Merge branch 'main' into tiled-upscaling-graph

docs/features/PROMPTS.md

@@ -120,7 +120,7 @@ Generate an image with a given prompt, record the seed of the image, and then
 use the `prompt2prompt` syntax to substitute words in the original prompt for
 words in a new prompt. This works for `img2img` as well.
 
-For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because of the word words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
+For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because the words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
   - `a cat playing with a ball in the forest`
   - `a dog playing with a ball in the forest`
 

docs/nodes/defaultNodes.md

@@ -1,13 +1,15 @@
 # List of Default Nodes
 
-The table below contains a list of the default nodes shipped with InvokeAI and their descriptions.
+The table below contains a list of the default nodes shipped with InvokeAI and
+their descriptions.
 
 | Node <img width=160 align="right">                            | Function                                                                                                                                             |
-|: ---------------------------------- | :--------------------------------------------------------------------------------------|
+| :------------------------------------------------------------ | :--------------------------------------------------------------------------------------------------------------------------------------------------- |
 | Add Integers                                                  | Adds two numbers                                                                                                                                     |
 | Boolean Primitive Collection                                  | A collection of boolean primitive values                                                                                                             |
 | Boolean Primitive                                             | A boolean primitive value                                                                                                                            |
 | Canny Processor                                               | Canny edge detection for ControlNet                                                                                                                  |
+| CenterPadCrop                                                 | Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image.                                          |
 | CLIP Skip                                                     | Skip layers in clip text_encoder model.                                                                                                              |
 | Collect                                                       | Collects values into a collection                                                                                                                    |
 | Color Correct                                                 | Shifts the colors of a target image to match the reference image, optionally using a mask to only color-correct certain regions of the target image. |
@@ -74,7 +76,7 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 | Noise                                                         | Generates latent noise.                                                                                                                              |
 | Normal BAE Processor                                          | Applies NormalBae processing to image                                                                                                                |
 | ONNX Latents to Image                                         | Generates an image from latents.                                                                                                                     |
-|ONNX Prompt (Raw) 			| A node to process inputs and produce outputs. May use dependency injection in __init__ to receive providers.|
+| ONNX Prompt (Raw)                                             | A node to process inputs and produce outputs. May use dependency injection in **init** to receive providers.                                         |
 | ONNX Text to Latents                                          | Generates latents from conditionings.                                                                                                                |
 | ONNX Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
 | OpenCV Inpaint                                                | Simple inpaint using opencv.                                                                                                                         |

invokeai/app/api/routers/app_info.py

@@ -1,7 +1,11 @@
 import typing
 from enum import Enum
+from importlib.metadata import PackageNotFoundError, version
 from pathlib import Path
+from platform import python_version
+from typing import Optional
 
+import torch
 from fastapi import Body
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
@@ -40,6 +44,24 @@ class AppVersion(BaseModel):
     version: str = Field(description="App version")
 
 
+class AppDependencyVersions(BaseModel):
+    """App depencency Versions Response"""
+
+    accelerate: str = Field(description="accelerate version")
+    compel: str = Field(description="compel version")
+    cuda: Optional[str] = Field(description="CUDA version")
+    diffusers: str = Field(description="diffusers version")
+    numpy: str = Field(description="Numpy version")
+    opencv: str = Field(description="OpenCV version")
+    onnx: str = Field(description="ONNX version")
+    pillow: str = Field(description="Pillow (PIL) version")
+    python: str = Field(description="Python version")
+    torch: str = Field(description="PyTorch version")
+    torchvision: str = Field(description="PyTorch Vision version")
+    transformers: str = Field(description="transformers version")
+    xformers: Optional[str] = Field(description="xformers version")
+
+
 class AppConfig(BaseModel):
     """App Config Response"""
 
@@ -54,6 +76,29 @@ async def get_version() -> AppVersion:
     return AppVersion(version=__version__)
 
 
+@app_router.get("/app_deps", operation_id="get_app_deps", status_code=200, response_model=AppDependencyVersions)
+async def get_app_deps() -> AppDependencyVersions:
+    try:
+        xformers = version("xformers")
+    except PackageNotFoundError:
+        xformers = None
+    return AppDependencyVersions(
+        accelerate=version("accelerate"),
+        compel=version("compel"),
+        cuda=torch.version.cuda,
+        diffusers=version("diffusers"),
+        numpy=version("numpy"),
+        opencv=version("opencv-python"),
+        onnx=version("onnx"),
+        pillow=version("pillow"),
+        python=python_version(),
+        torch=torch.version.__version__,
+        torchvision=version("torchvision"),
+        transformers=version("transformers"),
+        xformers=xformers,
+    )
+
+
 @app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
 async def get_config() -> AppConfig:
     infill_methods = ["tile", "lama", "cv2"]

invokeai/app/api/routers/model_records.py

@@ -141,7 +141,7 @@ async def del_model_record(
     status_code=201,
 )
 async def add_model_record(
-    config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")]
+    config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
 ) -> AnyModelConfig:
     """
     Add a model using the configuration information appropriate for its type.

invokeai/app/invocations/image.py

@@ -100,6 +100,61 @@ class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
+@invocation(
+    invocation_type="img_pad_crop",
+    title="Center Pad or Crop Image",
+    category="image",
+    tags=["image", "pad", "crop"],
+    version="1.0.0",
+)
+class CenterPadCropInvocation(BaseInvocation):
+    """Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image."""
+
+    image: ImageField = InputField(description="The image to crop")
+    left: int = InputField(
+        default=0,
+        description="Number of pixels to pad/crop from the left (negative values crop inwards, positive values pad outwards)",
+    )
+    right: int = InputField(
+        default=0,
+        description="Number of pixels to pad/crop from the right (negative values crop inwards, positive values pad outwards)",
+    )
+    top: int = InputField(
+        default=0,
+        description="Number of pixels to pad/crop from the top (negative values crop inwards, positive values pad outwards)",
+    )
+    bottom: int = InputField(
+        default=0,
+        description="Number of pixels to pad/crop from the bottom (negative values crop inwards, positive values pad outwards)",
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+
+        # Calculate and create new image dimensions
+        new_width = image.width + self.right + self.left
+        new_height = image.height + self.top + self.bottom
+        image_crop = Image.new(mode="RGBA", size=(new_width, new_height), color=(0, 0, 0, 0))
+
+        # Paste new image onto input
+        image_crop.paste(image, (self.left, self.top))
+
+        image_dto = context.services.images.create(
+            image=image_crop,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
+
+
 @invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.1.0")
 class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Pastes an image into another image."""

invokeai/app/invocations/latent.py

@@ -221,7 +221,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.4.0",
+    version="1.5.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -279,6 +279,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
         input=Input.Connection,
         ui_order=7,
     )
+    cfg_rescale_multiplier: float = InputField(
+        default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
+    )
     latents: Optional[LatentsField] = InputField(
         default=None,
         description=FieldDescriptions.latents,
@@ -338,6 +341,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             unconditioned_embeddings=uc,
             text_embeddings=c,
             guidance_scale=self.cfg_scale,
+            guidance_rescale_multiplier=self.cfg_rescale_multiplier,
             extra=extra_conditioning_info,
             postprocessing_settings=PostprocessingSettings(
                 threshold=0.0,  # threshold,
@@ -1190,12 +1194,12 @@ class CropLatentsCoreInvocation(BaseInvocation):
         description=FieldDescriptions.latents,
         input=Input.Connection,
     )
-    x_offset: int = InputField(
+    x: int = InputField(
         ge=0,
         multiple_of=LATENT_SCALE_FACTOR,
         description="The left x coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
     )
-    y_offset: int = InputField(
+    y: int = InputField(
         ge=0,
         multiple_of=LATENT_SCALE_FACTOR,
         description="The top y coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
@@ -1214,8 +1218,8 @@ class CropLatentsCoreInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.services.latents.get(self.latents.latents_name)
 
-        x1 = self.x_offset // LATENT_SCALE_FACTOR
+        x1 = self.x // LATENT_SCALE_FACTOR
-        y1 = self.y_offset // LATENT_SCALE_FACTOR
+        y1 = self.y // LATENT_SCALE_FACTOR
         x2 = x1 + (self.width // LATENT_SCALE_FACTOR)
         y2 = y1 + (self.height // LATENT_SCALE_FACTOR)
 

invokeai/app/invocations/metadata.py

@@ -127,6 +127,9 @@ class CoreMetadataInvocation(BaseInvocation):
     seed: Optional[int] = InputField(default=None, description="The seed used for noise generation")
     rand_device: Optional[str] = InputField(default=None, description="The device used for random number generation")
     cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
+    cfg_rescale_multiplier: Optional[float] = InputField(
+        default=None, description=FieldDescriptions.cfg_rescale_multiplier
+    )
     steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
     scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
     seamless_x: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the X axis")

invokeai/app/invocations/tiles.py

@@ -1,5 +1,3 @@
-from typing import Literal
-
 import numpy as np
 from PIL import Image
 from pydantic import BaseModel
@@ -7,7 +5,6 @@ from pydantic import BaseModel
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Input,
     InputField,
     InvocationContext,
     OutputField,
@@ -18,13 +15,7 @@ from invokeai.app.invocations.baseinvocation import (
 )
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.backend.tiles.tiles import (
+from invokeai.backend.tiles.tiles import calc_tiles_with_overlap, merge_tiles_with_linear_blending
-    calc_tiles_even_split,
-    calc_tiles_min_overlap,
-    calc_tiles_with_overlap,
-    merge_tiles_with_linear_blending,
-    merge_tiles_with_seam_blending,
-)
 from invokeai.backend.tiles.utils import Tile
 
 
@@ -65,92 +56,12 @@ class CalculateImageTilesInvocation(BaseInvocation):
         return CalculateImageTilesOutput(tiles=tiles)
 
 
-@invocation(
-    "calculate_image_tiles_Even_Split",
-    title="Calculate Image Tiles Even Split",
-    tags=["tiles"],
-    category="tiles",
-    version="1.0.0",
-)
-class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
-    """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
-
-    image_width: int = InputField(ge=1, default=1024, description="The image width, in pixels, to calculate tiles for.")
-    image_height: int = InputField(
-        ge=1, default=1024, description="The image height, in pixels, to calculate tiles for."
-    )
-    num_tiles_x: int = InputField(
-        default=2,
-        ge=1,
-        description="Number of tiles to divide image into on the x axis",
-    )
-    num_tiles_y: int = InputField(
-        default=2,
-        ge=1,
-        description="Number of tiles to divide image into on the y axis",
-    )
-    overlap: float = InputField(
-        default=0.25,
-        ge=0,
-        lt=1,
-        description="Overlap amount of tile size (0-1)",
-    )
-
-    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
-        tiles = calc_tiles_even_split(
-            image_height=self.image_height,
-            image_width=self.image_width,
-            num_tiles_x=self.num_tiles_x,
-            num_tiles_y=self.num_tiles_y,
-            overlap=self.overlap,
-        )
-        return CalculateImageTilesOutput(tiles=tiles)
-
-
-@invocation(
-    "calculate_image_tiles_min_overlap",
-    title="Calculate Image Tiles Minimum Overlap",
-    tags=["tiles"],
-    category="tiles",
-    version="1.0.0",
-)
-class CalculateImageTilesMinimumOverlapInvocation(BaseInvocation):
-    """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
-
-    image_width: int = InputField(ge=1, default=1024, description="The image width, in pixels, to calculate tiles for.")
-    image_height: int = InputField(
-        ge=1, default=1024, description="The image height, in pixels, to calculate tiles for."
-    )
-    tile_width: int = InputField(ge=1, default=576, description="The tile width, in pixels.")
-    tile_height: int = InputField(ge=1, default=576, description="The tile height, in pixels.")
-    min_overlap: int = InputField(
-        default=128,
-        ge=0,
-        description="minimum tile overlap size (must be a multiple of 8)",
-    )
-    round_to_8: bool = InputField(
-        default=False,
-        description="Round outputs down to the nearest 8 (for pulling from a large noise field)",
-    )
-
-    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
-        tiles = calc_tiles_min_overlap(
-            image_height=self.image_height,
-            image_width=self.image_width,
-            tile_height=self.tile_height,
-            tile_width=self.tile_width,
-            min_overlap=self.min_overlap,
-            round_to_8=self.round_to_8,
-        )
-        return CalculateImageTilesOutput(tiles=tiles)
-
-
 @invocation_output("tile_to_properties_output")
 class TileToPropertiesOutput(BaseInvocationOutput):
-    coords_top: int = OutputField(description="Top coordinate of the tile relative to its parent image.")
-    coords_bottom: int = OutputField(description="Bottom coordinate of the tile relative to its parent image.")
     coords_left: int = OutputField(description="Left coordinate of the tile relative to its parent image.")
     coords_right: int = OutputField(description="Right coordinate of the tile relative to its parent image.")
+    coords_top: int = OutputField(description="Top coordinate of the tile relative to its parent image.")
+    coords_bottom: int = OutputField(description="Bottom coordinate of the tile relative to its parent image.")
 
     # HACK: The width and height fields are 'meta' fields that can easily be calculated from the other fields on this
     # object. Including redundant fields that can cheaply/easily be re-calculated goes against conventional API design
@@ -174,10 +85,10 @@ class TileToPropertiesInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> TileToPropertiesOutput:
         return TileToPropertiesOutput(
-            coords_top=self.tile.coords.top,
-            coords_bottom=self.tile.coords.bottom,
             coords_left=self.tile.coords.left,
             coords_right=self.tile.coords.right,
+            coords_top=self.tile.coords.top,
+            coords_bottom=self.tile.coords.bottom,
             width=self.tile.coords.right - self.tile.coords.left,
             height=self.tile.coords.bottom - self.tile.coords.top,
             overlap_top=self.tile.overlap.top,
@@ -211,22 +122,13 @@ class PairTileImageInvocation(BaseInvocation):
         )
 
 
-BLEND_MODES = Literal["Linear", "Seam"]
+@invocation("merge_tiles_to_image", title="Merge Tiles to Image", tags=["tiles"], category="tiles", version="1.0.0")
-
-
-@invocation("merge_tiles_to_image", title="Merge Tiles to Image", tags=["tiles"], category="tiles", version="1.1.0")
 class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Merge multiple tile images into a single image."""
 
