WIP - simplify ModelLoadRegistry

## Summary

- This PR exposes a `tile_size` field on `ImageToLatentsInvocation` and
`LatentsToImageInvocation`.
  - Setting `tile_size = 0` preserves the default behaviour.
- This feature is primarily intended to support upscaling workflows that
require VAE encoding/decoding high resolution images. In the future, we
may want to expose the tile size as a global application config, but
that's a separate conversation.
- As a general rule, larger tile sizes produce better results at the
cost of higher memory usage.

### Example:

Original (5472x5472)

![orig](https://github.com/invoke-ai/InvokeAI/assets/14897797/af0a975d-11ed-4f3c-9e53-84f3da6c997e)

VAE roundtrip with 512x512 tiles (note the discoloration)

![vae_roundtrip_512x512](https://github.com/invoke-ai/InvokeAI/assets/14897797/d589ae3e-fe93-410a-904c-f61f0fc0f1f2)

VAE roundtrip with 1024x1024 tiles (some discoloration still present,
but less severe than at 512x512)

![vae_roundtrip_1024x1024](https://github.com/invoke-ai/InvokeAI/assets/14897797/d0bb9752-3bfa-444f-88c9-39a3ca89c748)


## Related Issues / Discussions

Related: #6144 

## QA Instructions

- [x] Test image generation via the Linear tab
- [x] Test VAE roundtrip with tiling disabled
- [x] Test VAE roundtrip with tiling and tile_size = 0
- [x] Test VAE roundtrip with tiling and tile_size > 0

## Merge Plan

No special instructions.

## Checklist

- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_

README.md

@@ -12,12 +12,24 @@
 
 Invoke is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. Invoke offers an industry leading web-based UI, and serves as the foundation for multiple commercial products.
 
-[Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]
+Invoke is available in two editions:
+
+| **Community Edition**                                                                                                      | **Professional Edition**                                                                            |
+|----------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------|
+| **For users looking for a locally installed, self-hosted and self-managed service**                                         | **For users or teams looking for a cloud-hosted, fully managed service**                            |
+| - Free to use under a commercially-friendly license                                                                         | - Monthly subscription fee with three different plan levels                                         |
+| - Download and install on compatible hardware                                                                               | - Offers additional benefits, including multi-user support, improved model training, and more                          |
+| - Includes all core studio features: generate, refine, iterate on images, and build workflows                               | - Hosted in the cloud for easy, secure model access and scalability                                               |
+| Quick Start -> [Installation and Updates][installation docs]                                                                     | More Information -> [www.invoke.com/pricing](https://www.invoke.com/pricing)                        |
 
-<div align="center">
 
 ![Highlighted Features - Canvas and Workflows](https://github.com/invoke-ai/InvokeAI/assets/31807370/708f7a82-084f-4860-bfbe-e2588c53548d)
 
+# Documentation
+| **Quick Links**                                                                                                      | 
+|----------------------------------------------------------------------------------------------------------------------------|
+|  [Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]  | 
+
 </div>
 
 ## Quick Start

docs/contributing/MODEL_MANAGER.md

@@ -73,15 +73,6 @@ model's lifetime it may be transformed in various ways, such as
 changing its precision or converting it from a .safetensors to a
 diffusers model.
 
-`ModelType`, `ModelFormat` and `BaseModelType` are string enums that
-are defined in `invokeai.backend.model_manager.config`. They are also
-imported by, and can be reexported from,
-`invokeai.app.services.model_manager.model_records`:
-
-```
-from invokeai.app.services.model_records import ModelType, ModelFormat, BaseModelType
-```
-
 The `path` field can be absolute or relative. If relative, it is taken
 to be relative to the `models_dir` setting in the user's
 `invokeai.yaml` file.

invokeai/app/api/routers/boards.py

@@ -118,15 +118,13 @@ async def list_boards(
     all: Optional[bool] = Query(default=None, description="Whether to list all boards"),
     offset: Optional[int] = Query(default=None, description="The page offset"),
     limit: Optional[int] = Query(default=None, description="The number of boards per page"),
+    include_archived: bool = Query(default=False, description="Whether or not to include archived boards in list"),
 ) -> Union[OffsetPaginatedResults[BoardDTO], list[BoardDTO]]:
     """Gets a list of boards"""
     if all:
-        return ApiDependencies.invoker.services.boards.get_all()
+        return ApiDependencies.invoker.services.boards.get_all(include_archived)
     elif offset is not None and limit is not None:
-        return ApiDependencies.invoker.services.boards.get_many(
-            offset,
-            limit,
-        )
+        return ApiDependencies.invoker.services.boards.get_many(offset, limit, include_archived)
     else:
         raise HTTPException(
             status_code=400,

invokeai/app/api/routers/images.py

@@ -9,9 +9,14 @@ from PIL import Image
 from pydantic import BaseModel, Field, JsonValue
 
 from invokeai.app.invocations.fields import MetadataField
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
+from invokeai.app.services.image_records.image_records_common import (
+    ImageCategory,
+    ImageRecordChanges,
+    ResourceOrigin,
+)
 from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 from ..dependencies import ApiDependencies
 
@@ -316,16 +321,14 @@ async def list_image_dtos(
     ),
     offset: int = Query(default=0, description="The page offset"),
     limit: int = Query(default=10, description="The number of images per page"),
+    order_dir: SQLiteDirection = Query(default=SQLiteDirection.Descending, description="The order of sort"),
+    starred_first: bool = Query(default=True, description="Whether to sort by starred images first"),
+    search_term: Optional[str] = Query(default=None, description="The term to search for"),
 ) -> OffsetPaginatedResults[ImageDTO]:
     """Gets a list of image DTOs"""
 
     image_dtos = ApiDependencies.invoker.services.images.get_many(
-        offset,
-        limit,
-        image_origin,
-        categories,
-        is_intermediate,
-        board_id,
+        offset, limit, starred_first, order_dir, image_origin, categories, is_intermediate, board_id, search_term
     )
 
     return image_dtos

invokeai/app/api/routers/model_manager.py

@@ -3,9 +3,9 @@
 
 import io
 import pathlib
-import shutil
 import traceback
 from copy import deepcopy
+from tempfile import TemporaryDirectory
 from typing import Any, Dict, List, Optional, Type
 
 from fastapi import Body, Path, Query, Response, UploadFile
@@ -19,7 +19,6 @@ from typing_extensions import Annotated
 from invokeai.app.services.model_images.model_images_common import ModelImageFileNotFoundException
 from invokeai.app.services.model_install.model_install_common import ModelInstallJob
 from invokeai.app.services.model_records import (
-    DuplicateModelException,
     InvalidModelException,
     ModelRecordChanges,
     UnknownModelException,
@@ -30,7 +29,6 @@ from invokeai.backend.model_manager.config import (
     MainCheckpointConfig,
     ModelFormat,
     ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
 from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
@@ -174,18 +172,6 @@ async def get_model_record(
         raise HTTPException(status_code=404, detail=str(e))
 
 
-# @model_manager_router.get("/summary", operation_id="list_model_summary")
-# async def list_model_summary(
-#     page: int = Query(default=0, description="The page to get"),
-#     per_page: int = Query(default=10, description="The number of models per page"),
-#     order_by: ModelRecordOrderBy = Query(default=ModelRecordOrderBy.Default, description="The attribute to order by"),
-# ) -> PaginatedResults[ModelSummary]:
-#     """Gets a page of model summary data."""
-#     record_store = ApiDependencies.invoker.services.model_manager.store
-#     results: PaginatedResults[ModelSummary] = record_store.list_models(page=page, per_page=per_page, order_by=order_by)
-#     return results
-
-
 class FoundModel(BaseModel):
     path: str = Field(description="Path to the model")
     is_installed: bool = Field(description="Whether or not the model is already installed")
@@ -746,39 +732,36 @@ async def convert_model(
         logger.error(f"The model with key {key} is not a main checkpoint model.")
         raise HTTPException(400, f"The model with key {key} is not a main checkpoint model.")
 
-    # loading the model will convert it into a cached diffusers file
-    try:
-        cc_size = loader.convert_cache.max_size
-        if cc_size == 0:  # temporary set the convert cache to a positive number so that cached model is written
-            loader._convert_cache.max_size = 1.0
-        loader.load_model(model_config, submodel_type=SubModelType.Scheduler)
-    finally:
-        loader._convert_cache.max_size = cc_size
+    with TemporaryDirectory(dir=ApiDependencies.invoker.services.configuration.models_path) as tmpdir:
+        convert_path = pathlib.Path(tmpdir) / pathlib.Path(model_config.path).stem
+        converted_model = loader.load_model(model_config)
+        # write the converted file to the convert path
+        raw_model = converted_model.model
+        assert hasattr(raw_model, "save_pretrained")
+        raw_model.save_pretrained(convert_path)
+        assert convert_path.exists()
 
-    # Get the path of the converted model from the loader
-    cache_path = loader.convert_cache.cache_path(key)
-    assert cache_path.exists()
+        # temporarily rename the original safetensors file so that there is no naming conflict
+        original_name = model_config.name
+        model_config.name = f"{original_name}.DELETE"
+        changes = ModelRecordChanges(name=model_config.name)
+        store.update_model(key, changes=changes)
 
-    # temporarily rename the original safetensors file so that there is no naming conflict
-    original_name = model_config.name
-    model_config.name = f"{original_name}.DELETE"
-    changes = ModelRecordChanges(name=model_config.name)
-    store.update_model(key, changes=changes)
-
-    # install the diffusers
-    try:
-        new_key = installer.install_path(
-            cache_path,
-            config={
-                "name": original_name,
-                "description": model_config.description,
-                "hash": model_config.hash,
-                "source": model_config.source,
-            },
-        )
-    except DuplicateModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
+        # install the diffusers
+        try:
+            new_key = installer.install_path(
+                convert_path,
+                config={
+                    "name": original_name,
+                    "description": model_config.description,
+                    "hash": model_config.hash,
+                    "source": model_config.source,
+                },
+            )
+        except Exception as e:
+            logger.error(str(e))
+            store.update_model(key, changes=ModelRecordChanges(name=original_name))
+            raise HTTPException(status_code=409, detail=str(e))
 
     # Update the model image if the model had one
     try:
@@ -791,8 +774,8 @@ async def convert_model(
     # delete the original safetensors file
     installer.delete(key)
 
-    # delete the cached version
-    shutil.rmtree(cache_path)
+    # delete the temporary directory
+    # shutil.rmtree(cache_path)
 
     # return the config record for the new diffusers directory
     new_config = store.get_model(new_key)

invokeai/app/invocations/constants.py

@@ -1,6 +1,5 @@
 from typing import Literal
 
-from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from invokeai.backend.util.devices import TorchDevice
 
 LATENT_SCALE_FACTOR = 8
@@ -11,9 +10,6 @@ factor is hard-coded to a literal '8' rather than using this constant.
 The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
 """
 
-SCHEDULER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
-"""A literal type representing the valid scheduler names."""
-
 IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
 """A literal type for PIL image modes supported by Invoke"""
 

invokeai/app/invocations/create_gradient_mask.py

@@ -19,8 +19,8 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
 from invokeai.app.invocations.model import UNetField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager import LoadedModel
 from invokeai.backend.model_manager.config import MainConfigBase, ModelVariantType
+from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 
 

invokeai/app/invocations/denoise_latents.py

@@ -17,7 +17,7 @@ from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPVisionModelWithProjection
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.controlnet_image_processors import ControlField
 from invokeai.app.invocations.fields import (
     ConditioningField,
@@ -53,7 +53,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     TextConditioningData,
     TextConditioningRegions,
 )
-from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
+from invokeai.backend.stable_diffusion.schedulers.schedulers import SCHEDULER_MAP, SCHEDULER_NAME_VALUES
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.hotfixes import ControlNetModel
 from invokeai.backend.util.mask import to_standard_float_mask
@@ -693,7 +693,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             raise ValueError("'latents' or 'noise' must be provided!")
 
         if noise is not None and noise.shape[1:] != latents.shape[1:]:
-            raise ValueError(f"Incompatable 'noise' and 'latents' shapes: {latents.shape=} {noise.shape=}")
+            raise ValueError(f"Incompatible 'noise' and 'latents' shapes: {latents.shape=} {noise.shape=}")
 
         # The seed comes from (in order of priority): the noise field, the latents field, or 0.
         seed = 0
@@ -736,7 +736,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         # The image prompts are then passed to prep_ip_adapter_data().
         image_prompts = self.prep_ip_adapter_image_prompts(context=context, ip_adapters=ip_adapters)
 
-        # get the unet's config so that we can pass the base to dispatch_progress()
+        # get the unet's config so that we can pass the base to sd_step_callback()
         unet_config = context.models.get_config(self.unet.unet.key)
 
         def step_callback(state: PipelineIntermediateState) -> None:

invokeai/app/invocations/fields.py

@@ -160,6 +160,8 @@ class FieldDescriptions:
     fp32 = "Whether or not to use full float32 precision"
     precision = "Precision to use"
     tiled = "Processing using overlapping tiles (reduce memory consumption)"
+    vae_tile_size = "The tile size for VAE tiling in pixels (image space). If set to 0, the default tile size for the "
+    "model will be used. Larger tile sizes generally produce better results at the cost of higher memory usage."
     detect_res = "Pixel resolution for detection"
     image_res = "Pixel resolution for output image"
     safe_mode = "Whether or not to use safe mode"

invokeai/app/invocations/image_to_latents.py

@@ -1,3 +1,4 @@
+from contextlib import nullcontext
 from functools import singledispatchmethod
 
 import einops
@@ -12,7 +13,7 @@ from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import DEFAULT_PRECISION
+from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
@@ -22,8 +23,9 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.model import VAEField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager import LoadedModel
+from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+from invokeai.backend.stable_diffusion.vae_tiling import patch_vae_tiling_params
 
 
 @invocation(
@@ -31,7 +33,7 @@ from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_t
     title="Image to Latents",
     tags=["latents", "image", "vae", "i2l"],
     category="latents",
-    version="1.0.2",
+    version="1.1.0",
 )
 class ImageToLatentsInvocation(BaseInvocation):
     """Encodes an image into latents."""
@@ -44,12 +46,17 @@ class ImageToLatentsInvocation(BaseInvocation):
         input=Input.Connection,
     )
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
+    # NOTE: tile_size = 0 is a special value. We use this rather than `int | None`, because the workflow UI does not
+    # offer a way to directly set None values.
+    tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
     fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
 
     @staticmethod
-    def vae_encode(vae_info: LoadedModel, upcast: bool, tiled: bool, image_tensor: torch.Tensor) -> torch.Tensor:
+    def vae_encode(
+        vae_info: LoadedModel, upcast: bool, tiled: bool, image_tensor: torch.Tensor, tile_size: int = 0
+    ) -> torch.Tensor:
         with vae_info as vae:
-            assert isinstance(vae, torch.nn.Module)
+            assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             orig_dtype = vae.dtype
             if upcast:
                 vae.to(dtype=torch.float32)
@@ -81,9 +88,18 @@ class ImageToLatentsInvocation(BaseInvocation):
             else:
                 vae.disable_tiling()
 
+            tiling_context = nullcontext()
+            if tile_size > 0:
+                tiling_context = patch_vae_tiling_params(
+                    vae,
+                    tile_sample_min_size=tile_size,
+                    tile_latent_min_size=tile_size // LATENT_SCALE_FACTOR,
+                    tile_overlap_factor=0.25,
+                )
+
             # non_noised_latents_from_image
             image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
-            with torch.inference_mode():
+            with torch.inference_mode(), tiling_context:
                 latents = ImageToLatentsInvocation._encode_to_tensor(vae, image_tensor)
 
             latents = vae.config.scaling_factor * latents
@@ -101,7 +117,9 @@ class ImageToLatentsInvocation(BaseInvocation):
         if image_tensor.dim() == 3:
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
-        latents = self.vae_encode(vae_info, self.fp32, self.tiled, image_tensor)
+        latents = self.vae_encode(
+            vae_info=vae_info, upcast=self.fp32, tiled=self.tiled, image_tensor=image_tensor, tile_size=self.tile_size
+        )
 
         latents = latents.to("cpu")
         name = context.tensors.save(tensor=latents)

invokeai/app/invocations/latents_to_image.py

@@ -1,3 +1,5 @@
+from contextlib import nullcontext
+
 import torch
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.models.attention_processor import (
@@ -8,10 +10,9 @@ from diffusers.models.attention_processor import (
 )
 from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
-from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import DEFAULT_PRECISION
+from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -23,8 +24,8 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.model import VAEField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion import set_seamless
+from invokeai.backend.stable_diffusion.vae_tiling import patch_vae_tiling_params
 from invokeai.backend.util.devices import TorchDevice
 
 
@@ -33,7 +34,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="Latents to Image",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.2.2",
+    version="1.3.0",
 )
 class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""
@@ -47,22 +48,21 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
     )
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
+    # NOTE: tile_size = 0 is a special value. We use this rather than `int | None`, because the workflow UI does not
+    # offer a way to directly set None values.
+    tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
     fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
 
-    @staticmethod
-    def vae_decode(
-        context: InvocationContext,
-        vae_info: LoadedModel,
-        seamless_axes: list[str],
-        latents: torch.Tensor,
-        use_fp32: bool,
-        use_tiling: bool,
-    ) -> Image.Image:
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+
+        vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
-        with set_seamless(vae_info.model, seamless_axes), vae_info as vae:
+        with set_seamless(vae_info.model, self.vae.seamless_axes), vae_info as vae:
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             latents = latents.to(vae.device)
-            if use_fp32:
+            if self.fp32:
                 vae.to(dtype=torch.float32)
 
                 use_torch_2_0_or_xformers = hasattr(vae.decoder, "mid_block") and isinstance(
@@ -87,15 +87,24 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 vae.to(dtype=torch.float16)
                 latents = latents.half()
 
-            if use_tiling or context.config.get().force_tiled_decode:
+            if self.tiled or context.config.get().force_tiled_decode:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()
 
+            tiling_context = nullcontext()
+            if self.tile_size > 0:
+                tiling_context = patch_vae_tiling_params(
+                    vae,
+                    tile_sample_min_size=self.tile_size,
+                    tile_latent_min_size=self.tile_size // LATENT_SCALE_FACTOR,
+                    tile_overlap_factor=0.25,
+                )
+
             # clear memory as vae decode can request a lot
             TorchDevice.empty_cache()
 
-            with torch.inference_mode():
+            with torch.inference_mode(), tiling_context:
                 # copied from diffusers pipeline
                 latents = latents / vae.config.scaling_factor
                 image = vae.decode(latents, return_dict=False)[0]
@@ -107,21 +116,6 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         TorchDevice.empty_cache()
 
-        return image
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.tensors.load(self.latents.latents_name)
-        vae_info = context.models.load(self.vae.vae)
-
-        image = self.vae_decode(
-            context=context,
-            vae_info=vae_info,
-            seamless_axes=self.vae.seamless_axes,
-            latents=latents,
-            use_fp32=self.fp32,
-            use_tiling=self.tiled,
-        )
         image_dto = context.images.save(image=image)
 
         return ImageOutput.build(image_dto)

invokeai/app/invocations/scheduler.py

@@ -1,5 +1,4 @@
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-from invokeai.app.invocations.constants import SCHEDULER_NAME_VALUES
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     InputField,
@@ -7,6 +6,7 @@ from invokeai.app.invocations.fields import (
     UIType,
 )
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.stable_diffusion.schedulers.schedulers import SCHEDULER_NAME_VALUES
 
 
 @invocation_output("scheduler_output")

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -7,8 +7,8 @@ from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
 from pydantic import field_validator
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.controlnet_image_processors import ControlField
 from invokeai.app.invocations.denoise_latents import DenoiseLatentsInvocation, get_scheduler
 from invokeai.app.invocations.fields import (
@@ -24,11 +24,12 @@ from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData
+from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, PipelineIntermediateState
 from invokeai.backend.stable_diffusion.multi_diffusion_pipeline import (
     MultiDiffusionPipeline,
     MultiDiffusionRegionConditioning,
 )
+from invokeai.backend.stable_diffusion.schedulers.schedulers import SCHEDULER_NAME_VALUES
 from invokeai.backend.tiles.tiles import (
     calc_tiles_min_overlap,
 )
@@ -55,15 +56,15 @@ def crop_controlnet_data(control_data: ControlNetData, latent_region: TBLR) -> C
     title="Tiled Multi-Diffusion Denoise Latents",
     tags=["upscale", "denoise"],
     category="latents",
-    # TODO(ryand): Reset to 1.0.0 right before release.
+    classification=Classification.Beta,
     version="1.0.0",
 )
 class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
     """Tiled Multi-Diffusion denoising.
 
-    This node handles automatically tiling the input image. Future iterations of
-    this node should allow the user to specify custom regions with different parameters for each region to harness the
-    full power of Multi-Diffusion.
+    This node handles automatically tiling the input image, and is primarily intended for global refinement of images
+    in tiled upscaling workflows. Future Multi-Diffusion nodes should allow the user to specify custom regions with
+    different parameters for each region to harness the full power of Multi-Diffusion.
 
     This node has a similar interface to the `DenoiseLatents` node, but it has a reduced feature set (no IP-Adapter,
     T2I-Adapter, masking, etc.).
@@ -85,21 +86,24 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
         description=FieldDescriptions.latents,
         input=Input.Connection,
     )
-    # TODO(ryand): Add multiple-of validation.
-    # TODO(ryand): Smaller defaults might make more sense.
-    tile_height: int = InputField(default=112, gt=0, description="Height of the tiles in latent space.")
-    tile_width: int = InputField(default=112, gt=0, description="Width of the tiles in latent space.")
-    tile_min_overlap: int = InputField(
-        default=16,
+    tile_height: int = InputField(
+        default=1024, gt=0, multiple_of=LATENT_SCALE_FACTOR, description="Height of the tiles in image space."
+    )
+    tile_width: int = InputField(
+        default=1024, gt=0, multiple_of=LATENT_SCALE_FACTOR, description="Width of the tiles in image space."
+    )
+    tile_overlap: int = InputField(
+        default=32,
+        multiple_of=LATENT_SCALE_FACTOR,
         gt=0,
-        description="The minimum overlap between adjacent tiles in latent space. The actual overlap may be larger than "
-        "this to evenly cover the entire image.",
+        description="The overlap between adjacent tiles in pixel space. (Of course, tile merging is applied in latent "
+        "space.) Tiles will be cropped during merging (if necessary) to ensure that they overlap by exactly this "
+        "amount.",
     )
     steps: int = InputField(default=18, gt=0, description=FieldDescriptions.steps)
     cfg_scale: float | list[float] = InputField(default=6.0, description=FieldDescriptions.cfg_scale, title="CFG Scale")
-    # TODO(ryand): The default here should probably be 0.0.
     denoising_start: float = InputField(
-        default=0.65,
+        default=0.0,
         ge=0,
         le=1,
         description=FieldDescriptions.denoising_start,
@@ -150,7 +154,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
                 self.config = FakeVae.FakeVaeConfig()
 
         return MultiDiffusionPipeline(
-            vae=FakeVae(),  # TODO: oh...
+            vae=FakeVae(),
             text_encoder=None,
             tokenizer=None,
             unet=unet,
@@ -162,19 +166,29 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
+        # Convert tile image-space dimensions to latent-space dimensions.
+        latent_tile_height = self.tile_height // LATENT_SCALE_FACTOR
+        latent_tile_width = self.tile_width // LATENT_SCALE_FACTOR
+        latent_tile_overlap = self.tile_overlap // LATENT_SCALE_FACTOR
+
         seed, noise, latents = DenoiseLatentsInvocation.prepare_noise_and_latents(context, self.noise, self.latents)
         _, _, latent_height, latent_width = latents.shape
 
         # Calculate the tile locations to cover the latent-space image.
-        # TODO(ryand): Add constraints on the tile params. Is there a multiple-of constraint?
         tiles = calc_tiles_min_overlap(
             image_height=latent_height,
             image_width=latent_width,
-            tile_height=self.tile_height,
-            tile_width=self.tile_width,
-            min_overlap=self.tile_min_overlap,
+            tile_height=latent_tile_height,
+            tile_width=latent_tile_width,
+            min_overlap=latent_tile_overlap,
         )
 
