Merge branch 'main' into ryan/model-cache-logging-only

docs/nodes/communityNodes.md

@@ -10,6 +10,20 @@ To use a community workflow, download the the `.json` node graph file and load i
 
 --------------------------------
 
+--------------------------------
+### Make 3D
+
+**Description:** Create compelling 3D stereo images from 2D originals.
+
+**Node Link:** [https://gitlab.com/srcrr/shift3d/-/raw/main/make3d.py](https://gitlab.com/srcrr/shift3d)
+
+**Example Node Graph:**  https://gitlab.com/srcrr/shift3d/-/raw/main/example-workflow.json?ref_type=heads&inline=false
+
+**Output Examples** 
+
+![Painting of a cozy delapidated house](https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png){: style="height:512px;width:512px"}
+![Photo of cute puppies](https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png){: style="height:512px;width:512px"}
+
 --------------------------------
 ### Ideal Size
 

invokeai/app/invocations/baseinvocation.py

@@ -68,6 +68,7 @@ class FieldDescriptions:
     height = "Height of output (px)"
     control = "ControlNet(s) to apply"
     ip_adapter = "IP-Adapter to apply"
+    t2i_adapter = "T2I-Adapter(s) to apply"
     denoised_latents = "Denoised latents tensor"
     latents = "Latents tensor"
     strength = "Strength of denoising (proportional to steps)"

invokeai/app/invocations/facetools.py

@@ -1,5 +1,4 @@
 import math
-import os
 import re
 from pathlib import Path
 from typing import Optional, TypedDict
@@ -11,6 +10,7 @@ from PIL import Image, ImageDraw, ImageFilter, ImageFont, ImageOps
 from PIL.Image import Image as ImageType
 from pydantic import validator
 
+import invokeai.assets.fonts as font_assets
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     InputField,
@@ -138,6 +138,7 @@ def generate_face_box_mask(
     chunk_x_offset: int = 0,
     chunk_y_offset: int = 0,
     draw_mesh: bool = True,
+    check_bounds: bool = True,
 ) -> list[FaceResultData]:
     result = []
     mask_pil = None
@@ -217,7 +218,7 @@ def generate_face_box_mask(
             im_width, im_height = pil_image.size
             over_w = im_width * 0.1
             over_h = im_height * 0.1
-            if (
+            if not check_bounds or (
                 (left_side >= -over_w)
                 and (right_side < im_width + over_w)
                 and (top_side >= -over_h)
@@ -345,6 +346,7 @@ def get_faces_list(
             chunk_x_offset=0,
             chunk_y_offset=0,
             draw_mesh=draw_mesh,
+            check_bounds=False,
         )
     if should_chunk or len(result) == 0:
         context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
@@ -402,7 +404,7 @@ def get_faces_list(
     return all_faces
 
 
-@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.0")
+@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.1")
 class FaceOffInvocation(BaseInvocation):
     """Bound, extract, and mask a face from an image using MediaPipe detection"""
 
@@ -496,7 +498,7 @@ class FaceOffInvocation(BaseInvocation):
         return output
 
 
-@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.0")
+@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.1")
 class FaceMaskInvocation(BaseInvocation):
     """Face mask creation using mediapipe face detection"""
 
@@ -614,7 +616,7 @@ class FaceMaskInvocation(BaseInvocation):
 
 
 @invocation(
-    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.0"
+    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.1"
 )
 class FaceIdentifierInvocation(BaseInvocation):
     """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
@@ -641,9 +643,9 @@ class FaceIdentifierInvocation(BaseInvocation):
             draw_mesh=False,
         )
 
-        path = Path(__file__).resolve().parent.parent.parent
+        # Note - font may be found either in the repo if running an editable install, or in the venv if running a package install
-        font_path = os.path.abspath(path / "assets/fonts/inter/Inter-Regular.ttf")
+        font_path = [x for x in [Path(y, "inter/Inter-Regular.ttf") for y in font_assets.__path__] if x.exists()]
-        font = ImageFont.truetype(font_path, FONT_SIZE)
+        font = ImageFont.truetype(font_path[0].as_posix(), FONT_SIZE)
 
         # Paste face IDs on the output image
         draw = ImageDraw.Draw(image)

invokeai/app/invocations/latent.py

@@ -11,6 +11,7 @@ import torchvision.transforms as T
 from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.models import UNet2DConditionModel
+from diffusers.models.adapter import FullAdapterXL, T2IAdapter
 from diffusers.models.attention_processor import (
     AttnProcessor2_0,
     LoRAAttnProcessor2_0,
@@ -33,6 +34,7 @@ from invokeai.app.invocations.primitives import (
     LatentsOutput,
     build_latents_output,
 )
+from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
@@ -47,6 +49,7 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
     ControlNetData,
     IPAdapterData,
     StableDiffusionGeneratorPipeline,
+    T2IAdapterData,
     image_resized_to_grid_as_tensor,
 )
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
@@ -196,7 +199,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.1.0",
+    version="1.2.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -226,9 +229,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
     ip_adapter: Optional[IPAdapterField] = InputField(
         description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection, ui_order=6
     )
+    t2i_adapter: Union[T2IAdapterField, list[T2IAdapterField]] = InputField(
+        description=FieldDescriptions.t2i_adapter, title="T2I-Adapter", default=None, input=Input.Connection, ui_order=7
+    )
     latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
     denoise_mask: Optional[DenoiseMaskField] = InputField(
-        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=7
+        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=8
     )
 
     @validator("cfg_scale")
@@ -451,6 +457,91 @@ class DenoiseLatentsInvocation(BaseInvocation):
             end_step_percent=ip_adapter.end_step_percent,
         )
 