     # Inputs
     tiles_with_images: list[TileWithImage] = InputField(description="A list of tile images with tile properties.")
-    blend_mode: BLEND_MODES = InputField(
-        default="Seam",
-        description="blending type Linear or Seam",
-        input=Input.Direct,
-    )
     blend_amount: int = InputField(
-        default=32,
         ge=0,
         description="The amount to blend adjacent tiles in pixels. Must be <= the amount of overlap between adjacent tiles.",
     )
@@ -256,16 +158,10 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
         channels = tile_np_images[0].shape[-1]
         dtype = tile_np_images[0].dtype
         np_image = np.zeros(shape=(height, width, channels), dtype=dtype)
-        if self.blend_mode == "Linear":
+
         merge_tiles_with_linear_blending(
             dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
         )
-        else:
-            merge_tiles_with_seam_blending(
-                dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
-            )
-
-        # Convert into a PIL image and save
         pil_image = Image.fromarray(np_image)
 
         image_dto = context.services.images.create(

invokeai/app/services/latents_storage/latents_storage_disk.py

@@ -5,6 +5,8 @@ from typing import Union
 
 import torch
 
+from invokeai.app.services.invoker import Invoker
+
 from .latents_storage_base import LatentsStorageBase
 
 
@@ -17,6 +19,10 @@ class DiskLatentsStorage(LatentsStorageBase):
         self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
         self.__output_folder.mkdir(parents=True, exist_ok=True)
 
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+        self._delete_all_latents()
+
     def get(self, name: str) -> torch.Tensor:
         latent_path = self.get_path(name)
         return torch.load(latent_path)
@@ -32,3 +38,21 @@ class DiskLatentsStorage(LatentsStorageBase):
 
     def get_path(self, name: str) -> Path:
         return self.__output_folder / name
+
+    def _delete_all_latents(self) -> None:
+        """
+        Deletes all latents from disk.
+        Must be called after we have access to `self._invoker` (e.g. in `start()`).
+        """
+        deleted_latents_count = 0
+        freed_space = 0
+        for latents_file in Path(self.__output_folder).glob("*"):
+            if latents_file.is_file():
+                freed_space += latents_file.stat().st_size
+                deleted_latents_count += 1
+                latents_file.unlink()
+        if deleted_latents_count > 0:
+            freed_space_in_mb = round(freed_space / 1024 / 1024, 2)
+            self._invoker.services.logger.info(
+                f"Deleted {deleted_latents_count} latents files (freed {freed_space_in_mb}MB)"
+            )

invokeai/app/services/latents_storage/latents_storage_forward_cache.py

@@ -5,6 +5,8 @@ from typing import Dict, Optional
 
 import torch
 
+from invokeai.app.services.invoker import Invoker
+
 from .latents_storage_base import LatentsStorageBase
 
 
@@ -23,6 +25,18 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
         self.__cache_ids = Queue()
         self.__max_cache_size = max_cache_size
 
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+        start_op = getattr(self.__underlying_storage, "start", None)
+        if callable(start_op):
+            start_op(invoker)
+
+    def stop(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+        stop_op = getattr(self.__underlying_storage, "stop", None)
+        if callable(stop_op):
+            stop_op(invoker)
+
     def get(self, name: str) -> torch.Tensor:
         cache_item = self.__get_cache(name)
         if cache_item is not None:

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -42,6 +42,7 @@ class SqliteSessionQueue(SessionQueueBase):
         self._set_in_progress_to_canceled()
         prune_result = self.prune(DEFAULT_QUEUE_ID)
         local_handler.register(event_name=EventServiceBase.queue_event, _func=self._on_session_event)
+        if prune_result.deleted > 0:
             self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
 
     def __init__(self, db: SqliteDatabase) -> None:

invokeai/app/services/shared/graph.py

@@ -207,10 +207,12 @@ class IterateInvocationOutput(BaseInvocationOutput):
     item: Any = OutputField(
         description="The item being iterated over", title="Collection Item", ui_type=UIType._CollectionItem
     )
+    index: int = OutputField(description="The index of the item", title="Index")
+    total: int = OutputField(description="The total number of items", title="Total")
 
 
 # TODO: Fill this out and move to invocations
-@invocation("iterate", version="1.0.0")
+@invocation("iterate", version="1.1.0")
 class IterateInvocation(BaseInvocation):
     """Iterates over a list of items"""
 
@@ -221,7 +223,7 @@ class IterateInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> IterateInvocationOutput:
         """Produces the outputs as values"""
-        return IterateInvocationOutput(item=self.collection[self.index])
+        return IterateInvocationOutput(item=self.collection[self.index], index=self.index, total=len(self.collection))
 
 
 @invocation_output("collect_output")

invokeai/app/services/shared/sqlite.py

@@ -1,6 +1,7 @@
 import sqlite3
 import threading
 from logging import Logger
+from pathlib import Path
 
 from invokeai.app.services.config import InvokeAIAppConfig
 
@@ -8,25 +9,20 @@ sqlite_memory = ":memory:"
 
 
 class SqliteDatabase:
-    conn: sqlite3.Connection
-    lock: threading.RLock
-    _logger: Logger
-    _config: InvokeAIAppConfig
-
     def __init__(self, config: InvokeAIAppConfig, logger: Logger):
         self._logger = logger
         self._config = config
 
         if self._config.use_memory_db:
-            location = sqlite_memory
+            self.db_path = sqlite_memory
             logger.info("Using in-memory database")
         else:
             db_path = self._config.db_path
             db_path.parent.mkdir(parents=True, exist_ok=True)
-            location = str(db_path)
+            self.db_path = str(db_path)
-            self._logger.info(f"Using database at {location}")
+            self._logger.info(f"Using database at {self.db_path}")
 
-        self.conn = sqlite3.connect(location, check_same_thread=False)
+        self.conn = sqlite3.connect(self.db_path, check_same_thread=False)
         self.lock = threading.RLock()
         self.conn.row_factory = sqlite3.Row
 
@@ -37,10 +33,16 @@ class SqliteDatabase:
 
     def clean(self) -> None:
         try:
+            if self.db_path == sqlite_memory:
+                return
+            initial_db_size = Path(self.db_path).stat().st_size
             self.lock.acquire()
             self.conn.execute("VACUUM;")
             self.conn.commit()
-            self._logger.info("Cleaned database")
+            final_db_size = Path(self.db_path).stat().st_size
+            freed_space_in_mb = round((initial_db_size - final_db_size) / 1024 / 1024, 2)
+            if freed_space_in_mb > 0:
+                self._logger.info(f"Cleaned database (freed {freed_space_in_mb}MB)")
         except Exception as e:
             self._logger.error(f"Error cleaning database: {e}")
             raise e

invokeai/app/shared/fields.py

@@ -2,6 +2,7 @@ class FieldDescriptions:
     denoising_start = "When to start denoising, expressed a percentage of total steps"
     denoising_end = "When to stop denoising, expressed a percentage of total steps"
     cfg_scale = "Classifier-Free Guidance scale"
+    cfg_rescale_multiplier = "Rescale multiplier for CFG guidance, used for models trained with zero-terminal SNR"
     scheduler = "Scheduler to use during inference"
     positive_cond = "Positive conditioning tensor"
     negative_cond = "Negative conditioning tensor"

invokeai/backend/ip_adapter/ip_adapter.py

@@ -54,6 +54,44 @@ class ImageProjModel(torch.nn.Module):
         return clip_extra_context_tokens
 
 
+class MLPProjModel(torch.nn.Module):
+    """SD model with image prompt"""
+
+    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
+        super().__init__()
+
+        self.proj = torch.nn.Sequential(
+            torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
+            torch.nn.GELU(),
+            torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
+            torch.nn.LayerNorm(cross_attention_dim),
+        )
+
+    @classmethod
+    def from_state_dict(cls, state_dict: dict[torch.Tensor]):
+        """Initialize an MLPProjModel from a state_dict.
+
+        The cross_attention_dim and clip_embeddings_dim are inferred from the shape of the tensors in the state_dict.
+
+        Args:
+            state_dict (dict[torch.Tensor]): The state_dict of model weights.
+
+        Returns:
+            MLPProjModel
+        """
+        cross_attention_dim = state_dict["proj.3.weight"].shape[0]
+        clip_embeddings_dim = state_dict["proj.0.weight"].shape[0]
+
+        model = cls(cross_attention_dim, clip_embeddings_dim)
+
+        model.load_state_dict(state_dict)
+        return model
+
+    def forward(self, image_embeds):
+        clip_extra_context_tokens = self.proj(image_embeds)
+        return clip_extra_context_tokens
+
+
 class IPAdapter:
     """IP-Adapter: https://arxiv.org/pdf/2308.06721.pdf"""
 
@@ -130,6 +168,13 @@ class IPAdapterPlus(IPAdapter):
         return image_prompt_embeds, uncond_image_prompt_embeds
 
 
+class IPAdapterFull(IPAdapterPlus):
+    """IP-Adapter Plus with full features."""
+
+    def _init_image_proj_model(self, state_dict: dict[torch.Tensor]):
+        return MLPProjModel.from_state_dict(state_dict).to(self.device, dtype=self.dtype)
+
+
 class IPAdapterPlusXL(IPAdapterPlus):
     """IP-Adapter Plus for SDXL."""
 
@@ -149,11 +194,9 @@ def build_ip_adapter(
 ) -> Union[IPAdapter, IPAdapterPlus]:
     state_dict = torch.load(ip_adapter_ckpt_path, map_location="cpu")
 
-    # Determine if the state_dict is from an IPAdapter or IPAdapterPlus based on the image_proj weights that it
+    if "proj.weight" in state_dict["image_proj"]:  # IPAdapter (with ImageProjModel).
-    # contains.
+        return IPAdapter(state_dict, device=device, dtype=dtype)
-    is_plus = "proj.weight" not in state_dict["image_proj"]
+    elif "proj_in.weight" in state_dict["image_proj"]:  # IPAdaterPlus or IPAdapterPlusXL (with Resampler).
-
-    if is_plus:
         cross_attention_dim = state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[-1]
         if cross_attention_dim == 768:
             # SD1 IP-Adapter Plus
@@ -163,5 +206,7 @@ def build_ip_adapter(
             return IPAdapterPlusXL(state_dict, device=device, dtype=dtype)
         else:
             raise Exception(f"Unsupported IP-Adapter Plus cross-attention dimension: {cross_attention_dim}.")
+    elif "proj.0.weight" in state_dict["image_proj"]:  # IPAdapterFull (with MLPProjModel).
+        return IPAdapterFull(state_dict, device=device, dtype=dtype)
     else:
-        return IPAdapter(state_dict, device=device, dtype=dtype)
+        raise ValueError(f"'{ip_adapter_ckpt_path}' has an unrecognized IP-Adapter model architecture.")

invokeai/backend/model_management/lora.py

@@ -192,20 +192,33 @@ class ModelPatcher:
                     trigger += f"-!pad-{i}"
                 return f"<{trigger}>"
 
+            def _get_ti_embedding(model_embeddings, ti):
+                # for SDXL models, select the embedding that matches the text encoder's dimensions
+                if ti.embedding_2 is not None:
+                    return (
+                        ti.embedding_2
+                        if ti.embedding_2.shape[1] == model_embeddings.weight.data[0].shape[0]
+                        else ti.embedding
+                    )
+                else:
+                    return ti.embedding
+
             # modify tokenizer
             new_tokens_added = 0
             for ti_name, ti in ti_list:
-                for i in range(ti.embedding.shape[0]):
+                ti_embedding = _get_ti_embedding(text_encoder.get_input_embeddings(), ti)
+
+                for i in range(ti_embedding.shape[0]):
                     new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
 
             # modify text_encoder
             text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
             model_embeddings = text_encoder.get_input_embeddings()
 
-            for ti_name, ti in ti_list:
+            for ti_name, _ in ti_list:
                 ti_tokens = []
-                for i in range(ti.embedding.shape[0]):
+                for i in range(ti_embedding.shape[0]):
-                    embedding = ti.embedding[i]
+                    embedding = ti_embedding[i]
                     trigger = _get_trigger(ti_name, i)
 
                     token_id = ti_tokenizer.convert_tokens_to_ids(trigger)
@@ -273,6 +286,7 @@ class ModelPatcher:
 
 class TextualInversionModel:
     embedding: torch.Tensor  # [n, 768]|[n, 1280]
+    embedding_2: Optional[torch.Tensor] = None  # [n, 768]|[n, 1280]   - for SDXL models
 
     @classmethod
     def from_checkpoint(
@@ -296,8 +310,8 @@ class TextualInversionModel:
         if "string_to_param" in state_dict:
             if len(state_dict["string_to_param"]) > 1:
                 print(
-                    f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first'
+                    f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first',
-                    " token will be used."
+                    " token will be used.",
                 )
 
             result.embedding = next(iter(state_dict["string_to_param"].values()))
@@ -306,6 +320,11 @@ class TextualInversionModel:
         elif "emb_params" in state_dict:
             result.embedding = state_dict["emb_params"]
 
+        # v5(sdxl safetensors file)
+        elif "clip_g" in state_dict and "clip_l" in state_dict:
+            result.embedding = state_dict["clip_g"]
+            result.embedding_2 = state_dict["clip_l"]
+
         # v4(diffusers bin files)
         else:
             result.embedding = next(iter(state_dict.values()))
@@ -342,6 +361,13 @@ class TextualInversionManager(BaseTextualInversionManager):
             if token_id in self.pad_tokens:
                 new_token_ids.extend(self.pad_tokens[token_id])
 
+        # Do not exceed the max model input size
+        # The -2 here is compensating for compensate compel.embeddings_provider.get_token_ids(),
+        # which first removes and then adds back the start and end tokens.
+        max_length = list(self.tokenizer.max_model_input_sizes.values())[0] - 2
+        if len(new_token_ids) > max_length:
+            new_token_ids = new_token_ids[0:max_length]
+
         return new_token_ids
 
 
@@ -490,24 +516,31 @@ class ONNXModelPatcher:
                     trigger += f"-!pad-{i}"
                 return f"<{trigger}>"
 
+            # modify text_encoder
+            orig_embeddings = text_encoder.tensors["text_model.embeddings.token_embedding.weight"]
+
             # modify tokenizer
             new_tokens_added = 0
             for ti_name, ti in ti_list:
-                for i in range(ti.embedding.shape[0]):
+                if ti.embedding_2 is not None:
-                    new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
+                    ti_embedding = (
+                        ti.embedding_2 if ti.embedding_2.shape[1] == orig_embeddings.shape[0] else ti.embedding
+                    )
+                else:
+                    ti_embedding = ti.embedding
 
-            # modify text_encoder
+                for i in range(ti_embedding.shape[0]):
-            orig_embeddings = text_encoder.tensors["text_model.embeddings.token_embedding.weight"]
+                    new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
 
             embeddings = np.concatenate(
                 (np.copy(orig_embeddings), np.zeros((new_tokens_added, orig_embeddings.shape[1]))),
                 axis=0,
             )
 
-            for ti_name, ti in ti_list:
+            for ti_name, _ in ti_list:
                 ti_tokens = []
-                for i in range(ti.embedding.shape[0]):
+                for i in range(ti_embedding.shape[0]):
-                    embedding = ti.embedding[i].detach().numpy()
+                    embedding = ti_embedding[i].detach().numpy()
                     trigger = _get_trigger(ti_name, i)
 
                     token_id = ti_tokenizer.convert_tokens_to_ids(trigger)

invokeai/backend/model_management/model_probe.py

@@ -373,12 +373,16 @@ class TextualInversionCheckpointProbe(CheckpointProbeBase):
             token_dim = list(checkpoint["string_to_param"].values())[0].shape[-1]
         elif "emb_params" in checkpoint:
             token_dim = checkpoint["emb_params"].shape[-1]
+        elif "clip_g" in checkpoint:
+            token_dim = checkpoint["clip_g"].shape[-1]
         else:
             token_dim = list(checkpoint.values())[0].shape[0]
         if token_dim == 768:
             return BaseModelType.StableDiffusion1
         elif token_dim == 1024:
             return BaseModelType.StableDiffusion2
+        elif token_dim == 1280:
+            return BaseModelType.StableDiffusionXL
         else:
             return None
 

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -607,10 +607,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if isinstance(guidance_scale, list):
             guidance_scale = guidance_scale[step_index]
 