+        # Get the unet's config so that we can pass the base to sd_step_callback().
+        unet_config = context.models.get_config(self.unet.unet.key)
+
+        def step_callback(state: PipelineIntermediateState) -> None:
+            context.util.sd_step_callback(state, unet_config.base)
+
         # Prepare an iterator that yields the UNet's LoRA models and their weights.
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.unet.loras:
@@ -205,8 +219,8 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
                 positive_conditioning_field=self.positive_conditioning,
                 negative_conditioning_field=self.negative_conditioning,
                 unet=unet,
-                latent_height=self.tile_height,
-                latent_width=self.tile_width,
+                latent_height=latent_tile_height,
+                latent_width=latent_tile_width,
                 cfg_scale=self.cfg_scale,
                 steps=self.steps,
                 cfg_rescale_multiplier=self.cfg_rescale_multiplier,
@@ -233,7 +247,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
             for tile, tile_controlnet_data in zip(tiles, controlnet_data_tiles, strict=True):
                 multi_diffusion_conditioning.append(
                     MultiDiffusionRegionConditioning(
-                        region=tile.coords,
+                        region=tile,
                         text_conditioning_data=conditioning_data,
                         control_data=tile_controlnet_data,
                     )
@@ -251,17 +265,17 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
             # Run Multi-Diffusion denoising.
             result_latents = pipeline.multi_diffusion_denoise(
                 multi_diffusion_conditioning=multi_diffusion_conditioning,
+                target_overlap=latent_tile_overlap,
                 latents=latents,
                 scheduler_step_kwargs=scheduler_step_kwargs,
                 noise=noise,
                 timesteps=timesteps,
                 init_timestep=init_timestep,
-                # TODO(ryand): Add proper callback.
-                callback=lambda x: None,
+                callback=step_callback,
             )
 
-        # TODO(ryand): I copied this from DenoiseLatentsInvocation. I'm not sure if it's actually important.
         result_latents = result_latents.to("cpu")
+        # TODO(ryand): I copied this from DenoiseLatentsInvocation. I'm not sure if it's actually important.
         TorchDevice.empty_cache()
 
         name = context.tensors.save(tensor=result_latents)

invokeai/app/invocations/tiled_stable_diffusion_refine.py

@@ -1,380 +0,0 @@
-from contextlib import ExitStack
-from typing import Iterator, Tuple
-
-import numpy as np
-import numpy.typing as npt
-import torch
-from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
-from PIL import Image
-from pydantic import field_validator
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
-from invokeai.app.invocations.denoise_latents import DenoiseLatentsInvocation, get_scheduler
-from invokeai.app.invocations.fields import (
-    ConditioningField,
-    FieldDescriptions,
-    ImageField,
-    Input,
-    InputField,
-    UIType,
-)
-from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
-from invokeai.app.invocations.latents_to_image import LatentsToImageInvocation
-from invokeai.app.invocations.model import ModelIdentifierField, UNetField, VAEField
-from invokeai.app.invocations.noise import get_noise
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, prepare_control_image
-from invokeai.backend.lora import LoRAModelRaw
-from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, image_resized_to_grid_as_tensor
-from invokeai.backend.tiles.tiles import calc_tiles_with_overlap, merge_tiles_with_linear_blending
-from invokeai.backend.tiles.utils import Tile
-from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.hotfixes import ControlNetModel
-
-
-@invocation(
-    "tiled_stable_diffusion_refine",
-    title="Tiled Stable Diffusion Refine",
-    tags=["upscale", "denoise"],
-    category="latents",
-    version="1.0.0",
-)
-class TiledStableDiffusionRefineInvocation(BaseInvocation):
-    """A tiled Stable Diffusion pipeline for refining high resolution images. This invocation is intended to be used to
-    refine an image after upscaling i.e. it is the second step in a typical "tiled upscaling" workflow.
-    """
-
-    image: ImageField = InputField(description="Image to be refined.")
-
-    positive_conditioning: ConditioningField = InputField(
-        description=FieldDescriptions.positive_cond, input=Input.Connection
-    )
-    negative_conditioning: ConditioningField = InputField(
-        description=FieldDescriptions.negative_cond, input=Input.Connection
-    )
-    # TODO(ryand): Add multiple-of validation.
-    tile_height: int = InputField(default=512, gt=0, description="Height of the tiles.")
-    tile_width: int = InputField(default=512, gt=0, description="Width of the tiles.")
-    tile_overlap: int = InputField(
-        default=16,
-        gt=0,
-        description="Target overlap between adjacent tiles (the last row/column may overlap more than this).",
-    )
-    steps: int = InputField(default=18, gt=0, description=FieldDescriptions.steps)
-    cfg_scale: float | list[float] = InputField(default=6.0, description=FieldDescriptions.cfg_scale, title="CFG Scale")
-    denoising_start: float = InputField(
-        default=0.65,
-        ge=0,
-        le=1,
-        description=FieldDescriptions.denoising_start,
-    )
-    denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
-    scheduler: SCHEDULER_NAME_VALUES = InputField(
-        default="euler",
-        description=FieldDescriptions.scheduler,
-        ui_type=UIType.Scheduler,
-    )
-    unet: UNetField = InputField(
-        description=FieldDescriptions.unet,
-        input=Input.Connection,
-        title="UNet",
-    )
-    cfg_rescale_multiplier: float = InputField(
-        title="CFG Rescale Multiplier", default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
-    )
-    vae: VAEField = InputField(
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-    )
-    vae_fp32: bool = InputField(
-        default=DEFAULT_PRECISION == torch.float32, description="Whether to use float32 precision when running the VAE."
-    )
-    # HACK(ryand): We probably want to allow the user to control all of the parameters in ControlField. But, we akwardly
-    # don't want to use the image field. Figure out how best to handle this.
-    # TODO(ryand): Currently, there is no ControlNet preprocessor applied to the tile images. In other words, we pretty
-    # much assume that it is a tile ControlNet. We need to decide how we want to handle this. E.g. find a way to support
-    # CN preprocessors, raise a clear warning when a non-tile CN model is selected, hardcode the supported CN models,
-    # etc.
-    control_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
-    )
-    control_weight: float = InputField(default=0.6)
-
-    @field_validator("cfg_scale")
-    def ge_one(cls, v: list[float] | float) -> list[float] | float:
-        """Validate that all cfg_scale values are >= 1"""
-        if isinstance(v, list):
-            for i in v:
-                if i < 1:
-                    raise ValueError("cfg_scale must be greater than 1")
-        else:
-            if v < 1:
-                raise ValueError("cfg_scale must be greater than 1")
-        return v
-
-    @staticmethod
-    def crop_latents_to_tile(latents: torch.Tensor, image_tile: Tile) -> torch.Tensor:
-        """Crop the latent-space tensor to the area corresponding to the image-space tile.
-        The tile coordinates must be divisible by the LATENT_SCALE_FACTOR.
-        """
-        for coord in [image_tile.coords.top, image_tile.coords.left, image_tile.coords.right, image_tile.coords.bottom]:
-            if coord % LATENT_SCALE_FACTOR != 0:
-                raise ValueError(
-                    f"The tile coordinates must all be divisible by the latent scale factor"
-                    f" ({LATENT_SCALE_FACTOR}). {image_tile.coords=}."
-                )
-        assert latents.dim() == 4  # We expect: (batch_size, channels, height, width).
-
-        top = image_tile.coords.top // LATENT_SCALE_FACTOR
-        left = image_tile.coords.left // LATENT_SCALE_FACTOR
-        bottom = image_tile.coords.bottom // LATENT_SCALE_FACTOR
-        right = image_tile.coords.right // LATENT_SCALE_FACTOR
-        return latents[..., top:bottom, left:right]
-
-    def run_controlnet(
-        self,
-        image: Image.Image,
-        controlnet_model: ControlNetModel,
-        weight: float,
-        do_classifier_free_guidance: bool,
-        width: int,
-        height: int,
-        device: torch.device,
-        dtype: torch.dtype,
-        control_mode: CONTROLNET_MODE_VALUES = "balanced",
-        resize_mode: CONTROLNET_RESIZE_VALUES = "just_resize_simple",
-    ) -> ControlNetData:
-        control_image = prepare_control_image(
-            image=image,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            width=width,
-            height=height,
-            device=device,
-            dtype=dtype,
-            control_mode=control_mode,
-            resize_mode=resize_mode,
-        )
-        return ControlNetData(
-            model=controlnet_model,
-            image_tensor=control_image,
-            weight=weight,
-            begin_step_percent=0.0,
-            end_step_percent=1.0,
-            control_mode=control_mode,
-            # Any resizing needed should currently be happening in prepare_control_image(), but adding resize_mode to
-            # ControlNetData in case needed in the future.
-            resize_mode=resize_mode,
-        )
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        # TODO(ryand): Expose the seed parameter.
-        seed = 0
-
-        # Load the input image.
-        input_image = context.images.get_pil(self.image.image_name)
-
-        # Calculate the tile locations to cover the image.
-        # We have selected this tiling strategy to make it easy to achieve tile coords that are multiples of 8. This
-        # facilitates conversions between image space and latent space.
-        # TODO(ryand): Expose these tiling parameters. (Keep in mind the multiple-of constraints on these params.)
-        tiles = calc_tiles_with_overlap(
-            image_height=input_image.height,
-            image_width=input_image.width,
-            tile_height=self.tile_height,
-            tile_width=self.tile_width,
-            overlap=self.tile_overlap,
-        )
-
-        # Convert the input image to a torch.Tensor.
-        input_image_torch = image_resized_to_grid_as_tensor(input_image.convert("RGB"), multiple_of=LATENT_SCALE_FACTOR)
-        input_image_torch = input_image_torch.unsqueeze(0)  # Add a batch dimension.
-        # Validate our assumptions about the shape of input_image_torch.
-        assert input_image_torch.dim() == 4  # We expect: (batch_size, channels, height, width).
-        assert input_image_torch.shape[:2] == (1, 3)
-
-        # Split the input image into tiles in torch.Tensor format.
-        image_tiles_torch: list[torch.Tensor] = []
-        for tile in tiles:
-            image_tile = input_image_torch[
-                :,
-                :,
-                tile.coords.top : tile.coords.bottom,
-                tile.coords.left : tile.coords.right,
-            ]
-            image_tiles_torch.append(image_tile)
-
-        # Split the input image into tiles in numpy format.
-        # TODO(ryand): We currently maintain both np.ndarray and torch.Tensor tiles. Ideally, all operations should work
-        # with torch.Tensor tiles.
-        input_image_np = np.array(input_image)
-        image_tiles_np: list[npt.NDArray[np.uint8]] = []
-        for tile in tiles:
-            image_tile_np = input_image_np[
-                tile.coords.top : tile.coords.bottom,
-                tile.coords.left : tile.coords.right,
-                :,
-            ]
-            image_tiles_np.append(image_tile_np)
-
-        # VAE-encode each image tile independently.
-        # TODO(ryand): Is there any advantage to VAE-encoding the entire image before splitting it into tiles? What
-        # about for decoding?
-        vae_info = context.models.load(self.vae.vae)
-        latent_tiles: list[torch.Tensor] = []
-        for image_tile_torch in image_tiles_torch:
-            latent_tiles.append(
-                ImageToLatentsInvocation.vae_encode(
-                    vae_info=vae_info, upcast=self.vae_fp32, tiled=False, image_tensor=image_tile_torch
-                )
-            )
-
-        # Generate noise with dimensions corresponding to the full image in latent space.
-        # It is important that the noise tensor is generated at the full image dimension and then tiled, rather than
-        # generating for each tile independently. This ensures that overlapping regions between tiles use the same
-        # noise.
-        assert input_image_torch.shape[2] % LATENT_SCALE_FACTOR == 0
-        assert input_image_torch.shape[3] % LATENT_SCALE_FACTOR == 0
-        global_noise = get_noise(
-            width=input_image_torch.shape[3],
-            height=input_image_torch.shape[2],
-            device=TorchDevice.choose_torch_device(),
-            seed=seed,
-            downsampling_factor=LATENT_SCALE_FACTOR,
-            use_cpu=True,
-        )
-
-        # Crop the global noise into tiles.
-        noise_tiles = [self.crop_latents_to_tile(latents=global_noise, image_tile=t) for t in tiles]
-
-        # Prepare an iterator that yields the UNet's LoRA models and their weights.
-        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
-            for lora in self.unet.loras:
-                lora_info = context.models.load(lora.lora)
-                assert isinstance(lora_info.model, LoRAModelRaw)
-                yield (lora_info.model, lora.weight)
-                del lora_info
-
-        # Load the UNet model.
-        unet_info = context.models.load(self.unet.unet)
-
-        refined_latent_tiles: list[torch.Tensor] = []
-        with ExitStack() as exit_stack, unet_info as unet, ModelPatcher.apply_lora_unet(unet, _lora_loader()):
-            assert isinstance(unet, UNet2DConditionModel)
-            scheduler = get_scheduler(
-                context=context,
-                scheduler_info=self.unet.scheduler,
-                scheduler_name=self.scheduler,
-                seed=seed,
-            )
-            pipeline = DenoiseLatentsInvocation.create_pipeline(unet=unet, scheduler=scheduler)
-
-            # Prepare the prompt conditioning data. The same prompt conditioning is applied to all tiles.
-            # Assume that all tiles have the same shape.
-            _, _, latent_height, latent_width = latent_tiles[0].shape
-            conditioning_data = DenoiseLatentsInvocation.get_conditioning_data(
-                context=context,
-                positive_conditioning_field=self.positive_conditioning,
-                negative_conditioning_field=self.negative_conditioning,
-                unet=unet,
-                latent_height=latent_height,
-                latent_width=latent_width,
-                cfg_scale=self.cfg_scale,
-                steps=self.steps,
-                cfg_rescale_multiplier=self.cfg_rescale_multiplier,
-            )
-
-            # Load the ControlNet model.
-            # TODO(ryand): Support multiple ControlNet models.
-            controlnet_model = exit_stack.enter_context(context.models.load(self.control_model))
-            assert isinstance(controlnet_model, ControlNetModel)
-
-            # Denoise (i.e. "refine") each tile independently.
-            for image_tile_np, latent_tile, noise_tile in zip(image_tiles_np, latent_tiles, noise_tiles, strict=True):
-                assert latent_tile.shape == noise_tile.shape
-
-                # Prepare a PIL Image for ControlNet processing.
-                # TODO(ryand): This is a bit awkward that we have to prepare both torch.Tensor and PIL.Image versions of
-                # the tiles. Ideally, the ControlNet code should be able to work with Tensors.
-                image_tile_pil = Image.fromarray(image_tile_np)
-
-                # Run the ControlNet on the image tile.
-                height, width, _ = image_tile_np.shape
-                # The height and width must be evenly divisible by LATENT_SCALE_FACTOR. This is enforced earlier, but we
-                # validate this assumption here.
-                assert height % LATENT_SCALE_FACTOR == 0
-                assert width % LATENT_SCALE_FACTOR == 0
-                controlnet_data = self.run_controlnet(
-                    image=image_tile_pil,
-                    controlnet_model=controlnet_model,
-                    weight=self.control_weight,
-                    do_classifier_free_guidance=True,
-                    width=width,
-                    height=height,
-                    device=controlnet_model.device,
-                    dtype=controlnet_model.dtype,
-                    control_mode="balanced",
-                    resize_mode="just_resize_simple",
-                )
-
-                timesteps, init_timestep, scheduler_step_kwargs = DenoiseLatentsInvocation.init_scheduler(
-                    scheduler,
-                    device=unet.device,
-                    steps=self.steps,
-                    denoising_start=self.denoising_start,
-                    denoising_end=self.denoising_end,
-                    seed=seed,
-                )
-
-                # TODO(ryand): Think about when/if latents/noise should be moved off of the device to save VRAM.
-                latent_tile = latent_tile.to(device=unet.device, dtype=unet.dtype)
-                noise_tile = noise_tile.to(device=unet.device, dtype=unet.dtype)
-                refined_latent_tile = pipeline.latents_from_embeddings(
-                    latents=latent_tile,
-                    timesteps=timesteps,
-                    init_timestep=init_timestep,
-                    noise=noise_tile,
-                    seed=seed,
-                    mask=None,
-                    masked_latents=None,
-                    scheduler_step_kwargs=scheduler_step_kwargs,
-                    conditioning_data=conditioning_data,
-                    control_data=[controlnet_data],
-                    ip_adapter_data=None,
-                    t2i_adapter_data=None,
-                    callback=lambda x: None,
-                )
-                refined_latent_tiles.append(refined_latent_tile)
-
-        # VAE-decode each refined latent tile independently.
-        refined_image_tiles: list[Image.Image] = []
-        for refined_latent_tile in refined_latent_tiles:
-            refined_image_tile = LatentsToImageInvocation.vae_decode(
-                context=context,
-                vae_info=vae_info,
-                seamless_axes=self.vae.seamless_axes,
-                latents=refined_latent_tile,
-                use_fp32=self.vae_fp32,
-                use_tiling=False,
-            )
-            refined_image_tiles.append(refined_image_tile)
-
-        # TODO(ryand): I copied this from DenoiseLatentsInvocation. I'm not sure if it's actually important.
-        TorchDevice.empty_cache()
-
-        # Merge the refined image tiles back into a single image.
-        refined_image_tiles_np = [np.array(t) for t in refined_image_tiles]
-        merged_image_np = np.zeros(shape=(input_image.height, input_image.width, 3), dtype=np.uint8)
-        # TODO(ryand): Tune the blend_amount. Should this be exposed as a parameter?
-        merge_tiles_with_linear_blending(
-            dst_image=merged_image_np, tiles=tiles, tile_images=refined_image_tiles_np, blend_amount=self.tile_overlap
-        )
-
-        # Save the refined image and return its reference.
-        merged_image_pil = Image.fromarray(merged_image_np)
-        image_dto = context.images.save(image=merged_image_pil)
-
-        return ImageOutput.build(image_dto)

invokeai/app/services/board_records/board_records_base.py

@@ -40,16 +40,12 @@ class BoardRecordStorageBase(ABC):
 
     @abstractmethod
     def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
+        self, offset: int = 0, limit: int = 10, include_archived: bool = False
     ) -> OffsetPaginatedResults[BoardRecord]:
         """Gets many board records."""
         pass
 
     @abstractmethod
-    def get_all(
-        self,
-    ) -> list[BoardRecord]:
+    def get_all(self, include_archived: bool = False) -> list[BoardRecord]:
         """Gets all board records."""
         pass

invokeai/app/services/board_records/board_records_common.py

@@ -22,6 +22,8 @@ class BoardRecord(BaseModelExcludeNull):
     """The updated timestamp of the image."""
     cover_image_name: Optional[str] = Field(default=None, description="The name of the cover image of the board.")
     """The name of the cover image of the board."""
+    archived: bool = Field(description="Whether or not the board is archived.")
+    """Whether or not the board is archived."""
 
 
 def deserialize_board_record(board_dict: dict) -> BoardRecord:
@@ -35,6 +37,7 @@ def deserialize_board_record(board_dict: dict) -> BoardRecord:
     created_at = board_dict.get("created_at", get_iso_timestamp())
     updated_at = board_dict.get("updated_at", get_iso_timestamp())
     deleted_at = board_dict.get("deleted_at", get_iso_timestamp())
+    archived = board_dict.get("archived", False)
 
     return BoardRecord(
         board_id=board_id,
@@ -43,12 +46,14 @@ def deserialize_board_record(board_dict: dict) -> BoardRecord:
         created_at=created_at,
         updated_at=updated_at,
         deleted_at=deleted_at,
+        archived=archived,
     )
 
 
 class BoardChanges(BaseModel, extra="forbid"):
     board_name: Optional[str] = Field(default=None, description="The board's new name.")
     cover_image_name: Optional[str] = Field(default=None, description="The name of the board's new cover image.")
+    archived: Optional[bool] = Field(default=None, description="Whether or not the board is archived")
 
 
 class BoardRecordNotFoundException(Exception):

invokeai/app/services/board_records/board_records_sqlite.py

@@ -125,6 +125,17 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
                     (changes.cover_image_name, board_id),
                 )
 
+            # Change the archived status of a board
+            if changes.archived is not None:
+                self._cursor.execute(
+                    """--sql
+                    UPDATE boards
+                    SET archived = ?
+                    WHERE board_id = ?;
+                    """,
+                    (changes.archived, board_id),
+                )
+
             self._conn.commit()
         except sqlite3.Error as e:
             self._conn.rollback()
@@ -134,35 +145,49 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
         return self.get(board_id)
 
     def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
+        self, offset: int = 0, limit: int = 10, include_archived: bool = False
     ) -> OffsetPaginatedResults[BoardRecord]:
         try:
             self._lock.acquire()
 
-            # Get all the boards
-            self._cursor.execute(
-                """--sql
+            # Build base query
+            base_query = """
                 SELECT *
                 FROM boards
+                {archived_filter}
                 ORDER BY created_at DESC
                 LIMIT ? OFFSET ?;
-                """,
-                (limit, offset),
-            )
+            """
+
+            # Determine archived filter condition
+            if include_archived:
+                archived_filter = ""
+            else:
+                archived_filter = "WHERE archived = 0"
+
+            final_query = base_query.format(archived_filter=archived_filter)
+
+            # Execute query to fetch boards
+            self._cursor.execute(final_query, (limit, offset))
 
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
             boards = [deserialize_board_record(dict(r)) for r in result]
 
-            # Get the total number of boards
-            self._cursor.execute(
-                """--sql
-                SELECT COUNT(*)
-                FROM boards
-                WHERE 1=1;
+            # Determine count query
+            if include_archived:
+                count_query = """
+                    SELECT COUNT(*)
+                    FROM boards;
                 """
-            )
+            else:
+                count_query = """
+                    SELECT COUNT(*)
+                    FROM boards
+                    WHERE archived = 0;
+                """
+
+            # Execute count query
+            self._cursor.execute(count_query)
 
             count = cast(int, self._cursor.fetchone()[0])
 
@@ -174,20 +199,25 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
         finally:
             self._lock.release()
 
-    def get_all(
-        self,
-    ) -> list[BoardRecord]:
+    def get_all(self, include_archived: bool = False) -> list[BoardRecord]:
         try:
             self._lock.acquire()
 
-            # Get all the boards
-            self._cursor.execute(
-                """--sql
+            base_query = """
                 SELECT *
                 FROM boards
+                {archived_filter}
                 ORDER BY created_at DESC
-                """
-            )
+            """
+
+            if include_archived:
+                archived_filter = ""
+            else:
+                archived_filter = "WHERE archived = 0"
+
+            final_query = base_query.format(archived_filter=archived_filter)
+
+            self._cursor.execute(final_query)
 
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
             boards = [deserialize_board_record(dict(r)) for r in result]

invokeai/app/services/boards/boards_base.py

@@ -44,16 +44,12 @@ class BoardServiceABC(ABC):
 
     @abstractmethod
     def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
+        self, offset: int = 0, limit: int = 10, include_archived: bool = False
     ) -> OffsetPaginatedResults[BoardDTO]:
         """Gets many boards."""
         pass
 
     @abstractmethod
-    def get_all(
-        self,
-    ) -> list[BoardDTO]:
+    def get_all(self, include_archived: bool = False) -> list[BoardDTO]:
         """Gets all boards."""
         pass

invokeai/app/services/boards/boards_default.py

@@ -48,8 +48,10 @@ class BoardService(BoardServiceABC):
     def delete(self, board_id: str) -> None:
         self.__invoker.services.board_records.delete(board_id)
 
-    def get_many(self, offset: int = 0, limit: int = 10) -> OffsetPaginatedResults[BoardDTO]:
-        board_records = self.__invoker.services.board_records.get_many(offset, limit)
+    def get_many(
+        self, offset: int = 0, limit: int = 10, include_archived: bool = False
+    ) -> OffsetPaginatedResults[BoardDTO]:
+        board_records = self.__invoker.services.board_records.get_many(offset, limit, include_archived)
         board_dtos = []
         for r in board_records.items:
             cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
@@ -63,8 +65,8 @@ class BoardService(BoardServiceABC):
 
         return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
 
-    def get_all(self) -> list[BoardDTO]:
-        board_records = self.__invoker.services.board_records.get_all()
+    def get_all(self, include_archived: bool = False) -> list[BoardDTO]:
+        board_records = self.__invoker.services.board_records.get_all(include_archived)
         board_dtos = []
         for r in board_records:
             cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)

invokeai/app/services/config/config_default.py

@@ -3,6 +3,7 @@
 
 from __future__ import annotations
 
+import copy
 import locale
 import os
 import re
@@ -25,14 +26,13 @@ DB_FILE = Path("invokeai.db")
 LEGACY_INIT_FILE = Path("invokeai.init")
 DEFAULT_RAM_CACHE = 10.0
 DEFAULT_VRAM_CACHE = 0.25
-DEFAULT_CONVERT_CACHE = 20.0
 DEVICE = Literal["auto", "cpu", "cuda", "cuda:1", "mps"]
 PRECISION = Literal["auto", "float16", "bfloat16", "float32"]
 ATTENTION_TYPE = Literal["auto", "normal", "xformers", "sliced", "torch-sdp"]
 ATTENTION_SLICE_SIZE = Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8]
 LOG_FORMAT = Literal["plain", "color", "syslog", "legacy"]
 LOG_LEVEL = Literal["debug", "info", "warning", "error", "critical"]
-CONFIG_SCHEMA_VERSION = "4.0.1"
+CONFIG_SCHEMA_VERSION = "4.0.2"
 
 
 def get_default_ram_cache_size() -> float:
@@ -85,7 +85,7 @@ class InvokeAIAppConfig(BaseSettings):
         log_tokenization: Enable logging of parsed prompt tokens.
         patchmatch: Enable patchmatch inpaint code.
         models_dir: Path to the models directory.
-        convert_cache_dir: Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.
+        convert_cache_dir: Path to the converted models cache directory (DEPRECATED, but do not delete because it is needed for migration from previous versions).
         download_cache_dir: Path to the directory that contains dynamically downloaded models.
         legacy_conf_dir: Path to directory of legacy checkpoint config files.
         db_dir: Path to InvokeAI databases directory.
@@ -102,7 +102,6 @@ class InvokeAIAppConfig(BaseSettings):
         profiles_dir: Path to profiles output directory.
         ram: Maximum memory amount used by memory model cache for rapid switching (GB).
         vram: Amount of VRAM reserved for model storage (GB).
-        convert_cache: Maximum size of on-disk converted models cache (GB).
         lazy_offload: Keep models in VRAM until their space is needed.
         log_memory_usage: If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.
         device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
@@ -113,6 +112,7 @@ class InvokeAIAppConfig(BaseSettings):
         force_tiled_decode: Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).
         pil_compress_level: The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.
         max_queue_size: Maximum number of items in the session queue.
+        clear_queue_on_startup: Empties session queue on startup.
         allow_nodes: List of nodes to allow. Omit to allow all.
         deny_nodes: List of nodes to deny. Omit to deny none.
         node_cache_size: How many cached nodes to keep in memory.
@@ -147,7 +147,7 @@ class InvokeAIAppConfig(BaseSettings):
 
     # PATHS
     models_dir:                    Path = Field(default=Path("models"),     description="Path to the models directory.")
-    convert_cache_dir:             Path = Field(default=Path("models/.convert_cache"), description="Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.")
+    convert_cache_dir:             Path = Field(default=Path("models/.convert_cache"), description="Path to the converted models cache directory (DEPRECATED, but do not delete because it is needed for migration from previous versions).")
     download_cache_dir:            Path = Field(default=Path("models/.download_cache"), description="Path to the directory that contains dynamically downloaded models.")
     legacy_conf_dir:               Path = Field(default=Path("configs"), description="Path to directory of legacy checkpoint config files.")
     db_dir:                        Path = Field(default=Path("databases"),  description="Path to InvokeAI databases directory.")
@@ -169,9 +169,8 @@ class InvokeAIAppConfig(BaseSettings):
     profiles_dir:                  Path = Field(default=Path("profiles"),   description="Path to profiles output directory.")
 