+    def run_t2i_adapters(
+        self,
+        context: InvocationContext,
+        t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
+        latents_shape: list[int],
+        do_classifier_free_guidance: bool,
+    ) -> Optional[list[T2IAdapterData]]:
+        if t2i_adapter is None:
+            return None
+
+        # Handle the possibility that t2i_adapter could be a list or a single T2IAdapterField.
+        if isinstance(t2i_adapter, T2IAdapterField):
+            t2i_adapter = [t2i_adapter]
+
+        if len(t2i_adapter) == 0:
+            return None
+
+        t2i_adapter_data = []
+        for t2i_adapter_field in t2i_adapter:
+            t2i_adapter_model_info = context.services.model_manager.get_model(
+                model_name=t2i_adapter_field.t2i_adapter_model.model_name,
+                model_type=ModelType.T2IAdapter,
+                base_model=t2i_adapter_field.t2i_adapter_model.base_model,
+                context=context,
+            )
+            image = context.services.images.get_pil_image(t2i_adapter_field.image.image_name)
+
+            # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
+            if t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusion1:
+                max_unet_downscale = 8
+            elif t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusionXL:
+                max_unet_downscale = 4
+            else:
+                raise ValueError(
+                    f"Unexpected T2I-Adapter base model type: '{t2i_adapter_field.t2i_adapter_model.base_model}'."
+                )
+
+            t2i_adapter_model: T2IAdapter
+            with t2i_adapter_model_info as t2i_adapter_model:
+                total_downscale_factor = t2i_adapter_model.total_downscale_factor
+                if isinstance(t2i_adapter_model.adapter, FullAdapterXL):
+                    # HACK(ryand): Work around a bug in FullAdapterXL. This is being addressed upstream in diffusers by
+                    # this PR: https://github.com/huggingface/diffusers/pull/5134.
+                    total_downscale_factor = total_downscale_factor // 2
+
+                # Resize the T2I-Adapter input image.
+                # We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
+                # result will match the latent image's dimensions after max_unet_downscale is applied.
+                t2i_input_height = latents_shape[2] // max_unet_downscale * total_downscale_factor
+                t2i_input_width = latents_shape[3] // max_unet_downscale * total_downscale_factor
+
+                # Note: We have hard-coded `do_classifier_free_guidance=False`. This is because we only want to prepare
+                # a single image. If CFG is enabled, we will duplicate the resultant tensor after applying the
+                # T2I-Adapter model.
+                #
+                # Note: We re-use the `prepare_control_image(...)` from ControlNet for T2I-Adapter, because it has many
+                # of the same requirements (e.g. preserving binary masks during resize).
+                t2i_image = prepare_control_image(
+                    image=image,
+                    do_classifier_free_guidance=False,
+                    width=t2i_input_width,
+                    height=t2i_input_height,
+                    num_channels=t2i_adapter_model.config.in_channels,
+                    device=t2i_adapter_model.device,
+                    dtype=t2i_adapter_model.dtype,
+                    resize_mode=t2i_adapter_field.resize_mode,
+                )
+
+                adapter_state = t2i_adapter_model(t2i_image)
+
+            if do_classifier_free_guidance:
+                for idx, value in enumerate(adapter_state):
+                    adapter_state[idx] = torch.cat([value] * 2, dim=0)
+
+            t2i_adapter_data.append(
+                T2IAdapterData(
+                    adapter_state=adapter_state,
+                    weight=t2i_adapter_field.weight,
+                    begin_step_percent=t2i_adapter_field.begin_step_percent,
+                    end_step_percent=t2i_adapter_field.end_step_percent,
+                )
+            )
+
+        return t2i_adapter_data
+
     # original idea by https://github.com/AmericanPresidentJimmyCarter
     # TODO: research more for second order schedulers timesteps
     def init_scheduler(self, scheduler, device, steps, denoising_start, denoising_end):
@@ -522,6 +613,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             mask, masked_latents = self.prep_inpaint_mask(context, latents)
 
+            # TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets,
+            # below. Investigate whether this is appropriate.
+            t2i_adapter_data = self.run_t2i_adapters(
+                context, self.t2i_adapter, latents.shape, do_classifier_free_guidance=True
+            )
+
             # Get the source node id (we are invoking the prepared node)
             graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
             source_node_id = graph_execution_state.prepared_source_mapping[self.id]
@@ -602,8 +699,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     masked_latents=masked_latents,
                     num_inference_steps=num_inference_steps,
                     conditioning_data=conditioning_data,
-                    control_data=controlnet_data,  # list[ControlNetData],
+                    control_data=controlnet_data,
-                    ip_adapter_data=ip_adapter_data,  # IPAdapterData,
+                    ip_adapter_data=ip_adapter_data,
+                    t2i_adapter_data=t2i_adapter_data,
                     callback=step_callback,
                 )
 

invokeai/app/invocations/t2i_adapter.py

@@ -0,0 +1,83 @@
+from typing import Union
+
+from pydantic import BaseModel, Field
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIType,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
+from invokeai.app.invocations.primitives import ImageField
+from invokeai.backend.model_management.models.base import BaseModelType
+
+
+class T2IAdapterModelField(BaseModel):
+    model_name: str = Field(description="Name of the T2I-Adapter model")
+    base_model: BaseModelType = Field(description="Base model")
+
+
+class T2IAdapterField(BaseModel):
+    image: ImageField = Field(description="The T2I-Adapter image prompt.")
+    t2i_adapter_model: T2IAdapterModelField = Field(description="The T2I-Adapter model to use.")
+    weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
+
+
+@invocation_output("t2i_adapter_output")
+class T2IAdapterOutput(BaseInvocationOutput):
+    t2i_adapter: T2IAdapterField = OutputField(description=FieldDescriptions.t2i_adapter, title="T2I Adapter")
+
+
+@invocation(
+    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.0"
+)
+class T2IAdapterInvocation(BaseInvocation):
+    """Collects T2I-Adapter info to pass to other nodes."""
+
+    # Inputs
+    image: ImageField = InputField(description="The IP-Adapter image prompt.")
+    ip_adapter_model: T2IAdapterModelField = InputField(
+        description="The T2I-Adapter model.",
+        title="T2I-Adapter Model",
+        input=Input.Direct,
+        ui_order=-1,
+    )
+    weight: Union[float, list[float]] = InputField(
+        default=1, ge=0, description="The weight given to the T2I-Adapter", ui_type=UIType.Float, title="Weight"
+    )
+    begin_step_percent: float = InputField(
+        default=0, ge=-1, le=2, description="When the T2I-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = InputField(
+        default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = InputField(
+        default="just_resize",
+        description="The resize mode applied to the T2I-Adapter input image so that it matches the target output size.",
+    )
+
+    def invoke(self, context: InvocationContext) -> T2IAdapterOutput:
+        return T2IAdapterOutput(
+            t2i_adapter=T2IAdapterField(
+                image=self.image,
+                t2i_adapter_model=self.ip_adapter_model,
+                weight=self.weight,
+                begin_step_percent=self.begin_step_percent,
+                end_step_percent=self.end_step_percent,
+                resize_mode=self.resize_mode,
+            )
+        )

invokeai/app/invocations/upscale.py

@@ -4,12 +4,14 @@ from typing import Literal
 
 import cv2 as cv
 import numpy as np
+import torch
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from PIL import Image
 from realesrgan import RealESRGANer
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
+from invokeai.backend.util.devices import choose_torch_device
 