-        noise_pred = self.invokeai_diffuser._combine(
+        noise_pred = self.invokeai_diffuser._combine(uc_noise_pred, c_noise_pred, guidance_scale)
-            uc_noise_pred,
+        guidance_rescale_multiplier = conditioning_data.guidance_rescale_multiplier
+        if guidance_rescale_multiplier > 0:
+            noise_pred = self._rescale_cfg(
+                noise_pred,
                 c_noise_pred,
-            guidance_scale,
+                guidance_rescale_multiplier,
             )
 
         # compute the previous noisy sample x_t -> x_t-1
@@ -634,6 +637,16 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         return step_output
 
+    @staticmethod
+    def _rescale_cfg(total_noise_pred, pos_noise_pred, multiplier=0.7):
+        """Implementation of Algorithm 2 from https://arxiv.org/pdf/2305.08891.pdf."""
+        ro_pos = torch.std(pos_noise_pred, dim=(1, 2, 3), keepdim=True)
+        ro_cfg = torch.std(total_noise_pred, dim=(1, 2, 3), keepdim=True)
+
+        x_rescaled = total_noise_pred * (ro_pos / ro_cfg)
+        x_final = multiplier * x_rescaled + (1.0 - multiplier) * total_noise_pred
+        return x_final
+
     def _unet_forward(
         self,
         latents,

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -67,13 +67,17 @@ class IPAdapterConditioningInfo:
 class ConditioningData:
     unconditioned_embeddings: BasicConditioningInfo
     text_embeddings: BasicConditioningInfo
-    guidance_scale: Union[float, List[float]]
     """
     Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
     `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
     Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
     images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
     """
+    guidance_scale: Union[float, List[float]]
+    """ for models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7 .
+     ref [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf)
+    """
+    guidance_rescale_multiplier: float = 0
     extra: Optional[ExtraConditioningInfo] = None
     scheduler_args: dict[str, Any] = field(default_factory=dict)
     """

invokeai/backend/tiles/tiles.py

@@ -1,8 +1,9 @@
 import math
+from typing import Union
 
 import numpy as np
 
-from invokeai.backend.tiles.utils import TBLR, Tile, calc_overlap, paste, seam_blend
+from invokeai.backend.tiles.utils import TBLR, Tile, paste
 
 
 def calc_tiles_with_overlap(
@@ -62,117 +63,31 @@ def calc_tiles_with_overlap(
 
             tiles.append(tile)
 
-    return calc_overlap(tiles, num_tiles_x, num_tiles_y)
+    def get_tile_or_none(idx_y: int, idx_x: int) -> Union[Tile, None]:
+        if idx_y < 0 or idx_y > num_tiles_y or idx_x < 0 or idx_x > num_tiles_x:
+            return None
+        return tiles[idx_y * num_tiles_x + idx_x]
 
-
+    # Iterate over tiles again and calculate overlaps.
-def calc_tiles_even_split(
-    image_height: int, image_width: int, num_tiles_x: int, num_tiles_y: int, overlap: float = 0
-) -> list[Tile]:
-    """Calculate the tile coordinates for a given image shape with the number of tiles requested.
-
-    Args:
-        image_height (int): The image height in px.
-        image_width (int): The image width in px.
-        num_x_tiles (int): The number of tile to split the image into on the X-axis.
-        num_y_tiles (int): The number of tile to split the image into on the Y-axis.
-        overlap (int, optional): The target overlap amount of the tiles size. Defaults to 0.
-
-    Returns:
-        list[Tile]: A list of tiles that cover the image shape. Ordered from left-to-right, top-to-bottom.
-    """
-
-    # Ensure tile size is divisible by 8
-    if image_width % 8 != 0 or image_height % 8 != 0:
-        raise ValueError(f"image size (({image_width}, {image_height})) must be divisible by 8")
-
-    # Calculate the overlap size based on the percentage and adjust it to be divisible by 8 (rounding up)
-    overlap_x = 8 * math.ceil(int((image_width / num_tiles_x) * overlap) / 8)
-    overlap_y = 8 * math.ceil(int((image_height / num_tiles_y) * overlap) / 8)
-
-    # Calculate the tile size based on the number of tiles and overlap, and ensure it's divisible by 8 (rounding down)
-    tile_size_x = 8 * math.floor(((image_width + overlap_x * (num_tiles_x - 1)) // num_tiles_x) / 8)
-    tile_size_y = 8 * math.floor(((image_height + overlap_y * (num_tiles_y - 1)) // num_tiles_y) / 8)
-
-    # tiles[y * num_tiles_x + x] is the tile for the y'th row, x'th column.
-    tiles: list[Tile] = []
-
-    # Calculate tile coordinates. (Ignore overlap values for now.)
     for tile_idx_y in range(num_tiles_y):
-        # Calculate the top and bottom of the row
-        top = tile_idx_y * (tile_size_y - overlap_y)
-        bottom = min(top + tile_size_y, image_height)
-        # For the last row adjust bottom to be the height of the image
-        if tile_idx_y == num_tiles_y - 1:
-            bottom = image_height
-
         for tile_idx_x in range(num_tiles_x):
-            # Calculate the left & right coordinate of each tile
+            cur_tile = get_tile_or_none(tile_idx_y, tile_idx_x)
-            left = tile_idx_x * (tile_size_x - overlap_x)
+            top_neighbor_tile = get_tile_or_none(tile_idx_y - 1, tile_idx_x)
-            right = min(left + tile_size_x, image_width)
+            left_neighbor_tile = get_tile_or_none(tile_idx_y, tile_idx_x - 1)
-            # For the last tile in the row adjust right to be the width of the image
-            if tile_idx_x == num_tiles_x - 1:
-                right = image_width
 
-            tile = Tile(
+            assert cur_tile is not None
-                coords=TBLR(top=top, bottom=bottom, left=left, right=right),
-                overlap=TBLR(top=0, bottom=0, left=0, right=0),
-            )
 
-            tiles.append(tile)
+            # Update cur_tile top-overlap and corresponding top-neighbor bottom-overlap.
+            if top_neighbor_tile is not None:
+                cur_tile.overlap.top = max(0, top_neighbor_tile.coords.bottom - cur_tile.coords.top)
+                top_neighbor_tile.overlap.bottom = cur_tile.overlap.top
 
-    return calc_overlap(tiles, num_tiles_x, num_tiles_y)
+            # Update cur_tile left-overlap and corresponding left-neighbor right-overlap.
+            if left_neighbor_tile is not None:
+                cur_tile.overlap.left = max(0, left_neighbor_tile.coords.right - cur_tile.coords.left)
+                left_neighbor_tile.overlap.right = cur_tile.overlap.left
 
-
+    return tiles
-def calc_tiles_min_overlap(
-    image_height: int, image_width: int, tile_height: int, tile_width: int, min_overlap: int, round_to_8: bool
-) -> list[Tile]:
-    """Calculate the tile coordinates for a given image shape under a simple tiling scheme with overlaps.
-
-    Args:
-        image_height (int): The image height in px.
-        image_width (int): The image width in px.
-        tile_height (int): The tile height in px. All tiles will have this height.
-        tile_width (int): The tile width in px. All tiles will have this width.
-        min_overlap (int): The target minimum overlap between adjacent tiles. If the tiles do not evenly cover the image
-            shape, then the overlap will be spread between the tiles.
-
-    Returns:
-        list[Tile]: A list of tiles that cover the image shape. Ordered from left-to-right, top-to-bottom.
-    """
-    assert image_height >= tile_height
-    assert image_width >= tile_width
-    assert min_overlap < tile_height
-    assert min_overlap < tile_width
-
-    num_tiles_x = math.ceil((image_width - min_overlap) / (tile_width - min_overlap)) if tile_width < image_width else 1
-    num_tiles_y = (
-        math.ceil((image_height - min_overlap) / (tile_height - min_overlap)) if tile_height < image_height else 1
-    )
-
-    # tiles[y * num_tiles_x + x] is the tile for the y'th row, x'th column.
-    tiles: list[Tile] = []
-
-    # Calculate tile coordinates. (Ignore overlap values for now.)
-    for tile_idx_y in range(num_tiles_y):
-        top = (tile_idx_y * (image_height - tile_height)) // (num_tiles_y - 1) if num_tiles_y > 1 else 0
-        if round_to_8:
-            top = 8 * (top // 8)
-        bottom = top + tile_height
-
-        for tile_idx_x in range(num_tiles_x):
-            left = (tile_idx_x * (image_width - tile_width)) // (num_tiles_x - 1) if num_tiles_x > 1 else 0
-            if round_to_8:
-                left = 8 * (left // 8)
-            right = left + tile_width
-
-            tile = Tile(
-                coords=TBLR(top=top, bottom=bottom, left=left, right=right),
-                overlap=TBLR(top=0, bottom=0, left=0, right=0),
-            )
-
-            tiles.append(tile)
-
-    return calc_overlap(tiles, num_tiles_x, num_tiles_y)
 
 
 def merge_tiles_with_linear_blending(
@@ -284,91 +199,3 @@ def merge_tiles_with_linear_blending(
             ),
             mask=mask,
         )
-
-
-def merge_tiles_with_seam_blending(
-    dst_image: np.ndarray, tiles: list[Tile], tile_images: list[np.ndarray], blend_amount: int
-):
-    """Merge a set of image tiles into `dst_image` with seam blending between the tiles.
-
-    We expect every tile edge to either:
-    1) have an overlap of 0, because it is aligned with the image edge, or
-    2) have an overlap >= blend_amount.
-    If neither of these conditions are satisfied, we raise an exception.
-
-    The seam blending is centered on a seam of least energy of the overlap between adjacent tiles.
-
-    Args:
-        dst_image (np.ndarray): The destination image. Shape: (H, W, C).
-        tiles (list[Tile]): The list of tiles describing the locations of the respective `tile_images`.
-        tile_images (list[np.ndarray]): The tile images to merge into `dst_image`.
-        blend_amount (int): The amount of blending (in px) between adjacent overlapping tiles.
-    """
-    # Sort tiles and images first by left x coordinate, then by top y coordinate. During tile processing, we want to
-    # iterate over tiles left-to-right, top-to-bottom.
-    tiles_and_images = list(zip(tiles, tile_images, strict=True))
-    tiles_and_images = sorted(tiles_and_images, key=lambda x: x[0].coords.left)
-    tiles_and_images = sorted(tiles_and_images, key=lambda x: x[0].coords.top)
-
-    # Organize tiles into rows.
-    tile_and_image_rows: list[list[tuple[Tile, np.ndarray]]] = []
-    cur_tile_and_image_row: list[tuple[Tile, np.ndarray]] = []
-    first_tile_in_cur_row, _ = tiles_and_images[0]
-    for tile_and_image in tiles_and_images:
-        tile, _ = tile_and_image
-        if not (
-            tile.coords.top == first_tile_in_cur_row.coords.top
-            and tile.coords.bottom == first_tile_in_cur_row.coords.bottom
-        ):
-            # Store the previous row, and start a new one.
-            tile_and_image_rows.append(cur_tile_and_image_row)
-            cur_tile_and_image_row = []
-            first_tile_in_cur_row, _ = tile_and_image
-
-        cur_tile_and_image_row.append(tile_and_image)
-    tile_and_image_rows.append(cur_tile_and_image_row)
-
-    for tile_and_image_row in tile_and_image_rows:
-        first_tile_in_row, _ = tile_and_image_row[0]
-        row_height = first_tile_in_row.coords.bottom - first_tile_in_row.coords.top
-        row_image = np.zeros((row_height, dst_image.shape[1], dst_image.shape[2]), dtype=dst_image.dtype)
-
-        # Blend the tiles in the row horizontally.
-        for tile, tile_image in tile_and_image_row:
-            # We expect the tiles to be ordered left-to-right.
-            # For each tile:
-            # - extract the overlap regions and pass to seam_blend()
-            # - apply blended region to the row_image
-            # - apply the un-blended region to the row_image
-            tile_height, tile_width, _ = tile_image.shape
-            overlap_size = tile.overlap.left
-            # Left blending:
-            if overlap_size > 0:
-                assert overlap_size >= blend_amount
-
-                overlap_coord_right = tile.coords.left + overlap_size
-                src_overlap = row_image[:, tile.coords.left : overlap_coord_right]
-                dst_overlap = tile_image[:, :overlap_size]
-                blended_overlap = seam_blend(src_overlap, dst_overlap, blend_amount, x_seam=False)
-                row_image[:, tile.coords.left : overlap_coord_right] = blended_overlap
-                row_image[:, overlap_coord_right : tile.coords.right] = tile_image[:, overlap_size:]
-            else:
-                # no overlap just paste the tile
-                row_image[:, tile.coords.left : tile.coords.right] = tile_image
-
-        # Blend the row into the dst_image
-        # We assume that the entire row has the same vertical overlaps as the first_tile_in_row.
-        # Rows are processed in the same way as tiles (extract overlap, blend, apply)
-        row_overlap_size = first_tile_in_row.overlap.top
-        if row_overlap_size > 0:
-            assert row_overlap_size >= blend_amount
-
-            overlap_coords_bottom = first_tile_in_row.coords.top + row_overlap_size
-            src_overlap = dst_image[first_tile_in_row.coords.top : overlap_coords_bottom, :]
-            dst_overlap = row_image[:row_overlap_size, :]
-            blended_overlap = seam_blend(src_overlap, dst_overlap, blend_amount, x_seam=True)
-            dst_image[first_tile_in_row.coords.top : overlap_coords_bottom, :] = blended_overlap
-            dst_image[overlap_coords_bottom : first_tile_in_row.coords.bottom, :] = row_image[row_overlap_size:, :]
-        else:
-            # no overlap just paste the row
-            row_image[first_tile_in_row.coords.top:first_tile_in_row.coords.bottom, :] = row_image

invokeai/backend/tiles/utils.py

@@ -1,9 +1,6 @@
-import math
+from typing import Optional
-from typing import Optional, Union
 
-import cv2
 import numpy as np
-#from PIL import Image
 from pydantic import BaseModel, Field
 