     # CACHE
-    ram:                          float = Field(default_factory=get_default_ram_cache_size, gt=0, description="Maximum memory amount used by memory model cache for rapid switching (GB).")
-    vram:                         float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (GB).")
-    convert_cache:                float = Field(default=DEFAULT_CONVERT_CACHE, ge=0, description="Maximum size of on-disk converted models cache (GB).")
+    ram:                           float = Field(default_factory=get_default_ram_cache_size, gt=0, description="Maximum memory amount used by memory model cache for rapid switching (GB).")
+    vram:                          float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (GB).")
     lazy_offload:                  bool = Field(default=True,               description="Keep models in VRAM until their space is needed.")
     log_memory_usage:              bool = Field(default=False,              description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.")
 
@@ -186,6 +185,7 @@ class InvokeAIAppConfig(BaseSettings):
     force_tiled_decode:            bool = Field(default=False,              description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).")
     pil_compress_level:             int = Field(default=1,                  description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.")
     max_queue_size:                 int = Field(default=10000, gt=0,        description="Maximum number of items in the session queue.")
+    clear_queue_on_startup:        bool = Field(default=False,              description="Empties session queue on startup.")
 
     # NODES
     allow_nodes:    Optional[list[str]] = Field(default=None,               description="List of nodes to allow. Omit to allow all.")
@@ -355,14 +355,14 @@ class DefaultInvokeAIAppConfig(InvokeAIAppConfig):
         return (init_settings,)
 
 
-def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
-    """Migrate a v3 config dictionary to a current config object.
+def migrate_v3_config_dict(config_dict: dict[str, Any]) -> dict[str, Any]:
+    """Migrate a v3 config dictionary to a v4.0.0.
 
     Args:
         config_dict: A dictionary of settings from a v3 config file.
 
     Returns:
-        An instance of `InvokeAIAppConfig` with the migrated settings.
+        An `InvokeAIAppConfig` config dict.
 
     """
     parsed_config_dict: dict[str, Any] = {}
@@ -396,32 +396,41 @@ def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
             elif k in InvokeAIAppConfig.model_fields:
                 # skip unknown fields
                 parsed_config_dict[k] = v
-    # When migrating the config file, we should not include currently-set environment variables.
-    config = DefaultInvokeAIAppConfig.model_validate(parsed_config_dict)
-
-    return config
+    parsed_config_dict["schema_version"] = "4.0.0"
+    return parsed_config_dict
 
 
-def migrate_v4_0_0_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
-    """Migrate v4.0.0 config dictionary to a current config object.
+def migrate_v4_0_0_to_4_0_1_config_dict(config_dict: dict[str, Any]) -> dict[str, Any]:
+    """Migrate v4.0.0 config dictionary to a v4.0.1 config dictionary
 
     Args:
         config_dict: A dictionary of settings from a v4.0.0 config file.
 
     Returns:
-        An instance of `InvokeAIAppConfig` with the migrated settings.
+        A config dict with the settings migrated to v4.0.1.
     """
-    parsed_config_dict: dict[str, Any] = {}
-    for k, v in config_dict.items():
-        # autocast was removed from precision in v4.0.1
-        if k == "precision" and v == "autocast":
-            parsed_config_dict["precision"] = "auto"
-        else:
-            parsed_config_dict[k] = v
-        if k == "schema_version":
-            parsed_config_dict[k] = CONFIG_SCHEMA_VERSION
-    config = DefaultInvokeAIAppConfig.model_validate(parsed_config_dict)
-    return config
+    parsed_config_dict: dict[str, Any] = copy.deepcopy(config_dict)
+    # precision "autocast" was replaced by "auto" in v4.0.1
+    if parsed_config_dict.get("precision") == "autocast":
+        parsed_config_dict["precision"] = "auto"
+    parsed_config_dict["schema_version"] = "4.0.1"
+    return parsed_config_dict
+
+
+def migrate_v4_0_1_to_4_0_2_config_dict(config_dict: dict[str, Any]) -> dict[str, Any]:
+    """Migrate v4.0.1 config dictionary to a v4.0.2 config dictionary.
+
+    Args:
+        config_dict: A dictionary of settings from a v4.0.1 config file.
+
+    Returns:
+        An config dict with the settings migrated to v4.0.2.
+    """
+    parsed_config_dict: dict[str, Any] = copy.deepcopy(config_dict)
+    # convert_cache was removed in 4.0.2
+    parsed_config_dict.pop("convert_cache", None)
+    parsed_config_dict["schema_version"] = "4.0.2"
+    return parsed_config_dict
 
 
 def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
@@ -435,27 +444,31 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
     """
     assert config_path.suffix == ".yaml"
     with open(config_path, "rt", encoding=locale.getpreferredencoding()) as file:
-        loaded_config_dict = yaml.safe_load(file)
+        loaded_config_dict: dict[str, Any] = yaml.safe_load(file)
 
     assert isinstance(loaded_config_dict, dict)
 
+    migrated = False
     if "InvokeAI" in loaded_config_dict:
-        # This is a v3 config file, attempt to migrate it
+        migrated = True
+        loaded_config_dict = migrate_v3_config_dict(loaded_config_dict)  # pyright: ignore [reportUnknownArgumentType]
+    if loaded_config_dict["schema_version"] == "4.0.0":
+        migrated = True
+        loaded_config_dict = migrate_v4_0_0_to_4_0_1_config_dict(loaded_config_dict)
+    if loaded_config_dict["schema_version"] == "4.0.1":
+        migrated = True
+        loaded_config_dict = migrate_v4_0_1_to_4_0_2_config_dict(loaded_config_dict)
+
+    if migrated:
         shutil.copy(config_path, config_path.with_suffix(".yaml.bak"))
         try:
-            # loaded_config_dict could be the wrong shape, but we will catch all exceptions below
-            migrated_config = migrate_v3_config_dict(loaded_config_dict)  # pyright: ignore [reportUnknownArgumentType]
+            # load and write without environment variables
+            migrated_config = DefaultInvokeAIAppConfig.model_validate(loaded_config_dict)
+            migrated_config.write_file(config_path)
         except Exception as e:
             shutil.copy(config_path.with_suffix(".yaml.bak"), config_path)
             raise RuntimeError(f"Failed to load and migrate v3 config file {config_path}: {e}") from e
-        migrated_config.write_file(config_path)
-        return migrated_config
 
-    if loaded_config_dict["schema_version"] == "4.0.0":
-        loaded_config_dict = migrate_v4_0_0_config_dict(loaded_config_dict)
-        loaded_config_dict.write_file(config_path)
-
-    # Attempt to load as a v4 config file
     try:
         # Meta is not included in the model fields, so we need to validate it separately
         config = InvokeAIAppConfig.model_validate(loaded_config_dict)

invokeai/app/services/image_records/image_records_base.py

@@ -4,6 +4,7 @@ from typing import Optional
 
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin
 
@@ -37,10 +38,13 @@ class ImageRecordStorageBase(ABC):
         self,
         offset: int = 0,
         limit: int = 10,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
         image_origin: Optional[ResourceOrigin] = None,
         categories: Optional[list[ImageCategory]] = None,
         is_intermediate: Optional[bool] = None,
         board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
     ) -> OffsetPaginatedResults[ImageRecord]:
         """Gets a page of image records."""
         pass

invokeai/app/services/image_records/image_records_sqlite.py

@@ -5,6 +5,7 @@ from typing import Optional, Union, cast
 
 from invokeai.app.invocations.fields import MetadataField, MetadataFieldValidator
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 
 from .image_records_base import ImageRecordStorageBase
@@ -144,10 +145,13 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         self,
         offset: int = 0,
         limit: int = 10,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
         image_origin: Optional[ResourceOrigin] = None,
         categories: Optional[list[ImageCategory]] = None,
         is_intermediate: Optional[bool] = None,
         board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
     ) -> OffsetPaginatedResults[ImageRecord]:
         try:
             self._lock.acquire()
@@ -208,9 +212,21 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                 """
                 query_params.append(board_id)
 
-            query_pagination = """--sql
-            ORDER BY images.starred DESC, images.created_at DESC LIMIT ? OFFSET ?
-            """
+            # Search term condition
+            if search_term:
+                query_conditions += """--sql
+                AND images.metadata LIKE ?
+                """
+                query_params.append(f"%{search_term.lower()}%")
+
+            if starred_first:
+                query_pagination = f"""--sql
+                ORDER BY images.starred DESC, images.created_at {order_dir.value} LIMIT ? OFFSET ?
+                """
+            else:
+                query_pagination = f"""--sql
+                ORDER BY images.created_at {order_dir.value} LIMIT ? OFFSET ?
+                """
 
             # Final images query with pagination
             images_query += query_conditions + query_pagination + ";"

invokeai/app/services/images/images_base.py

@@ -12,6 +12,7 @@ from invokeai.app.services.image_records.image_records_common import (
 )
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 
 class ImageServiceABC(ABC):
@@ -116,10 +117,13 @@ class ImageServiceABC(ABC):
         self,
         offset: int = 0,
         limit: int = 10,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
         image_origin: Optional[ResourceOrigin] = None,
         categories: Optional[list[ImageCategory]] = None,
         is_intermediate: Optional[bool] = None,
         board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
     ) -> OffsetPaginatedResults[ImageDTO]:
         """Gets a paginated list of image DTOs."""
         pass

invokeai/app/services/images/images_default.py

@@ -5,6 +5,7 @@ from PIL.Image import Image as PILImageType
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 from ..image_files.image_files_common import (
     ImageFileDeleteException,
@@ -73,7 +74,12 @@ class ImageService(ImageServiceABC):
                 session_id=session_id,
             )
             if board_id is not None:
-                self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
+                try:
+                    self.__invoker.services.board_image_records.add_image_to_board(
+                        board_id=board_id, image_name=image_name
+                    )
+                except Exception as e:
+                    self.__invoker.services.logger.warn(f"Failed to add image to board {board_id}: {str(e)}")
             self.__invoker.services.image_files.save(
                 image_name=image_name, image=image, metadata=metadata, workflow=workflow, graph=graph
             )
@@ -202,19 +208,25 @@ class ImageService(ImageServiceABC):
         self,
         offset: int = 0,
         limit: int = 10,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
         image_origin: Optional[ResourceOrigin] = None,
         categories: Optional[list[ImageCategory]] = None,
         is_intermediate: Optional[bool] = None,
         board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
     ) -> OffsetPaginatedResults[ImageDTO]:
         try:
             results = self.__invoker.services.image_records.get_many(
                 offset,
                 limit,
+                starred_first,
+                order_dir,
                 image_origin,
                 categories,
                 is_intermediate,
                 board_id,
+                search_term,
             )
 
             image_dtos = [

invokeai/app/services/model_load/model_load_base.py

@@ -6,8 +6,7 @@ from pathlib import Path
 from typing import Callable, Optional
 
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
-from invokeai.backend.model_manager.load import LoadedModel, LoadedModelWithoutConfig
-from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
+from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
 
 
@@ -28,11 +27,6 @@ class ModelLoadServiceBase(ABC):
     def ram_cache(self) -> ModelCacheBase[AnyModel]:
         """Return the RAM cache used by this loader."""
 
-    @property
-    @abstractmethod
-    def convert_cache(self) -> ModelConvertCacheBase:
-        """Return the checkpoint convert cache used by this loader."""
-
     @abstractmethod
     def load_model_from_path(
         self, model_path: Path, loader: Optional[Callable[[Path], AnyModel]] = None

invokeai/app/services/model_load/model_load_default.py

@@ -2,7 +2,7 @@
 """Implementation of model loader service."""
 
 from pathlib import Path
-from typing import Callable, Optional, Type
+from typing import Callable, Optional
 
 from picklescan.scanner import scan_file_path
 from safetensors.torch import load_file as safetensors_load_file
@@ -11,14 +11,9 @@ from torch import load as torch_load
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.invoker import Invoker
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
-from invokeai.backend.model_manager.load import (
-    LoadedModel,
-    LoadedModelWithoutConfig,
-    ModelLoaderRegistry,
-    ModelLoaderRegistryBase,
-)
-from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
+from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
@@ -33,8 +28,7 @@ class ModelLoadService(ModelLoadServiceBase):
         self,
         app_config: InvokeAIAppConfig,
         ram_cache: ModelCacheBase[AnyModel],
-        convert_cache: ModelConvertCacheBase,
-        registry: Optional[Type[ModelLoaderRegistryBase]] = ModelLoaderRegistry,
+        registry: ModelLoaderRegistry,
     ):
         """Initialize the model load service."""
         logger = InvokeAILogger.get_logger(self.__class__.__name__)
@@ -42,7 +36,6 @@ class ModelLoadService(ModelLoadServiceBase):
         self._logger = logger
         self._app_config = app_config
         self._ram_cache = ram_cache
-        self._convert_cache = convert_cache
         self._registry = registry
 
     def start(self, invoker: Invoker) -> None:
@@ -53,11 +46,6 @@ class ModelLoadService(ModelLoadServiceBase):
         """Return the RAM cache used by this loader."""
         return self._ram_cache
 
-    @property
-    def convert_cache(self) -> ModelConvertCacheBase:
-        """Return the checkpoint convert cache used by this loader."""
-        return self._convert_cache
-
     def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """
         Given a model's configuration, load it and return the LoadedModel object.
@@ -76,7 +64,6 @@ class ModelLoadService(ModelLoadServiceBase):
             app_config=self._app_config,
             logger=self._logger,
             ram_cache=self._ram_cache,
-            convert_cache=self._convert_cache,
         ).load_model(model_config, submodel_type)
 
         if hasattr(self, "_invoker"):

invokeai/app/services/model_manager/__init__.py

@@ -1,17 +0,0 @@
-"""Initialization file for model manager service."""
-
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelType, SubModelType
-from invokeai.backend.model_manager.load import LoadedModel
-
-from .model_manager_default import ModelManagerService, ModelManagerServiceBase
-
-__all__ = [
-    "ModelManagerServiceBase",
-    "ModelManagerService",
-    "AnyModel",
-    "AnyModelConfig",
-    "BaseModelType",
-    "ModelType",
-    "SubModelType",
-    "LoadedModel",
-]

invokeai/app/services/model_manager/model_manager_default.py

@@ -7,7 +7,8 @@ import torch
 from typing_extensions import Self
 
 from invokeai.app.services.invoker import Invoker
-from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache, ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_cache.model_cache_default import ModelCache
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
@@ -86,11 +87,9 @@ class ModelManagerService(ModelManagerServiceBase):
             logger=logger,
             execution_device=execution_device or TorchDevice.choose_torch_device(),
         )
-        convert_cache = ModelConvertCache(cache_path=app_config.convert_cache_path, max_size=app_config.convert_cache)
         loader = ModelLoadService(
             app_config=app_config,
             ram_cache=ram_cache,
-            convert_cache=convert_cache,
             registry=ModelLoaderRegistry,
         )
         installer = ModelInstallService(

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -37,10 +37,14 @@ class SqliteSessionQueue(SessionQueueBase):
     def start(self, invoker: Invoker) -> None:
         self.__invoker = invoker
         self._set_in_progress_to_canceled()
-        prune_result = self.prune(DEFAULT_QUEUE_ID)
-
-        if prune_result.deleted > 0:
-            self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
+        if self.__invoker.services.configuration.clear_queue_on_startup:
+            clear_result = self.clear(DEFAULT_QUEUE_ID)
+            if clear_result.deleted > 0:
+                self.__invoker.services.logger.info(f"Cleared all {clear_result.deleted} queue items")
+        else:
+            prune_result = self.prune(DEFAULT_QUEUE_ID)
+            if prune_result.deleted > 0:
+                self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
 
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()

invokeai/app/services/shared/graph.py

@@ -652,7 +652,7 @@ class Graph(BaseModel):
         output_fields = [get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
 
         # Input type must be a list
-        if get_origin(input_field) != list:
+        if get_origin(input_field) is not list:
             return False
 
         # Validate that all outputs match the input type

invokeai/app/services/shared/sqlite/sqlite_util.py

@@ -14,6 +14,8 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_8 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_9 import build_migration_9
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_10 import build_migration_10
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_11 import build_migration_11
+from invokeai.app.services.shared.sqlite_migrator.migrations.migration_12 import build_migration_12
+from invokeai.app.services.shared.sqlite_migrator.migrations.migration_13 import build_migration_13
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -45,6 +47,8 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_9())
     migrator.register_migration(build_migration_10())
     migrator.register_migration(build_migration_11(app_config=config, logger=logger))
+    migrator.register_migration(build_migration_12(app_config=config))
+    migrator.register_migration(build_migration_13())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_12.py

@@ -0,0 +1,35 @@
+import shutil
+import sqlite3
+
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration12Callback:
+    def __init__(self, app_config: InvokeAIAppConfig) -> None:
+        self._app_config = app_config
+
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        self._remove_model_convert_cache_dir()
+
+    def _remove_model_convert_cache_dir(self) -> None:
+        """
+        Removes unused model convert cache directory
+        """
+        convert_cache = self._app_config.convert_cache_path
+        shutil.rmtree(convert_cache, ignore_errors=True)
+
+
+def build_migration_12(app_config: InvokeAIAppConfig) -> Migration:
+    """
+    Build the migration from database version 11 to 12.
+
+    This migration removes the now-unused model convert cache directory.
+    """
+    migration_12 = Migration(
+        from_version=11,
+        to_version=12,
+        callback=Migration12Callback(app_config),
+    )
+
+    return migration_12

invokeai/app/services/shared/sqlite_migrator/migrations/migration_13.py

@@ -0,0 +1,31 @@
+import sqlite3
+
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration13Callback:
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        self._add_archived_col(cursor)
+
+    def _add_archived_col(self, cursor: sqlite3.Cursor) -> None:
+        """
+        - Adds `archived` columns to the board table.
+        """
+
+        cursor.execute("ALTER TABLE boards ADD COLUMN archived BOOLEAN DEFAULT FALSE;")
+
+
+def build_migration_13() -> Migration:
+    """
+    Build the migration from database version 12 to 13..
+
+    This migration does the following:
+    - Adds `archived` columns to the board table.
+    """
+    migration_13 = Migration(
+        from_version=12,
+        to_version=13,
+        callback=Migration13Callback(),
+    )
+
+    return migration_13

invokeai/backend/ip_adapter/ip_adapter.py

@@ -11,6 +11,7 @@ from PIL import Image
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
 from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionWeights
+from invokeai.backend.model_manager.load.model_size_utils import calc_module_size
 
 from ..raw_model import RawModel
 from .resampler import Resampler
@@ -137,10 +138,7 @@ class IPAdapter(RawModel):
         self.attn_weights.to(device=self.device, dtype=self.dtype, non_blocking=non_blocking)
 
     def calc_size(self):
-        # workaround for circular import
-        from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
-
-        return calc_model_size_by_data(self._image_proj_model) + calc_model_size_by_data(self.attn_weights)
+        return calc_module_size(self._image_proj_model) + calc_module_size(self.attn_weights)
 
     def _init_image_proj_model(
         self, state_dict: dict[str, torch.Tensor]

invokeai/backend/lora.py

@@ -10,6 +10,7 @@ from safetensors.torch import load_file
 from typing_extensions import Self
 
 from invokeai.backend.model_manager import BaseModelType
+from invokeai.backend.util.devices import TorchDevice
 
 from .raw_model import RawModel
 
@@ -521,7 +522,7 @@ class LoRAModelRaw(RawModel):  # (torch.nn.Module):
             # lower memory consumption by removing already parsed layer values
             state_dict[layer_key].clear()
 
-            layer.to(device=device, dtype=dtype, non_blocking=True)
+            layer.to(device=device, dtype=dtype, non_blocking=TorchDevice.get_non_blocking(device))
             model.layers[layer_key] = layer
 
         return model

invokeai/backend/model_hash/hash_validator.py

@@ -12,7 +12,9 @@ def validate_hash(hash: str):
         map = json.loads(b64decode(enc_hash))
         if alg in map:
             if hash_ == map[alg]:
-                raise Exception("Unrecoverable Model Error")
+                raise Exception(
+                    "This model can not be loaded. If you're looking for help, consider visiting https://www.redirectionprogram.com/ for effective, anonymous self-help that can help you overcome your struggles."
+                )
 
 
 hashes: list[str] = [

invokeai/backend/model_manager/__init__.py

@@ -13,7 +13,6 @@ from .config import (
     SchedulerPredictionType,
     SubModelType,
 )
-from .load import LoadedModel
 from .probe import ModelProbe
 from .search import ModelSearch
 
@@ -23,7 +22,6 @@ __all__ = [
     "BaseModelType",
     "ModelRepoVariant",
     "InvalidModelConfigException",
-    "LoadedModel",
     "ModelConfigFactory",
     "ModelFormat",
     "ModelProbe",

invokeai/backend/model_manager/config.py

@@ -24,20 +24,21 @@ import time
 from enum import Enum
 from typing import Literal, Optional, Type, TypeAlias, Union
 
+import diffusers
 import torch
 from diffusers.models.modeling_utils import ModelMixin
 from pydantic import BaseModel, ConfigDict, Discriminator, Field, Tag, TypeAdapter
 from typing_extensions import Annotated, Any, Dict
 