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 
@@ -22,13 +24,19 @@ ESRGAN_MODELS = Literal[
     "RealESRGAN_x2plus.pth",
 ]
 
+if choose_torch_device() == torch.device("mps"):
+    from torch import mps
 
-@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.0.0")
+
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0")
 class ESRGANInvocation(BaseInvocation):
     """Upscales an image using RealESRGAN."""
 
     image: ImageField = InputField(description="The input image")
     model_name: ESRGAN_MODELS = InputField(default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use")
+    tile_size: int = InputField(
+        default=400, ge=0, description="Tile size for tiled ESRGAN upscaling (0=tiling disabled)"
+    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
@@ -86,9 +94,11 @@ class ESRGANInvocation(BaseInvocation):
             model_path=str(models_path / esrgan_model_path),
             model=rrdbnet_model,
             half=False,
+            tile=self.tile_size,
         )
 
         # prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
+        # TODO: This strips the alpha... is that okay?
         cv_image = cv.cvtColor(np.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
 
         # We can pass an `outscale` value here, but it just resizes the image by that factor after
@@ -99,6 +109,10 @@ class ESRGANInvocation(BaseInvocation):
         # back to PIL
         pil_image = Image.fromarray(cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)).convert("RGBA")
 
+        torch.cuda.empty_cache()
+        if choose_torch_device() == torch.device("mps"):
+            mps.empty_cache()
+
         image_dto = context.services.images.create(
             image=pil_image,
             image_origin=ResourceOrigin.INTERNAL,

invokeai/app/services/config/invokeai_config.py

@@ -255,6 +255,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
     force_tiled_decode  : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
     force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
+    png_compress_level  : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", category="Generation", )
 
     # QUEUE
     max_queue_size      : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", category="Queue", )

invokeai/app/services/graph.py

@@ -2,7 +2,7 @@
 
 import copy
 import itertools
-from typing import Annotated, Any, Optional, Union, cast, get_args, get_origin, get_type_hints
+from typing import Annotated, Any, Optional, Union, get_args, get_origin, get_type_hints
 
 import networkx as nx
 from pydantic import BaseModel, root_validator, validator
@@ -170,6 +170,18 @@ class NodeIdMismatchError(ValueError):
     pass
 
 
+class InvalidSubGraphError(ValueError):
+    pass
+
+
+class CyclicalGraphError(ValueError):
+    pass
+
+
+class UnknownGraphValidationError(ValueError):
+    pass
+
+
 # TODO: Create and use an Empty output?
 @invocation_output("graph_output")
 class GraphInvocationOutput(BaseInvocationOutput):
@@ -254,59 +266,6 @@ class Graph(BaseModel):
         default_factory=list,
     )
 
-    @root_validator
-    def validate_nodes_and_edges(cls, values):
-        """Validates that all edges match nodes in the graph"""
-        nodes = cast(Optional[dict[str, BaseInvocation]], values.get("nodes"))
-        edges = cast(Optional[list[Edge]], values.get("edges"))
-
-        if nodes is not None:
-            # Validate that all node ids are unique
-            node_ids = [n.id for n in nodes.values()]
-            duplicate_node_ids = set([node_id for node_id in node_ids if node_ids.count(node_id) >= 2])
-            if duplicate_node_ids:
-                raise DuplicateNodeIdError(f"Node ids must be unique, found duplicates {duplicate_node_ids}")
-
-            # Validate that all node ids match the keys in the nodes dict
-            for k, v in nodes.items():
-                if k != v.id:
-                    raise NodeIdMismatchError(f"Node ids must match, got {k} and {v.id}")
-
-        if edges is not None and nodes is not None:
-            # Validate that all edges match nodes in the graph
-            node_ids = set([e.source.node_id for e in edges] + [e.destination.node_id for e in edges])
-            missing_node_ids = [node_id for node_id in node_ids if node_id not in nodes]
-            if missing_node_ids:
-                raise NodeNotFoundError(
-                    f"All edges must reference nodes in the graph, missing nodes: {missing_node_ids}"
-                )
-
-            # Validate that all edge fields match node fields in the graph
-            for edge in edges:
-                source_node = nodes.get(edge.source.node_id, None)
-                if source_node is None:
-                    raise NodeFieldNotFoundError(f"Edge source node {edge.source.node_id} does not exist in the graph")
-
-                destination_node = nodes.get(edge.destination.node_id, None)
-                if destination_node is None:
-                    raise NodeFieldNotFoundError(
-                        f"Edge destination node {edge.destination.node_id} does not exist in the graph"
-                    )
-
-                # output fields are not on the node object directly, they are on the output type
-                if edge.source.field not in source_node.get_output_type().__fields__:
-                    raise NodeFieldNotFoundError(
-                        f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
-                    )
-
-                # input fields are on the node
-                if edge.destination.field not in destination_node.__fields__:
-                    raise NodeFieldNotFoundError(
-                        f"Edge destination field {edge.destination.field} does not exist in node {edge.destination.node_id}"
-                    )
-
-        return values
-
     def add_node(self, node: BaseInvocation) -> None:
         """Adds a node to a graph
 
@@ -377,53 +336,108 @@ class Graph(BaseModel):
         except KeyError:
             pass
 
-    def is_valid(self) -> bool:
+    def validate_self(self) -> None:
-        """Validates the graph."""
+        """
+        Validates the graph.
+
+        Raises an exception if the graph is invalid:
+        - `DuplicateNodeIdError`
+        - `NodeIdMismatchError`
+        - `InvalidSubGraphError`
+        - `NodeNotFoundError`
+        - `NodeFieldNotFoundError`
+        - `CyclicalGraphError`
+        - `InvalidEdgeError`
+        """
+
+        # Validate that all node ids are unique
+        node_ids = [n.id for n in self.nodes.values()]
+        duplicate_node_ids = set([node_id for node_id in node_ids if node_ids.count(node_id) >= 2])
+        if duplicate_node_ids:
+            raise DuplicateNodeIdError(f"Node ids must be unique, found duplicates {duplicate_node_ids}")
+
+        # Validate that all node ids match the keys in the nodes dict
+        for k, v in self.nodes.items():
+            if k != v.id:
+                raise NodeIdMismatchError(f"Node ids must match, got {k} and {v.id}")
 