 
@@ -48,130 +45,3 @@ def paste(dst_image: np.ndarray, src_image: np.ndarray, box: TBLR, mask: Optiona
         mask = np.expand_dims(mask, -1)
         dst_image_box = dst_image[box.top : box.bottom, box.left : box.right]
         dst_image[box.top : box.bottom, box.left : box.right] = src_image * mask + dst_image_box * (1.0 - mask)
-
-
-def calc_overlap(tiles: list[Tile], num_tiles_x, num_tiles_y) -> list[Tile]:
-    """Calculate and update the overlap of a list of tiles.
-
-    Args:
-        tiles (list[Tile]): The list of tiles describing the locations of the respective `tile_images`.
-        num_tiles_x: the number of tiles on the x axis.
-        num_tiles_y: the number of tiles on the y axis.
-    """
-    def get_tile_or_none(idx_y: int, idx_x: int) -> Union[Tile, None]:
-        if idx_y < 0 or idx_y > num_tiles_y or idx_x < 0 or idx_x > num_tiles_x:
-            return None
-        return tiles[idx_y * num_tiles_x + idx_x]
-
-    for tile_idx_y in range(num_tiles_y):
-        for tile_idx_x in range(num_tiles_x):
-            cur_tile = get_tile_or_none(tile_idx_y, tile_idx_x)
-            top_neighbor_tile = get_tile_or_none(tile_idx_y - 1, tile_idx_x)
-            left_neighbor_tile = get_tile_or_none(tile_idx_y, tile_idx_x - 1)
-
-            assert cur_tile is not None
-
-            # Update cur_tile top-overlap and corresponding top-neighbor bottom-overlap.
-            if top_neighbor_tile is not None:
-                cur_tile.overlap.top = max(0, top_neighbor_tile.coords.bottom - cur_tile.coords.top)
-                top_neighbor_tile.overlap.bottom = cur_tile.overlap.top
-
-            # Update cur_tile left-overlap and corresponding left-neighbor right-overlap.
-            if left_neighbor_tile is not None:
-                cur_tile.overlap.left = max(0, left_neighbor_tile.coords.right - cur_tile.coords.left)
-                left_neighbor_tile.overlap.right = cur_tile.overlap.left
-    return tiles
-
-
-def seam_blend(ia1: np.ndarray, ia2: np.ndarray, blend_amount: int, x_seam: bool,) -> np.ndarray:
-    """Blend two overlapping tile sections using a seams to find a path.
-
-    It is assumed that input images will be RGB np arrays and are the same size.
-
-    Args:
-        ia1 (torch.Tensor): Image array 1 Shape: (H, W, C).
-        ia2 (torch.Tensor): Image array 2 Shape: (H, W, C).
-        x_seam (bool): If the images should be blended on the x axis or not.
-        blend_amount (int): The size of the blur to use on the seam. Half of this value will be used to avoid the edges of the image.
-    """
-
-    def shift(arr, num, fill_value=255.0):
-        result = np.full_like(arr, fill_value)
-        if num > 0:
-            result[num:] = arr[:-num]
-        elif num < 0:
-            result[:num] = arr[-num:]
-        else:
-            result[:] = arr
-        return result
-
-    # Assume RGB and convert to grey
-    iag1 = np.dot(ia1, [0.2989, 0.5870, 0.1140])
-    iag2 = np.dot(ia2, [0.2989, 0.5870, 0.1140])
-
-    # Calc Difference between the images
-    ia = iag2 - iag1
-
-    # If the seam is on the X-axis rotate the array so we can treat it like a vertical seam
-    if x_seam:
-        ia = np.rot90(ia, 1)
-
-    # Calc max and min X & Y limits
-    # gutter is used to avoid the blur hitting the edge of the image
-    gutter = math.ceil(blend_amount / 2) if blend_amount > 0 else 0
-    max_y, max_x = ia.shape
-    max_x -= gutter
-    min_x = gutter
-
-    # Calc the energy in the difference
-    energy = np.abs(np.gradient(ia, axis=0)) + np.abs(np.gradient(ia, axis=1))
-
-    #Find the starting position of the seam
-    res = np.copy(energy)
-    for y in range(1, max_y):
-        row = res[y, :]
-        rowl = shift(row, -1)
-        rowr = shift(row, 1)
-        res[y, :] = res[y - 1, :] + np.min([row, rowl, rowr], axis=0)
-
-    # create an array max_y long
-    lowest_energy_line = np.empty([max_y], dtype="uint16")
-    lowest_energy_line[max_y - 1] = np.argmin(res[max_y - 1, min_x : max_x - 1])
-
-    #Calc the path of the seam
-    for ypos in range(max_y - 2, -1, -1):
-        lowest_pos = lowest_energy_line[ypos + 1]
-        lpos = lowest_pos - 1
-        rpos = lowest_pos + 1
-        lpos = np.clip(lpos, min_x, max_x - 1)
-        rpos = np.clip(rpos, min_x, max_x - 1)
-        lowest_energy_line[ypos] = np.argmin(energy[ypos, lpos : rpos + 1]) + lpos
-
-    # Draw the mask
-    mask = np.zeros_like(ia)
-    for ypos in range(0, max_y):
-        to_fill = lowest_energy_line[ypos]
-        mask[ypos, :to_fill] = 1
-
-    # If the seam is on the X-axis rotate the array back
-    if x_seam:
-        mask = np.rot90(mask, 3)
-
-    # blur the seam mask if required
-    if blend_amount > 0:
-        mask = cv2.blur(mask, (blend_amount, blend_amount))
-
-    # copy ia2 over ia1 while applying the seam mask
-    mask = np.expand_dims(mask, -1)
-    blended_image = ia1 * mask + ia2 * (1.0 - mask)
-
-    # for debugging to see the final blended overlap image
-    #image = Image.fromarray((mask * 255.0).astype("uint8"))
-    #i1 = Image.fromarray(ia1.astype("uint8"))
-    #i2 = Image.fromarray(ia2.astype("uint8"))
-    #bimage = Image.fromarray(blended_image.astype("uint8"))
-
-    #print(f"{ia1.shape}, {ia2.shape}, {mask.shape}, {blended_image.shape}")
-    #print(f"{i1.size}, {i2.size}, {image.size}, {bimage.size}")
-
-    return blended_image

invokeai/backend/util/logging.py

@@ -342,9 +342,8 @@ class InvokeAILogger(object):  # noqa D102
         cls, name: str = "InvokeAI", config: InvokeAIAppConfig = InvokeAIAppConfig.get_config()
     ) -> logging.Logger:  # noqa D102
         if name in cls.loggers:
-            logger = cls.loggers[name]
+            return cls.loggers[name]
-            logger.handlers.clear()
+
-        else:
         logger = logging.getLogger(name)
         logger.setLevel(config.log_level.upper())  # yes, strings work here
         for ch in cls.get_loggers(config):
@@ -358,7 +357,7 @@ class InvokeAILogger(object):  # noqa D102
         handlers = []
         for handler in handler_strs:
             handler_name, *args = handler.split("=", 2)
-            args = args[0] if len(args) > 0 else None
+            arg = args[0] if len(args) > 0 else None
 
             # console and file get the fancy formatter.
             # syslog gets a simple one
@@ -370,16 +369,16 @@ class InvokeAILogger(object):  # noqa D102
                 handlers.append(ch)
 
             elif handler_name == "syslog":
-                ch = cls._parse_syslog_args(args)
+                ch = cls._parse_syslog_args(arg)
                 handlers.append(ch)
 
             elif handler_name == "file":
-                ch = cls._parse_file_args(args)
+                ch = cls._parse_file_args(arg)
                 ch.setFormatter(formatter())
                 handlers.append(ch)
 
             elif handler_name == "http":
-                ch = cls._parse_http_args(args)
+                ch = cls._parse_http_args(arg)
                 handlers.append(ch)
         return handlers
 

invokeai/frontend/web/package.json

@@ -75,6 +75,7 @@
     "framer-motion": "^10.16.4",
     "i18next": "^23.6.0",
     "i18next-http-backend": "^2.3.1",
+    "idb-keyval": "^6.2.1",
     "konva": "^9.2.3",
     "lodash-es": "^4.17.21",
     "nanostores": "^0.9.4",

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

@@ -803,8 +803,7 @@
         "canny": "Canny",
         "hedDescription": "Ganzheitlich verschachtelte Kantenerkennung",
         "scribble": "Scribble",
-        "maxFaces": "Maximal Anzahl Gesichter",
+        "maxFaces": "Maximal Anzahl Gesichter"
-        "unstarImage": "Markierung aufheben"
     },
     "queue": {
         "status": "Status",

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

@@ -243,7 +243,6 @@
         "setControlImageDimensions": "Set Control Image Dimensions To W/H",
         "showAdvanced": "Show Advanced",
         "toggleControlNet": "Toggle this ControlNet",
-        "unstarImage": "Unstar Image",
         "w": "W",
         "weight": "Weight",
         "enableIPAdapter": "Enable IP Adapter",
@@ -378,6 +377,8 @@
         "showGenerations": "Show Generations",
         "showUploads": "Show Uploads",
         "singleColumnLayout": "Single Column Layout",
+        "starImage": "Star Image",
+        "unstarImage": "Unstar Image",
         "unableToLoad": "Unable to load Gallery",
         "uploads": "Uploads",
         "deleteSelection": "Delete Selection",
@@ -599,6 +600,7 @@
     },
     "metadata": {
         "cfgScale": "CFG scale",
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
         "createdBy": "Created By",
         "fit": "Image to image fit",
         "generationMode": "Generation Mode",
@@ -977,6 +979,7 @@
         "unsupportedAnyOfLength": "too many union members ({{count}})",
         "unsupportedMismatchedUnion": "mismatched CollectionOrScalar type with base types {{firstType}} and {{secondType}}",
         "unableToParseFieldType": "unable to parse field type",
+        "unableToExtractEnumOptions": "unable to extract enum options",
         "uNetField": "UNet",
         "uNetFieldDescription": "UNet submodel.",
         "unhandledInputProperty": "Unhandled input property",
@@ -1032,6 +1035,8 @@
             "setType": "Set cancel type"
         },
         "cfgScale": "CFG Scale",
+        "cfgRescaleMultiplier": "CFG Rescale Multiplier",
+        "cfgRescale": "CFG Rescale",
         "clipSkip": "CLIP Skip",
         "clipSkipWithLayerCount": "CLIP Skip {{layerCount}}",
         "closeViewer": "Close Viewer",
@@ -1470,6 +1475,12 @@
                 "Controls how much your prompt influences the generation process."
             ]
         },
+        "paramCFGRescaleMultiplier": {
+            "heading": "CFG Rescale Multiplier",
+            "paragraphs": [
+                "Rescale multiplier for CFG guidance, used for models trained using zero-terminal SNR (ztsnr). Suggested value 0.7."
+            ]
+        },
         "paramDenoisingStrength": {
             "heading": "Denoising Strength",
             "paragraphs": [

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

@@ -91,7 +91,19 @@
         "controlNet": "ControlNet",
         "auto": "Automatico",
         "simple": "Semplice",
-        "details": "Dettagli"
+        "details": "Dettagli",
+        "format": "formato",
+        "unknown": "Sconosciuto",
+        "folder": "Cartella",
+        "error": "Errore",
+        "installed": "Installato",
+        "template": "Schema",
+        "outputs": "Uscite",
+        "data": "Dati",
+        "somethingWentWrong": "Qualcosa è andato storto",
+        "copyError": "$t(gallery.copy) Errore",
+        "input": "Ingresso",
+        "notInstalled": "Non $t(common.installed)"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -122,7 +134,14 @@
         "preparingDownload": "Preparazione del download",
         "preparingDownloadFailed": "Problema durante la preparazione del download",
         "downloadSelection": "Scarica gli elementi selezionati",
-        "noImageSelected": "Nessuna immagine selezionata"
+        "noImageSelected": "Nessuna immagine selezionata",
+        "deleteSelection": "Elimina la selezione",
+        "image": "immagine",
+        "drop": "Rilascia",
+        "unstarImage": "Rimuovi preferenza immagine",
+        "dropOrUpload": "$t(gallery.drop) o carica",
+        "starImage": "Immagine preferita",
+        "dropToUpload": "$t(gallery.drop) per aggiornare"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tasti rapidi",
@@ -477,7 +496,8 @@
         "modelType": "Tipo di modello",
         "customConfigFileLocation": "Posizione del file di configurazione personalizzato",
         "vaePrecision": "Precisione VAE",
-        "noModelSelected": "Nessun modello selezionato"
+        "noModelSelected": "Nessun modello selezionato",
+        "conversionNotSupported": "Conversione non supportata"
     },
     "parameters": {
         "images": "Immagini",
@@ -838,7 +858,8 @@
         "menu": "Menu",
         "showGalleryPanel": "Mostra il pannello Galleria",
         "loadMore": "Carica altro",
-        "mode": "Modalità"
+        "mode": "Modalità",
+        "resetUI": "$t(accessibility.reset) l'Interfaccia Utente"
     },
     "ui": {
         "hideProgressImages": "Nascondi avanzamento immagini",
@@ -1040,7 +1061,15 @@
         "updateAllNodes": "Aggiorna tutti i nodi",
         "unableToUpdateNodes_one": "Impossibile aggiornare {{count}} nodo",
         "unableToUpdateNodes_many": "Impossibile aggiornare {{count}} nodi",
-        "unableToUpdateNodes_other": "Impossibile aggiornare {{count}} nodi"
+        "unableToUpdateNodes_other": "Impossibile aggiornare {{count}} nodi",
+        "addLinearView": "Aggiungi alla vista Lineare",
+        "outputFieldInInput": "Campo di uscita in ingresso",
+        "unableToMigrateWorkflow": "Impossibile migrare il flusso di lavoro",
+        "unableToUpdateNode": "Impossibile aggiornare nodo",
+        "unknownErrorValidatingWorkflow": "Errore sconosciuto durante la convalida del flusso di lavoro",
+        "collectionFieldType": "{{name}} Raccolta",
+        "collectionOrScalarFieldType": "{{name}} Raccolta|Scalare",
+        "nodeVersion": "Versione Nodo"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1062,7 +1091,10 @@
         "deleteBoardOnly": "Elimina solo la Bacheca",
         "deleteBoard": "Elimina Bacheca",
         "deleteBoardAndImages": "Elimina Bacheca e Immagini",
-        "deletedBoardsCannotbeRestored": "Le bacheche eliminate non possono essere ripristinate"
+        "deletedBoardsCannotbeRestored": "Le bacheche eliminate non possono essere ripristinate",
+        "movingImagesToBoard_one": "Spostare {{count}} immagine nella bacheca:",
+        "movingImagesToBoard_many": "Spostare {{count}} immagini nella bacheca:",
+        "movingImagesToBoard_other": "Spostare {{count}} immagini nella bacheca:"
     },
     "controlnet": {
         "contentShuffleDescription": "Rimescola il contenuto di un'immagine",
@@ -1136,7 +1168,8 @@
         "megaControl": "Mega ControlNet",
         "minConfidence": "Confidenza minima",
         "scribble": "Scribble",
-        "amult": "Angolo di illuminazione"
+        "amult": "Angolo di illuminazione",
+        "coarse": "Approssimativo"
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
@@ -1204,7 +1237,8 @@
     "embedding": {
         "noMatchingEmbedding": "Nessun Incorporamento corrispondente",
         "addEmbedding": "Aggiungi Incorporamento",
-        "incompatibleModel": "Modello base incompatibile:"
+        "incompatibleModel": "Modello base incompatibile:",
+        "noEmbeddingsLoaded": "Nessun incorporamento caricato"
     },
     "models": {
         "noMatchingModels": "Nessun modello corrispondente",
@@ -1217,7 +1251,8 @@
         "noRefinerModelsInstalled": "Nessun modello SDXL Refiner installato",
         "noLoRAsInstalled": "Nessun LoRA installato",
         "esrganModel": "Modello ESRGAN",
-        "addLora": "Aggiungi LoRA"
+        "addLora": "Aggiungi LoRA",
+        "noLoRAsLoaded": "Nessuna LoRA caricata"
     },
     "invocationCache": {
         "disable": "Disabilita",
@@ -1233,7 +1268,8 @@
         "enable": "Abilita",
         "clear": "Svuota",
         "maxCacheSize": "Dimensione max cache",
-        "cacheSize": "Dimensione cache"
+        "cacheSize": "Dimensione cache",
+        "useCache": "Usa Cache"
     },
     "dynamicPrompts": {
         "seedBehaviour": {