-from invokeai.app.invocations.constants import SCHEDULER_NAME_VALUES
 from invokeai.app.util.misc import uuid_string
 from invokeai.backend.model_hash.hash_validator import validate_hash
+from invokeai.backend.stable_diffusion.schedulers.schedulers import SCHEDULER_NAME_VALUES
 
 from ..raw_model import RawModel
 
 # ModelMixin is the base class for all diffusers and transformers models
 # RawModel is the InvokeAI wrapper class for ip_adapters, loras, textual_inversion and onnx runtime
-AnyModel = Union[ModelMixin, RawModel, torch.nn.Module, Dict[str, torch.Tensor]]
+AnyModel = Union[ModelMixin, RawModel, torch.nn.Module, Dict[str, torch.Tensor], diffusers.DiffusionPipeline]
 
 
 class InvalidModelConfigException(Exception):

invokeai/backend/model_manager/convert_ckpt_to_diffusers.py

@@ -1,83 +0,0 @@
-# Adapted for use in InvokeAI by Lincoln Stein, July 2023
-#
-"""Conversion script for the Stable Diffusion checkpoints."""
-
-from pathlib import Path
-from typing import Optional
-
-import torch
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
-    convert_ldm_vae_checkpoint,
-    create_vae_diffusers_config,
-    download_controlnet_from_original_ckpt,
-    download_from_original_stable_diffusion_ckpt,
-)
-from omegaconf import DictConfig
-
-from . import AnyModel
-
-
-def convert_ldm_vae_to_diffusers(
-    checkpoint: torch.Tensor | dict[str, torch.Tensor],
-    vae_config: DictConfig,
-    image_size: int,
-    dump_path: Optional[Path] = None,
-    precision: torch.dtype = torch.float16,
-) -> AutoencoderKL:
-    """Convert a checkpoint-style VAE into a Diffusers VAE"""
-    vae_config = create_vae_diffusers_config(vae_config, image_size=image_size)
-    converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
-
-    vae = AutoencoderKL(**vae_config)
-    vae.load_state_dict(converted_vae_checkpoint)
-    vae.to(precision)
-
-    if dump_path:
-        vae.save_pretrained(dump_path, safe_serialization=True)
-
-    return vae
-
-
-def convert_ckpt_to_diffusers(
-    checkpoint_path: str | Path,
-    dump_path: Optional[str | Path] = None,
-    precision: torch.dtype = torch.float16,
-    use_safetensors: bool = True,
-    **kwargs,
-) -> AnyModel:
-    """
-    Takes all the arguments of download_from_original_stable_diffusion_ckpt(),
-    and in addition a path-like object indicating the location of the desired diffusers
-    model to be written.
-    """
-    pipe = download_from_original_stable_diffusion_ckpt(Path(checkpoint_path).as_posix(), **kwargs)
-    pipe = pipe.to(precision)
-
-    # TO DO: save correct repo variant
-    if dump_path:
-        pipe.save_pretrained(
-            dump_path,
-            safe_serialization=use_safetensors,
-        )
-    return pipe
-
-
-def convert_controlnet_to_diffusers(
-    checkpoint_path: Path,
-    dump_path: Optional[Path] = None,
-    precision: torch.dtype = torch.float16,
-    **kwargs,
-) -> AnyModel:
-    """
-    Takes all the arguments of download_controlnet_from_original_ckpt(),
-    and in addition a path-like object indicating the location of the desired diffusers
-    model to be written.
-    """
-    pipe = download_controlnet_from_original_ckpt(checkpoint_path.as_posix(), **kwargs)
-    pipe = pipe.to(precision)
-
-    # TO DO: save correct repo variant
-    if dump_path:
-        pipe.save_pretrained(dump_path, safe_serialization=True)
-    return pipe

invokeai/backend/model_manager/load/__init__.py

@@ -1,29 +1 @@
-# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development Team
-"""
-Init file for the model loader.
-"""
 
-from importlib import import_module
-from pathlib import Path
-
-from .convert_cache.convert_cache_default import ModelConvertCache
-from .load_base import LoadedModel, LoadedModelWithoutConfig, ModelLoaderBase
-from .load_default import ModelLoader
-from .model_cache.model_cache_default import ModelCache
-from .model_loader_registry import ModelLoaderRegistry, ModelLoaderRegistryBase
-
-# This registers the subclasses that implement loaders of specific model types
-loaders = [x.stem for x in Path(Path(__file__).parent, "model_loaders").glob("*.py") if x.stem != "__init__"]
-for module in loaders:
-    import_module(f"{__package__}.model_loaders.{module}")
-
-__all__ = [
-    "LoadedModel",
-    "LoadedModelWithoutConfig",
-    "ModelCache",
-    "ModelConvertCache",
-    "ModelLoaderBase",
-    "ModelLoader",
-    "ModelLoaderRegistryBase",
-    "ModelLoaderRegistry",
-]

invokeai/backend/model_manager/load/convert_cache/__init__.py

@@ -1,4 +0,0 @@
-from .convert_cache_base import ModelConvertCacheBase
-from .convert_cache_default import ModelConvertCache
-
-__all__ = ["ModelConvertCacheBase", "ModelConvertCache"]

invokeai/backend/model_manager/load/convert_cache/convert_cache_base.py

@@ -1,28 +0,0 @@
-"""
-Disk-based converted model cache.
-"""
-
-from abc import ABC, abstractmethod
-from pathlib import Path
-
-
-class ModelConvertCacheBase(ABC):
-    @property
-    @abstractmethod
-    def max_size(self) -> float:
-        """Return the maximum size of this cache directory."""
-        pass
-
-    @abstractmethod
-    def make_room(self, size: float) -> None:
-        """
-        Make sufficient room in the cache directory for a model of max_size.
-
-        :param size: Size required (GB)
-        """
-        pass
-
-    @abstractmethod
-    def cache_path(self, key: str) -> Path:
-        """Return the path for a model with the indicated key."""
-        pass

invokeai/backend/model_manager/load/convert_cache/convert_cache_default.py

@@ -1,83 +0,0 @@
-"""
-Placeholder for convert cache implementation.
-"""
-
-import shutil
-from pathlib import Path
-
-from invokeai.backend.util import GIG, directory_size
-from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.backend.util.util import safe_filename
-
-from .convert_cache_base import ModelConvertCacheBase
-
-
-class ModelConvertCache(ModelConvertCacheBase):
-    def __init__(self, cache_path: Path, max_size: float = 10.0):
-        """Initialize the convert cache with the base directory and a limit on its maximum size (in GBs)."""
-        if not cache_path.exists():
-            cache_path.mkdir(parents=True)
-        self._cache_path = cache_path
-        self._max_size = max_size
-
-        # adjust cache size at startup in case it has been changed
-        if self._cache_path.exists():
-            self.make_room(0.0)
-
-    @property
-    def max_size(self) -> float:
-        """Return the maximum size of this cache directory (GB)."""
-        return self._max_size
-
-    @max_size.setter
-    def max_size(self, value: float) -> None:
-        """Set the maximum size of this cache directory (GB)."""
-        self._max_size = value
-
-    def cache_path(self, key: str) -> Path:
-        """Return the path for a model with the indicated key."""
-        key = safe_filename(self._cache_path, key)
-        return self._cache_path / key
-
-    def make_room(self, size: float) -> None:
-        """
-        Make sufficient room in the cache directory for a model of max_size.
-
-        :param size: Size required (GB)
-        """
-        size_needed = directory_size(self._cache_path) + size
-        max_size = int(self.max_size) * GIG
-        logger = InvokeAILogger.get_logger()
-
-        if size_needed <= max_size:
-            return
-
-        logger.debug(
-            f"Convert cache has gotten too large {(size_needed / GIG):4.2f} > {(max_size / GIG):4.2f}G.. Trimming."
-        )
-
-        # For this to work, we make the assumption that the directory contains
-        # a 'model_index.json', 'unet/config.json' file, or a 'config.json' file at top level.
-        # This should be true for any diffusers model.
-        def by_atime(path: Path) -> float:
-            for config in ["model_index.json", "unet/config.json", "config.json"]:
-                sentinel = path / config
-                if sentinel.exists():
-                    return sentinel.stat().st_atime
-
-            # no sentinel file found! - pick the most recent file in the directory
-            try:
-                atimes = sorted([x.stat().st_atime for x in path.iterdir() if x.is_file()], reverse=True)
-                return atimes[0]
-            except IndexError:
-                return 0.0
-
-        # sort by last access time - least accessed files will be at the end
-        lru_models = sorted(self._cache_path.iterdir(), key=by_atime, reverse=True)
-        logger.debug(f"cached models in descending atime order: {lru_models}")
-        while size_needed > max_size and len(lru_models) > 0:
-            next_victim = lru_models.pop()
-            victim_size = directory_size(next_victim)
-            logger.debug(f"Removing cached converted model {next_victim} to free {victim_size / GIG} GB")
-            shutil.rmtree(next_victim)
-            size_needed -= victim_size

invokeai/backend/model_manager/load/global_model_loader_registry.py

@@ -0,0 +1,8 @@
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+
+
+def _build_model_loader_registry():
+    return ModelLoaderRegistry()
+
+
+MODEL_LOADER_REGISTRY = _build_model_loader_registry()

invokeai/backend/model_manager/load/load_base.py

@@ -18,7 +18,6 @@ from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.convert_cache.convert_cache_base import ModelConvertCacheBase
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
 
 
@@ -112,7 +111,6 @@ class ModelLoaderBase(ABC):
         app_config: InvokeAIAppConfig,
         logger: Logger,
         ram_cache: ModelCacheBase[AnyModel],
-        convert_cache: ModelConvertCacheBase,
     ):
         """Initialize the loader."""
         pass
@@ -138,12 +136,6 @@ class ModelLoaderBase(ABC):
         """Return size in bytes of the model, calculated before loading."""
         pass
 
-    @property
-    @abstractmethod
-    def convert_cache(self) -> ModelConvertCacheBase:
-        """Return the convert cache associated with this loader."""
-        pass
-
     @property
     @abstractmethod
     def ram_cache(self) -> ModelCacheBase[AnyModel]:

invokeai/backend/model_manager/load/load_default.py

@@ -12,11 +12,10 @@ from invokeai.backend.model_manager import (
     InvalidModelConfigException,
     SubModelType,
 )
-from invokeai.backend.model_manager.config import DiffusersConfigBase, ModelType
-from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
+from invokeai.backend.model_manager.config import DiffusersConfigBase
 from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
-from invokeai.backend.model_manager.load.model_util import calc_model_size_by_fs
+from invokeai.backend.model_manager.load.model_size_utils import calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.util.devices import TorchDevice
 
@@ -30,13 +29,11 @@ class ModelLoader(ModelLoaderBase):
         app_config: InvokeAIAppConfig,
         logger: Logger,
         ram_cache: ModelCacheBase[AnyModel],
-        convert_cache: ModelConvertCacheBase,
     ):
         """Initialize the loader."""
         self._app_config = app_config
         self._logger = logger
         self._ram_cache = ram_cache
-        self._convert_cache = convert_cache
         self._torch_dtype = TorchDevice.choose_torch_dtype()
 
     def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
@@ -50,23 +47,15 @@ class ModelLoader(ModelLoaderBase):
         :param submodel_type: an ModelType enum indicating the portion of
                the model to retrieve (e.g. ModelType.Vae)
         """
-        if model_config.type is ModelType.Main and not submodel_type:
-            raise InvalidModelConfigException("submodel_type is required when loading a main model")
-
         model_path = self._get_model_path(model_config)
 
         if not model_path.exists():
             raise InvalidModelConfigException(f"Files for model '{model_config.name}' not found at {model_path}")
 
         with skip_torch_weight_init():
-            locker = self._convert_and_load(model_config, model_path, submodel_type)
+            locker = self._load_and_cache(model_config, submodel_type)
         return LoadedModel(config=model_config, _locker=locker)
 
-    @property
-    def convert_cache(self) -> ModelConvertCacheBase:
-        """Return the convert cache associated with this loader."""
-        return self._convert_cache
-
     @property
     def ram_cache(self) -> ModelCacheBase[AnyModel]:
         """Return the ram cache associated with this loader."""
@@ -76,20 +65,14 @@ class ModelLoader(ModelLoaderBase):
         model_base = self._app_config.models_path
         return (model_base / config.path).resolve()
 
-    def _convert_and_load(
-        self, config: AnyModelConfig, model_path: Path, submodel_type: Optional[SubModelType] = None
-    ) -> ModelLockerBase:
+    def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> ModelLockerBase:
         try:
             return self._ram_cache.get(config.key, submodel_type)
         except IndexError:
             pass
 
-        cache_path: Path = self._convert_cache.cache_path(str(model_path))
-        if self._needs_conversion(config, model_path, cache_path):
-            loaded_model = self._do_convert(config, model_path, cache_path, submodel_type)
-        else:
-            config.path = str(cache_path) if cache_path.exists() else str(self._get_model_path(config))
-            loaded_model = self._load_model(config, submodel_type)
+        config.path = str(self._get_model_path(config))
+        loaded_model = self._load_model(config, submodel_type)
 
         self._ram_cache.put(
             config.key,
@@ -113,28 +96,6 @@ class ModelLoader(ModelLoaderBase):
             variant=config.repo_variant if isinstance(config, DiffusersConfigBase) else None,
         )
 
-    def _do_convert(
-        self, config: AnyModelConfig, model_path: Path, cache_path: Path, submodel_type: Optional[SubModelType] = None
-    ) -> AnyModel:
-        self.convert_cache.make_room(calc_model_size_by_fs(model_path))
-        pipeline = self._convert_model(config, model_path, cache_path if self.convert_cache.max_size > 0 else None)
-        if submodel_type:
-            # Proactively load the various submodels into the RAM cache so that we don't have to re-convert
-            # the entire pipeline every time a new submodel is needed.
-            for subtype in SubModelType:
-                if subtype == submodel_type:
-                    continue
-                if submodel := getattr(pipeline, subtype.value, None):
-                    self._ram_cache.put(config.key, submodel_type=subtype, model=submodel)
-        return getattr(pipeline, submodel_type.value) if submodel_type else pipeline
-
-    def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
-        return False
-
-    # This needs to be implemented in subclasses that handle checkpoints
-    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
-        raise NotImplementedError
-
     # This needs to be implemented in the subclass
     def _load_model(
         self,

invokeai/backend/model_manager/load/model_cache/model_cache_default.py

@@ -285,9 +285,11 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 else:
                     new_dict: Dict[str, torch.Tensor] = {}
                     for k, v in cache_entry.state_dict.items():
-                        new_dict[k] = v.to(torch.device(target_device), copy=True, non_blocking=True)
+                        new_dict[k] = v.to(
+                            target_device, copy=True, non_blocking=TorchDevice.get_non_blocking(target_device)
+                        )
                     cache_entry.model.load_state_dict(new_dict, assign=True)
-            cache_entry.model.to(target_device, non_blocking=True)
+            cache_entry.model.to(target_device, non_blocking=TorchDevice.get_non_blocking(target_device))
             cache_entry.device = target_device
         except Exception as e:  # blow away cache entry
             self._delete_cache_entry(cache_entry)

invokeai/backend/model_manager/load/model_loader_registry.py

@@ -1,48 +1,34 @@
 # Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
-"""
-This module implements a system in which model loaders register the
-type, base and format of models that they know how to load.
+from typing import Optional, Tuple, Type
 
-Use like this:
-
-  cls, model_config, submodel_type = ModelLoaderRegistry.get_implementation(model_config, submodel_type)  # type: ignore
-  loaded_model = cls(
-       app_config=app_config,
-       logger=logger,
-       ram_cache=ram_cache,
-       convert_cache=convert_cache
-    ).load_model(model_config, submodel_type)
-
-"""
-
-from abc import ABC, abstractmethod
-from typing import Callable, Dict, Optional, Tuple, Type, TypeVar
-
-from ..config import (
-    AnyModelConfig,
-    BaseModelType,
-    ModelConfigBase,
-    ModelFormat,
-    ModelType,
-    SubModelType,
-)
-from . import ModelLoaderBase
+from invokeai.backend.model_manager.config import BaseModelType, ModelConfigBase, ModelFormat, ModelType
+from invokeai.backend.model_manager.load.load_base import AnyModelConfig, ModelLoaderBase, SubModelType
 
 
-class ModelLoaderRegistryBase(ABC):
-    """This class allows model loaders to register their type, base and format."""
+class ModelLoaderRegistry:
+    """A registry that tracks which model loader class to use for a given model type/format/base combination."""
+
+    def __init__(self):
+        self._registry: dict[str, Type[ModelLoaderBase]] = {}
 
-    @classmethod
-    @abstractmethod
     def register(
-        cls, type: ModelType, format: ModelFormat, base: BaseModelType = BaseModelType.Any
-    ) -> Callable[[Type[ModelLoaderBase]], Type[ModelLoaderBase]]:
-        """Define a decorator which registers the subclass of loader."""
+        self,
+        loader_class: Type[ModelLoaderBase],
+        type: ModelType,
+        format: ModelFormat,
+        base: BaseModelType = BaseModelType.Any,
+    ):
+        """Register a model loader class."""
+        key = self._to_registry_key(base, type, format)
+        if key in self._registry:
+            raise RuntimeError(
+                f"{loader_class.__name__} is trying to register as a loader for {base}/{type}/{format}, but this type "
+                f"of model has already been registered by {self._registry[key].__name__}"
+            )
+        self._registry[key] = loader_class
 
-    @classmethod
-    @abstractmethod
     def get_implementation(
-        cls, config: AnyModelConfig, submodel_type: Optional[SubModelType]
+        self, config: AnyModelConfig, submodel_type: Optional[SubModelType]
     ) -> Tuple[Type[ModelLoaderBase], ModelConfigBase, Optional[SubModelType]]:
         """
         Get subclass of ModelLoaderBase registered to handle base and type.
@@ -56,46 +42,13 @@ class ModelLoaderRegistryBase(ABC):
         in, in the event that a submodel type is provided.
         """
 
-
-TModelLoader = TypeVar("TModelLoader", bound=ModelLoaderBase)
-
-
-class ModelLoaderRegistry(ModelLoaderRegistryBase):
-    """
-    This class allows model loaders to register their type, base and format.
-    """
-
-    _registry: Dict[str, Type[ModelLoaderBase]] = {}
-
-    @classmethod
-    def register(
-        cls, type: ModelType, format: ModelFormat, base: BaseModelType = BaseModelType.Any
-    ) -> Callable[[Type[TModelLoader]], Type[TModelLoader]]:
-        """Define a decorator which registers the subclass of loader."""
-
-        def decorator(subclass: Type[TModelLoader]) -> Type[TModelLoader]:
-            key = cls._to_registry_key(base, type, format)
-            if key in cls._registry:
-                raise Exception(
-                    f"{subclass.__name__} is trying to register as a loader for {base}/{type}/{format}, but this type of model has already been registered by {cls._registry[key].__name__}"
-                )
-            cls._registry[key] = subclass
-            return subclass
-
-        return decorator
-
-    @classmethod
-    def get_implementation(
-        cls, config: AnyModelConfig, submodel_type: Optional[SubModelType]
-    ) -> Tuple[Type[ModelLoaderBase], ModelConfigBase, Optional[SubModelType]]:
-        """Get subclass of ModelLoaderBase registered to handle base and type."""
-
-        key1 = cls._to_registry_key(config.base, config.type, config.format)  # for a specific base type
-        key2 = cls._to_registry_key(BaseModelType.Any, config.type, config.format)  # with wildcard Any
-        implementation = cls._registry.get(key1) or cls._registry.get(key2)
+        key1 = self._to_registry_key(config.base, config.type, config.format)  # for a specific base type
+        key2 = self._to_registry_key(BaseModelType.Any, config.type, config.format)  # with wildcard Any
+        implementation = self._registry.get(key1, None) or self._registry.get(key2, None)
         if not implementation:
             raise NotImplementedError(
-                f"No subclass of LoadedModel is registered for base={config.base}, type={config.type}, format={config.format}"
+                f"No subclass of ModelLoaderBase is registered for base={config.base}, type={config.type}, "
+                f"format={config.format}"
             )
         return implementation, config, submodel_type
 

invokeai/backend/model_manager/load/model_loaders/controlnet.py

@@ -1,9 +1,10 @@
 # Copyright (c) 2024, Lincoln D. Stein and the InvokeAI Development Team
 """Class for ControlNet model loading in InvokeAI."""
 
-from pathlib import Path
 from typing import Optional
 
+from diffusers import ControlNetModel
+
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
@@ -11,8 +12,7 @@ from invokeai.backend.model_manager import (
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.config import CheckpointConfigBase
-from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_controlnet_to_diffusers
+from invokeai.backend.model_manager.config import ControlNetCheckpointConfig, SubModelType
 
 from .. import ModelLoaderRegistry
 from .generic_diffusers import GenericDiffusersLoader
@@ -23,36 +23,15 @@ from .generic_diffusers import GenericDiffusersLoader
 class ControlNetLoader(GenericDiffusersLoader):
     """Class to load ControlNet models."""
 
-    def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
-        if not isinstance(config, CheckpointConfigBase):
-            return False
-        elif (
-            dest_path.exists()
-            and (dest_path / "config.json").stat().st_mtime >= (config.converted_at or 0.0)
-            and (dest_path / "config.json").stat().st_mtime >= model_path.stat().st_mtime
-        ):
-            return False
-        else:
-            return True
-
-    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
-        assert isinstance(config, CheckpointConfigBase)
-        image_size = (
-            512
-            if config.base == BaseModelType.StableDiffusion1
-            else 768
-            if config.base == BaseModelType.StableDiffusion2
-            else 1024
-        )
-
-        self._logger.info(f"Converting {model_path} to diffusers format")
-        with open(self._app_config.legacy_conf_path / config.config_path, "r") as config_stream:
-            result = convert_controlnet_to_diffusers(
-                model_path,
-                output_path,
-                original_config_file=config_stream,
-                image_size=image_size,
-                precision=self._torch_dtype,
-                from_safetensors=model_path.suffix == ".safetensors",
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if isinstance(config, ControlNetCheckpointConfig):
+            return ControlNetModel.from_single_file(
+                config.path,
+                torch_dtype=self._torch_dtype,
             )
-        return result
+        else:
+            return super()._load_model(config, submodel_type)

invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py

@@ -18,8 +18,8 @@ from invokeai.backend.model_manager import (
     SubModelType,
 )
 from invokeai.backend.model_manager.config import DiffusersConfigBase
-
-from .. import ModelLoader, ModelLoaderRegistry
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)

invokeai/backend/model_manager/load/model_loaders/ip_adapter.py

@@ -8,7 +8,8 @@ import torch
 
 from invokeai.backend.ip_adapter.ip_adapter import build_ip_adapter
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelFormat, ModelType, SubModelType
-from invokeai.backend.model_manager.load import ModelLoader, ModelLoaderRegistry
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.raw_model import RawModel
 
 

invokeai/backend/model_manager/load/model_loaders/lora.py

@@ -15,7 +15,6 @@ from invokeai.backend.model_manager import (
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
 
 from .. import ModelLoader, ModelLoaderRegistry
@@ -32,10 +31,9 @@ class LoRALoader(ModelLoader):
         app_config: InvokeAIAppConfig,
         logger: Logger,
         ram_cache: ModelCacheBase[AnyModel],
-        convert_cache: ModelConvertCacheBase,
     ):
         """Initialize the loader."""
-        super().__init__(app_config, logger, ram_cache, convert_cache)
+        super().__init__(app_config, logger, ram_cache)
         self._model_base: Optional[BaseModelType] = None
 
     def _load_model(

invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

@@ -4,22 +4,28 @@
 from pathlib import Path
 from typing import Optional
 
+from diffusers import (
+    StableDiffusionInpaintPipeline,
+    StableDiffusionPipeline,
+    StableDiffusionXLInpaintPipeline,
+    StableDiffusionXLPipeline,
+)
+
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
     BaseModelType,
     ModelFormat,
     ModelType,
-    SchedulerPredictionType,
+    ModelVariantType,
     SubModelType,
 )
 from invokeai.backend.model_manager.config import (
     CheckpointConfigBase,
     DiffusersConfigBase,
     MainCheckpointConfig,
-    ModelVariantType,
 )
-from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
+from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 from .. import ModelLoaderRegistry
 from .generic_diffusers import GenericDiffusersLoader
@@ -48,8 +54,12 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
         config: AnyModelConfig,
         submodel_type: Optional[SubModelType] = None,
     ) -> AnyModel:
-        if not submodel_type is not None:
+        if isinstance(config, CheckpointConfigBase):
+            return self._load_from_singlefile(config, submodel_type)
+
+        if submodel_type is None:
             raise Exception("A submodel type must be provided when loading main pipelines.")
+
         model_path = Path(config.path)
         load_class = self.get_hf_load_class(model_path, submodel_type)
         repo_variant = config.repo_variant if isinstance(config, DiffusersConfigBase) else None
@@ -71,46 +81,58 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
 
         return result
 
-    def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
-        if not isinstance(config, CheckpointConfigBase):
-            return False
-        elif (
-            dest_path.exists()
-            and (dest_path / "model_index.json").stat().st_mtime >= (config.converted_at or 0.0)
-            and (dest_path / "model_index.json").stat().st_mtime >= model_path.stat().st_mtime
-        ):
-            return False
-        else:
-            return True
-
-    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
+    def _load_from_singlefile(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        load_classes = {
+            BaseModelType.StableDiffusion1: {
+                ModelVariantType.Normal: StableDiffusionPipeline,
+                ModelVariantType.Inpaint: StableDiffusionInpaintPipeline,
+            },
+            BaseModelType.StableDiffusion2: {
+                ModelVariantType.Normal: StableDiffusionPipeline,
+                ModelVariantType.Inpaint: StableDiffusionInpaintPipeline,
+            },
+            BaseModelType.StableDiffusionXL: {
+                ModelVariantType.Normal: StableDiffusionXLPipeline,
+                ModelVariantType.Inpaint: StableDiffusionXLInpaintPipeline,
+            },
+        }
         assert isinstance(config, MainCheckpointConfig)
-        base = config.base
-
+        try:
+            load_class = load_classes[config.base][config.variant]
+        except KeyError as e:
+            raise Exception(f"No diffusers pipeline known for base={config.base}, variant={config.variant}") from e
         prediction_type = config.prediction_type.value
         upcast_attention = config.upcast_attention
-        image_size = (
-            1024
-            if base == BaseModelType.StableDiffusionXL
-            else 768
-            if config.prediction_type == SchedulerPredictionType.VPrediction and base == BaseModelType.StableDiffusion2
-            else 512
-        )
 
-        self._logger.info(f"Converting {model_path} to diffusers format")
+        # Without SilenceWarnings we get log messages like this:
+        # site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+        # warnings.warn(
+        # Some weights of the model checkpoint were not used when initializing CLIPTextModel:
+        # ['text_model.embeddings.position_ids']
+        # Some weights of the model checkpoint were not used when initializing CLIPTextModelWithProjection:
+        # ['text_model.embeddings.position_ids']
 
-        loaded_model = convert_ckpt_to_diffusers(
-            model_path,
-            output_path,
-            model_type=self.model_base_to_model_type[base],
-            original_config_file=self._app_config.legacy_conf_path / config.config_path,
-            extract_ema=True,
-            from_safetensors=model_path.suffix == ".safetensors",
-            precision=self._torch_dtype,
-            prediction_type=prediction_type,
-            image_size=image_size,
-            upcast_attention=upcast_attention,
-            load_safety_checker=False,
-            num_in_channels=VARIANT_TO_IN_CHANNEL_MAP[config.variant],
-        )
-        return loaded_model
+        with SilenceWarnings():
+            pipeline = load_class.from_single_file(
+                config.path,
+                torch_dtype=self._torch_dtype,
+                prediction_type=prediction_type,
+                upcast_attention=upcast_attention,
+                load_safety_checker=False,
+            )
+
+        if not submodel_type:
+            return pipeline
+
+        # Proactively load the various submodels into the RAM cache so that we don't have to re-load
+        # the entire pipeline every time a new submodel is needed.
+        for subtype in SubModelType:
+            if subtype == submodel_type:
+                continue
+            if submodel := getattr(pipeline, subtype.value, None):
+                self._ram_cache.put(config.key, submodel_type=subtype, model=submodel)
+        return getattr(pipeline, submodel_type.value)

invokeai/backend/model_manager/load/model_loaders/vae.py

@@ -1,12 +1,9 @@
 # Copyright (c) 2024, Lincoln D. Stein and the InvokeAI Development Team
 """Class for VAE model loading in InvokeAI."""
 
