48C1554,1153.86 1513.98,1093.17 1470.76,1058.64C1411.24,1011.1 1338.98,1012.58 1319.15,1014.03L1214.23,1014.03L1214.23,796.992C1214.23,722.828 1154.02,662.617 1079.85,662.617L530.507,662.617C456.343,662.617 396.131,722.828 396.131,796.992L396.131,1346.34C396.131,1420.5 456.343,1480.71 530.507,1480.71L1079.85,1480.71C1154.02,1480.71 1214.23,1420.5 1214.23,1346.34L1214.23,1130.69Z"
-    />
-  ),
-  defaultProps: {
-    boxSize: '24px',
-  },
-});
-
-export default NodesIcon;