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

@@ -72,5 +72,13 @@
     },
     "unifiedCanvas": {
         "betaPreserveMasked": "마스크 레이어 유지"
+    },
+    "accessibility": {
+        "previousImage": "이전 이미지",
+        "modifyConfig": "Config 수정",
+        "nextImage": "다음 이미지",
+        "mode": "모드",
+        "menu": "메뉴",
+        "modelSelect": "모델 선택"
     }
 }

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

@@ -99,7 +99,17 @@
         "data": "数据",
         "safetensors": "Safetensors",
         "outpaint": "外扩绘制",
-        "details": "详情"
+        "details": "详情",
+        "format": "格式",
+        "unknown": "未知",
+        "folder": "文件夹",
+        "error": "错误",
+        "installed": "已安装",
+        "file": "文件",
+        "somethingWentWrong": "出了点问题",
+        "copyError": "$t(gallery.copy) 错误",
+        "input": "输入",
+        "notInstalled": "非 $t(common.installed)"
     },
     "gallery": {
         "generations": "生成的图像",
@@ -130,7 +140,12 @@
         "preparingDownload": "准备下载",
         "preparingDownloadFailed": "准备下载时出现问题",
         "downloadSelection": "下载所选内容",
-        "noImageSelected": "无选中的图像"
+        "noImageSelected": "无选中的图像",
+        "deleteSelection": "删除所选内容",
+        "image": "图像",
+        "drop": "弃用",
+        "dropOrUpload": "$t(gallery.drop) 或上传",
+        "dropToUpload": "$t(gallery.drop) 以上传"
     },
     "hotkeys": {
         "keyboardShortcuts": "键盘快捷键",
@@ -486,7 +501,8 @@
         "alpha": "Alpha",
         "vaePrecision": "VAE 精度",
         "checkpointOrSafetensors": "$t(common.checkpoint) / $t(common.safetensors)",
-        "noModelSelected": "无选中的模型"
+        "noModelSelected": "无选中的模型",
+        "conversionNotSupported": "转换尚未支持"
     },
     "parameters": {
         "images": "图像",
@@ -615,7 +631,10 @@
         "seamlessX": "无缝 X",
         "seamlessY": "无缝 Y",
         "maskEdge": "遮罩边缘",
-        "unmasked": "取消遮罩"
+        "unmasked": "取消遮罩",
+        "cfgRescaleMultiplier": "CFG 重缩放倍数",
+        "cfgRescale": "CFG 重缩放",
+        "useSize": "使用尺寸"
     },
     "settings": {
         "models": "模型",
@@ -655,7 +674,8 @@
         "clearIntermediatesDisabled": "队列为空才能清理中间产物",
         "enableNSFWChecker": "启用成人内容检测器",
         "enableInvisibleWatermark": "启用不可见水印",
-        "enableInformationalPopovers": "启用信息弹窗"
+        "enableInformationalPopovers": "启用信息弹窗",
+        "reloadingIn": "重新加载中"
     },
     "toast": {
         "tempFoldersEmptied": "临时文件夹已清空",
@@ -739,7 +759,8 @@
         "imageUploadFailed": "图像上传失败",
         "problemImportingMask": "导入遮罩时出现问题",
         "baseModelChangedCleared_other": "基础模型已更改, 已清除或禁用 {{count}} 个不兼容的子模型",
-        "setAsCanvasInitialImage": "设为画布初始图像"
+        "setAsCanvasInitialImage": "设为画布初始图像",
+        "invalidUpload": "无效的上传"
     },
     "unifiedCanvas": {
         "layer": "图层",
@@ -748,7 +769,7 @@
         "maskingOptions": "遮罩选项",
         "enableMask": "启用遮罩",
         "preserveMaskedArea": "保留遮罩区域",
-        "clearMask": "清除遮罩",
+        "clearMask": "清除遮罩 (Shift+C)",
         "brush": "刷子",
         "eraser": "橡皮擦",
         "fillBoundingBox": "填充选择区域",
@@ -801,7 +822,8 @@
         "betaPreserveMasked": "保留遮罩层",
         "antialiasing": "抗锯齿",
         "showResultsOn": "显示结果 (开)",
-        "showResultsOff": "显示结果 (关)"
+        "showResultsOff": "显示结果 (关)",
+        "saveMask": "保存 $t(unifiedCanvas.mask)"
     },
     "accessibility": {
         "modelSelect": "模型选择",
@@ -826,7 +848,9 @@
         "menu": "菜单",
         "showGalleryPanel": "显示图库浮窗",
         "loadMore": "加载更多",
-        "mode": "模式"
+        "mode": "模式",
+        "resetUI": "$t(accessibility.reset) UI",
+        "createIssue": "创建问题"
     },
     "ui": {
         "showProgressImages": "显示处理中的图片",
@@ -877,7 +901,7 @@
         "animatedEdges": "边缘动效",
         "nodeTemplate": "节点模板",
         "pickOne": "选择一个",
-        "unableToLoadWorkflow": "无法验证工作流",
+        "unableToLoadWorkflow": "无法加载工作流",
         "snapToGrid": "对齐网格",
         "noFieldsLinearview": "线性视图中未添加任何字段",
         "nodeSearch": "检索节点",
@@ -929,7 +953,7 @@
         "skippingUnknownOutputType": "跳过未知类型的输出",
         "latentsFieldDescription": "Latents 可以在节点间传递。",
         "denoiseMaskFieldDescription": "去噪遮罩可以在节点间传递",
-        "missingTemplate": "缺失模板",
+        "missingTemplate": "无效的节点：类型为 {{type}} 的节点 {{node}} 缺失模板（无已安装模板？）",
         "outputSchemaNotFound": "未找到输出模式",
         "latentsPolymorphicDescription": "Latents 可以在节点间传递。",
         "colorFieldDescription": "一种 RGBA 颜色。",
@@ -957,7 +981,7 @@
         "collectionItem": "项目合集",
         "controlCollectionDescription": "节点间传递的控制信息。",
         "skippedReservedInput": "跳过保留的输入",
-        "outputFields": "输出",
+        "outputFields": "输出区域",
         "edge": "边缘",
         "inputNode": "输入节点",
         "enumDescription": "枚举 (Enums) 可能是多个选项的一个数值。",
@@ -992,7 +1016,7 @@
         "string": "字符串",
         "inputFields": "输入",
         "uNetFieldDescription": "UNet 子模型。",
-        "mismatchedVersion": "不匹配的版本",
+        "mismatchedVersion": "无效的节点：类型为 {{type}} 的节点 {{node}} 版本不匹配（是否尝试更新？）",
         "vaeFieldDescription": "Vae 子模型。",
         "imageFieldDescription": "图像可以在节点间传递。",
         "outputNode": "输出节点",
@@ -1050,8 +1074,36 @@
         "latentsPolymorphic": "Latents 多态",
         "conditioningField": "条件",
         "latentsField": "Latents",
-        "updateAllNodes": "更新所有节点",
+        "updateAllNodes": "更新节点",
-        "unableToUpdateNodes_other": "{{count}} 个节点无法完成更新"
+        "unableToUpdateNodes_other": "{{count}} 个节点无法完成更新",
+        "inputFieldTypeParseError": "无法解析 {{node}} 的输入类型 {{field}}。({{message}})",
+        "unsupportedArrayItemType": "不支持的数组类型 \"{{type}}\"",
+        "addLinearView": "添加到线性视图",
+        "targetNodeFieldDoesNotExist": "无效的边缘：{{node}} 的目标/输入区域 {{field}} 不存在",
+        "unsupportedMismatchedUnion": "合集或标量类型与基类 {{firstType}} 和 {{secondType}} 不匹配",
+        "allNodesUpdated": "已更新所有节点",
+        "sourceNodeDoesNotExist": "无效的边缘：{{node}} 的源/输出节点不存在",
+        "unableToExtractEnumOptions": "无法提取枚举选项",
+        "unableToParseFieldType": "无法解析类型",
+        "outputFieldInInput": "输入中的输出区域",
+        "unrecognizedWorkflowVersion": "无法识别的工作流架构版本：{{version}}",
+        "outputFieldTypeParseError": "无法解析 {{node}} 的输出类型 {{field}}。({{message}})",
+        "sourceNodeFieldDoesNotExist": "无效的边缘：{{node}} 的源/输出区域 {{field}} 不存在",
+        "unableToGetWorkflowVersion": "无法获取工作流架构版本",
+        "nodePack": "节点包",
+        "unableToExtractSchemaNameFromRef": "无法从参考中提取架构名",
+        "unableToMigrateWorkflow": "无法迁移工作流",
+        "unknownOutput": "未知输出：{{name}}",
+        "unableToUpdateNode": "无法更新节点",
+        "unknownErrorValidatingWorkflow": "验证工作流时出现未知错误",
+        "collectionFieldType": "{{name}} 合集",
+        "unknownNodeType": "未知节点类型",
+        "targetNodeDoesNotExist": "无效的边缘：{{node}} 的目标/输入节点不存在",
+        "unknownFieldType": "$t(nodes.unknownField) 类型：{{type}}",
+        "collectionOrScalarFieldType": "{{name}} 合集 | 标量",
+        "nodeVersion": "节点版本",
+        "deletedInvalidEdge": "已删除无效的边缘 {{source}} -> {{target}}",
+        "unknownInput": "未知输入：{{name}}"
     },
     "controlnet": {
         "resize": "直接缩放",
@@ -1137,8 +1189,7 @@
         "openPose": "Openpose",
         "controlAdapter_other": "Control Adapters",
         "lineartAnime": "Lineart Anime",
-        "canny": "Canny",
+        "canny": "Canny"
-        "unstarImage": "取消收藏图像"
     },
     "queue": {
         "status": "状态",
@@ -1246,7 +1297,8 @@
         "fit": "图生图匹配",
         "recallParameters": "召回参数",
         "noRecallParameters": "未找到要召回的参数",
-        "vae": "VAE"
+        "vae": "VAE",
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)"
     },
     "models": {
         "noMatchingModels": "无相匹配的模型",
@@ -1259,7 +1311,8 @@
         "noRefinerModelsInstalled": "无已安装的 SDXL Refiner 模型",
         "noLoRAsInstalled": "无已安装的 LoRA",
         "esrganModel": "ESRGAN 模型",
-        "addLora": "添加 LoRA"
+        "addLora": "添加 LoRA",
+        "noLoRAsLoaded": "无已加载的 LoRA"
     },
     "boards": {
         "autoAddBoard": "自动添加面板",
@@ -1281,12 +1334,14 @@
         "deleteBoardOnly": "仅删除面板",
         "deleteBoard": "删除面板",
         "deleteBoardAndImages": "删除面板和图像",
-        "deletedBoardsCannotbeRestored": "已删除的面板无法被恢复"
+        "deletedBoardsCannotbeRestored": "已删除的面板无法被恢复",
+        "movingImagesToBoard_other": "移动 {{count}} 张图像到面板："
     },
     "embedding": {
         "noMatchingEmbedding": "不匹配的 Embedding",
         "addEmbedding": "添加 Embedding",
-        "incompatibleModel": "不兼容的基础模型："
+        "incompatibleModel": "不兼容的基础模型：",
+        "noEmbeddingsLoaded": "无已加载的 Embedding"
     },
     "dynamicPrompts": {
         "seedBehaviour": {
@@ -1515,6 +1570,12 @@
                 "ControlNet 为生成过程提供引导，为生成具有受控构图、结构、样式的图像提供帮助，具体的功能由所选的模型决定。"
             ],
             "heading": "ControlNet"
+        },
+        "paramCFGRescaleMultiplier": {
+            "heading": "CFG 重缩放倍数",
+            "paragraphs": [
+                "CFG 引导的重缩放倍率，用于通过 zero-terminal SNR (ztsnr) 训练的模型。推荐设为 0.7。"
+            ]
         }
     },
     "invocationCache": {
@@ -1531,7 +1592,8 @@
         "enable": "启用",
         "clear": "清除",
         "maxCacheSize": "最大缓存大小",
-        "cacheSize": "缓存大小"
+        "cacheSize": "缓存大小",
+        "useCache": "使用缓存"
     },
     "hrf": {
         "enableHrf": "启用高分辨率修复",

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

@@ -21,6 +21,7 @@ import GlobalHotkeys from './GlobalHotkeys';
 import PreselectedImage from './PreselectedImage';
 import Toaster from './Toaster';
 import { useSocketIO } from 'app/hooks/useSocketIO';
+import { useClearStorage } from 'common/hooks/useClearStorage';
 
 const DEFAULT_CONFIG = {};
 
@@ -36,15 +37,16 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage }: Props) => {
   const language = useAppSelector(languageSelector);
   const logger = useLogger('system');
   const dispatch = useAppDispatch();
+  const clearStorage = useClearStorage();
 
   // singleton!
   useSocketIO();
 
   const handleReset = useCallback(() => {
-    localStorage.clear();
+    clearStorage();
     location.reload();
     return false;
-  }, []);
+  }, [clearStorage]);
 
   useEffect(() => {
     i18n.changeLanguage(language);

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

@@ -7,21 +7,23 @@ import { $headerComponent } from 'app/store/nanostores/headerComponent';
 import { $isDebugging } from 'app/store/nanostores/isDebugging';
 import { $projectId } from 'app/store/nanostores/projectId';
 import { $queueId, DEFAULT_QUEUE_ID } from 'app/store/nanostores/queueId';
-import { store } from 'app/store/store';
+import { $store } from 'app/store/nanostores/store';
+import { createStore } from 'app/store/store';
 import { PartialAppConfig } from 'app/types/invokeai';
+import Loading from 'common/components/Loading/Loading';
+import AppDndContext from 'features/dnd/components/AppDndContext';
+import 'i18n';
 import React, {
   PropsWithChildren,
   ReactNode,
   lazy,
   memo,
   useEffect,
+  useMemo,
 } from 'react';
 import { Provider } from 'react-redux';
 import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares';
 import { ManagerOptions, SocketOptions } from 'socket.io-client';
-import Loading from 'common/components/Loading/Loading';
-import AppDndContext from 'features/dnd/components/AppDndContext';
-import 'i18n';
 
 const App = lazy(() => import('./App'));
 const ThemeLocaleProvider = lazy(() => import('./ThemeLocaleProvider'));
@@ -137,6 +139,14 @@ const InvokeAIUI = ({
     };
   }, [isDebugging]);
 
+  const store = useMemo(() => {
+    return createStore(projectId);
+  }, [projectId]);
+
+  useEffect(() => {
+    $store.set(store);
+  }, [store]);
+
   return (
     <React.StrictMode>
       <Provider store={store}>

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

@@ -9,9 +9,9 @@ import { TOAST_OPTIONS, theme as invokeAITheme } from 'theme/theme';
 
 import '@fontsource-variable/inter';
 import { MantineProvider } from '@mantine/core';
+import { useMantineTheme } from 'mantine-theme/theme';
 import 'overlayscrollbars/overlayscrollbars.css';
 import 'theme/css/overlayscrollbars.css';
-import { useMantineTheme } from 'mantine-theme/theme';
 
 type ThemeLocaleProviderProps = {
   children: ReactNode;

invokeai/frontend/web/src/app/hooks/useSocketIO.ts

@@ -3,8 +3,8 @@ import { $authToken } from 'app/store/nanostores/authToken';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
 import { $isDebugging } from 'app/store/nanostores/isDebugging';
 import { useAppDispatch } from 'app/store/storeHooks';
-import { MapStore, WritableAtom, atom, map } from 'nanostores';
+import { MapStore, atom, map } from 'nanostores';
-import { useEffect } from 'react';
+import { useEffect, useMemo } from 'react';
 import {
   ClientToServerEvents,
   ServerToClientEvents,
@@ -16,57 +16,10 @@ import { ManagerOptions, Socket, SocketOptions, io } from 'socket.io-client';
 declare global {
   interface Window {
     $socketOptions?: MapStore<Partial<ManagerOptions & SocketOptions>>;
-    $socketUrl?: WritableAtom<string>;
   }
 }
 