-from pathlib import Path
 from typing import Optional
 
-import torch
-from omegaconf import DictConfig, OmegaConf
-from safetensors.torch import load_file as safetensors_load_file
+from diffusers import AutoencoderKL
 
 from invokeai.backend.model_manager import (
     AnyModelConfig,
@@ -14,56 +11,26 @@ from invokeai.backend.model_manager import (
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.config import AnyModel, CheckpointConfigBase
-from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_ldm_vae_to_diffusers
+from invokeai.backend.model_manager.config import AnyModel, SubModelType, VAECheckpointConfig
 
 from .. import ModelLoaderRegistry
 from .generic_diffusers import GenericDiffusersLoader
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.VAE, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.VAE, format=ModelFormat.Checkpoint)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.VAE, format=ModelFormat.Checkpoint)
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.VAE, format=ModelFormat.Checkpoint)
 class VAELoader(GenericDiffusersLoader):
     """Class to load VAE models."""
 
-    def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
-        if not isinstance(config, CheckpointConfigBase):
-            return False
-        elif (
-            dest_path.exists()
-            and (dest_path / "config.json").stat().st_mtime >= (config.converted_at or 0.0)
-            and (dest_path / "config.json").stat().st_mtime >= model_path.stat().st_mtime
-        ):
-            return False
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if isinstance(config, VAECheckpointConfig):
+            return AutoencoderKL.from_single_file(
+                config.path,
+                torch_dtype=self._torch_dtype,
+            )
         else:
-            return True
-
-    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
-        # TODO(MM2): check whether sdxl VAE models convert.
-        if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
-            raise Exception(f"VAE conversion not supported for model type: {config.base}")
-        else:
-            assert isinstance(config, CheckpointConfigBase)
-            config_file = self._app_config.legacy_conf_path / config.config_path
-
-        if model_path.suffix == ".safetensors":
-            checkpoint = safetensors_load_file(model_path, device="cpu")
-        else:
-            checkpoint = torch.load(model_path, map_location="cpu")
-
-        # sometimes weights are hidden under "state_dict", and sometimes not
-        if "state_dict" in checkpoint:
-            checkpoint = checkpoint["state_dict"]
-
-        ckpt_config = OmegaConf.load(config_file)
-        assert isinstance(ckpt_config, DictConfig)
-        self._logger.info(f"Converting {model_path} to diffusers format")
-        vae_model = convert_ldm_vae_to_diffusers(
-            checkpoint=checkpoint,
-            vae_config=ckpt_config,
-            image_size=512,
-            precision=self._torch_dtype,
-            dump_path=output_path,
-        )
-        return vae_model
+            return super()._load_model(config, submodel_type)

invokeai/backend/model_manager/load/model_size_utils.py

@@ -0,0 +1,79 @@
+import json
+from pathlib import Path
+from typing import Optional
+
+import torch
+
+
+def calc_module_size(model: torch.nn.Module) -> int:
+    """Estimate the size of a torch.nn.Module in bytes."""
+    mem_params = sum([param.nelement() * param.element_size() for param in model.parameters()])
+    mem_bufs = sum([buf.nelement() * buf.element_size() for buf in model.buffers()])
+    mem: int = mem_params + mem_bufs  # in bytes
+    return mem
+
+
+def calc_model_size_by_fs(model_path: Path, subfolder: Optional[str] = None, variant: Optional[str] = None) -> int:
+    """Estimate the size of a model on disk in bytes."""
+    if model_path.is_file():
+        return model_path.stat().st_size
+
+    if subfolder is not None:
+        model_path = model_path / subfolder
+
+    # this can happen when, for example, the safety checker is not downloaded.
+    if not model_path.exists():
+        return 0
+
+    all_files = [f for f in model_path.iterdir() if (model_path / f).is_file()]
+
+    fp16_files = {f for f in all_files if ".fp16." in f.name or ".fp16-" in f.name}
+    bit8_files = {f for f in all_files if ".8bit." in f.name or ".8bit-" in f.name}
+    other_files = set(all_files) - fp16_files - bit8_files
+
+    if not variant:  # ModelRepoVariant.DEFAULT evaluates to empty string for compatability with HF
+        files = other_files
+    elif variant == "fp16":
+        files = fp16_files
+    elif variant == "8bit":
+        files = bit8_files
+    else:
+        raise NotImplementedError(f"Unknown variant: {variant}")
+
+    # try read from index if exists
+    index_postfix = ".index.json"
+    if variant is not None:
+        index_postfix = f".index.{variant}.json"
+
+    for file in files:
+        if not file.name.endswith(index_postfix):
+            continue
+        try:
+            with open(model_path / file, "r") as f:
+                index_data = json.loads(f.read())
+            return int(index_data["metadata"]["total_size"])
+        except Exception:
+            pass
+
+    # calculate files size if there is no index file
+    formats = [
+        (".safetensors",),  # safetensors
+        (".bin",),  # torch
+        (".onnx", ".pb"),  # onnx
+        (".msgpack",),  # flax
+        (".ckpt",),  # tf
+        (".h5",),  # tf2
+    ]
+
+    for file_format in formats:
+        model_files = [f for f in files if f.suffix in file_format]
+        if len(model_files) == 0:
+            continue
+
+        model_size = 0
+        for model_file in model_files:
+            file_stats = (model_path / model_file).stat()
+            model_size += file_stats.st_size
+        return model_size
+
+    return 0  # scheduler/feature_extractor/tokenizer - models without loading to gpu

invokeai/backend/model_manager/load/model_util.py

@@ -1,14 +1,11 @@
 # Copyright (c) 2024 The InvokeAI Development Team
 """Various utility functions needed by the loader and caching system."""
 
-import json
-from pathlib import Path
-from typing import Optional
-
 import torch
 from diffusers import DiffusionPipeline
 
 from invokeai.backend.model_manager.config import AnyModel
+from invokeai.backend.model_manager.load.model_size_utils import calc_module_size
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
 
 
@@ -17,7 +14,7 @@ def calc_model_size_by_data(model: AnyModel) -> int:
     if isinstance(model, DiffusionPipeline):
         return _calc_pipeline_by_data(model)
     elif isinstance(model, torch.nn.Module):
-        return _calc_model_by_data(model)
+        return calc_module_size(model)
     elif isinstance(model, IAIOnnxRuntimeModel):
         return _calc_onnx_model_by_data(model)
     else:
@@ -30,84 +27,11 @@ def _calc_pipeline_by_data(pipeline: DiffusionPipeline) -> int:
     for submodel_key in pipeline.components.keys():
         submodel = getattr(pipeline, submodel_key)
         if submodel is not None and isinstance(submodel, torch.nn.Module):
-            res += _calc_model_by_data(submodel)
+            res += calc_module_size(submodel)
     return res
 
 
-def _calc_model_by_data(model: torch.nn.Module) -> int:
-    mem_params = sum([param.nelement() * param.element_size() for param in model.parameters()])
-    mem_bufs = sum([buf.nelement() * buf.element_size() for buf in model.buffers()])
-    mem: int = mem_params + mem_bufs  # in bytes
-    return mem
-
-
 def _calc_onnx_model_by_data(model: IAIOnnxRuntimeModel) -> int:
     tensor_size = model.tensors.size() * 2  # The session doubles this
     mem = tensor_size  # in bytes
     return mem
-
-
-def calc_model_size_by_fs(model_path: Path, subfolder: Optional[str] = None, variant: Optional[str] = None) -> int:
-    """Estimate the size of a model on disk in bytes."""
-    if model_path.is_file():
-        return model_path.stat().st_size
-
-    if subfolder is not None:
-        model_path = model_path / subfolder
-
-    # this can happen when, for example, the safety checker is not downloaded.
-    if not model_path.exists():
-        return 0
-
-    all_files = [f for f in model_path.iterdir() if (model_path / f).is_file()]
-
-    fp16_files = {f for f in all_files if ".fp16." in f.name or ".fp16-" in f.name}
-    bit8_files = {f for f in all_files if ".8bit." in f.name or ".8bit-" in f.name}
-    other_files = set(all_files) - fp16_files - bit8_files
-
-    if not variant:  # ModelRepoVariant.DEFAULT evaluates to empty string for compatability with HF
-        files = other_files
-    elif variant == "fp16":
-        files = fp16_files
-    elif variant == "8bit":
-        files = bit8_files
-    else:
-        raise NotImplementedError(f"Unknown variant: {variant}")
-
-    # try read from index if exists
-    index_postfix = ".index.json"
-    if variant is not None:
-        index_postfix = f".index.{variant}.json"
-
-    for file in files:
-        if not file.name.endswith(index_postfix):
-            continue
-        try:
-            with open(model_path / file, "r") as f:
-                index_data = json.loads(f.read())
-            return int(index_data["metadata"]["total_size"])
-        except Exception:
-            pass
-
-    # calculate files size if there is no index file
-    formats = [
-        (".safetensors",),  # safetensors
-        (".bin",),  # torch
-        (".onnx", ".pb"),  # onnx
-        (".msgpack",),  # flax
-        (".ckpt",),  # tf
-        (".h5",),  # tf2
-    ]
-
-    for file_format in formats:
-        model_files = [f for f in files if f.suffix in file_format]
-        if len(model_files) == 0:
-            continue
-
-        model_size = 0
-        for model_file in model_files:
-            file_stats = (model_path / model_file).stat()
-            model_size += file_stats.st_size
-        return model_size
-
-    return 0  # scheduler/feature_extractor/tokenizer - models without loading to gpu

invokeai/backend/model_manager/probe.py

@@ -312,6 +312,8 @@ class ModelProbe(object):
             config_file = (
                 "stable-diffusion/v1-inference.yaml"
                 if base_type is BaseModelType.StableDiffusion1
+                else "stable-diffusion/sd_xl_base.yaml"
+                if base_type is BaseModelType.StableDiffusionXL
                 else "stable-diffusion/v2-inference.yaml"
             )
         else:
@@ -451,8 +453,16 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
 
 class VaeCheckpointProbe(CheckpointProbeBase):
     def get_base_type(self) -> BaseModelType:
-        # I can't find any standalone 2.X VAEs to test with!
-        return BaseModelType.StableDiffusion1
+        # VAEs of all base types have the same structure, so we wimp out and
+        # guess using the name.
+        for regexp, basetype in [
+            (r"xl", BaseModelType.StableDiffusionXL),
+            (r"sd2", BaseModelType.StableDiffusion2),
+            (r"vae", BaseModelType.StableDiffusion1),
+        ]:
+            if re.search(regexp, self.model_path.name, re.IGNORECASE):
+                return basetype
+        raise InvalidModelConfigException("Cannot determine base type")
 
 
 class LoRACheckpointProbe(CheckpointProbeBase):

invokeai/backend/model_manager/starter_models.py

@@ -294,8 +294,8 @@ STARTER_MODELS: list[StarterModel] = [
     StarterModel(
         name="canny-sdxl",
         base=BaseModelType.StableDiffusionXL,
-        source="diffusers/controlnet-canny-sdxl-1.0",
-        description="Controlnet weights trained on sdxl-1.0 with canny conditioning.",
+        source="xinsir/controlnet-canny-sdxl-1.0",
+        description="Controlnet weights trained on sdxl-1.0 with canny conditioning, by Xinsir.",
         type=ModelType.ControlNet,
     ),
     StarterModel(
@@ -326,6 +326,20 @@ STARTER_MODELS: list[StarterModel] = [
         description="Controlnet weights trained on sdxl-1.0 with Zoe's preprocessor (32 bits).",
         type=ModelType.ControlNet,
     ),
+    StarterModel(
+        name="openpose-sdxl",
+        base=BaseModelType.StableDiffusionXL,
+        source="xinsir/controlnet-openpose-sdxl-1.0",
+        description="Controlnet weights trained on sdxl-1.0 compatible with the DWPose processor by Xinsir.",
+        type=ModelType.ControlNet,
+    ),
+    StarterModel(
+        name="scribble-sdxl",
+        base=BaseModelType.StableDiffusionXL,
+        source="xinsir/controlnet-scribble-sdxl-1.0",
+        description="Controlnet weights trained on sdxl-1.0 compatible with various lineart processors and black/white sketches by Xinsir.",
+        type=ModelType.ControlNet,
+    ),
     # endregion
     # region T2I Adapter
     StarterModel(

invokeai/backend/model_patcher.py

@@ -16,6 +16,7 @@ from invokeai.app.shared.models import FreeUConfig
 from invokeai.backend.model_manager import AnyModel
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
+from invokeai.backend.util.devices import TorchDevice
 
 from .lora import LoRAModelRaw
 from .textual_inversion import TextualInversionManager, TextualInversionModelRaw
@@ -139,12 +140,15 @@ class ModelPatcher:
                         # We intentionally move to the target device first, then cast. Experimentally, this was found to
                         # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
                         # same thing in a single call to '.to(...)'.
-                        layer.to(device=device, non_blocking=True)
-                        layer.to(dtype=torch.float32, non_blocking=True)
+                        layer.to(device=device, non_blocking=TorchDevice.get_non_blocking(device))
+                        layer.to(dtype=torch.float32, non_blocking=TorchDevice.get_non_blocking(device))
                         # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
                         # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
                         layer_weight = layer.get_weight(module.weight) * (lora_weight * layer_scale)
-                        layer.to(device=torch.device("cpu"), non_blocking=True)
+                        layer.to(
+                            device=TorchDevice.CPU_DEVICE,
+                            non_blocking=TorchDevice.get_non_blocking(TorchDevice.CPU_DEVICE),
+                        )
 
                         assert isinstance(layer_weight, torch.Tensor)  # mypy thinks layer_weight is a float|Any ??!
                         if module.weight.shape != layer_weight.shape:
@@ -153,7 +157,7 @@ class ModelPatcher:
                             layer_weight = layer_weight.reshape(module.weight.shape)
 
                         assert isinstance(layer_weight, torch.Tensor)  # mypy thinks layer_weight is a float|Any ??!
-                        module.weight += layer_weight.to(dtype=dtype, non_blocking=True)
+                        module.weight += layer_weight.to(dtype=dtype, non_blocking=TorchDevice.get_non_blocking(device))
 
             yield  # wait for context manager exit
 
@@ -161,7 +165,9 @@ class ModelPatcher:
             assert hasattr(model, "get_submodule")  # mypy not picking up fact that torch.nn.Module has get_submodule()
             with torch.no_grad():
                 for module_key, weight in original_weights.items():
-                    model.get_submodule(module_key).weight.copy_(weight, non_blocking=True)
+                    model.get_submodule(module_key).weight.copy_(
+                        weight, non_blocking=TorchDevice.get_non_blocking(weight.device)
+                    )
 
     @classmethod
     @contextmanager

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -255,8 +255,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         # Validate assumptions about input tensor shapes.
         batch_size, latent_channels, latent_height, latent_width = latents.shape
         assert latent_channels == 4
-        assert masked_ref_image_latents.shape == [1, 4, latent_height, latent_width]
-        assert inpainting_mask == [1, 1, latent_height, latent_width]
+        assert list(masked_ref_image_latents.shape) == [1, 4, latent_height, latent_width]
+        assert list(inpainting_mask.shape) == [1, 1, latent_height, latent_width]
 
         # Repeat original_image_latents and inpainting_mask to match the latents batch size.
         original_image_latents = masked_ref_image_latents.expand(batch_size, -1, -1, -1)
@@ -299,9 +299,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 HACK(ryand): seed is only used in a particular case when `noise` is None, but we need to re-generate the
                 same noise used earlier in the pipeline. This should really be handled in a clearer way.
             timesteps: The timestep schedule for the denoising process.
-            init_timestep: The first timestep in the schedule.
-                TODO(ryand): I'm pretty sure this should always be the same as timesteps[0:1]. Confirm that that is the
-                case, and remove this duplicate param.
+            init_timestep: The first timestep in the schedule. This is used to determine the initial noise level, so
+                should be populated if you want noise applied *even* if timesteps is empty.
             callback: A callback function that is called to report progress during the denoising process.
             control_data: ControlNet data.
             ip_adapter_data: IP-Adapter data.
@@ -316,9 +315,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 SD UNet model.
             is_gradient_mask: A flag indicating whether `mask` is a gradient mask or not.
         """
-        # TODO(ryand): Figure out why this condition is necessary, and document it. My guess is that it's to handle
-        # cases where densoisings_start and denoising_end are set such that there are no timesteps.
-        if init_timestep.shape[0] == 0 or timesteps.shape[0] == 0:
+        if init_timestep.shape[0] == 0:
             return latents
 
         orig_latents = latents.clone()

invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py

@@ -13,17 +13,13 @@ from invokeai.backend.stable_diffusion.diffusers_pipeline import (
     StableDiffusionGeneratorPipeline,
 )
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import TextConditioningData
-from invokeai.backend.tiles.utils import TBLR
-
-# The maximum number of regions with compatible sizes that will be batched together.
-# Larger batch sizes improve speed, but require more device memory.
-MAX_REGION_BATCH_SIZE = 4
+from invokeai.backend.tiles.utils import Tile
 
 
 @dataclass
 class MultiDiffusionRegionConditioning:
     # Region coords in latent space.
-    region: TBLR
+    region: Tile
     text_conditioning_data: TextConditioningData
     control_data: list[ControlNetData]
 
@@ -31,31 +27,8 @@ class MultiDiffusionRegionConditioning:
 class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
     """A Stable Diffusion pipeline that uses Multi-Diffusion (https://arxiv.org/pdf/2302.08113) for denoising."""
 
-    def _split_into_region_batches(
-        self, multi_diffusion_conditioning: list[MultiDiffusionRegionConditioning]
-    ) -> list[list[MultiDiffusionRegionConditioning]]:
-        # Group the regions by shape. Only regions with the same shape can be batched together.
-        conditioning_by_shape: dict[tuple[int, int], list[MultiDiffusionRegionConditioning]] = {}
-        for region_conditioning in multi_diffusion_conditioning:
-            shape_hw = (
-                region_conditioning.region.bottom - region_conditioning.region.top,
-                region_conditioning.region.right - region_conditioning.region.left,
-            )
-            # In python, a tuple of hashable objects is hashable, so can be used as a key in a dict.
-            if shape_hw not in conditioning_by_shape:
-                conditioning_by_shape[shape_hw] = []
-            conditioning_by_shape[shape_hw].append(region_conditioning)
-
-        # Split the regions into batches, respecting the MAX_REGION_BATCH_SIZE constraint.
-        region_conditioning_batches = []
-        for region_conditioning_batch in conditioning_by_shape.values():
-            for i in range(0, len(region_conditioning_batch), MAX_REGION_BATCH_SIZE):
-                region_conditioning_batches.append(region_conditioning_batch[i : i + MAX_REGION_BATCH_SIZE])
-
-        return region_conditioning_batches
-
     def _check_regional_prompting(self, multi_diffusion_conditioning: list[MultiDiffusionRegionConditioning]):
-        """Check the input conditioning and confirm that regional prompting is not used."""
+        """Validate that regional conditioning is not used."""
         for region_conditioning in multi_diffusion_conditioning:
             if (
                 region_conditioning.text_conditioning_data.cond_regions is not None
@@ -66,6 +39,7 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
     def multi_diffusion_denoise(
         self,
         multi_diffusion_conditioning: list[MultiDiffusionRegionConditioning],
+        target_overlap: int,
         latents: torch.Tensor,
         scheduler_step_kwargs: dict[str, Any],
         noise: Optional[torch.Tensor],
@@ -75,9 +49,7 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
     ) -> torch.Tensor:
         self._check_regional_prompting(multi_diffusion_conditioning)
 
-        # TODO(ryand): Figure out why this condition is necessary, and document it. My guess is that it's to handle
-        # cases where densoisings_start and denoising_end are set such that there are no timesteps.
-        if init_timestep.shape[0] == 0 or timesteps.shape[0] == 0:
+        if init_timestep.shape[0] == 0:
             return latents
 
         batch_size, _, latent_height, latent_width = latents.shape
@@ -94,24 +66,16 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
         # cropping into regions.
         self._adjust_memory_efficient_attention(latents)
 
-        # Populate a weighted mask that will be used to combine the results from each region after every step.
-        # For now, we assume that each region has the same weight (1.0).
-        region_weight_mask = torch.zeros(
-            (1, 1, latent_height, latent_width), device=latents.device, dtype=latents.dtype
-        )
-        for region_conditioning in multi_diffusion_conditioning:
-            region = region_conditioning.region
-            region_weight_mask[:, :, region.top : region.bottom, region.left : region.right] += 1.0
-
-        # Group the region conditioning into batches for faster processing.
-        # region_conditioning_batches[b][r] is the r'th region in the b'th batch.
-        region_conditioning_batches = self._split_into_region_batches(multi_diffusion_conditioning)
-
         # Many of the diffusers schedulers are stateful (i.e. they update internal state in each call to step()). Since
         # we are calling step() multiple times at the same timestep (once for each region batch), we must maintain a
         # separate scheduler state for each region batch.
+        # TODO(ryand): This solution allows all schedulers to **run**, but does not fully solve the issue of scheduler
+        # statefulness. Some schedulers store previous model outputs in their state, but these values become incorrect
+        # as Multi-Diffusion blending is applied (e.g. the PNDMScheduler). This can result in a blurring effect when
+        # multiple MultiDiffusion regions overlap. Solving this properly would require a case-by-case review of each
+        # scheduler to determine how it's state needs to be updated for compatibilty with Multi-Diffusion.
         region_batch_schedulers: list[SchedulerMixin] = [
-            copy.deepcopy(self.scheduler) for _ in region_conditioning_batches
+            copy.deepcopy(self.scheduler) for _ in multi_diffusion_conditioning
         ]
 
         callback(
@@ -128,72 +92,68 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
             batched_t = t.expand(batch_size)
 
             merged_latents = torch.zeros_like(latents)
+            merged_latents_weights = torch.zeros(
+                (1, 1, latent_height, latent_width), device=latents.device, dtype=latents.dtype
+            )
             merged_pred_original: torch.Tensor | None = None
-            for region_batch_idx, region_conditioning_batch in enumerate(region_conditioning_batches):
+            for region_idx, region_conditioning in enumerate(multi_diffusion_conditioning):
                 # Switch to the scheduler for the region batch.
-                self.scheduler = region_batch_schedulers[region_batch_idx]
+                self.scheduler = region_batch_schedulers[region_idx]
 
-                # TODO(ryand): This logic has not yet been tested with input latents with a batch_size > 1.
+                # Crop the inputs to the region.
+                region_latents = latents[
+                    :,
+                    :,
+                    region_conditioning.region.coords.top : region_conditioning.region.coords.bottom,
+                    region_conditioning.region.coords.left : region_conditioning.region.coords.right,
+                ]
 