         # Validate all subgraphs
         for gn in (n for n in self.nodes.values() if isinstance(n, GraphInvocation)):
-            if not gn.graph.is_valid():
+            try:
-                return False
+                gn.graph.validate_self()
+            except Exception as e:
+                raise InvalidSubGraphError(f"Subgraph {gn.id} is invalid") from e
 
-        # Validate all edges reference nodes in the graph
+        # Validate that all edges match nodes and fields in the graph
-        node_ids = set([e.source.node_id for e in self.edges] + [e.destination.node_id for e in self.edges])
+        for edge in self.edges:
-        if not all((self.has_node(node_id) for node_id in node_ids)):
+            source_node = self.nodes.get(edge.source.node_id, None)
-            return False
+            if source_node is None:
+                raise NodeNotFoundError(f"Edge source node {edge.source.node_id} does not exist in the graph")
+
+            destination_node = self.nodes.get(edge.destination.node_id, None)
+            if destination_node is None:
+                raise NodeNotFoundError(f"Edge destination node {edge.destination.node_id} does not exist in the graph")
+
+            # output fields are not on the node object directly, they are on the output type
+            if edge.source.field not in source_node.get_output_type().__fields__:
+                raise NodeFieldNotFoundError(
+                    f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
+                )
+
+            # input fields are on the node
+            if edge.destination.field not in destination_node.__fields__:
+                raise NodeFieldNotFoundError(
+                    f"Edge destination field {edge.destination.field} does not exist in node {edge.destination.node_id}"
+                )
 
         # Validate there are no cycles
         g = self.nx_graph_flat()
         if not nx.is_directed_acyclic_graph(g):
-            return False
+            raise CyclicalGraphError("Graph contains cycles")
 
         # Validate all edge connections are valid
-        if not all(
+        for e in self.edges:
-            (
+            if not are_connections_compatible(
-                are_connections_compatible(
                 self.get_node(e.source.node_id),
                 e.source.field,
                 self.get_node(e.destination.node_id),
                 e.destination.field,
+            ):
+                raise InvalidEdgeError(
+                    f"Invalid edge from {e.source.node_id}.{e.source.field} to {e.destination.node_id}.{e.destination.field}"
                 )
-                for e in self.edges
-            )
-        ):
-            return False
 
-        # Validate all iterators
+        # Validate all iterators & collectors
-        # TODO: may need to validate all iterators in subgraphs so edge connections in parent graphs will be available
+        # TODO: may need to validate all iterators & collectors in subgraphs so edge connections in parent graphs will be available
-        if not all(
+        for n in self.nodes.values():
-            (self._is_iterator_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, IterateInvocation))
+            if isinstance(n, IterateInvocation) and not self._is_iterator_connection_valid(n.id):
-        ):
+                raise InvalidEdgeError(f"Invalid iterator node {n.id}")
-            return False
+            if isinstance(n, CollectInvocation) and not self._is_collector_connection_valid(n.id):
+                raise InvalidEdgeError(f"Invalid collector node {n.id}")
 
-        # Validate all collectors
+        return None
-        # TODO: may need to validate all collectors in subgraphs so edge connections in parent graphs will be available
-        if not all(
-            (self._is_collector_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, CollectInvocation))
-        ):
-            return False
 
+    def is_valid(self) -> bool:
+        """
+        Checks if the graph is valid.
+
+        Raises `UnknownGraphValidationError` if there is a problem validating the graph (not a validation error).
+        """
+        try:
+            self.validate_self()
             return True
+        except (
+            DuplicateNodeIdError,
+            NodeIdMismatchError,
+            InvalidSubGraphError,
+            NodeNotFoundError,
+            NodeFieldNotFoundError,
+            CyclicalGraphError,
+            InvalidEdgeError,
+        ):
+            return False
+        except Exception as e:
+            raise UnknownGraphValidationError(f"Problem validating graph {e}") from e
 
     def _validate_edge(self, edge: Edge):
         """Validates that a new edge doesn't create a cycle in the graph"""
@@ -804,6 +818,12 @@ class GraphExecutionState(BaseModel):
         default_factory=dict,
     )
 
+    @validator("graph")
+    def graph_is_valid(cls, v: Graph):
+        """Validates that the graph is valid"""
+        v.validate_self()
+        return v
+
     class Config:
         schema_extra = {
             "required": [

invokeai/app/services/image_file_storage.py

@@ -9,6 +9,7 @@ from PIL import Image, PngImagePlugin
 from PIL.Image import Image as PILImageType
 from send2trash import send2trash
 
+from invokeai.app.services.config.invokeai_config import InvokeAIAppConfig
 from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
 
 
@@ -79,6 +80,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
     __cache_ids: Queue  # TODO: this is an incredibly naive cache
     __cache: Dict[Path, PILImageType]
     __max_cache_size: int
+    __compress_level: int
 
     def __init__(self, output_folder: Union[str, Path]):
         self.__cache = dict()
@@ -87,7 +89,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
 
         self.__output_folder: Path = output_folder if isinstance(output_folder, Path) else Path(output_folder)
         self.__thumbnails_folder = self.__output_folder / "thumbnails"
-
+        self.__compress_level = InvokeAIAppConfig.get_config().png_compress_level
         # Validate required output folders at launch
         self.__validate_storage_folders()
 
@@ -134,7 +136,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
                 if original_workflow is not None:
                     pnginfo.add_text("invokeai_workflow", original_workflow)
 