-const makeSocketOptions = (): Partial<ManagerOptions & SocketOptions> => {
-  const socketOptions: Parameters<typeof io>[0] = {
-    timeout: 60000,
-    path: '/ws/socket.io',
-    autoConnect: false, // achtung! removing this breaks the dynamic middleware
-    forceNew: true,
-  };
-
-  // if building in package mode, replace socket url with open api base url minus the http protocol
-  if (['nodes', 'package'].includes(import.meta.env.MODE)) {
-    const authToken = $authToken.get();
-    if (authToken) {
-      // TODO: handle providing jwt to socket.io
-      socketOptions.auth = { token: authToken };
-    }
-
-    socketOptions.transports = ['websocket', 'polling'];
-  }
-
-  return socketOptions;
-};
-
-const makeSocketUrl = (): string => {
-  const wsProtocol = window.location.protocol === 'https:' ? 'wss' : 'ws';
-  let socketUrl = `${wsProtocol}://${window.location.host}`;
-  if (['nodes', 'package'].includes(import.meta.env.MODE)) {
-    const baseUrl = $baseUrl.get();
-    if (baseUrl) {
-      //eslint-disable-next-line
-      socketUrl = baseUrl.replace(/^https?\:\/\//i, '');
-    }
-  }
-  return socketUrl;
-};
-
-const makeSocket = (): Socket<ServerToClientEvents, ClientToServerEvents> => {
-  const socketOptions = makeSocketOptions();
-  const socketUrl = $socketUrl.get();
-  const socket: Socket<ServerToClientEvents, ClientToServerEvents> = io(
-    socketUrl,
-    { ...socketOptions, ...$socketOptions.get() }
-  );
-  return socket;
-};
-
 export const $socketOptions = map<Partial<ManagerOptions & SocketOptions>>({});
-export const $socketUrl = atom<string>(makeSocketUrl());
 export const $isSocketInitialized = atom<boolean>(false);
 
 /**
@@ -74,23 +27,50 @@ export const $isSocketInitialized = atom<boolean>(false);
  */
 export const useSocketIO = () => {
   const dispatch = useAppDispatch();
-  const socketOptions = useStore($socketOptions);
-  const socketUrl = useStore($socketUrl);
   const baseUrl = useStore($baseUrl);
   const authToken = useStore($authToken);
+  const addlSocketOptions = useStore($socketOptions);
+
+  const socketUrl = useMemo(() => {
+    const wsProtocol = window.location.protocol === 'https:' ? 'wss' : 'ws';
+    if (baseUrl) {
+      return baseUrl.replace(/^https?:\/\//i, '');
+    }
+
+    return `${wsProtocol}://${window.location.host}`;
+  }, [baseUrl]);
+
+  const socketOptions = useMemo(() => {
+    const options: Parameters<typeof io>[0] = {
+      timeout: 60000,
+      path: '/ws/socket.io',
+      autoConnect: false, // achtung! removing this breaks the dynamic middleware
+      forceNew: true,
+    };
+
+    if (authToken) {
+      options.auth = { token: authToken };
+      options.transports = ['websocket', 'polling'];
+    }
+
+    return { ...options, ...addlSocketOptions };
+  }, [authToken, addlSocketOptions]);
 
   useEffect(() => {
     if ($isSocketInitialized.get()) {
       // Singleton!
       return;
     }
-    const socket = makeSocket();
+
+    const socket: Socket<ServerToClientEvents, ClientToServerEvents> = io(
+      socketUrl,
+      socketOptions
+    );
     setEventListeners({ dispatch, socket });
     socket.connect();
 
     if ($isDebugging.get()) {
       window.$socketOptions = $socketOptions;
-      window.$socketUrl = $socketUrl;
       console.log('Socket initialized', socket);
     }
 
@@ -99,11 +79,10 @@ export const useSocketIO = () => {
     return () => {
       if ($isDebugging.get()) {
         window.$socketOptions = undefined;
-        window.$socketUrl = undefined;
         console.log('Socket teardown', socket);
       }
       socket.disconnect();
       $isSocketInitialized.set(false);
     };
-  }, [dispatch, socketOptions, socketUrl, baseUrl, authToken]);
+  }, [dispatch, socketOptions, socketUrl]);
 };

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

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

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

@@ -23,16 +23,16 @@ import systemReducer from 'features/system/store/systemSlice';
 import hotkeysReducer from 'features/ui/store/hotkeysSlice';
 import uiReducer from 'features/ui/store/uiSlice';
 import dynamicMiddlewares from 'redux-dynamic-middlewares';
-import { rememberEnhancer, rememberReducer } from 'redux-remember';
+import { Driver, rememberEnhancer, rememberReducer } from 'redux-remember';
 import { api } from 'services/api';
-import { LOCALSTORAGE_PREFIX } from './constants';
+import { STORAGE_PREFIX } from './constants';
 import { serialize } from './enhancers/reduxRemember/serialize';
 import { unserialize } from './enhancers/reduxRemember/unserialize';
 import { actionSanitizer } from './middleware/devtools/actionSanitizer';
 import { actionsDenylist } from './middleware/devtools/actionsDenylist';
 import { stateSanitizer } from './middleware/devtools/stateSanitizer';
 import { listenerMiddleware } from './middleware/listenerMiddleware';
-import { $store } from './nanostores/store';
+import { createStore as createIDBKeyValStore, get, set } from 'idb-keyval';
 
 const allReducers = {
   canvas: canvasReducer,
@@ -74,16 +74,28 @@ const rememberedKeys: (keyof typeof allReducers)[] = [
   'modelmanager',
 ];
 
-export const store = configureStore({
+// Create a custom idb-keyval store (just needed to customize the name)
+export const idbKeyValStore = createIDBKeyValStore('invoke', 'invoke-store');
+
+// Create redux-remember driver, wrapping idb-keyval
+const idbKeyValDriver: Driver = {
+  getItem: (key) => get(key, idbKeyValStore),
+  setItem: (key, value) => set(key, value, idbKeyValStore),
+};
+
+export const createStore = (uniqueStoreKey?: string) =>
+  configureStore({
     reducer: rememberedRootReducer,
     enhancers: (existingEnhancers) => {
       return existingEnhancers
         .concat(
-        rememberEnhancer(window.localStorage, rememberedKeys, {
+          rememberEnhancer(idbKeyValDriver, rememberedKeys, {
             persistDebounce: 300,
             serialize,
             unserialize,
-          prefix: LOCALSTORAGE_PREFIX,
+            prefix: uniqueStoreKey
+              ? `${STORAGE_PREFIX}${uniqueStoreKey}-`
+              : STORAGE_PREFIX,
           })
         )
         .concat(autoBatchEnhancer());
@@ -121,10 +133,11 @@ export const store = configureStore({
     },
   });
 
-export type AppGetState = typeof store.getState;
+export type AppGetState = ReturnType<
-export type RootState = ReturnType<typeof store.getState>;
+  ReturnType<typeof createStore>['getState']
+>;
+export type RootState = ReturnType<ReturnType<typeof createStore>['getState']>;
 // eslint-disable-next-line @typescript-eslint/no-explicit-any
 export type AppThunkDispatch = ThunkDispatch<RootState, any, AnyAction>;
-export type AppDispatch = typeof store.dispatch;
+export type AppDispatch = ReturnType<typeof createStore>['dispatch'];
 export const stateSelector = (state: RootState) => state;
-$store.set(store);

invokeai/frontend/web/src/common/components/IAIInformationalPopover/constants.ts

@@ -25,6 +25,7 @@ export type Feature =
   | 'lora'
   | 'noiseUseCPU'
   | 'paramCFGScale'
+  | 'paramCFGRescaleMultiplier'
   | 'paramDenoisingStrength'
   | 'paramIterations'
   | 'paramModel'

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

@@ -0,0 +1,12 @@
+import { idbKeyValStore } from 'app/store/store';
+import { clear } from 'idb-keyval';
+import { useCallback } from 'react';
+
+export const useClearStorage = () => {
+  const clearStorage = useCallback(() => {
+    clear(idbKeyValStore);
+    localStorage.clear();
+  }, []);
+
+  return clearStorage;
+};

invokeai/frontend/web/src/features/controlAdapters/components/ControlAdapterImagePreview.tsx

@@ -5,14 +5,19 @@ import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAIDndImage from 'common/components/IAIDndImage';
+import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
 import { setBoundingBoxDimensions } from 'features/canvas/store/canvasSlice';
+import { useControlAdapterControlImage } from 'features/controlAdapters/hooks/useControlAdapterControlImage';
+import { useControlAdapterProcessedControlImage } from 'features/controlAdapters/hooks/useControlAdapterProcessedControlImage';
+import { useControlAdapterProcessorType } from 'features/controlAdapters/hooks/useControlAdapterProcessorType';
+import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
 import {
   TypesafeDraggableData,
   TypesafeDroppableData,
 } from 'features/dnd/types';
 import { setHeight, setWidth } from 'features/parameters/store/generationSlice';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
-import { memo, useCallback, useMemo, useState } from 'react';
+import { memo, useCallback, useEffect, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 import { FaRulerVertical, FaSave, FaUndo } from 'react-icons/fa';
 import {
@@ -22,11 +27,6 @@ import {
   useRemoveImageFromBoardMutation,
 } from 'services/api/endpoints/images';
 import { PostUploadAction } from 'services/api/types';
-import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
-import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { useControlAdapterControlImage } from 'features/controlAdapters/hooks/useControlAdapterControlImage';
-import { useControlAdapterProcessedControlImage } from 'features/controlAdapters/hooks/useControlAdapterProcessedControlImage';
-import { useControlAdapterProcessorType } from 'features/controlAdapters/hooks/useControlAdapterProcessorType';
 
 type Props = {
   id: string;
@@ -35,13 +35,15 @@ type Props = {
 
 const selector = createSelector(
   stateSelector,
-  ({ controlAdapters, gallery }) => {
+  ({ controlAdapters, gallery, system }) => {
     const { pendingControlImages } = controlAdapters;
     const { autoAddBoardId } = gallery;
+    const { isConnected } = system;
 
     return {
       pendingControlImages,
       autoAddBoardId,
+      isConnected,
     };
   },
   defaultSelectorOptions
@@ -55,18 +57,19 @@ const ControlAdapterImagePreview = ({ isSmall, id }: Props) => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
-  const { pendingControlImages, autoAddBoardId } = useAppSelector(selector);
+  const { pendingControlImages, autoAddBoardId, isConnected } =
+    useAppSelector(selector);
   const activeTabName = useAppSelector(activeTabNameSelector);
 
   const [isMouseOverImage, setIsMouseOverImage] = useState(false);
 
-  const { currentData: controlImage } = useGetImageDTOQuery(
+  const { currentData: controlImage, isError: isErrorControlImage } =
-    controlImageName ?? skipToken
+    useGetImageDTOQuery(controlImageName ?? skipToken);
-  );
 
-  const { currentData: processedControlImage } = useGetImageDTOQuery(
+  const {
-    processedControlImageName ?? skipToken
+    currentData: processedControlImage,
-  );
+    isError: isErrorProcessedControlImage,
+  } = useGetImageDTOQuery(processedControlImageName ?? skipToken);
 
   const [changeIsIntermediate] = useChangeImageIsIntermediateMutation();
   const [addToBoard] = useAddImageToBoardMutation();
@@ -158,6 +161,17 @@ const ControlAdapterImagePreview = ({ isSmall, id }: Props) => {
     !pendingControlImages.includes(id) &&
     processorType !== 'none';
 
+  useEffect(() => {
+    if (isConnected && (isErrorControlImage || isErrorProcessedControlImage)) {
+      handleResetControlImage();
+    }
+  }, [
+    handleResetControlImage,
+    isConnected,
+    isErrorControlImage,
+    isErrorProcessedControlImage,
+  ]);
+
   return (
     <Flex
       onMouseEnter={handleMouseEnter}

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

@@ -73,7 +73,13 @@ const BoardContextMenu = ({
         addToast({
           title: t('gallery.preparingDownload'),
           status: 'success',
-          ...(response.response ? { description: response.response } : {}),
+          ...(response.response
+            ? {
+                description: response.response,
+                duration: null,
+                isClosable: true,
+              }
+            : {}),
         })
       );
     } catch {

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/MultipleSelectionMenuItems.tsx

@@ -59,7 +59,13 @@ const MultipleSelectionMenuItems = () => {
         addToast({
           title: t('gallery.preparingDownload'),
           status: 'success',
-          ...(response.response ? { description: response.response } : {}),
+          ...(response.response
+            ? {
+                description: response.response,
+                duration: null,
+                isClosable: true,
+              }
+            : {}),
         })
       );
     } catch {

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx

@@ -234,14 +234,14 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
           icon={customStarUi ? customStarUi.off.icon : <MdStar />}
           onClickCapture={handleUnstarImage}
         >
-          {customStarUi ? customStarUi.off.text : t('controlnet.unstarImage')}
+          {customStarUi ? customStarUi.off.text : t('gallery.unstarImage')}
         </MenuItem>
       ) : (
         <MenuItem
           icon={customStarUi ? customStarUi.on.icon : <MdStarBorder />}
           onClickCapture={handleStarImage}
         >
-          {customStarUi ? customStarUi.on.text : `Star Image`}
+          {customStarUi ? customStarUi.on.text : t('gallery.starImage')}
         </MenuItem>
       )}
       <MenuItem

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx

@@ -29,6 +29,7 @@ const ImageMetadataActions = (props: Props) => {
     recallNegativePrompt,
     recallSeed,
     recallCfgScale,
+    recallCfgRescaleMultiplier,
     recallModel,
     recallScheduler,
     recallVaeModel,
@@ -85,6 +86,10 @@ const ImageMetadataActions = (props: Props) => {
     recallCfgScale(metadata?.cfg_scale);
   }, [metadata?.cfg_scale, recallCfgScale]);
 
+  const handleRecallCfgRescaleMultiplier = useCallback(() => {
+    recallCfgRescaleMultiplier(metadata?.cfg_rescale_multiplier);
+  }, [metadata?.cfg_rescale_multiplier, recallCfgRescaleMultiplier]);
+
   const handleRecallStrength = useCallback(() => {
     recallStrength(metadata?.strength);
   }, [metadata?.strength, recallStrength]);
@@ -243,6 +248,14 @@ const ImageMetadataActions = (props: Props) => {
           onClick={handleRecallCfgScale}
         />
       )}
+      {metadata.cfg_rescale_multiplier !== undefined &&
+        metadata.cfg_rescale_multiplier !== null && (
+          <ImageMetadataItem
+            label={t('metadata.cfgRescaleMultiplier')}
+            value={metadata.cfg_rescale_multiplier}
+            onClick={handleRecallCfgRescaleMultiplier}
+          />
+        )}
       {metadata.strength && (
         <ImageMetadataItem
           label={t('metadata.strength')}