-                # Prepare the latents for the region batch.
-                batch_latents = torch.cat(
-                    [
-                        latents[
-                            :,
-                            :,
-                            region_conditioning.region.top : region_conditioning.region.bottom,
-                            region_conditioning.region.left : region_conditioning.region.right,
-                        ]
-                        for region_conditioning in region_conditioning_batch
-                    ],
-                )
-
-                # TODO(ryand): Do we have to repeat the text_conditioning_data to match the batch size? Or does step()
-                # handle broadcasting properly?
-
-                # TODO(ryand): Resume here!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
                 # Run the denoising step on the region.
                 step_output = self.step(
                     t=batched_t,
-                    latents=batch_latents,
+                    latents=region_latents,
                     conditioning_data=region_conditioning.text_conditioning_data,
                     step_index=i,
-                    total_step_count=total_step_count,
+                    total_step_count=len(timesteps),
                     scheduler_step_kwargs=scheduler_step_kwargs,
                     mask_guidance=None,
                     mask=None,
                     masked_latents=None,
                     control_data=region_conditioning.control_data,
                 )
-                # Run a denoising step on the region.
-                # step_output = self._region_step(
-                #     region_conditioning=region_conditioning,
-                #     t=batched_t,
-                #     latents=latents,
-                #     step_index=i,
-                #     total_step_count=len(timesteps),
-                #     scheduler_step_kwargs=scheduler_step_kwargs,
-                # )
 
                 # Store the results from the region.
+                # If two tiles overlap by more than the target overlap amount, crop the left and top edges of the
+                # affected tiles to achieve the target overlap.
                 region = region_conditioning.region
-                merged_latents[:, :, region.top : region.bottom, region.left : region.right] += step_output.prev_sample
+                top_adjustment = max(0, region.overlap.top - target_overlap)
+                left_adjustment = max(0, region.overlap.left - target_overlap)
+                region_height_slice = slice(region.coords.top + top_adjustment, region.coords.bottom)
+                region_width_slice = slice(region.coords.left + left_adjustment, region.coords.right)
+                merged_latents[:, :, region_height_slice, region_width_slice] += step_output.prev_sample[
+                    :, :, top_adjustment:, left_adjustment:
+                ]
+                # For now, we treat every region as having the same weight.
+                merged_latents_weights[:, :, region_height_slice, region_width_slice] += 1.0
+
                 pred_orig_sample = getattr(step_output, "pred_original_sample", None)
                 if pred_orig_sample is not None:
                     # If one region has pred_original_sample, then we can assume that all regions will have it, because
                     # they all use the same scheduler.
                     if merged_pred_original is None:
                         merged_pred_original = torch.zeros_like(latents)
-                    merged_pred_original[:, :, region.top : region.bottom, region.left : region.right] += (
-                        pred_orig_sample
-                    )
+                    merged_pred_original[:, :, region_height_slice, region_width_slice] += pred_orig_sample[
+                        :, :, top_adjustment:, left_adjustment:
+                    ]
 
             # Normalize the merged results.
-            latents = torch.where(region_weight_mask > 0, merged_latents / region_weight_mask, merged_latents)
+            latents = torch.where(merged_latents_weights > 0, merged_latents / merged_latents_weights, merged_latents)
+            # For debugging, uncomment this line to visualize the region seams:
+            # latents = torch.where(merged_latents_weights > 1, 0.0, latents)
             predicted_original = None
             if merged_pred_original is not None:
                 predicted_original = torch.where(
-                    region_weight_mask > 0, merged_pred_original / region_weight_mask, merged_pred_original
+                    merged_latents_weights > 0, merged_pred_original / merged_latents_weights, merged_pred_original
                 )
 
             callback(
@@ -208,35 +168,3 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
             )
 
         return latents
-
-    @torch.inference_mode()
-    def _region_batch_step(
-        self,
-        region_conditioning: MultiDiffusionRegionConditioning,
-        t: torch.Tensor,
-        latents: torch.Tensor,
-        step_index: int,
-        total_step_count: int,
-        scheduler_step_kwargs: dict[str, Any],
-    ):
-        # Crop the inputs to the region.
-        region_latents = latents[
-            :,
-            :,
-            region_conditioning.region.top : region_conditioning.region.bottom,
-            region_conditioning.region.left : region_conditioning.region.right,
-        ]
-
-        # Run the denoising step on the region.
-        return self.step(
-            t=t,
-            latents=region_latents,
-            conditioning_data=region_conditioning.text_conditioning_data,
-            step_index=step_index,
-            total_step_count=total_step_count,
-            scheduler_step_kwargs=scheduler_step_kwargs,
-            mask_guidance=None,
-            mask=None,
-            masked_latents=None,
-            control_data=region_conditioning.control_data,
-        )

invokeai/backend/stable_diffusion/schedulers/__init__.py

@@ -1,3 +0,0 @@
-from .schedulers import SCHEDULER_MAP  # noqa: F401
-
-__all__ = ["SCHEDULER_MAP"]

invokeai/backend/stable_diffusion/schedulers/schedulers.py

@@ -1,3 +1,5 @@
+from typing import Literal
+
 from diffusers import (
     DDIMScheduler,
     DDPMScheduler,
@@ -43,3 +45,9 @@ SCHEDULER_MAP = {
     "lcm": (LCMScheduler, {}),
     "tcd": (TCDScheduler, {}),
 }
+
+
+# HACK(ryand): Passing a tuple of keys to Literal works at runtime, but not at type-check time. See the docs here for
+# more info: https://typing.readthedocs.io/en/latest/spec/literal.html#parameters-at-runtime. For now, we are ignoring
+# this error. In the future, we should fix this type handling.
+SCHEDULER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]  # type: ignore

invokeai/backend/stable_diffusion/vae_tiling.py

@@ -0,0 +1,35 @@
+from contextlib import contextmanager
+
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
+
+
+@contextmanager
+def patch_vae_tiling_params(
+    vae: AutoencoderKL | AutoencoderTiny,
+    tile_sample_min_size: int,
+    tile_latent_min_size: int,
+    tile_overlap_factor: float,
+):
+    """Patch the parameters that control the VAE tiling tile size and overlap.
+
+    These parameters are not explicitly exposed in the VAE's API, but they have a significant impact on the quality of
+    the outputs. As a general rule, bigger tiles produce better results, but this comes at the cost of higher memory
+    usage.
+    """
+    # Record initial config.
+    orig_tile_sample_min_size = vae.tile_sample_min_size
+    orig_tile_latent_min_size = vae.tile_latent_min_size
+    orig_tile_overlap_factor = vae.tile_overlap_factor
+
+    try:
+        # Apply target config.
+        vae.tile_sample_min_size = tile_sample_min_size
+        vae.tile_latent_min_size = tile_latent_min_size
+        vae.tile_overlap_factor = tile_overlap_factor
+        yield
+    finally:
+        # Restore initial config.
+        vae.tile_sample_min_size = orig_tile_sample_min_size
+        vae.tile_latent_min_size = orig_tile_latent_min_size
+        vae.tile_overlap_factor = orig_tile_overlap_factor

invokeai/backend/util/devices.py

@@ -42,6 +42,10 @@ PRECISION_TO_NAME: Dict[torch.dtype, TorchPrecisionNames] = {v: k for k, v in NA
 class TorchDevice:
     """Abstraction layer for torch devices."""
 
+    CPU_DEVICE = torch.device("cpu")
+    CUDA_DEVICE = torch.device("cuda")
+    MPS_DEVICE = torch.device("mps")
+
     @classmethod
     def choose_torch_device(cls) -> torch.device:
         """Return the torch.device to use for accelerated inference."""
@@ -108,3 +112,15 @@ class TorchDevice:
     @classmethod
     def _to_dtype(cls, precision_name: TorchPrecisionNames) -> torch.dtype:
         return NAME_TO_PRECISION[precision_name]
+
+    @staticmethod
+    def get_non_blocking(to_device: torch.device) -> bool:
+        """Return the non_blocking flag to be used when moving a tensor to a given device.
+        MPS may have unexpected errors with non-blocking operations - we should not use non-blocking when moving _to_ MPS.
+        When moving _from_ MPS, we can use non-blocking operations.
+
+        See:
+        - https://github.com/pytorch/pytorch/issues/107455
+        - https://discuss.pytorch.org/t/should-we-set-non-blocking-to-true/38234/28
+        """
+        return False if to_device.type == "mps" else True

invokeai/backend/util/test_utils.py

@@ -5,9 +5,10 @@ from typing import Optional, Union
 import pytest
 import torch
 
-from invokeai.app.services.model_manager import ModelManagerServiceBase
+from invokeai.app.services.model_manager.model_manager_base import ModelManagerServiceBase
 from invokeai.app.services.model_records import UnknownModelException
-from invokeai.backend.model_manager import BaseModelType, LoadedModel, ModelType, SubModelType
+from invokeai.backend.model_manager import BaseModelType, ModelType, SubModelType
+from invokeai.backend.model_manager.load.load_base import LoadedModel
 
 
 @pytest.fixture(scope="session")

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

@@ -17,7 +17,10 @@
     },
     "boards": {
         "addBoard": "Add Board",
+        "archiveBoard": "Archive Board",
+        "archived": "Archived",
         "autoAddBoard": "Auto-Add Board",
+        "selectedForAutoAdd": "Selected for Auto-Add",
         "bottomMessage": "Deleting this board and its images will reset any features currently using them.",
         "cancel": "Cancel",
         "changeBoard": "Change Board",
@@ -36,8 +39,13 @@
         "searchBoard": "Search Boards...",
         "selectBoard": "Select a Board",
         "topMessage": "This board contains images used in the following features:",
+        "unarchiveBoard": "Unarchive Board",
         "uncategorized": "Uncategorized",
-        "downloadBoard": "Download Board"
+        "downloadBoard": "Download Board",
+        "imagesWithCount_one": "{{count}} image",
+        "imagesWithCount_other": "{{count}} images",
+        "assetsWithCount_one": "{{count}} asset",
+        "assetsWithCount_other": "{{count}} assets"
     },
     "accordions": {
         "generation": {
@@ -364,6 +372,10 @@
         "image": "image",
         "loading": "Loading",
         "loadMore": "Load More",
+        "newestFirst": "Newest First",
+        "oldestFirst": "Oldest First",
+        "sortDirection": "Sort Direction",
+        "showStarredImagesFirst": "Show Starred Images First",
         "noImageSelected": "No Image Selected",
         "noImagesInGallery": "No Images to Display",
         "setCurrentImage": "Set as Current Image",
@@ -381,6 +393,10 @@
         "viewerImage": "Viewer Image",
         "compareImage": "Compare Image",
         "openInViewer": "Open in Viewer",
+        "searchImages": "Search by Metadata",
+        "selectAllOnPage": "Select All On Page",
+        "selectAllOnBoard": "Select All On Board",
+        "showArchivedBoards": "Show Archived Boards",
         "selectForCompare": "Select for Compare",
         "selectAnImageToCompare": "Select an Image to Compare",
         "slider": "Slider",

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

@@ -23,6 +23,7 @@ import { addEnqueueRequestedCanvasListener } from 'app/store/middleware/listener
 import { addEnqueueRequestedLinear } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedLinear';
 import { addEnqueueRequestedNodes } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedNodes';
 import { addGalleryImageClickedListener } from 'app/store/middleware/listenerMiddleware/listeners/galleryImageClicked';
+import { addGalleryOffsetChangedListener } from 'app/store/middleware/listenerMiddleware/listeners/galleryOffsetChanged';
 import { addGetOpenAPISchemaListener } from 'app/store/middleware/listenerMiddleware/listeners/getOpenAPISchema';
 import { addImageAddedToBoardFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageAddedToBoard';
 import { addRequestedSingleImageDeletionListener } from 'app/store/middleware/listenerMiddleware/listeners/imageDeleted';
@@ -51,6 +52,8 @@ import { addUpscaleRequestedListener } from 'app/store/middleware/listenerMiddle
 import { addWorkflowLoadRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/workflowLoadRequested';
 import type { AppDispatch, RootState } from 'app/store/store';
 
+import { addArchivedOrDeletedBoardListener } from './listeners/addArchivedOrDeletedBoardListener';
+
 export const listenerMiddleware = createListenerMiddleware();
 
 export type AppStartListening = TypedStartListening<RootState, AppDispatch>;
@@ -77,6 +80,7 @@ addImagesUnstarredListener(startAppListening);
 
 // Gallery
 addGalleryImageClickedListener(startAppListening);
+addGalleryOffsetChangedListener(startAppListening);
 
 // User Invoked
 addEnqueueRequestedCanvasListener(startAppListening);
@@ -116,6 +120,7 @@ addControlNetAutoProcessListener(startAppListening);
 addImageAddedToBoardFulfilledListener(startAppListening);
 addImageRemovedFromBoardFulfilledListener(startAppListening);
 addBoardIdSelectedListener(startAppListening);
+addArchivedOrDeletedBoardListener(startAppListening);
 
 // Node schemas
 addGetOpenAPISchemaListener(startAppListening);

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

@@ -0,0 +1,48 @@
+import { isAnyOf } from '@reduxjs/toolkit';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';
+import {
+  autoAddBoardIdChanged,
+  boardIdSelected,
+  galleryViewChanged,
+  shouldShowArchivedBoardsChanged,
+} from 'features/gallery/store/gallerySlice';
+import { boardsApi } from 'services/api/endpoints/boards';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    matcher: isAnyOf(
+      // Updating a board may change its archived status
+      boardsApi.endpoints.updateBoard.matchFulfilled,
+      // If the selected/auto-add board was deleted from a different session, we'll only know during the list request,
+      boardsApi.endpoints.listAllBoards.matchFulfilled,
+      // If a board is deleted, we'll need to reset the auto-add board
+      imagesApi.endpoints.deleteBoard.matchFulfilled,
+      imagesApi.endpoints.deleteBoardAndImages.matchFulfilled,
+      // When we change the visibility of archived boards, we may need to reset the auto-add board
+      shouldShowArchivedBoardsChanged
+    ),
+    effect: async (action, { dispatch, getState }) => {
+      /**
+       * The auto-add board shouldn't be set to an archived board or deleted board. When we archive a board, delete
+       * a board, or change a the archived board visibility flag, we may need to reset the auto-add board.
+       */
+
+      const state = getState();
+      const queryArgs = selectListBoardsQueryArgs(state);
+      const queryResult = boardsApi.endpoints.listAllBoards.select(queryArgs)(state);
+      const autoAddBoardId = state.gallery.autoAddBoardId;
+
+      if (!queryResult.data) {
+        return;
+      }
+
+      if (!queryResult.data.find((board) => board.board_id === autoAddBoardId)) {
+        dispatch(autoAddBoardIdChanged('none'));
+        dispatch(boardIdSelected({ boardId: 'none' }));
+        dispatch(galleryViewChanged('images'));
+      }
+    },
+  });
+};

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

@@ -2,8 +2,7 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
 import { imagesApi } from 'services/api/endpoints/images';
-import type { ImageCache } from 'services/api/types';
-import { getListImagesUrl, imagesSelectors } from 'services/api/util';
+import { getListImagesUrl } from 'services/api/util';
 
 export const addFirstListImagesListener = (startAppListening: AppStartListening) => {
   startAppListening({
@@ -18,13 +17,10 @@ export const addFirstListImagesListener = (startAppListening: AppStartListening)
       cancelActiveListeners();
       unsubscribe();
 
-      // TODO: figure out how to type the predicate
-      const data = action.payload as ImageCache;
+      const data = action.payload;
 
-      if (data.ids.length > 0) {
-        // Select the first image
-        const firstImage = imagesSelectors.selectAll(data)[0];
-        dispatch(imageSelected(firstImage ?? null));
+      if (data.items.length > 0) {
+        dispatch(imageSelected(data.items[0] ?? null));
       }
     },
   });

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

@@ -1,9 +1,13 @@
 import { isAnyOf } from '@reduxjs/toolkit';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { boardIdSelected, galleryViewChanged, imageSelected } from 'features/gallery/store/gallerySlice';
-import { ASSETS_CATEGORIES, IMAGE_CATEGORIES } from 'features/gallery/store/types';
+import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
+import {
+  boardIdSelected,
+  galleryViewChanged,
+  imageSelected,
+  selectionChanged,
+} from 'features/gallery/store/gallerySlice';
 import { imagesApi } from 'services/api/endpoints/images';
-import { imagesSelectors } from 'services/api/util';
 
 export const addBoardIdSelectedListener = (startAppListening: AppStartListening) => {
   startAppListening({
@@ -14,14 +18,9 @@ export const addBoardIdSelectedListener = (startAppListening: AppStartListening)
 
       const state = getState();
 
-      const board_id = boardIdSelected.match(action) ? action.payload.boardId : state.gallery.selectedBoardId;
+      const queryArgs = selectListImagesQueryArgs(state);
 
-      const galleryView = galleryViewChanged.match(action) ? action.payload : state.gallery.galleryView;
-
-      // when a board is selected, we need to wait until the board has loaded *some* images, then select the first one
-      const categories = galleryView === 'images' ? IMAGE_CATEGORIES : ASSETS_CATEGORIES;
-
-      const queryArgs = { board_id: board_id ?? 'none', categories };
+      dispatch(selectionChanged([]));
 
       // wait until the board has some images - maybe it already has some from a previous fetch
       // must use getState() to ensure we do not have stale state
@@ -35,11 +34,12 @@ export const addBoardIdSelectedListener = (startAppListening: AppStartListening)
         const { data: boardImagesData } = imagesApi.endpoints.listImages.select(queryArgs)(getState());
 
         if (boardImagesData && boardIdSelected.match(action) && action.payload.selectedImageName) {
-          const selectedImage = imagesSelectors.selectById(boardImagesData, action.payload.selectedImageName);
+          const selectedImage = boardImagesData.items.find(
+            (item) => item.image_name === action.payload.selectedImageName
+          );
           dispatch(imageSelected(selectedImage || null));
         } else if (boardImagesData) {
-          const firstImage = imagesSelectors.selectAll(boardImagesData)[0];
-          dispatch(imageSelected(firstImage || null));
+          dispatch(imageSelected(boardImagesData.items[0] || null));
         } else {
           // board has no images - deselect
           dispatch(imageSelected(null));

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

@@ -4,7 +4,6 @@ import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelecto
 import { imageToCompareChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
 import { imagesApi } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
-import { imagesSelectors } from 'services/api/util';
 
 export const galleryImageClicked = createAction<{
   imageDTO: ImageDTO;
@@ -32,14 +31,14 @@ export const addGalleryImageClickedListener = (startAppListening: AppStartListen
       const { imageDTO, shiftKey, ctrlKey, metaKey, altKey } = action.payload;
       const state = getState();
       const queryArgs = selectListImagesQueryArgs(state);
-      const { data: listImagesData } = imagesApi.endpoints.listImages.select(queryArgs)(state);
+      const queryResult = imagesApi.endpoints.listImages.select(queryArgs)(state);
 
-      if (!listImagesData) {
+      if (!queryResult.data) {
         // Should never happen if we have clicked a gallery image
         return;
       }
 
-      const imageDTOs = imagesSelectors.selectAll(listImagesData);
+      const imageDTOs = queryResult.data.items;
       const selection = state.gallery.selection;
 
       if (altKey) {

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

@@ -0,0 +1,119 @@
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
+import { imageToCompareChanged, offsetChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addGalleryOffsetChangedListener = (startAppListening: AppStartListening) => {
+  /**
+   * When the user changes pages in the gallery, we need to wait until the next page of images is loaded, then maybe
+   * update the selection.
+   *
+   * There are a three scenarios:
+   *
+   * 1. The page is changed by clicking the pagination buttons. No changes to selection are needed.
+   *
+   * 2. The page is changed by using the arrow keys (without alt).
+   * - When going backwards, select the last image.
+   * - When going forwards, select the first image.
+   *
+   * 3. The page is changed by using the arrows keys with alt. This means the user is changing the comparison image.
+   * - When going backwards, select the last image _as the comparison image_.
+   * - When going forwards, select the first image _as the comparison image_.
+   */
+  startAppListening({
+    actionCreator: offsetChanged,
+    effect: async (action, { dispatch, getState, getOriginalState, take, cancelActiveListeners }) => {
+      // Cancel any active listeners to prevent the selection from changing without user input
+      cancelActiveListeners();
+
+      const { withHotkey } = action.payload;
+
+      if (!withHotkey) {
+        // User changed pages by clicking the pagination buttons - no changes to selection
+        return;
+      }
+
+      const originalState = getOriginalState();
+      const prevOffset = originalState.gallery.offset;
+      const offset = getState().gallery.offset;
+
+      if (offset === prevOffset) {
+        // The page didn't change - bail
+        return;
+      }
+
+      /**
+       * We need to wait until the next page of images is loaded before updating the selection, so we use the correct
+       * page of images.
+       *
+       * The simplest way to do it would be to use `take` to wait for the next fulfilled action, but RTK-Q doesn't
+       * dispatch an action on cache hits. This means the `take` will only return if the cache is empty. If the user
+       * changes to a cached page - a common situation - the `take` will never resolve.
+       *
+       * So we need to take a two-step approach. First, check if we have data in the cache for the page of images. If
+       * we have data cached, use it to update the selection. If we don't have data cached, wait for the next fulfilled
+       * action, which updates the cache, then use the cache to update the selection.
+       */
+
+      // Check if we have data in the cache for the page of images
+      const queryArgs = selectListImagesQueryArgs(getState());
+      let { data } = imagesApi.endpoints.listImages.select(queryArgs)(getState());
+
+      // No data yet - wait for the network request to complete
+      if (!data) {
+        const takeResult = await take(imagesApi.endpoints.listImages.matchFulfilled, 5000);
+        if (!takeResult) {
+          // The request didn't complete in time - bail
+          return;
+        }
+        data = takeResult[0].payload;
+      }
+
+      // We awaited a network request - state could have changed, get fresh state
+      const state = getState();
+      const { selection, imageToCompare } = state.gallery;
+      const imageDTOs = data?.items;
+
+      if (!imageDTOs) {
+        // The page didn't load - bail
+        return;
+      }
+
+      if (withHotkey === 'arrow') {
+        // User changed pages by using the arrow keys - selection changes to first or last image depending
+        if (offset < prevOffset) {
+          // We've gone backwards
+          const lastImage = imageDTOs[imageDTOs.length - 1];
+          if (!selection.some((selectedImage) => selectedImage.image_name === lastImage?.image_name)) {
+            dispatch(selectionChanged(lastImage ? [lastImage] : []));
+          }
+        } else {
+          // We've gone forwards
+          const firstImage = imageDTOs[0];
+          if (!selection.some((selectedImage) => selectedImage.image_name === firstImage?.image_name)) {
+            dispatch(selectionChanged(firstImage ? [firstImage] : []));
+          }
+        }
+        return;
+      }
+
+      if (withHotkey === 'alt+arrow') {
+        // User changed pages by using the arrow keys with alt - comparison image changes to first or last depending
+        if (offset < prevOffset) {
+          // We've gone backwards
+          const lastImage = imageDTOs[imageDTOs.length - 1];
+          if (lastImage && imageToCompare?.image_name !== lastImage.image_name) {
+            dispatch(imageToCompareChanged(lastImage));
+          }
+        } else {
+          // We've gone forwards
+          const firstImage = imageDTOs[0];
+          if (firstImage && imageToCompare?.image_name !== firstImage.image_name) {
+            dispatch(imageToCompareChanged(firstImage));
+          }
+        }
+        return;
+      }
+    },
+  });
+};

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

@@ -22,11 +22,10 @@ import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { isImageFieldInputInstance } from 'features/nodes/types/field';
 import { isInvocationNode } from 'features/nodes/types/invocation';
-import { clamp, forEach } from 'lodash-es';
+import { forEach } from 'lodash-es';
 import { api } from 'services/api';
 import { imagesApi } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
-import { imagesSelectors } from 'services/api/util';
 
 const deleteNodesImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
   state.nodes.present.nodes.forEach((node) => {
@@ -118,32 +117,7 @@ export const addRequestedSingleImageDeletionListener = (startAppListening: AppSt
       }
 
       dispatch(isModalOpenChanged(false));
-
       const state = getState();
-      const lastSelectedImage = state.gallery.selection[state.gallery.selection.length - 1]?.image_name;
-
-      if (imageDTO && imageDTO?.image_name === lastSelectedImage) {
-        const { image_name } = imageDTO;
-
-        const baseQueryArgs = selectListImagesQueryArgs(state);
-        const { data } = imagesApi.endpoints.listImages.select(baseQueryArgs)(state);
-
-        const cachedImageDTOs = data ? imagesSelectors.selectAll(data) : [];
-
-        const deletedImageIndex = cachedImageDTOs.findIndex((i) => i.image_name === image_name);
-
-        const filteredImageDTOs = cachedImageDTOs.filter((i) => i.image_name !== image_name);
-
-        const newSelectedImageIndex = clamp(deletedImageIndex, 0, filteredImageDTOs.length - 1);
-
-        const newSelectedImageDTO = filteredImageDTOs[newSelectedImageIndex];
-
-        if (newSelectedImageDTO) {
-          dispatch(imageSelected(newSelectedImageDTO));
-        } else {
-          dispatch(imageSelected(null));
-        }
-      }
 