-            image.save(image_path, "PNG", pnginfo=pnginfo)
+            image.save(image_path, "PNG", pnginfo=pnginfo, compress_level=self.__compress_level)
 
             thumbnail_name = get_thumbnail_name(image_name)
             thumbnail_path = self.get_path(thumbnail_name, thumbnail=True)

invokeai/app/services/image_record_storage.py

@@ -584,7 +584,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                 FROM images
                 JOIN board_images ON images.image_name = board_images.image_name
                 WHERE board_images.board_id = ?
-                ORDER BY images.created_at DESC
+                ORDER BY images.starred DESC, images.created_at DESC
                 LIMIT 1;
                 """,
                 (board_id,),

invokeai/app/services/session_processor/session_processor_default.py

@@ -1,3 +1,4 @@
+import traceback
 from threading import BoundedSemaphore
 from threading import Event as ThreadEvent
 from threading import Thread
@@ -123,6 +124,10 @@ class DefaultSessionProcessor(SessionProcessorBase):
                         continue
                 except Exception as e:
                     self.__invoker.services.logger.error(f"Error in session processor: {e}")
+                    if queue_item is not None:
+                        self.__invoker.services.session_queue.cancel_queue_item(
+                            queue_item.item_id, error=traceback.format_exc()
+                        )
                     poll_now_event.wait(POLLING_INTERVAL)
                     continue
         except Exception as e:

invokeai/app/services/session_queue/session_queue_base.py

@@ -80,7 +80,7 @@ class SessionQueueBase(ABC):
         pass
 
     @abstractmethod
-    def cancel_queue_item(self, item_id: int) -> SessionQueueItem:
+    def cancel_queue_item(self, item_id: int, error: Optional[str] = None) -> SessionQueueItem:
         """Cancels a session queue item"""
         pass
 

invokeai/app/services/session_queue/session_queue_common.py

@@ -123,6 +123,11 @@ class Batch(BaseModel):
                     raise NodeNotFoundError(f"Field {batch_data.field_name} not found in node {batch_data.node_path}")
         return values
 
+    @validator("graph")
+    def validate_graph(cls, v: Graph):
+        v.validate_self()
+        return v
+
     class Config:
         schema_extra = {
             "required": [

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -555,10 +555,11 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return PruneResult(deleted=count)
 
-    def cancel_queue_item(self, item_id: int) -> SessionQueueItem:
+    def cancel_queue_item(self, item_id: int, error: Optional[str] = None) -> SessionQueueItem:
         queue_item = self.get_queue_item(item_id)
         if queue_item.status not in ["canceled", "failed", "completed"]:
-            queue_item = self._set_queue_item_status(item_id=item_id, status="canceled")
+            status = "failed" if error is not None else "canceled"
+            queue_item = self._set_queue_item_status(item_id=item_id, status=status, error=error)
             self.__invoker.services.queue.cancel(queue_item.session_id)
             self.__invoker.services.events.emit_session_canceled(
                 queue_item_id=queue_item.item_id,

invokeai/app/util/controlnet_utils.py

@@ -265,22 +265,41 @@ def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device:
 
 
 def prepare_control_image(
-    # image used to be Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor, List[torch.Tensor]]
-    # but now should be able to assume that image is a single PIL.Image, which simplifies things
     image: Image,
-    # FIXME: need to fix hardwiring of width and height, change to basing on latents dimensions?
+    width: int,
-    # latents_to_match_resolution, # TorchTensor of shape (batch_size, 3, height, width)
+    height: int,
-    width=512,  # should be 8 * latent.shape[3]
+    num_channels: int = 3,
-    height=512,  # should be 8 * latent height[2]
-    # batch_size=1, # currently no batching
-    # num_images_per_prompt=1, # currently only single image
     device="cuda",
     dtype=torch.float16,
     do_classifier_free_guidance=True,
     control_mode="balanced",
     resize_mode="just_resize_simple",
 ):
-    # FIXME: implement "crop_resize_simple" and "fill_resize_simple", or pull them out
+    """Pre-process images for ControlNets or T2I-Adapters.
+
+    Args:
+        image (Image): The PIL image to pre-process.
+        width (int): The target width in pixels.
+        height (int): The target height in pixels.
+        num_channels (int, optional): The target number of image channels. This is achieved by converting the input
+            image to RGB, then naively taking the first `num_channels` channels. The primary use case is converting a
+            RGB image to a single-channel grayscale image. Raises if `num_channels` cannot be achieved. Defaults to 3.
+        device (str, optional): The target device for the output image. Defaults to "cuda".
+        dtype (_type_, optional): The dtype for the output image. Defaults to torch.float16.
+        do_classifier_free_guidance (bool, optional): If True, repeat the output image along the batch dimension.
+            Defaults to True.
+        control_mode (str, optional): Defaults to "balanced".
+        resize_mode (str, optional): Defaults to "just_resize_simple".
+
+    Raises:
+        NotImplementedError: If resize_mode == "crop_resize_simple".
+        NotImplementedError: If resize_mode == "fill_resize_simple".
+        ValueError: If `resize_mode` is not recognized.
+        ValueError: If `num_channels` is out of range.
+
+    Returns:
+        torch.Tensor: The pre-processed input tensor.
+    """
     if (
         resize_mode == "just_resize_simple"
         or resize_mode == "crop_resize_simple"
@@ -289,10 +308,10 @@ def prepare_control_image(
         image = image.convert("RGB")
         if resize_mode == "just_resize_simple":
             image = image.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
-        elif resize_mode == "crop_resize_simple":  # not yet implemented
+        elif resize_mode == "crop_resize_simple":
-            pass
+            raise NotImplementedError(f"prepare_control_image is not implemented for resize_mode='{resize_mode}'.")
-        elif resize_mode == "fill_resize_simple":  # not yet implemented
+        elif resize_mode == "fill_resize_simple":
-            pass
+            raise NotImplementedError(f"prepare_control_image is not implemented for resize_mode='{resize_mode}'.")
         nimage = np.array(image)
         nimage = nimage[None, :]
         nimage = np.concatenate([nimage], axis=0)
@@ -313,9 +332,11 @@ def prepare_control_image(
             device=device,
         )
     else:
-        pass
+        raise ValueError(f"Unsupported resize_mode: '{resize_mode}'.")
-        print("ERROR: invalid resize_mode ==> ", resize_mode)
+
-        exit(1)
+    if timage.shape[1] < num_channels or num_channels <= 0:
+        raise ValueError(f"Cannot achieve the target of num_channels={num_channels}.")
+    timage = timage[:, :num_channels, :, :]
 
     timage = timage.to(device=device, dtype=dtype)
     cfg_injection = control_mode == "more_control" or control_mode == "unbalanced"

invokeai/backend/ip_adapter/ip_adapter.py

@@ -218,6 +218,20 @@ class IPAdapterPlus(IPAdapter):
         return image_prompt_embeds, uncond_image_prompt_embeds
 