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

@@ -1,6 +1,6 @@
 import { Flex, Text } from '@chakra-ui/react';
 import { skipToken } from '@reduxjs/toolkit/dist/query';
-import { useAppDispatch } from 'app/store/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIDndImage from 'common/components/IAIDndImage';
 import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
 import {
@@ -13,7 +13,7 @@ import {
   ImageFieldInputTemplate,
 } from 'features/nodes/types/field';
 import { FieldComponentProps } from './types';
-import { memo, useCallback, useMemo } from 'react';
+import { memo, useCallback, useEffect, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { FaUndo } from 'react-icons/fa';
 import { useGetImageDTOQuery } from 'services/api/endpoints/images';
@@ -24,8 +24,8 @@ const ImageFieldInputComponent = (
 ) => {
   const { nodeId, field } = props;
   const dispatch = useAppDispatch();
-
+  const isConnected = useAppSelector((state) => state.system.isConnected);
-  const { currentData: imageDTO } = useGetImageDTOQuery(
+  const { currentData: imageDTO, isError } = useGetImageDTOQuery(
     field.value?.image_name ?? skipToken
   );
 
@@ -67,6 +67,12 @@ const ImageFieldInputComponent = (
     [nodeId, field.name]
   );
 
+  useEffect(() => {
+    if (isConnected && isError) {
+      handleReset();
+    }
+  }, [handleReset, isConnected, isError]);
+
   return (
     <Flex
       className="nodrag"

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

@@ -43,10 +43,10 @@ export class NodeUpdateError extends Error {
 }
 
 /**
- * FieldTypeParseError
+ * FieldParseError
  * Raised when a field cannot be parsed from a field schema.
  */
-export class FieldTypeParseError extends Error {
+export class FieldParseError extends Error {
   /**
    * Create FieldTypeParseError
    * @param {String} message
@@ -56,18 +56,3 @@ export class FieldTypeParseError extends Error {
     this.name = this.constructor.name;
   }
 }
-
-/**
- * UnsupportedFieldTypeError
- * Raised when an unsupported field type is parsed.
- */
-export class UnsupportedFieldTypeError extends Error {
-  /**
-   * Create UnsupportedFieldTypeError
-   * @param {String} message
-   */
-  constructor(message: string) {
-    super(message);
-    this.name = this.constructor.name;
-  }
-}

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

@@ -51,6 +51,7 @@ export const zCoreMetadata = z
     seed: z.number().int().nullish().catch(null),
     rand_device: z.string().nullish().catch(null),
     cfg_scale: z.number().nullish().catch(null),
+    cfg_rescale_multiplier: z.number().nullish().catch(null),
     steps: z.number().int().nullish().catch(null),
     scheduler: z.string().nullish().catch(null),
     clip_skip: z.number().int().nullish().catch(null),

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

@@ -43,6 +43,7 @@ export const buildCanvasImageToImageGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     seed,
     steps,
@@ -316,6 +317,7 @@ export const buildCanvasImageToImageGraph = (
     {
       generation_mode: 'img2img',
       cfg_scale,
+      cfg_rescale_multiplier,
       width: !isUsingScaledDimensions
         ? width
         : scaledBoundingBoxDimensions.width,

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

@@ -45,6 +45,7 @@ export const buildCanvasSDXLImageToImageGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     seed,
     steps,
@@ -327,6 +328,7 @@ export const buildCanvasSDXLImageToImageGraph = (
     {
       generation_mode: 'img2img',
       cfg_scale,
+      cfg_rescale_multiplier,
       width: !isUsingScaledDimensions
         ? width
         : scaledBoundingBoxDimensions.width,

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

@@ -43,6 +43,7 @@ export const buildCanvasSDXLTextToImageGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     seed,
     steps,
@@ -306,6 +307,7 @@ export const buildCanvasSDXLTextToImageGraph = (
     {
       generation_mode: 'txt2img',
       cfg_scale,
+      cfg_rescale_multiplier,
       width: !isUsingScaledDimensions
         ? width
         : scaledBoundingBoxDimensions.width,

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

@@ -41,6 +41,7 @@ export const buildCanvasTextToImageGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     seed,
     steps,
@@ -294,6 +295,7 @@ export const buildCanvasTextToImageGraph = (
     {
       generation_mode: 'txt2img',
       cfg_scale,
+      cfg_rescale_multiplier,
       width: !isUsingScaledDimensions
         ? width
         : scaledBoundingBoxDimensions.width,

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

@@ -41,6 +41,7 @@ export const buildLinearImageToImageGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     seed,
     steps,
@@ -316,6 +317,7 @@ export const buildLinearImageToImageGraph = (
     {
       generation_mode: 'img2img',
       cfg_scale,
+      cfg_rescale_multiplier,
       height,
       width,
       positive_prompt: positivePrompt,

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

@@ -43,6 +43,7 @@ export const buildLinearSDXLImageToImageGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     seed,
     steps,
@@ -336,6 +337,7 @@ export const buildLinearSDXLImageToImageGraph = (
     {
       generation_mode: 'sdxl_img2img',
       cfg_scale,
+      cfg_rescale_multiplier,
       height,
       width,
       positive_prompt: positivePrompt,

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

@@ -34,6 +34,7 @@ export const buildLinearSDXLTextToImageGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     seed,
     steps,
@@ -230,6 +231,7 @@ export const buildLinearSDXLTextToImageGraph = (
     {
       generation_mode: 'sdxl_txt2img',
       cfg_scale,
+      cfg_rescale_multiplier,
       height,
       width,
       positive_prompt: positivePrompt,

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

@@ -38,6 +38,7 @@ export const buildLinearTextToImageGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     steps,
     width,
@@ -84,6 +85,7 @@ export const buildLinearTextToImageGraph = (
           id: DENOISE_LATENTS,
           is_intermediate,
           cfg_scale,
+          cfg_rescale_multiplier,
           scheduler,
           steps,
           denoising_start: 0,
@@ -239,6 +241,7 @@ export const buildLinearTextToImageGraph = (
     {
       generation_mode: 'txt2img',
       cfg_scale,
+      cfg_rescale_multiplier,
       height,
       width,
       positive_prompt: positivePrompt,

invokeai/frontend/web/src/features/nodes/util/schema/buildFieldInputTemplate.ts

@@ -23,7 +23,12 @@ import {
   VAEModelFieldInputTemplate,
   isStatefulFieldType,
 } from 'features/nodes/types/field';
-import { InvocationFieldSchema } from 'features/nodes/types/openapi';
+import {
+  InvocationFieldSchema,
+  isSchemaObject,
+} from 'features/nodes/types/openapi';
+import { t } from 'i18next';
+import { FieldParseError } from 'features/nodes/types/error';
 
 // eslint-disable-next-line @typescript-eslint/no-explicit-any
 type FieldInputTemplateBuilder<T extends FieldInputTemplate = any> = // valid `any`!
@@ -321,7 +326,28 @@ const buildImageFieldInputTemplate: FieldInputTemplateBuilder<
 const buildEnumFieldInputTemplate: FieldInputTemplateBuilder<
   EnumFieldInputTemplate
 > = ({ schemaObject, baseField, isCollection, isCollectionOrScalar }) => {
-  const options = schemaObject.enum ?? [];
+  let options: EnumFieldInputTemplate['options'] = [];
+  if (schemaObject.anyOf) {
+    const filteredAnyOf = schemaObject.anyOf.filter((i) => {
+      if (isSchemaObject(i)) {
+        if (i.type === 'null') {
+          return false;
+        }
+      }
+      return true;
+    });
+    const firstAnyOf = filteredAnyOf[0];
+    if (filteredAnyOf.length !== 1 || !isSchemaObject(firstAnyOf)) {
+      options = [];
+    } else {
+      options = firstAnyOf.enum ?? [];
+    }
+  } else {
+    options = schemaObject.enum ?? [];
+  }
+  if (options.length === 0) {
+    throw new FieldParseError(t('nodes.unableToExtractEnumOptions'));
+  }
   const template: EnumFieldInputTemplate = {
     ...baseField,
     type: {

invokeai/frontend/web/src/features/nodes/util/schema/parseFieldType.ts

@@ -1,10 +1,4 @@
-import { t } from 'i18next';
+import { FieldParseError } from 'features/nodes/types/error';
-import { isArray } from 'lodash-es';
-import { OpenAPIV3_1 } from 'openapi-types';
-import {
-  FieldTypeParseError,
-  UnsupportedFieldTypeError,
-} from 'features/nodes/types/error';
 import { FieldType } from 'features/nodes/types/field';
 import {
   OpenAPIV3_1SchemaOrRef,
@@ -14,6 +8,9 @@ import {
   isRefObject,
   isSchemaObject,
 } from 'features/nodes/types/openapi';
+import { t } from 'i18next';
+import { isArray } from 'lodash-es';
+import { OpenAPIV3_1 } from 'openapi-types';
 
 /**
  * Transforms an invocation output ref object to field type.
@@ -70,7 +67,7 @@ export const parseFieldType = (
           // This is a single ref type
           const name = refObjectToSchemaName(allOf[0]);
           if (!name) {
-            throw new FieldTypeParseError(
+            throw new FieldParseError(
               t('nodes.unableToExtractSchemaNameFromRef')
             );
           }
@@ -95,7 +92,7 @@ export const parseFieldType = (
           if (isRefObject(filteredAnyOf[0])) {
             const name = refObjectToSchemaName(filteredAnyOf[0]);
             if (!name) {
-              throw new FieldTypeParseError(
+              throw new FieldParseError(
                 t('nodes.unableToExtractSchemaNameFromRef')
               );
             }
@@ -120,7 +117,7 @@ export const parseFieldType = (
 
         if (filteredAnyOf.length !== 2) {
           // This is a union of more than 2 types, which we don't support
-          throw new UnsupportedFieldTypeError(
+          throw new FieldParseError(
             t('nodes.unsupportedAnyOfLength', {
               count: filteredAnyOf.length,
             })
@@ -167,7 +164,7 @@ export const parseFieldType = (
           };
         }
 
-        throw new UnsupportedFieldTypeError(
+        throw new FieldParseError(
           t('nodes.unsupportedMismatchedUnion', {
             firstType,
             secondType,
@@ -186,7 +183,7 @@ export const parseFieldType = (
         if (isSchemaObject(schemaObject.items)) {
           const itemType = schemaObject.items.type;
           if (!itemType || isArray(itemType)) {
-            throw new UnsupportedFieldTypeError(
+            throw new FieldParseError(
               t('nodes.unsupportedArrayItemType', {
                 type: itemType,
               })
@@ -196,7 +193,7 @@ export const parseFieldType = (
           const name = OPENAPI_TO_FIELD_TYPE_MAP[itemType];
           if (!name) {
             // it's 'null', 'object', or 'array' - skip
-            throw new UnsupportedFieldTypeError(
+            throw new FieldParseError(
               t('nodes.unsupportedArrayItemType', {
                 type: itemType,
               })
@@ -212,7 +209,7 @@ export const parseFieldType = (
         // This is a ref object, extract the type name
         const name = refObjectToSchemaName(schemaObject.items);
         if (!name) {
-          throw new FieldTypeParseError(
+          throw new FieldParseError(
             t('nodes.unableToExtractSchemaNameFromRef')
           );
         }
@@ -226,7 +223,7 @@ export const parseFieldType = (
         const name = OPENAPI_TO_FIELD_TYPE_MAP[schemaObject.type];
         if (!name) {
           // it's 'null', 'object', or 'array' - skip
-          throw new UnsupportedFieldTypeError(
+          throw new FieldParseError(
             t('nodes.unsupportedArrayItemType', {
               type: schemaObject.type,
             })
@@ -242,9 +239,7 @@ export const parseFieldType = (
   } else if (isRefObject(schemaObject)) {
     const name = refObjectToSchemaName(schemaObject);
     if (!name) {
-      throw new FieldTypeParseError(
+      throw new FieldParseError(t('nodes.unableToExtractSchemaNameFromRef'));
-        t('nodes.unableToExtractSchemaNameFromRef')
-      );
     }
     return {
       name,
@@ -252,5 +247,5 @@ export const parseFieldType = (
       isCollectionOrScalar: false,
     };
   }
-  throw new FieldTypeParseError(t('nodes.unableToParseFieldType'));
+  throw new FieldParseError(t('nodes.unableToParseFieldType'));
 };

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

@@ -1,12 +1,6 @@
 import { logger } from 'app/logging/logger';
 import { parseify } from 'common/util/serialize';
-import { t } from 'i18next';
+import { FieldParseError } from 'features/nodes/types/error';
-import { reduce } from 'lodash-es';
-import { OpenAPIV3_1 } from 'openapi-types';
-import {
-  FieldTypeParseError,
-  UnsupportedFieldTypeError,
-} from 'features/nodes/types/error';
 import {
   FieldInputTemplate,
   FieldOutputTemplate,
@@ -18,6 +12,9 @@ import {
   isInvocationOutputSchemaObject,
   isInvocationSchemaObject,
 } from 'features/nodes/types/openapi';
+import { t } from 'i18next';
+import { reduce } from 'lodash-es';
+import { OpenAPIV3_1 } from 'openapi-types';
 import { buildFieldInputTemplate } from './buildFieldInputTemplate';
 import { buildFieldOutputTemplate } from './buildFieldOutputTemplate';
 import { parseFieldType } from './parseFieldType';
@@ -133,10 +130,7 @@ export const parseSchema = (
 
           inputsAccumulator[propertyName] = fieldInputTemplate;
         } catch (e) {
-          if (
+          if (e instanceof FieldParseError) {
-            e instanceof FieldTypeParseError ||
-            e instanceof UnsupportedFieldTypeError
-          ) {
             logger('nodes').warn(
               {
                 node: type,
@@ -225,10 +219,7 @@ export const parseSchema = (
 
           outputsAccumulator[propertyName] = fieldOutputTemplate;
         } catch (e) {
-          if (
+          if (e instanceof FieldParseError) {
-            e instanceof FieldTypeParseError ||
-            e instanceof UnsupportedFieldTypeError
-          ) {
             logger('nodes').warn(
               {
                 node: type,

invokeai/frontend/web/src/features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse.tsx

@@ -9,21 +9,41 @@ import { useTranslation } from 'react-i18next';
 import { ParamCpuNoiseToggle } from 'features/parameters/components/Parameters/Noise/ParamCpuNoise';
 import ParamSeamless from 'features/parameters/components/Parameters/Seamless/ParamSeamless';
 import ParamClipSkip from './ParamClipSkip';
+import ParamCFGRescaleMultiplier from './ParamCFGRescaleMultiplier';
 
 const selector = createSelector(
   stateSelector,
   (state: RootState) => {
-    const { clipSkip, model, seamlessXAxis, seamlessYAxis, shouldUseCpuNoise } =
+    const {
-      state.generation;
+      clipSkip,
+      model,
+      seamlessXAxis,
+      seamlessYAxis,
+      shouldUseCpuNoise,
+      cfgRescaleMultiplier,
+    } = state.generation;
 