       // We need to reset the features where the image is in use - none of these work if their image(s) don't exist
       if (imageUsage.isCanvasImage) {
@@ -168,6 +142,20 @@ export const addRequestedSingleImageDeletionListener = (startAppListening: AppSt
       if (wasImageDeleted) {
         dispatch(api.util.invalidateTags([{ type: 'Board', id: imageDTO.board_id ?? 'none' }]));
       }
+
+      const lastSelectedImage = state.gallery.selection[state.gallery.selection.length - 1]?.image_name;
+
+      if (imageDTO && imageDTO?.image_name === lastSelectedImage) {
+        const baseQueryArgs = selectListImagesQueryArgs(state);
+        const { data } = imagesApi.endpoints.listImages.select(baseQueryArgs)(state);
+
+        if (data && data.items) {
+          const newlySelectedImage = data?.items.find((img) => img.image_name !== imageDTO?.image_name);
+          dispatch(imageSelected(newlySelectedImage || null));
+        } else {
+          dispatch(imageSelected(null));
+        }
+      }
     },
   });
 
@@ -188,10 +176,8 @@ export const addRequestedSingleImageDeletionListener = (startAppListening: AppSt
         const queryArgs = selectListImagesQueryArgs(state);
         const { data } = imagesApi.endpoints.listImages.select(queryArgs)(state);
 
-        const newSelectedImageDTO = data ? imagesSelectors.selectAll(data)[0] : undefined;
-
-        if (newSelectedImageDTO) {
-          dispatch(imageSelected(newSelectedImageDTO));
+        if (data && data.items[0]) {
+          dispatch(imageSelected(data.items[0]));
         } else {
           dispatch(imageSelected(null));
         }

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

@@ -15,7 +15,12 @@ import {
 } from 'features/controlLayers/store/controlLayersSlice';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 import { isValidDrop } from 'features/dnd/util/isValidDrop';
-import { imageSelected, imageToCompareChanged, isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
+import {
+  imageSelected,
+  imageToCompareChanged,
+  isImageViewerOpenChanged,
+  selectionChanged,
+} from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
@@ -216,6 +221,7 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
             board_id: boardId,
           })
         );
+        dispatch(selectionChanged([]));
         return;
       }
 
@@ -233,6 +239,7 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
             imageDTO,
           })
         );
+        dispatch(selectionChanged([]));
         return;
       }
 
@@ -248,6 +255,7 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
             board_id: boardId,
           })
         );
+        dispatch(selectionChanged([]));
         return;
       }
 
@@ -261,6 +269,7 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
             imageDTOs,
           })
         );
+        dispatch(selectionChanged([]));
         return;
       }
     },

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

@@ -11,6 +11,7 @@ import {
   ipaLayerImageChanged,
   rgLayerIPAdapterImageChanged,
 } from 'features/controlLayers/store/controlLayersSlice';
+import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { toast } from 'features/toast/toast';
@@ -62,7 +63,8 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
           );
 
           // Attempt to get the board's name for the toast
-          const { data } = boardsApi.endpoints.listAllBoards.select()(state);
+          const queryArgs = selectListBoardsQueryArgs(state);
+          const { data } = boardsApi.endpoints.listAllBoards.select(queryArgs)(state);
 
           // Fall back to just the board id if we can't find the board for some reason
           const board = data?.find((b) => b.board_id === autoAddBoardId);

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

@@ -8,14 +8,14 @@ import {
   galleryViewChanged,
   imageSelected,
   isImageViewerOpenChanged,
+  offsetChanged,
 } from 'features/gallery/store/gallerySlice';
-import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
 import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
 import { zNodeStatus } from 'features/nodes/types/invocation';
 import { CANVAS_OUTPUT } from 'features/nodes/util/graph/constants';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
-import { imagesAdapter } from 'services/api/util';
+import { getCategories, getListImagesUrl } from 'services/api/util';
 import { socketInvocationComplete } from 'services/events/actions';
 
 // These nodes output an image, but do not actually *save* an image, so we don't want to handle the gallery logic on them
@@ -52,24 +52,6 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
         }
 
         if (!imageDTO.is_intermediate) {
-          /**
-           * Cache updates for when an image result is received
-           * - add it to the no_board/images
-           */
-
-          dispatch(
-            imagesApi.util.updateQueryData(
-              'listImages',
-              {
-                board_id: imageDTO.board_id ?? 'none',
-                categories: IMAGE_CATEGORIES,
-              },
-              (draft) => {
-                imagesAdapter.addOne(draft, imageDTO);
-              }
-            )
-          );
-
           // update the total images for the board
           dispatch(
             boardsApi.util.updateQueryData('getBoardImagesTotal', imageDTO.board_id ?? 'none', (draft) => {
@@ -78,7 +60,18 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
             })
           );
 
-          dispatch(imagesApi.util.invalidateTags([{ type: 'Board', id: imageDTO.board_id ?? 'none' }]));
+          dispatch(
+            imagesApi.util.invalidateTags([
+              { type: 'Board', id: imageDTO.board_id ?? 'none' },
+              {
+                type: 'ImageList',
+                id: getListImagesUrl({
+                  board_id: imageDTO.board_id ?? 'none',
+                  categories: getCategories(imageDTO),
+                }),
+              },
+            ])
+          );
 
           const { shouldAutoSwitch } = gallery;
 
@@ -98,6 +91,8 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
               );
             }
 
+            dispatch(offsetChanged({ offset: 0 }));
+
             if (!imageDTO.board_id && gallery.selectedBoardId !== 'none') {
               dispatch(
                 boardIdSelected({

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

@@ -1,47 +1,37 @@
-import type { SystemStyleObject } from '@invoke-ai/ui-library';
+import type { IconButtonProps, SystemStyleObject } from '@invoke-ai/ui-library';
 import { IconButton } from '@invoke-ai/ui-library';
-import type { MouseEvent, ReactElement } from 'react';
-import { memo, useMemo } from 'react';
+import type { MouseEvent } from 'react';
+import { memo } from 'react';
 
-type Props = {
+const sx: SystemStyleObject = {
+  minW: 0,
+  svg: {
+    transitionProperty: 'common',
+    transitionDuration: 'normal',
+    fill: 'base.100',
+    _hover: { fill: 'base.50' },
+    filter: 'drop-shadow(0px 0px 0.1rem var(--invoke-colors-base-800))',
+  },
+};
+
+type Props = Omit<IconButtonProps, 'aria-label' | 'onClick' | 'tooltip'> & {
   onClick: (event: MouseEvent<HTMLButtonElement>) => void;
   tooltip: string;
-  icon?: ReactElement;
-  styleOverrides?: SystemStyleObject;
 };
 
 const IAIDndImageIcon = (props: Props) => {
-  const { onClick, tooltip, icon, styleOverrides } = props;
-
-  const sx = useMemo(
-    () => ({
-      position: 'absolute',
-      top: 1,
-      insetInlineEnd: 1,
-      p: 0,
-      minW: 0,
-      svg: {
-        transitionProperty: 'common',
-        transitionDuration: 'normal',
-        fill: 'base.100',
-        _hover: { fill: 'base.50' },
-        filter: 'drop-shadow(0px 0px 0.1rem var(--invoke-colors-base-800))',
-      },
-      ...styleOverrides,
-    }),
-    [styleOverrides]
-  );
+  const { onClick, tooltip, icon, ...rest } = props;
 
   return (
     <IconButton
       onClick={onClick}
       aria-label={tooltip}
-      tooltip={tooltip}
       icon={icon}
       size="sm"
       variant="link"
       sx={sx}
       data-testid={tooltip}
+      {...rest}
     />
   );
 };

invokeai/frontend/web/src/common/util/dateComparator.ts

@@ -1,16 +0,0 @@
-/**
- * Comparator function for sorting dates in ascending order
- */
-export const dateComparator = (a: string, b: string) => {
-  const dateA = new Date(a);
-  const dateB = new Date(b);
-
-  // sort in ascending order
-  if (dateA > dateB) {
-    return 1;
-  }
-  if (dateA < dateB) {
-    return -1;
-  }
-  return 0;
-};

invokeai/frontend/web/src/features/changeBoardModal/components/ChangeBoardModal.tsx

@@ -7,6 +7,7 @@ import {
   isModalOpenChanged,
   selectChangeBoardModalSlice,
 } from 'features/changeBoardModal/store/slice';
+import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';
 import { memo, useCallback, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 import { useListAllBoardsQuery } from 'services/api/endpoints/boards';
@@ -20,7 +21,8 @@ const selectImagesToChange = createMemoizedSelector(
 const ChangeBoardModal = () => {
   const dispatch = useAppDispatch();
   const [selectedBoard, setSelectedBoard] = useState<string | null>();
-  const { data: boards, isFetching } = useListAllBoardsQuery();
+  const queryArgs = useAppSelector(selectListBoardsQueryArgs);
+  const { data: boards, isFetching } = useListAllBoardsQuery(queryArgs);
   const isModalOpen = useAppSelector((s) => s.changeBoardModal.isModalOpen);
   const imagesToChange = useAppSelector(selectImagesToChange);
   const [addImagesToBoard] = useAddImagesToBoardMutation();

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

@@ -1,4 +1,3 @@
-import type { SystemStyleObject } from '@invoke-ai/ui-library';
 import { Box, Flex, Spinner } from '@invoke-ai/ui-library';
 import { skipToken } from '@reduxjs/toolkit/query';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
@@ -185,25 +184,25 @@ const ControlAdapterImagePreview = ({ isSmall, id }: Props) => {
         />
       </Box>
 
-      <>
-        <IAIDndImageIcon
-          onClick={handleResetControlImage}
-          icon={controlImage ? <PiArrowCounterClockwiseBold size={16} /> : undefined}
-          tooltip={t('controlnet.resetControlImage')}
-        />
-        <IAIDndImageIcon
-          onClick={handleSaveControlImage}
-          icon={controlImage ? <PiFloppyDiskBold size={16} /> : undefined}
-          tooltip={t('controlnet.saveControlImage')}
-          styleOverrides={saveControlImageStyleOverrides}
-        />
-        <IAIDndImageIcon
-          onClick={handleSetControlImageToDimensions}
-          icon={controlImage ? <PiRulerBold size={16} /> : undefined}
-          tooltip={t('controlnet.setControlImageDimensions')}
-          styleOverrides={setControlImageDimensionsStyleOverrides}
-        />
-      </>
+      {controlImage && (
+        <Flex position="absolute" flexDir="column" top={1} insetInlineEnd={1} gap={1}>
+          <IAIDndImageIcon
+            onClick={handleResetControlImage}
+            icon={<PiArrowCounterClockwiseBold size={16} />}
+            tooltip={t('controlnet.resetControlImage')}
+          />
+          <IAIDndImageIcon
+            onClick={handleSaveControlImage}
+            icon={<PiFloppyDiskBold size={16} />}
+            tooltip={t('controlnet.saveControlImage')}
+          />
+          <IAIDndImageIcon
+            onClick={handleSetControlImageToDimensions}
+            icon={<PiRulerBold size={16} />}
+            tooltip={t('controlnet.setControlImageDimensions')}
+          />
+        </Flex>
+      )}
 
       {pendingControlImages.includes(id) && (
         <Flex
@@ -226,6 +225,3 @@ const ControlAdapterImagePreview = ({ isSmall, id }: Props) => {
 };
 
 export default memo(ControlAdapterImagePreview);
-
-const saveControlImageStyleOverrides: SystemStyleObject = { mt: 6 };
-const setControlImageDimensionsStyleOverrides: SystemStyleObject = { mt: 12 };

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/ControlAdapterImagePreview.tsx

@@ -1,4 +1,3 @@
-import type { SystemStyleObject } from '@invoke-ai/ui-library';
 import { Box, Flex, Spinner, useShiftModifier } from '@invoke-ai/ui-library';
 import { skipToken } from '@reduxjs/toolkit/query';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
@@ -160,7 +159,7 @@ export const ControlAdapterImagePreview = memo(
         onMouseEnter={handleMouseEnter}
         onMouseLeave={handleMouseLeave}
         position="relative"
-        w="full"
+        w={36}
         h={36}
         alignItems="center"
         justifyContent="center"
@@ -193,25 +192,27 @@ export const ControlAdapterImagePreview = memo(
           />
         </Box>
 
-        <>
-          <IAIDndImageIcon
-            onClick={handleResetControlImage}
-            icon={controlImage ? <PiArrowCounterClockwiseBold size={16} /> : undefined}
-            tooltip={t('controlnet.resetControlImage')}
-          />
-          <IAIDndImageIcon
-            onClick={handleSaveControlImage}
-            icon={controlImage ? <PiFloppyDiskBold size={16} /> : undefined}
-            tooltip={t('controlnet.saveControlImage')}
-            styleOverrides={saveControlImageStyleOverrides}
-          />
-          <IAIDndImageIcon
-            onClick={handleSetControlImageToDimensions}
-            icon={controlImage ? <PiRulerBold size={16} /> : undefined}
-            tooltip={shift ? t('controlnet.setControlImageDimensionsForce') : t('controlnet.setControlImageDimensions')}
-            styleOverrides={setControlImageDimensionsStyleOverrides}
-          />
-        </>
+        {controlImage && (
+          <Flex position="absolute" flexDir="column" top={1} insetInlineEnd={1} gap={1}>
+            <IAIDndImageIcon
+              onClick={handleResetControlImage}
+              icon={<PiArrowCounterClockwiseBold size={16} />}
+              tooltip={t('controlnet.resetControlImage')}
+            />
+            <IAIDndImageIcon
+              onClick={handleSaveControlImage}
+              icon={<PiFloppyDiskBold size={16} />}
+              tooltip={t('controlnet.saveControlImage')}
+            />
+            <IAIDndImageIcon
+              onClick={handleSetControlImageToDimensions}
+              icon={<PiRulerBold size={16} />}
+              tooltip={
+                shift ? t('controlnet.setControlImageDimensionsForce') : t('controlnet.setControlImageDimensions')
+              }
+            />
+          </Flex>
+        )}
 
         {controlAdapter.processorPendingBatchId !== null && (
           <Flex
@@ -235,6 +236,3 @@ export const ControlAdapterImagePreview = memo(
 );
 
 ControlAdapterImagePreview.displayName = 'ControlAdapterImagePreview';
-
-const saveControlImageStyleOverrides: SystemStyleObject = { mt: 6 };
-const setControlImageDimensionsStyleOverrides: SystemStyleObject = { mt: 12 };

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/IPAdapterImagePreview.tsx

@@ -1,4 +1,3 @@
-import type { SystemStyleObject } from '@invoke-ai/ui-library';
 import { Flex, useShiftModifier } from '@invoke-ai/ui-library';
 import { skipToken } from '@reduxjs/toolkit/query';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
@@ -82,7 +81,7 @@ export const IPAdapterImagePreview = memo(
     }, [handleResetControlImage, isConnected, isErrorControlImage]);
 
     return (
-      <Flex position="relative" w="full" h={36} alignItems="center" justifyContent="center">
+      <Flex position="relative" w={36} h={36} alignItems="center">
         <IAIDndImage
           draggableData={draggableData}
           droppableData={droppableData}
@@ -90,24 +89,25 @@ export const IPAdapterImagePreview = memo(
           postUploadAction={postUploadAction}
         />
 
-        <>
-          <IAIDndImageIcon
-            onClick={handleResetControlImage}
-            icon={controlImage ? <PiArrowCounterClockwiseBold size={16} /> : undefined}
-            tooltip={t('controlnet.resetControlImage')}
-          />
-          <IAIDndImageIcon
-            onClick={handleSetControlImageToDimensions}
-            icon={controlImage ? <PiRulerBold size={16} /> : undefined}
-            tooltip={shift ? t('controlnet.setControlImageDimensionsForce') : t('controlnet.setControlImageDimensions')}
-            styleOverrides={setControlImageDimensionsStyleOverrides}
-          />
-        </>
+        {controlImage && (
+          <Flex position="absolute" flexDir="column" top={1} insetInlineEnd={1} gap={1}>
+            <IAIDndImageIcon
+              onClick={handleResetControlImage}
+              icon={<PiArrowCounterClockwiseBold size={16} />}
+              tooltip={t('controlnet.resetControlImage')}
+            />
+            <IAIDndImageIcon
+              onClick={handleSetControlImageToDimensions}
+              icon={<PiRulerBold size={16} />}
+              tooltip={
+                shift ? t('controlnet.setControlImageDimensionsForce') : t('controlnet.setControlImageDimensions')
+              }
+            />
+          </Flex>
+        )}
       </Flex>
     );
   }
 );
 
 IPAdapterImagePreview.displayName = 'IPAdapterImagePreview';
-
-const setControlImageDimensionsStyleOverrides: SystemStyleObject = { mt: 6 };

invokeai/frontend/web/src/features/controlLayers/components/IILayer/InitialImagePreview.tsx

@@ -1,4 +1,3 @@
-import type { SystemStyleObject } from '@invoke-ai/ui-library';
 import { Flex, useShiftModifier } from '@invoke-ai/ui-library';
 import { skipToken } from '@reduxjs/toolkit/query';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
@@ -79,31 +78,34 @@ export const InitialImagePreview = memo(({ image, onChangeImage, droppableData,
   }, [onReset, isConnected, isErrorControlImage]);
 
   return (
-    <Flex position="relative" w="full" h={36} alignItems="center" justifyContent="center">
-      <IAIDndImage
-        draggableData={draggableData}
-        droppableData={droppableData}
-        imageDTO={imageDTO}
-        postUploadAction={postUploadAction}
-      />
+    <Flex w="full" alignItems="center" justifyContent="center">
+      <Flex position="relative" w={36} h={36} alignItems="center" justifyContent="center">
+        <IAIDndImage
+          draggableData={draggableData}
+          droppableData={droppableData}
+          imageDTO={imageDTO}
+          postUploadAction={postUploadAction}
+        />
 
-      <>
-        <IAIDndImageIcon
-          onClick={onReset}
-          icon={imageDTO ? <PiArrowCounterClockwiseBold size={16} /> : undefined}
-          tooltip={t('controlnet.resetControlImage')}
-        />
-        <IAIDndImageIcon
-          onClick={onUseSize}
-          icon={imageDTO ? <PiRulerBold size={16} /> : undefined}
-          tooltip={shift ? t('controlnet.setControlImageDimensionsForce') : t('controlnet.setControlImageDimensions')}
-          styleOverrides={useSizeStyleOverrides}
-        />
-      </>
+        {imageDTO && (
+          <Flex position="absolute" flexDir="column" top={1} insetInlineEnd={1} gap={1}>
+            <IAIDndImageIcon
+              onClick={onReset}
+              icon={<PiArrowCounterClockwiseBold size={16} />}
+              tooltip={t('controlnet.resetControlImage')}
+            />
+            <IAIDndImageIcon
+              onClick={onUseSize}
+              icon={<PiRulerBold size={16} />}
+              tooltip={
+                shift ? t('controlnet.setControlImageDimensionsForce') : t('controlnet.setControlImageDimensions')
+              }
+            />
+          </Flex>
+        )}
+      </Flex>
     </Flex>
   );
 });
 
 InitialImagePreview.displayName = 'InitialImagePreview';
-
-const useSizeStyleOverrides: SystemStyleObject = { mt: 6 };

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

@@ -11,25 +11,28 @@ const BoardAutoAddSelect = () => {
   const { t } = useTranslation();
   const autoAddBoardId = useAppSelector((s) => s.gallery.autoAddBoardId);
   const autoAssignBoardOnClick = useAppSelector((s) => s.gallery.autoAssignBoardOnClick);
-  const { options, hasBoards } = useListAllBoardsQuery(undefined, {
-    selectFromResult: ({ data }) => {
-      const options: ComboboxOption[] = [
-        {
-          label: t('controlnet.none'),
-          value: 'none',
-        },
-      ].concat(
-        (data ?? []).map(({ board_id, board_name }) => ({
-          label: board_name,
-          value: board_id,
-        }))
-      );
-      return {
-        options,
-        hasBoards: options.length > 1,
-      };
-    },
-  });
+  const { options, hasBoards } = useListAllBoardsQuery(
+    {},
+    {
+      selectFromResult: ({ data }) => {
+        const options: ComboboxOption[] = [
+          {
+            label: t('controlnet.none'),
+            value: 'none',
+          },
+        ].concat(
+          (data ?? []).map(({ board_id, board_name }) => ({
+            label: board_name,
+            value: board_id,
+          }))
+        );
+        return {
+          options,
+          hasBoards: options.length > 1,
+        };
+      },
+    }
+  );
 
   const onChange = useCallback<ComboboxOnChange>(
     (v) => {

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

@@ -3,11 +3,12 @@ import { ContextMenu, MenuGroup, MenuItem, MenuList } from '@invoke-ai/ui-librar
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { autoAddBoardIdChanged, selectGallerySlice } from 'features/gallery/store/gallerySlice';
-import type { BoardId } from 'features/gallery/store/types';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { toast } from 'features/toast/toast';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiDownloadBold, PiPlusBold } from 'react-icons/pi';
+import { PiArchiveBold, PiArchiveFill, PiDownloadBold, PiPlusBold } from 'react-icons/pi';
+import { useUpdateBoardMutation } from 'services/api/endpoints/boards';
 import { useBulkDownloadImagesMutation } from 'services/api/endpoints/images';
 import { useBoardName } from 'services/api/hooks/useBoardName';
 import type { BoardDTO } from 'services/api/types';
@@ -15,52 +16,85 @@ import type { BoardDTO } from 'services/api/types';
 import GalleryBoardContextMenuItems from './GalleryBoardContextMenuItems';
 
 type Props = {
-  board?: BoardDTO;
-  board_id: BoardId;
+  board: BoardDTO;
   children: ContextMenuProps<HTMLDivElement>['children'];
-  setBoardToDelete?: (board?: BoardDTO) => void;
+  setBoardToDelete: (board?: BoardDTO) => void;
 };
 
-const BoardContextMenu = ({ board, board_id, setBoardToDelete, children }: Props) => {
+const BoardContextMenu = ({ board, setBoardToDelete, children }: Props) => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const autoAssignBoardOnClick = useAppSelector((s) => s.gallery.autoAssignBoardOnClick);
   const selectIsSelectedForAutoAdd = useMemo(
-    () => createSelector(selectGallerySlice, (gallery) => board && board.board_id === gallery.autoAddBoardId),
-    [board]
+    () => createSelector(selectGallerySlice, (gallery) => board.board_id === gallery.autoAddBoardId),
+    [board.board_id]
   );
 
+  const [updateBoard] = useUpdateBoardMutation();
+
   const isSelectedForAutoAdd = useAppSelector(selectIsSelectedForAutoAdd);
-  const boardName = useBoardName(board_id);
+  const boardName = useBoardName(board.board_id);
   const isBulkDownloadEnabled = useFeatureStatus('bulkDownload');
 
   const [bulkDownload] = useBulkDownloadImagesMutation();
 
   const handleSetAutoAdd = useCallback(() => {
-    dispatch(autoAddBoardIdChanged(board_id));
-  }, [board_id, dispatch]);
+    dispatch(autoAddBoardIdChanged(board.board_id));
+  }, [board.board_id, dispatch]);
 
   const handleBulkDownload = useCallback(() => {
-    bulkDownload({ image_names: [], board_id: board_id });
-  }, [board_id, bulkDownload]);
+    bulkDownload({ image_names: [], board_id: board.board_id });
+  }, [board.board_id, bulkDownload]);
+
+  const handleArchive = useCallback(async () => {
+    try {
+      await updateBoard({
+        board_id: board.board_id,
+        changes: { archived: true },
+      }).unwrap();
+    } catch (error) {
+      toast({
+        status: 'error',
+        title: 'Unable to archive board',
+      });
+    }
+  }, [board.board_id, updateBoard]);
+
+  const handleUnarchive = useCallback(() => {
+    updateBoard({
+      board_id: board.board_id,
+      changes: { archived: false },
+    });
+  }, [board.board_id, updateBoard]);
 
   const renderMenuFunc = useCallback(
     () => (
       <MenuList visibility="visible">
         <MenuGroup title={boardName}>
-          <MenuItem
-            icon={<PiPlusBold />}
-            isDisabled={isSelectedForAutoAdd || autoAssignBoardOnClick}
-            onClick={handleSetAutoAdd}
-          >
-            {t('boards.menuItemAutoAdd')}
-          </MenuItem>
+          {!autoAssignBoardOnClick && (
+            <MenuItem icon={<PiPlusBold />} isDisabled={isSelectedForAutoAdd} onClick={handleSetAutoAdd}>
+              {isSelectedForAutoAdd ? t('boards.selectedForAutoAdd') : t('boards.menuItemAutoAdd')}
+            </MenuItem>
+          )}
           {isBulkDownloadEnabled && (
             <MenuItem icon={<PiDownloadBold />} onClickCapture={handleBulkDownload}>
               {t('boards.downloadBoard')}
             </MenuItem>
           )}
-          {board && <GalleryBoardContextMenuItems board={board} setBoardToDelete={setBoardToDelete} />}
+
+          {board.archived && (
+            <MenuItem icon={<PiArchiveBold />} onClick={handleUnarchive}>
+              {t('boards.unarchiveBoard')}
+            </MenuItem>
+          )}
+
+          {!board.archived && (
+            <MenuItem icon={<PiArchiveFill />} onClick={handleArchive}>
+              {t('boards.archiveBoard')}
+            </MenuItem>
+          )}
+
+          <GalleryBoardContextMenuItems board={board} setBoardToDelete={setBoardToDelete} />
         </MenuGroup>
       </MenuList>
     ),
@@ -74,6 +108,8 @@ const BoardContextMenu = ({ board, board_id, setBoardToDelete, children }: Props
       isSelectedForAutoAdd,
       setBoardToDelete,
       t,
+      handleArchive,
+      handleUnarchive,
     ]
   );
 

invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BoardTotalsTooltip.tsx

@@ -0,0 +1,22 @@
+import { useTranslation } from 'react-i18next';
+import { useGetBoardAssetsTotalQuery, useGetBoardImagesTotalQuery } from 'services/api/endpoints/boards';
+
+type Props = {
+  board_id: string;
+  isArchived: boolean;
+};
+
+export const BoardTotalsTooltip = ({ board_id, isArchived }: Props) => {
+  const { t } = useTranslation();
+  const { imagesTotal } = useGetBoardImagesTotalQuery(board_id, {
+    selectFromResult: ({ data }) => {
+      return { imagesTotal: data?.total ?? 0 };
+    },
+  });
+  const { assetsTotal } = useGetBoardAssetsTotalQuery(board_id, {
+    selectFromResult: ({ data }) => {
+      return { assetsTotal: data?.total ?? 0 };
+    },
+  });
+  return `${t('boards.imagesWithCount', { count: imagesTotal })}, ${t('boards.assetsWithCount', { count: assetsTotal })}${isArchived ? ` (${t('boards.archived')})` : ''}`;
+};