 
+class IPAdapterPlusXL(IPAdapterPlus):
+    """IP-Adapter Plus for SDXL."""
+
+    def _init_image_proj_model(self, state_dict):
+        return Resampler.from_state_dict(
+            state_dict=state_dict,
+            depth=4,
+            dim_head=64,
+            heads=20,
+            num_queries=self._num_tokens,
+            ff_mult=4,
+        ).to(self.device, dtype=self.dtype)
+
+
 def build_ip_adapter(
     ip_adapter_ckpt_path: str, device: torch.device, dtype: torch.dtype = torch.float16
 ) -> Union[IPAdapter, IPAdapterPlus]:
@@ -228,6 +242,14 @@ def build_ip_adapter(
     is_plus = "proj.weight" not in state_dict["image_proj"]
 
     if is_plus:
+        cross_attention_dim = state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[-1]
+        if cross_attention_dim == 768:
+            # SD1 IP-Adapter Plus
             return IPAdapterPlus(state_dict, device=device, dtype=dtype)
+        elif cross_attention_dim == 2048:
+            # SDXL IP-Adapter Plus
+            return IPAdapterPlusXL(state_dict, device=device, dtype=dtype)
+        else:
+            raise Exception(f"Unsupported IP-Adapter Plus cross-attention dimension: {cross_attention_dim}.")
     else:
         return IPAdapter(state_dict, device=device, dtype=dtype)

invokeai/backend/model_management/model_probe.py

@@ -57,6 +57,7 @@ class ModelProbe(object):
         "AutoencoderTiny": ModelType.Vae,
         "ControlNetModel": ModelType.ControlNet,
         "CLIPVisionModelWithProjection": ModelType.CLIPVision,
+        "T2IAdapter": ModelType.T2IAdapter,
     }
 
     @classmethod
@@ -408,6 +409,11 @@ class CLIPVisionCheckpointProbe(CheckpointProbeBase):
         raise NotImplementedError()
 
 
+class T2IAdapterCheckpointProbe(CheckpointProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        raise NotImplementedError()
+
+
 ########################################################
 # classes for probing folders
 #######################################################
@@ -595,6 +601,26 @@ class CLIPVisionFolderProbe(FolderProbeBase):
         return BaseModelType.Any
 
 
+class T2IAdapterFolderProbe(FolderProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        config_file = self.folder_path / "config.json"
+        if not config_file.exists():
+            raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
+        with open(config_file, "r") as file:
+            config = json.load(file)
+
+        adapter_type = config.get("adapter_type", None)
+        if adapter_type == "full_adapter_xl":
+            return BaseModelType.StableDiffusionXL
+        elif adapter_type == "full_adapter" or "light_adapter":
+            # I haven't seen any T2I adapter models for SD2, so assume that this is an SD1 adapter.
+            return BaseModelType.StableDiffusion1
+        else:
+            raise InvalidModelException(
+                f"Unable to determine base model for '{self.folder_path}' (adapter_type = {adapter_type})."
+            )
+
+
 ############## register probe classes ######
 ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.Vae, VaeFolderProbe)
@@ -603,6 +629,7 @@ ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInvers
 ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.IPAdapter, IPAdapterFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.CLIPVision, CLIPVisionFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.T2IAdapter, T2IAdapterFolderProbe)
 
 ModelProbe.register_probe("checkpoint", ModelType.Main, PipelineCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.Vae, VaeCheckpointProbe)
@@ -611,5 +638,6 @@ ModelProbe.register_probe("checkpoint", ModelType.TextualInversion, TextualInver
 ModelProbe.register_probe("checkpoint", ModelType.ControlNet, ControlNetCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.IPAdapter, IPAdapterCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.CLIPVision, CLIPVisionCheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.T2IAdapter, T2IAdapterCheckpointProbe)
 
 ModelProbe.register_probe("onnx", ModelType.ONNX, ONNXFolderProbe)

invokeai/backend/model_management/models/__init__.py

@@ -25,6 +25,7 @@ from .lora import LoRAModel
 from .sdxl import StableDiffusionXLModel
 from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
 from .stable_diffusion_onnx import ONNXStableDiffusion1Model, ONNXStableDiffusion2Model
+from .t2i_adapter import T2IAdapterModel
 from .textual_inversion import TextualInversionModel
 from .vae import VaeModel
 
@@ -38,6 +39,7 @@ MODEL_CLASSES = {
         ModelType.TextualInversion: TextualInversionModel,
         ModelType.IPAdapter: IPAdapterModel,
         ModelType.CLIPVision: CLIPVisionModel,
+        ModelType.T2IAdapter: T2IAdapterModel,
     },
     BaseModelType.StableDiffusion2: {
         ModelType.ONNX: ONNXStableDiffusion2Model,
@@ -48,6 +50,7 @@ MODEL_CLASSES = {
         ModelType.TextualInversion: TextualInversionModel,
         ModelType.IPAdapter: IPAdapterModel,
         ModelType.CLIPVision: CLIPVisionModel,
+        ModelType.T2IAdapter: T2IAdapterModel,
     },
     BaseModelType.StableDiffusionXL: {
         ModelType.Main: StableDiffusionXLModel,
@@ -59,6 +62,7 @@ MODEL_CLASSES = {
         ModelType.ONNX: ONNXStableDiffusion2Model,
         ModelType.IPAdapter: IPAdapterModel,
         ModelType.CLIPVision: CLIPVisionModel,
+        ModelType.T2IAdapter: T2IAdapterModel,
     },
     BaseModelType.StableDiffusionXLRefiner: {
         ModelType.Main: StableDiffusionXLModel,
@@ -70,6 +74,7 @@ MODEL_CLASSES = {
         ModelType.ONNX: ONNXStableDiffusion2Model,
         ModelType.IPAdapter: IPAdapterModel,
         ModelType.CLIPVision: CLIPVisionModel,
+        ModelType.T2IAdapter: T2IAdapterModel,
     },
     BaseModelType.Any: {
         ModelType.CLIPVision: CLIPVisionModel,
@@ -81,6 +86,7 @@ MODEL_CLASSES = {
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
         ModelType.IPAdapter: IPAdapterModel,
+        ModelType.T2IAdapter: T2IAdapterModel,
     },
     # BaseModelType.Kandinsky2_1: {
     #    ModelType.Main: Kandinsky2_1Model,

invokeai/backend/model_management/models/base.py

@@ -53,6 +53,7 @@ class ModelType(str, Enum):
     TextualInversion = "embedding"
     IPAdapter = "ip_adapter"
     CLIPVision = "clip_vision"
+    T2IAdapter = "t2i_adapter"
 
 
 class SubModelType(str, Enum):