-    return { clipSkip, model, seamlessXAxis, seamlessYAxis, shouldUseCpuNoise };
+    return {
+      clipSkip,
+      model,
+      seamlessXAxis,
+      seamlessYAxis,
+      shouldUseCpuNoise,
+      cfgRescaleMultiplier,
+    };
   },
   defaultSelectorOptions
 );
 
 export default function ParamAdvancedCollapse() {
-  const { clipSkip, model, seamlessXAxis, seamlessYAxis, shouldUseCpuNoise } =
+  const {
-    useAppSelector(selector);
+    clipSkip,
+    model,
+    seamlessXAxis,
+    seamlessYAxis,
+    shouldUseCpuNoise,
+    cfgRescaleMultiplier,
+  } = useAppSelector(selector);
   const { t } = useTranslation();
   const activeLabel = useMemo(() => {
     const activeLabel: string[] = [];
@@ -46,8 +66,20 @@ export default function ParamAdvancedCollapse() {
       activeLabel.push(t('parameters.seamlessY'));
     }
 
+    if (cfgRescaleMultiplier) {
+      activeLabel.push(t('parameters.cfgRescale'));
+    }
+
     return activeLabel.join(', ');
-  }, [clipSkip, model, seamlessXAxis, seamlessYAxis, shouldUseCpuNoise, t]);
+  }, [
+    cfgRescaleMultiplier,
+    clipSkip,
+    model,
+    seamlessXAxis,
+    seamlessYAxis,
+    shouldUseCpuNoise,
+    t,
+  ]);
 
   return (
     <IAICollapse label={t('common.advanced')} activeLabel={activeLabel}>
@@ -61,6 +93,8 @@ export default function ParamAdvancedCollapse() {
           </>
         )}
         <ParamCpuNoiseToggle />
+        <Divider />
+        <ParamCFGRescaleMultiplier />
       </Flex>
     </IAICollapse>
   );

invokeai/frontend/web/src/features/parameters/components/Parameters/Advanced/ParamCFGRescaleMultiplier.tsx

@@ -0,0 +1,60 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
+import IAISlider from 'common/components/IAISlider';
+import { setCfgRescaleMultiplier } from 'features/parameters/store/generationSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const selector = createSelector(
+  [stateSelector],
+  ({ generation, hotkeys }) => {
+    const { cfgRescaleMultiplier } = generation;
+    const { shift } = hotkeys;
+
+    return {
+      cfgRescaleMultiplier,
+      shift,
+    };
+  },
+  defaultSelectorOptions
+);
+
+const ParamCFGRescaleMultiplier = () => {
+  const { cfgRescaleMultiplier, shift } = useAppSelector(selector);
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const handleChange = useCallback(
+    (v: number) => dispatch(setCfgRescaleMultiplier(v)),
+    [dispatch]
+  );
+
+  const handleReset = useCallback(
+    () => dispatch(setCfgRescaleMultiplier(0)),
+    [dispatch]
+  );
+
+  return (
+    <IAIInformationalPopover feature="paramCFGRescaleMultiplier">
+      <IAISlider
+        label={t('parameters.cfgRescaleMultiplier')}
+        step={shift ? 0.01 : 0.05}
+        min={0}
+        max={0.99}
+        onChange={handleChange}
+        handleReset={handleReset}
+        value={cfgRescaleMultiplier}
+        sliderNumberInputProps={{ max: 0.99 }}
+        withInput
+        withReset
+        withSliderMarks
+        isInteger={false}
+      />
+    </IAIInformationalPopover>
+  );
+};
+
+export default memo(ParamCFGRescaleMultiplier);

invokeai/frontend/web/src/features/parameters/components/Parameters/ImageToImage/InitialImage.tsx

@@ -1,7 +1,7 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { skipToken } from '@reduxjs/toolkit/dist/query';
 import { stateSelector } from 'app/store/store';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAIDndImage from 'common/components/IAIDndImage';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
@@ -9,25 +9,30 @@ import {
   TypesafeDraggableData,
   TypesafeDroppableData,
 } from 'features/dnd/types';
-import { memo, useMemo } from 'react';
+import { clearInitialImage } from 'features/parameters/store/generationSlice';
+import { memo, useEffect, useMemo } from 'react';
 import { useGetImageDTOQuery } from 'services/api/endpoints/images';
 
 const selector = createSelector(
   [stateSelector],
   (state) => {
     const { initialImage } = state.generation;
+    const { isConnected } = state.system;
+
     return {
       initialImage,
       isResetButtonDisabled: !initialImage,
+      isConnected,
     };
   },
   defaultSelectorOptions
 );
 
 const InitialImage = () => {
-  const { initialImage } = useAppSelector(selector);
+  const dispatch = useAppDispatch();
+  const { initialImage, isConnected } = useAppSelector(selector);
 
-  const { currentData: imageDTO } = useGetImageDTOQuery(
+  const { currentData: imageDTO, isError } = useGetImageDTOQuery(
     initialImage?.imageName ?? skipToken
   );
 
@@ -49,6 +54,13 @@ const InitialImage = () => {
     []
   );
 
+  useEffect(() => {
+    if (isError && isConnected) {
+      // The image doesn't exist, reset init image
+      dispatch(clearInitialImage());
+    }
+  }, [dispatch, isConnected, isError]);
+
   return (
     <IAIDndImage
       imageDTO={imageDTO}

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

@@ -57,6 +57,7 @@ import {
   modelSelected,
 } from 'features/parameters/store/actions';
 import {
+  setCfgRescaleMultiplier,
   setCfgScale,
   setHeight,
   setHrfEnabled,
@@ -94,6 +95,7 @@ import {
   isParameterStrength,
   isParameterVAEModel,
   isParameterWidth,
+  isParameterCFGRescaleMultiplier,
 } from 'features/parameters/types/parameterSchemas';
 
 const selector = createSelector(
@@ -282,6 +284,21 @@ export const useRecallParameters = () => {
     [dispatch, parameterSetToast, parameterNotSetToast]
   );
 
+  /**
+   * Recall CFG rescale multiplier with toast
+   */
+  const recallCfgRescaleMultiplier = useCallback(
+    (cfgRescaleMultiplier: unknown) => {
+      if (!isParameterCFGRescaleMultiplier(cfgRescaleMultiplier)) {
+        parameterNotSetToast();
+        return;
+      }
+      dispatch(setCfgRescaleMultiplier(cfgRescaleMultiplier));
+      parameterSetToast();
+    },
+    [dispatch, parameterSetToast, parameterNotSetToast]
+  );
+
   /**
    * Recall model with toast
    */
@@ -799,6 +816,7 @@ export const useRecallParameters = () => {
 
       const {
         cfg_scale,
+        cfg_rescale_multiplier,
         height,
         model,
         positive_prompt,
@@ -831,6 +849,10 @@ export const useRecallParameters = () => {
         dispatch(setCfgScale(cfg_scale));
       }
 
+      if (isParameterCFGRescaleMultiplier(cfg_rescale_multiplier)) {
+        dispatch(setCfgRescaleMultiplier(cfg_rescale_multiplier));
+      }
+
       if (isParameterModel(model)) {
         dispatch(modelSelected(model));
       }
@@ -985,6 +1007,7 @@ export const useRecallParameters = () => {
     recallSDXLNegativeStylePrompt,
     recallSeed,
     recallCfgScale,
+    recallCfgRescaleMultiplier,
     recallModel,
     recallScheduler,
     recallVaeModel,

invokeai/frontend/web/src/features/parameters/store/generationSlice.ts

@@ -24,6 +24,7 @@ import {
   ParameterVAEModel,
   ParameterWidth,
   zParameterModel,
+  ParameterCFGRescaleMultiplier,
 } from 'features/parameters/types/parameterSchemas';
 
 export interface GenerationState {
@@ -31,6 +32,7 @@ export interface GenerationState {
   hrfStrength: ParameterStrength;
   hrfMethod: ParameterHRFMethod;
   cfgScale: ParameterCFGScale;
+  cfgRescaleMultiplier: ParameterCFGRescaleMultiplier;
   height: ParameterHeight;
   img2imgStrength: ParameterStrength;
   infillMethod: string;
@@ -76,6 +78,7 @@ export const initialGenerationState: GenerationState = {
   hrfEnabled: false,
   hrfMethod: 'ESRGAN',
   cfgScale: 7.5,
+  cfgRescaleMultiplier: 0,
   height: 512,
   img2imgStrength: 0.75,
   infillMethod: 'patchmatch',
@@ -145,9 +148,15 @@ export const generationSlice = createSlice({
         state.steps
       );
     },
-    setCfgScale: (state, action: PayloadAction<number>) => {
+    setCfgScale: (state, action: PayloadAction<ParameterCFGScale>) => {
       state.cfgScale = action.payload;
     },
+    setCfgRescaleMultiplier: (
+      state,
+      action: PayloadAction<ParameterCFGRescaleMultiplier>
+    ) => {
+      state.cfgRescaleMultiplier = action.payload;
+    },
     setThreshold: (state, action: PayloadAction<number>) => {
       state.threshold = action.payload;
     },
@@ -336,6 +345,7 @@ export const {
   resetParametersState,
   resetSeed,
   setCfgScale,
+  setCfgRescaleMultiplier,
   setWidth,
   setHeight,
   toggleSize,

invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts

@@ -77,6 +77,17 @@ export const isParameterCFGScale = (val: unknown): val is ParameterCFGScale =>
   zParameterCFGScale.safeParse(val).success;
 // #endregion
 
+// #region CFG Rescale Multiplier
+export const zParameterCFGRescaleMultiplier = z.number().gte(0).lt(1);
+export type ParameterCFGRescaleMultiplier = z.infer<
+  typeof zParameterCFGRescaleMultiplier
+>;
+export const isParameterCFGRescaleMultiplier = (
+  val: unknown
+): val is ParameterCFGRescaleMultiplier =>
+  zParameterCFGRescaleMultiplier.safeParse(val).success;
+// #endregion
+
 // #region Scheduler
 export const zParameterScheduler = zSchedulerField;
 export type ParameterScheduler = z.infer<typeof zParameterScheduler>;

invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx

@@ -14,11 +14,11 @@ import {
 } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { VALID_LOG_LEVELS } from 'app/logging/logger';
-import { LOCALSTORAGE_KEYS, LOCALSTORAGE_PREFIX } from 'app/store/constants';
 import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIButton from 'common/components/IAIButton';
 import IAIMantineSelect from 'common/components/IAIMantineSelect';
+import { useClearStorage } from 'common/hooks/useClearStorage';
 import {
   consoleLogLevelChanged,
   setEnableImageDebugging,
@@ -164,20 +164,14 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
     shouldEnableInformationalPopovers,
   } = useAppSelector(selector);
 
+  const clearStorage = useClearStorage();
+
   const handleClickResetWebUI = useCallback(() => {
-    // Only remove our keys
+    clearStorage();
-    Object.keys(window.localStorage).forEach((key) => {
-      if (
-        LOCALSTORAGE_KEYS.includes(key) ||
-        key.startsWith(LOCALSTORAGE_PREFIX)
-      ) {
-        localStorage.removeItem(key);
-      }
-    });
     onSettingsModalClose();
     onRefreshModalOpen();
     setInterval(() => setCountdown((prev) => prev - 1), 1000);
-  }, [onSettingsModalClose, onRefreshModalOpen]);
+  }, [clearStorage, onSettingsModalClose, onRefreshModalOpen]);
 
   useEffect(() => {
     if (countdown <= 0) {

invokeai/frontend/web/yarn.lock

@@ -4158,6 +4158,11 @@ i18next@^23.6.0:
   dependencies:
     "@babel/runtime" "^7.22.5"
 
+idb-keyval@^6.2.1:
+  version "6.2.1"
+  resolved "https://registry.yarnpkg.com/idb-keyval/-/idb-keyval-6.2.1.tgz#94516d625346d16f56f3b33855da11bfded2db33"
+  integrity sha512-8Sb3veuYCyrZL+VBt9LJfZjLUPWVvqn8tG28VqYNFCo43KHcKuq+b4EiXGeuaLAQWL2YmyDgMp2aSpH9JHsEQg==
+
 ieee754@^1.1.13:
   version "1.2.1"
   resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.2.1.tgz#8eb7a10a63fff25d15a57b001586d177d1b0d352"

pyproject.toml

@@ -54,7 +54,8 @@ dependencies = [
   "invisible-watermark~=0.2.0",            # needed to install SDXL base and refiner using their repo_ids
   "matplotlib",                            # needed for plotting of Penner easing functions
   "mediapipe",                             # needed for "mediapipeface" controlnet model
-  "numpy",
+  # Minimum numpy version of 1.24.0 is needed to use the 'strict' argument to np.testing.assert_array_equal().
+  "numpy>=1.24.0",
   "npyscreen",
   "omegaconf",
   "onnx",

tests/backend/ip_adapter/test_ip_adapter.py

@@ -37,6 +37,14 @@ def build_dummy_sd15_unet_input(torch_device):
             "unet_model_id": "runwayml/stable-diffusion-v1-5",
             "unet_model_name": "stable-diffusion-v1-5",
         },
+        # SD1.5, IPAdapterFull
+        {
+            "ip_adapter_model_id": "InvokeAI/ip-adapter-full-face_sd15",
+            "ip_adapter_model_name": "ip-adapter-full-face_sd15",
+            "base_model": BaseModelType.StableDiffusion1,
+            "unet_model_id": "runwayml/stable-diffusion-v1-5",
+            "unet_model_name": "stable-diffusion-v1-5",
+        },
     ],
 )
 @pytest.mark.slow

tests/backend/util/test_logging.py

@@ -0,0 +1,57 @@
+"""
+Test interaction of logging with configuration system.
+"""
+import io
+import logging
+import re
+
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.util.logging import LOG_FORMATTERS, InvokeAILogger
+
+
+# test formatting
+# Would prefer to use the capfd/capsys fixture here, but it is broken
+# when used with the logging module: https://github.com/pytest-dev/pytest/issue
+def test_formatting():
+    logger = InvokeAILogger.get_logger()
+    stream = io.StringIO()
+    handler = logging.StreamHandler(stream)
+    handler.setFormatter(LOG_FORMATTERS["plain"]())
+    logger.addHandler(handler)
+    logger.info("test1")
+    output = stream.getvalue()
+    assert re.search(r"\[InvokeAI\]::INFO --> test1$", output)
+
+    handler.setFormatter(LOG_FORMATTERS["legacy"]())
+    logger.info("test2")
+    output = stream.getvalue()
+    assert re.search(r">> test2$", output)
+
+
+# test independence of two loggers with different names
+def test_independence():
+    logger1 = InvokeAILogger.get_logger()
+    logger2 = InvokeAILogger.get_logger("Test")
+    assert logger1.name == "InvokeAI"
+    assert logger2.name == "Test"
+    assert logger1.level == logging.INFO
+    assert logger2.level == logging.INFO
+    logger2.setLevel(logging.DEBUG)
+    assert logger1.level == logging.INFO
+    assert logger2.level == logging.DEBUG
+
+
+# test that the logger is returned from two similar get_logger() calls
+def test_retrieval():
+    logger1 = InvokeAILogger.get_logger()
+    logger2 = InvokeAILogger.get_logger()
+    logger3 = InvokeAILogger.get_logger("Test")
+    assert logger1 == logger2
+    assert logger1 != logger3
+
+
+# test that the configuration is used to set the initial logging level
+def test_config():
+    config = InvokeAIAppConfig(log_level="debug")
+    logger1 = InvokeAILogger.get_logger("DebugTest", config=config)
+    assert logger1.level == logging.DEBUG