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

@@ -2,6 +2,7 @@ import { Collapse, Flex, Grid, GridItem } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
 import { overlayScrollbarsParams } from 'common/components/OverlayScrollbars/constants';
 import DeleteBoardModal from 'features/gallery/components/Boards/DeleteBoardModal';
+import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';
 import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';
 import type { CSSProperties } from 'react';
 import { memo, useState } from 'react';
@@ -26,7 +27,8 @@ const BoardsList = (props: Props) => {
   const { isOpen } = props;
   const selectedBoardId = useAppSelector((s) => s.gallery.selectedBoardId);
   const boardSearchText = useAppSelector((s) => s.gallery.boardSearchText);
-  const { data: boards } = useListAllBoardsQuery();
+  const queryArgs = useAppSelector(selectListBoardsQueryArgs);
+  const { data: boards } = useListAllBoardsQuery(queryArgs);
   const filteredBoards = boardSearchText
     ? boards?.filter((board) => board.board_name.toLowerCase().includes(boardSearchText.toLowerCase()))
     : boards;

invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/GalleryBoard.tsx

@@ -8,15 +8,12 @@ import SelectionOverlay from 'common/components/SelectionOverlay';
 import type { AddToBoardDropData } from 'features/dnd/types';
 import AutoAddIcon from 'features/gallery/components/Boards/AutoAddIcon';
 import BoardContextMenu from 'features/gallery/components/Boards/BoardContextMenu';
+import { BoardTotalsTooltip } from 'features/gallery/components/Boards/BoardsList/BoardTotalsTooltip';
 import { autoAddBoardIdChanged, boardIdSelected, selectGallerySlice } from 'features/gallery/store/gallerySlice';
 import { memo, useCallback, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiImagesSquare } from 'react-icons/pi';
-import {
-  useGetBoardAssetsTotalQuery,
-  useGetBoardImagesTotalQuery,
-  useUpdateBoardMutation,
-} from 'services/api/endpoints/boards';
+import { PiArchiveBold, PiImagesSquare } from 'react-icons/pi';
+import { useUpdateBoardMutation } from 'services/api/endpoints/boards';
 import { useGetImageDTOQuery } from 'services/api/endpoints/images';
 import type { BoardDTO } from 'services/api/types';
 
@@ -28,6 +25,14 @@ const editableInputStyles: SystemStyleObject = {
   },
 };
 
+const ArchivedIcon = () => {
+  return (
+    <Box position="absolute" top={1} insetInlineEnd={2} p={0} minW={0}>
+      <Icon as={PiArchiveBold} fill="base.300" filter="drop-shadow(0px 0px 0.1rem var(--invoke-colors-base-800))" />
+    </Box>
+  );
+};
+
 interface GalleryBoardProps {
   board: BoardDTO;
   isSelected: boolean;
@@ -36,6 +41,7 @@ interface GalleryBoardProps {
 
 const GalleryBoard = ({ board, isSelected, setBoardToDelete }: GalleryBoardProps) => {
   const dispatch = useAppDispatch();
+  const { t } = useTranslation();
   const autoAssignBoardOnClick = useAppSelector((s) => s.gallery.autoAssignBoardOnClick);
   const selectIsSelectedForAutoAdd = useMemo(
     () => createSelector(selectGallerySlice, (gallery) => board.board_id === gallery.autoAddBoardId),
@@ -51,17 +57,6 @@ const GalleryBoard = ({ board, isSelected, setBoardToDelete }: GalleryBoardProps
     setIsHovered(false);
   }, []);
 
-  const { data: imagesTotal } = useGetBoardImagesTotalQuery(board.board_id);
-  const { data: assetsTotal } = useGetBoardAssetsTotalQuery(board.board_id);
-  const tooltip = useMemo(() => {
-    if (imagesTotal?.total === undefined || assetsTotal?.total === undefined) {
-      return undefined;
-    }
-    return `${imagesTotal.total} image${imagesTotal.total === 1 ? '' : 's'}, ${
-      assetsTotal.total
-    } asset${assetsTotal.total === 1 ? '' : 's'}`;
-  }, [assetsTotal, imagesTotal]);
-
   const { currentData: coverImage } = useGetImageDTOQuery(board.cover_image_name ?? skipToken);
 
   const { board_name, board_id } = board;
@@ -117,7 +112,7 @@ const GalleryBoard = ({ board, isSelected, setBoardToDelete }: GalleryBoardProps
   const handleChange = useCallback((newBoardName: string) => {
     setLocalBoardName(newBoardName);
   }, []);
-  const { t } = useTranslation();
+
   return (
     <Box w="full" h="full" userSelect="none">
       <Flex
@@ -130,9 +125,12 @@ const GalleryBoard = ({ board, isSelected, setBoardToDelete }: GalleryBoardProps
         w="full"
         h="full"
       >
-        <BoardContextMenu board={board} board_id={board_id} setBoardToDelete={setBoardToDelete}>
+        <BoardContextMenu board={board} setBoardToDelete={setBoardToDelete}>
           {(ref) => (
-            <Tooltip label={tooltip} openDelay={1000}>
+            <Tooltip
+              label={<BoardTotalsTooltip board_id={board.board_id} isArchived={Boolean(board.archived)} />}
+              openDelay={1000}
+            >
               <Flex
                 ref={ref}
                 onClick={handleSelectBoard}
@@ -145,6 +143,7 @@ const GalleryBoard = ({ board, isSelected, setBoardToDelete }: GalleryBoardProps
                 cursor="pointer"
                 bg="base.800"
               >
+                {board.archived && <ArchivedIcon />}
                 {coverImage?.thumbnail_url ? (
                   <Image
                     src={coverImage?.thumbnail_url}

invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/NoBoardBoard.tsx

@@ -4,12 +4,12 @@ import IAIDroppable from 'common/components/IAIDroppable';
 import SelectionOverlay from 'common/components/SelectionOverlay';
 import type { RemoveFromBoardDropData } from 'features/dnd/types';
 import AutoAddIcon from 'features/gallery/components/Boards/AutoAddIcon';
-import BoardContextMenu from 'features/gallery/components/Boards/BoardContextMenu';
+import { BoardTotalsTooltip } from 'features/gallery/components/Boards/BoardsList/BoardTotalsTooltip';
+import NoBoardBoardContextMenu from 'features/gallery/components/Boards/NoBoardBoardContextMenu';
 import { autoAddBoardIdChanged, boardIdSelected } from 'features/gallery/store/gallerySlice';
 import InvokeLogoSVG from 'public/assets/images/invoke-symbol-wht-lrg.svg';
 import { memo, useCallback, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
-import { useGetBoardAssetsTotalQuery, useGetBoardImagesTotalQuery } from 'services/api/endpoints/boards';
 import { useBoardName } from 'services/api/hooks/useBoardName';
 
 interface Props {
@@ -29,17 +29,6 @@ const NoBoardBoard = memo(({ isSelected }: Props) => {
   }, [dispatch, autoAssignBoardOnClick]);
   const [isHovered, setIsHovered] = useState(false);
 
-  const { data: imagesTotal } = useGetBoardImagesTotalQuery('none');
-  const { data: assetsTotal } = useGetBoardAssetsTotalQuery('none');
-  const tooltip = useMemo(() => {
-    if (imagesTotal?.total === undefined || assetsTotal?.total === undefined) {
-      return undefined;
-    }
-    return `${imagesTotal.total} image${imagesTotal.total === 1 ? '' : 's'}, ${
-      assetsTotal.total
-    } asset${assetsTotal.total === 1 ? '' : 's'}`;
-  }, [assetsTotal, imagesTotal]);
-
   const handleMouseOver = useCallback(() => {
     setIsHovered(true);
   }, []);
@@ -69,9 +58,9 @@ const NoBoardBoard = memo(({ isSelected }: Props) => {
         w="full"
         h="full"
       >
-        <BoardContextMenu board_id="none">
+        <NoBoardBoardContextMenu>
           {(ref) => (
-            <Tooltip label={tooltip} openDelay={1000}>
+            <Tooltip label={<BoardTotalsTooltip board_id="none" isArchived={false} />} openDelay={1000}>
               <Flex
                 ref={ref}
                 onClick={handleSelectBoard}
@@ -122,7 +111,7 @@ const NoBoardBoard = memo(({ isSelected }: Props) => {
               </Flex>
             </Tooltip>
           )}
-        </BoardContextMenu>
+        </NoBoardBoardContextMenu>
       </Flex>
     </Box>
   );

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

@@ -0,0 +1,55 @@
+import type { ContextMenuProps } from '@invoke-ai/ui-library';
+import { ContextMenu, MenuGroup, MenuItem, MenuList } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { autoAddBoardIdChanged } from 'features/gallery/store/gallerySlice';
+import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiDownloadBold, PiPlusBold } from 'react-icons/pi';
+import { useBulkDownloadImagesMutation } from 'services/api/endpoints/images';
+
+type Props = {
+  children: ContextMenuProps<HTMLDivElement>['children'];
+};
+
+const NoBoardBoardContextMenu = ({ children }: Props) => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const autoAssignBoardOnClick = useAppSelector((s) => s.gallery.autoAssignBoardOnClick);
+  const isSelectedForAutoAdd = useAppSelector((s) => s.gallery.autoAddBoardId === 'none');
+  const isBulkDownloadEnabled = useFeatureStatus('bulkDownload');
+
+  const [bulkDownload] = useBulkDownloadImagesMutation();
+
+  const handleSetAutoAdd = useCallback(() => {
+    dispatch(autoAddBoardIdChanged('none'));
+  }, [dispatch]);
+
+  const handleBulkDownload = useCallback(() => {
+    bulkDownload({ image_names: [], board_id: 'none' });
+  }, [bulkDownload]);
+
+  const renderMenuFunc = useCallback(
+    () => (
+      <MenuList visibility="visible">
+        <MenuGroup title={t('boards.uncategorized')}>
+          {!autoAssignBoardOnClick && (
+            <MenuItem icon={<PiPlusBold />} isDisabled={isSelectedForAutoAdd} onClick={handleSetAutoAdd}>
+              {isSelectedForAutoAdd ? t('boards.selectedForAutoAdd') : t('boards.menuItemAutoAdd')}
+            </MenuItem>
+          )}
+          {isBulkDownloadEnabled && (
+            <MenuItem icon={<PiDownloadBold />} onClickCapture={handleBulkDownload}>
+              {t('boards.downloadBoard')}
+            </MenuItem>
+          )}
+        </MenuGroup>
+      </MenuList>
+    ),
+    [autoAssignBoardOnClick, handleBulkDownload, handleSetAutoAdd, isBulkDownloadEnabled, isSelectedForAutoAdd, t]
+  );
+
+  return <ContextMenu renderMenu={renderMenuFunc}>{children}</ContextMenu>;
+};
+
+export default memo(NoBoardBoardContextMenu);

invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover.tsx

@@ -1,111 +0,0 @@
-import type { FormLabelProps } from '@invoke-ai/ui-library';
-import {
-  Checkbox,
-  CompositeSlider,
-  Flex,
-  FormControl,
-  FormControlGroup,
-  FormLabel,
-  IconButton,
-  Popover,
-  PopoverBody,
-  PopoverContent,
-  PopoverTrigger,
-  Switch,
-} from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import {
-  alwaysShowImageSizeBadgeChanged,
-  autoAssignBoardOnClickChanged,
-  setGalleryImageMinimumWidth,
-  shouldAutoSwitchChanged,
-} from 'features/gallery/store/gallerySlice';
-import type { ChangeEvent } from 'react';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { RiSettings4Fill } from 'react-icons/ri';
-
-import BoardAutoAddSelect from './Boards/BoardAutoAddSelect';
-
-const formLabelProps: FormLabelProps = {
-  flexGrow: 1,
-};
-
-const GallerySettingsPopover = () => {
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-  const galleryImageMinimumWidth = useAppSelector((s) => s.gallery.galleryImageMinimumWidth);
-  const shouldAutoSwitch = useAppSelector((s) => s.gallery.shouldAutoSwitch);
-  const autoAssignBoardOnClick = useAppSelector((s) => s.gallery.autoAssignBoardOnClick);
-  const alwaysShowImageSizeBadge = useAppSelector((s) => s.gallery.alwaysShowImageSizeBadge);
-
-  const handleChangeGalleryImageMinimumWidth = useCallback(
-    (v: number) => {
-      dispatch(setGalleryImageMinimumWidth(v));
-    },
-    [dispatch]
-  );
-
-  const handleChangeAutoSwitch = useCallback(
-    (e: ChangeEvent<HTMLInputElement>) => {
-      dispatch(shouldAutoSwitchChanged(e.target.checked));
-    },
-    [dispatch]
-  );
-
-  const handleChangeAutoAssignBoardOnClick = useCallback(
-    (e: ChangeEvent<HTMLInputElement>) => dispatch(autoAssignBoardOnClickChanged(e.target.checked)),
-    [dispatch]
-  );
-
-  const handleChangeAlwaysShowImageSizeBadgeChanged = useCallback(
-    (e: ChangeEvent<HTMLInputElement>) => dispatch(alwaysShowImageSizeBadgeChanged(e.target.checked)),
-    [dispatch]
-  );
-
-  return (
-    <Popover isLazy>
-      <PopoverTrigger>
-        <IconButton
-          tooltip={t('gallery.gallerySettings')}
-          aria-label={t('gallery.gallerySettings')}
-          size="sm"
-          icon={<RiSettings4Fill />}
-        />
-      </PopoverTrigger>
-      <PopoverContent>
-        <PopoverBody>
-          <Flex direction="column" gap={2}>
-            <FormControl>
-              <FormLabel>{t('gallery.galleryImageSize')}</FormLabel>
-              <CompositeSlider
-                value={galleryImageMinimumWidth}
-                onChange={handleChangeGalleryImageMinimumWidth}
-                min={45}
-                max={256}
-                defaultValue={90}
-              />
-            </FormControl>
-            <FormControlGroup formLabelProps={formLabelProps}>
-              <FormControl>
-                <FormLabel>{t('gallery.autoSwitchNewImages')}</FormLabel>
-                <Switch isChecked={shouldAutoSwitch} onChange={handleChangeAutoSwitch} />
-              </FormControl>
-              <FormControl>
-                <FormLabel>{t('gallery.autoAssignBoardOnClick')}</FormLabel>
-                <Checkbox isChecked={autoAssignBoardOnClick} onChange={handleChangeAutoAssignBoardOnClick} />
-              </FormControl>
-              <FormControl>
-                <FormLabel>{t('gallery.alwaysShowImageSizeBadge')}</FormLabel>
-                <Checkbox isChecked={alwaysShowImageSizeBadge} onChange={handleChangeAlwaysShowImageSizeBadgeChanged} />
-              </FormControl>
-            </FormControlGroup>
-            <BoardAutoAddSelect />
-          </Flex>
-        </PopoverBody>
-      </PopoverContent>
-    </Popover>
-  );
-};
-
-export default memo(GallerySettingsPopover);

invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover/AlwaysShowImageSizeCheckbox.tsx

@@ -0,0 +1,26 @@
+import { Checkbox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { alwaysShowImageSizeBadgeChanged } from 'features/gallery/store/gallerySlice';
+import type { ChangeEvent } from 'react';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const GallerySettingsPopover = () => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const alwaysShowImageSizeBadge = useAppSelector((s) => s.gallery.alwaysShowImageSizeBadge);
+
+  const onChange = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => dispatch(alwaysShowImageSizeBadgeChanged(e.target.checked)),
+    [dispatch]
+  );
+
+  return (
+    <FormControl>
+      <FormLabel flexGrow={1}>{t('gallery.alwaysShowImageSizeBadge')}</FormLabel>
+      <Checkbox isChecked={alwaysShowImageSizeBadge} onChange={onChange} />
+    </FormControl>
+  );
+};
+
+export default memo(GallerySettingsPopover);

invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover/AutoAssignBoardCheckbox.tsx

@@ -0,0 +1,26 @@
+import { Checkbox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { autoAssignBoardOnClickChanged } from 'features/gallery/store/gallerySlice';
+import type { ChangeEvent } from 'react';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const GallerySettingsPopover = () => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const autoAssignBoardOnClick = useAppSelector((s) => s.gallery.autoAssignBoardOnClick);
+
+  const onChange = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => dispatch(autoAssignBoardOnClickChanged(e.target.checked)),
+    [dispatch]
+  );
+
+  return (
+    <FormControl>
+      <FormLabel flexGrow={1}>{t('gallery.autoAssignBoardOnClick')}</FormLabel>
+      <Checkbox isChecked={autoAssignBoardOnClick} onChange={onChange} />
+    </FormControl>
+  );
+};
+
+export default memo(GallerySettingsPopover);

invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover/AutoSwitchCheckbox.tsx

@@ -0,0 +1,28 @@
+import { Checkbox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { shouldAutoSwitchChanged } from 'features/gallery/store/gallerySlice';
+import type { ChangeEvent } from 'react';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const GallerySettingsPopover = () => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const shouldAutoSwitch = useAppSelector((s) => s.gallery.shouldAutoSwitch);
+
+  const onChange = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      dispatch(shouldAutoSwitchChanged(e.target.checked));
+    },
+    [dispatch]
+  );
+
+  return (
+    <FormControl>
+      <FormLabel flexGrow={1}>{t('gallery.autoSwitchNewImages')}</FormLabel>
+      <Checkbox isChecked={shouldAutoSwitch} onChange={onChange} />
+    </FormControl>
+  );
+};
+
+export default memo(GallerySettingsPopover);

invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover/GallerySettingsPopover.tsx

@@ -0,0 +1,41 @@
+import { Divider, Flex, IconButton, Popover, PopoverBody, PopoverContent, PopoverTrigger } from '@invoke-ai/ui-library';
+import BoardAutoAddSelect from 'features/gallery/components/Boards/BoardAutoAddSelect';
+import AlwaysShowImageSizeCheckbox from 'features/gallery/components/GallerySettingsPopover/AlwaysShowImageSizeCheckbox';
+import AutoAssignBoardCheckbox from 'features/gallery/components/GallerySettingsPopover/AutoAssignBoardCheckbox';
+import AutoSwitchCheckbox from 'features/gallery/components/GallerySettingsPopover/AutoSwitchCheckbox';
+import ImageMinimumWidthSlider from 'features/gallery/components/GallerySettingsPopover/ImageMinimumWidthSlider';
+import ShowArchivedBoardsCheckbox from 'features/gallery/components/GallerySettingsPopover/ShowArchivedBoardsCheckbox';
+import ShowStarredFirstCheckbox from 'features/gallery/components/GallerySettingsPopover/ShowStarredFirstCheckbox';
+import SortDirectionCombobox from 'features/gallery/components/GallerySettingsPopover/SortDirectionCombobox';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { RiSettings4Fill } from 'react-icons/ri';
+
+const GallerySettingsPopover = () => {
+  const { t } = useTranslation();
+
+  return (
+    <Popover isLazy>
+      <PopoverTrigger>
+        <IconButton aria-label={t('gallery.gallerySettings')} size="sm" icon={<RiSettings4Fill />} />
+      </PopoverTrigger>
+      <PopoverContent>
+        <PopoverBody>
+          <Flex direction="column" gap={2}>
+            <ImageMinimumWidthSlider />
+            <AutoSwitchCheckbox />
+            <AutoAssignBoardCheckbox />
+            <AlwaysShowImageSizeCheckbox />
+            <ShowArchivedBoardsCheckbox />
+            <BoardAutoAddSelect />
+            <Divider pt={2} />
+            <ShowStarredFirstCheckbox />
+            <SortDirectionCombobox />
+          </Flex>
+        </PopoverBody>
+      </PopoverContent>
+    </Popover>
+  );
+};
+
+export default memo(GallerySettingsPopover);

invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover/ImageMinimumWidthSlider.tsx

@@ -0,0 +1,26 @@
+import { CompositeSlider, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { setGalleryImageMinimumWidth } from 'features/gallery/store/gallerySlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const GallerySettingsPopover = () => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const galleryImageMinimumWidth = useAppSelector((s) => s.gallery.galleryImageMinimumWidth);
+
+  const onChange = useCallback(
+    (v: number) => {
+      dispatch(setGalleryImageMinimumWidth(v));
+    },
+    [dispatch]
+  );
+  return (
+    <FormControl>
+      <FormLabel>{t('gallery.galleryImageSize')}</FormLabel>
+      <CompositeSlider value={galleryImageMinimumWidth} onChange={onChange} min={45} max={256} defaultValue={90} />
+    </FormControl>
+  );
+};
+
+export default memo(GallerySettingsPopover);

invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover/ShowArchivedBoardsCheckbox.tsx

@@ -0,0 +1,28 @@
+import { Checkbox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { shouldShowArchivedBoardsChanged } from 'features/gallery/store/gallerySlice';
+import type { ChangeEvent } from 'react';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const GallerySettingsPopover = () => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const shouldShowArchivedBoards = useAppSelector((s) => s.gallery.shouldShowArchivedBoards);
+
+  const onChange = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      dispatch(shouldShowArchivedBoardsChanged(e.target.checked));
+    },
+    [dispatch]
+  );
+
+  return (
+    <FormControl>
+      <FormLabel flexGrow={1}>{t('gallery.showArchivedBoards')}</FormLabel>
+      <Checkbox isChecked={shouldShowArchivedBoards} onChange={onChange} />
+    </FormControl>
+  );
+};
+
+export default memo(GallerySettingsPopover);

invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover/ShowStarredFirstCheckbox.tsx

@@ -0,0 +1,30 @@
+import { FormControl, FormLabel, Switch } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { starredFirstChanged } from 'features/gallery/store/gallerySlice';
+import type { ChangeEvent } from 'react';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const GallerySettingsPopover = () => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const starredFirst = useAppSelector((s) => s.gallery.starredFirst);
+
+  const onChange = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      dispatch(starredFirstChanged(e.target.checked));
+    },
+    [dispatch]
+  );
+
+  return (
+    <FormControl w="full">
+      <FormLabel flexGrow={1} m={0}>
+        {t('gallery.showStarredImagesFirst')}
+      </FormLabel>
+      <Switch size="sm" isChecked={starredFirst} onChange={onChange} />
+    </FormControl>
+  );
+};
+
+export default memo(GallerySettingsPopover);

invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover/SortDirectionCombobox.tsx

@@ -0,0 +1,45 @@
+import type { ComboboxOption } from '@invoke-ai/ui-library';
+import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import type { SingleValue } from 'chakra-react-select';
+import { orderDirChanged } from 'features/gallery/store/gallerySlice';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { assert } from 'tsafe';
+
+const GallerySettingsPopover = () => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const orderDir = useAppSelector((s) => s.gallery.orderDir);
+
+  const options = useMemo<ComboboxOption[]>(
+    () => [
+      { value: 'DESC', label: t('gallery.newestFirst') },
+      { value: 'ASC', label: t('gallery.oldestFirst') },
+    ],
+    [t]
+  );
+
+  const onChange = useCallback(
+    (v: SingleValue<ComboboxOption>) => {
+      assert(v?.value === 'ASC' || v?.value === 'DESC');
+      dispatch(orderDirChanged(v.value));
+    },
+    [dispatch]
+  );
+
+  const value = useMemo(() => {
+    return options.find((opt) => opt.value === orderDir);
+  }, [orderDir, options]);
+
+  return (
+    <FormControl>
+      <FormLabel flexGrow={1} m={0}>
+        {t('gallery.sortDirection')}
+      </FormLabel>
+      <Combobox isSearchable={false} value={value} options={options} onChange={onChange} />
+    </FormControl>
+  );
+};
+
+export default memo(GallerySettingsPopover);