invokeai/backend/model_management/models/t2i_adapter.py

@@ -0,0 +1,102 @@
+import os
+from enum import Enum
+from typing import Literal, Optional
+
+import torch
+from diffusers import T2IAdapter
+
+from invokeai.backend.model_management.models.base import (
+    BaseModelType,
+    EmptyConfigLoader,
+    InvalidModelException,
+    ModelBase,
+    ModelConfigBase,
+    ModelNotFoundException,
+    ModelType,
+    SubModelType,
+    calc_model_size_by_data,
+    calc_model_size_by_fs,
+    classproperty,
+)
+
+
+class T2IAdapterModelFormat(str, Enum):
+    Diffusers = "diffusers"
+
+
+class T2IAdapterModel(ModelBase):
+    class DiffusersConfig(ModelConfigBase):
+        model_format: Literal[T2IAdapterModelFormat.Diffusers]
+
+    def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
+        assert model_type == ModelType.T2IAdapter
+        super().__init__(model_path, base_model, model_type)
+
+        config = EmptyConfigLoader.load_config(self.model_path, config_name="config.json")
+
+        model_class_name = config.get("_class_name", None)
+        if model_class_name not in {"T2IAdapter"}:
+            raise InvalidModelException(f"Invalid T2I-Adapter model. Unknown _class_name: '{model_class_name}'.")
+
+        self.model_class = self._hf_definition_to_type(["diffusers", model_class_name])
+        self.model_size = calc_model_size_by_fs(self.model_path)
+
+    def get_size(self, child_type: Optional[SubModelType] = None):
+        if child_type is not None:
+            raise ValueError(f"T2I-Adapters do not have child models. Invalid child type: '{child_type}'.")
+        return self.model_size
+
+    def get_model(
+        self,
+        torch_dtype: Optional[torch.dtype],
+        child_type: Optional[SubModelType] = None,
+    ) -> T2IAdapter:
+        if child_type is not None:
+            raise ValueError(f"T2I-Adapters do not have child models. Invalid child type: '{child_type}'.")
+
+        model = None
+        for variant in ["fp16", None]:
+            try:
+                model = self.model_class.from_pretrained(
+                    self.model_path,
+                    torch_dtype=torch_dtype,
+                    variant=variant,
+                )
+                break
+            except Exception:
+                pass
+        if not model:
+            raise ModelNotFoundException()
+
+        # Calculate a more accurate size after loading the model into memory.
+        self.model_size = calc_model_size_by_data(model)
+        return model
+
+    @classproperty
+    def save_to_config(cls) -> bool:
+        return False
+
+    @classmethod
+    def detect_format(cls, path: str):
+        if not os.path.exists(path):
+            raise ModelNotFoundException(f"Model not found at '{path}'.")
+
+        if os.path.isdir(path):
+            if os.path.exists(os.path.join(path, "config.json")):
+                return T2IAdapterModelFormat.Diffusers
+
+        raise InvalidModelException(f"Unsupported T2I-Adapter format: '{path}'.")
+
+    @classmethod
+    def convert_if_required(
+        cls,
+        model_path: str,
+        output_path: str,
+        config: ModelConfigBase,
+        base_model: BaseModelType,
+    ) -> str:
+        format = cls.detect_format(model_path)
+        if format == T2IAdapterModelFormat.Diffusers:
+            return model_path
+        else:
+            raise ValueError(f"Unsupported format: '{format}'.")

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -173,6 +173,16 @@ class IPAdapterData:
     end_step_percent: float = Field(default=1.0)
 
 
+@dataclass
+class T2IAdapterData:
+    """A structure containing the information required to apply conditioning from a single T2I-Adapter model."""
+
+    adapter_state: dict[torch.Tensor] = Field()
+    weight: Union[float, list[float]] = Field(default=1.0)
+    begin_step_percent: float = Field(default=0.0)
+    end_step_percent: float = Field(default=1.0)
+
+
 @dataclass
 class InvokeAIStableDiffusionPipelineOutput(StableDiffusionPipelineOutput):
     r"""
@@ -327,6 +337,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         callback: Callable[[PipelineIntermediateState], None] = None,
         control_data: List[ControlNetData] = None,
         ip_adapter_data: Optional[IPAdapterData] = None,
+        t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
         mask: Optional[torch.Tensor] = None,
         masked_latents: Optional[torch.Tensor] = None,
         seed: Optional[int] = None,
@@ -379,6 +390,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 additional_guidance=additional_guidance,
                 control_data=control_data,
                 ip_adapter_data=ip_adapter_data,
+                t2i_adapter_data=t2i_adapter_data,
                 callback=callback,
             )
         finally:
@@ -399,6 +411,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         additional_guidance: List[Callable] = None,
         control_data: List[ControlNetData] = None,
         ip_adapter_data: Optional[IPAdapterData] = None,
+        t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
         callback: Callable[[PipelineIntermediateState], None] = None,
     ):
         self._adjust_memory_efficient_attention(latents)
@@ -454,6 +467,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     additional_guidance=additional_guidance,
                     control_data=control_data,
                     ip_adapter_data=ip_adapter_data,
+                    t2i_adapter_data=t2i_adapter_data,
                 )
                 latents = step_output.prev_sample
 
@@ -500,6 +514,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         additional_guidance: List[Callable] = None,
         control_data: List[ControlNetData] = None,
         ip_adapter_data: Optional[IPAdapterData] = None,
+        t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
     ):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
@@ -527,11 +542,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 # otherwise, set IP-Adapter scale to 0, so it has no effect
                 ip_adapter_data.ip_adapter_model.set_scale(0.0)
 
-        # handle ControlNet(s)
+        # Handle ControlNet(s) and T2I-Adapter(s)
-        # default is no controlnet, so set controlnet processing output to None
+        down_block_additional_residuals = None
-        controlnet_down_block_samples, controlnet_mid_block_sample = None, None
+        mid_block_additional_residual = None
-        if control_data is not None:
+        if control_data is not None and t2i_adapter_data is not None:
-            controlnet_down_block_samples, controlnet_mid_block_sample = self.invokeai_diffuser.do_controlnet_step(
+            # TODO(ryand): This is a limitation of the UNet2DConditionModel API, not a fundamental incompatibility
+            # between ControlNets and T2I-Adapters. We will try to fix this upstream in diffusers.
+            raise Exception("ControlNet(s) and T2I-Adapter(s) cannot be used simultaneously (yet).")
+        elif control_data is not None:
+            down_block_additional_residuals, mid_block_additional_residual = self.invokeai_diffuser.do_controlnet_step(
                 control_data=control_data,
                 sample=latent_model_input,
                 timestep=timestep,
@@ -539,6 +558,32 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 total_step_count=total_step_count,
                 conditioning_data=conditioning_data,
             )
+        elif t2i_adapter_data is not None:
+            accum_adapter_state = None
+            for single_t2i_adapter_data in t2i_adapter_data:
+                # Determine the T2I-Adapter weights for the current denoising step.
+                first_t2i_adapter_step = math.floor(single_t2i_adapter_data.begin_step_percent * total_step_count)
+                last_t2i_adapter_step = math.ceil(single_t2i_adapter_data.end_step_percent * total_step_count)
+                t2i_adapter_weight = (
+                    single_t2i_adapter_data.weight[step_index]
+                    if isinstance(single_t2i_adapter_data.weight, list)
+                    else single_t2i_adapter_data.weight
+                )
+                if step_index < first_t2i_adapter_step or step_index > last_t2i_adapter_step:
+                    # If the current step is outside of the T2I-Adapter's begin/end step range, then set its weight to 0
+                    # so it has no effect.
+                    t2i_adapter_weight = 0.0
+
+                # Apply the t2i_adapter_weight, and accumulate.
+                if accum_adapter_state is None:
+                    # Handle the first T2I-Adapter.
+                    accum_adapter_state = [val * t2i_adapter_weight for val in single_t2i_adapter_data.adapter_state]
+                else:
+                    # Add to the previous adapter states.
+                    for idx, value in enumerate(single_t2i_adapter_data.adapter_state):
+                        accum_adapter_state[idx] += value * t2i_adapter_weight
+
+            down_block_additional_residuals = accum_adapter_state
 
         uc_noise_pred, c_noise_pred = self.invokeai_diffuser.do_unet_step(
             sample=latent_model_input,
@@ -547,8 +592,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             total_step_count=total_step_count,
             conditioning_data=conditioning_data,
             # extra:
-            down_block_additional_residuals=controlnet_down_block_samples,  # from controlnet(s)
+            down_block_additional_residuals=down_block_additional_residuals,
-            mid_block_additional_residual=controlnet_mid_block_sample,  # from controlnet(s)
+            mid_block_additional_residual=mid_block_additional_residual,
         )
 
         guidance_scale = conditioning_data.guidance_scale

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-bbfac5eb.js

@@ -1,4 +1,4 @@
-import{w as s,hY as T,v as l,a2 as I,hZ as R,ae as V,h_ as z,h$ as j,i0 as D,i1 as F,i2 as G,i3 as W,i4 as K,aG as Y,i5 as Z,i6 as H}from"./index-94062f76.js";import{M as U}from"./MantineProvider-a057bfc9.js";var P=String.raw,E=P`
+import{w as s,h$ as T,v as l,a2 as I,i0 as R,ae as V,i1 as z,i2 as j,i3 as D,i4 as F,i5 as G,i6 as W,i7 as K,aG as H,i8 as U,i9 as Y}from"./index-6f7e7659.js";import{M as Z}from"./MantineProvider-2072a471.js";var P=String.raw,E=P`
   :root,
   :host {
     --chakra-vh: 100vh;
@@ -277,4 +277,4 @@ import{w as s,hY as T,v as l,a2 as I,hZ as R,ae as V,h_ as z,h$ as j,i0 as D,i1
       }
 
       ${E}
-    `}),g={light:"chakra-ui-light",dark:"chakra-ui-dark"};function Q(e={}){const{preventTransition:o=!0}=e,n={setDataset:r=>{const t=o?n.preventTransition():void 0;document.documentElement.dataset.theme=r,document.documentElement.style.colorScheme=r,t==null||t()},setClassName(r){document.body.classList.add(r?g.dark:g.light),document.body.classList.remove(r?g.light:g.dark)},query(){return window.matchMedia("(prefers-color-scheme: dark)")},getSystemTheme(r){var t;return((t=n.query().matches)!=null?t:r==="dark")?"dark":"light"},addListener(r){const t=n.query(),i=a=>{r(a.matches?"dark":"light")};return typeof t.addListener=="function"?t.addListener(i):t.addEventListener("change",i),()=>{typeof t.removeListener=="function"?t.removeListener(i):t.removeEventListener("change",i)}},preventTransition(){const r=document.createElement("style");return r.appendChild(document.createTextNode("*{-webkit-transition:none!important;-moz-transition:none!important;-o-transition:none!important;-ms-transition:none!important;transition:none!important}")),document.head.appendChild(r),()=>{window.getComputedStyle(document.body),requestAnimationFrame(()=>{requestAnimationFrame(()=>{document.head.removeChild(r)})})}}};return n}var X="chakra-ui-color-mode";function L(e){return{ssr:!1,type:"localStorage",get(o){if(!(globalThis!=null&&globalThis.document))return o;let n;try{n=localStorage.getItem(e)||o}catch{}return n||o},set(o){try{localStorage.setItem(e,o)}catch{}}}}var ee=L(X),M=()=>{};function S(e,o){return e.type==="cookie"&&e.ssr?e.get(o):o}function O(e){const{value:o,children:n,options:{useSystemColorMode:r,initialColorMode:t,disableTransitionOnChange:i}={},colorModeManager:a=ee}=e,d=t==="dark"?"dark":"light",[u,p]=l.useState(()=>S(a,d)),[y,b]=l.useState(()=>S(a)),{getSystemTheme:w,setClassName:k,setDataset:x,addListener:$}=l.useMemo(()=>Q({preventTransition:i}),[i]),v=t==="system"&&!u?y:u,c=l.useCallback(h=>{const f=h==="system"?w():h;p(f),k(f==="dark"),x(f),a.set(f)},[a,w,k,x]);I(()=>{t==="system"&&b(w())},[]),l.useEffect(()=>{const h=a.get();if(h){c(h);return}if(t==="system"){c("system");return}c(d)},[a,d,t,c]);const C=l.useCallback(()=>{c(v==="dark"?"light":"dark")},[v,c]);l.useEffect(()=>{if(r)return $(c)},[r,$,c]);const A=l.useMemo(()=>({colorMode:o??v,toggleColorMode:o?M:C,setColorMode:o?M:c,forced:o!==void 0}),[v,C,c,o]);return s.jsx(R.Provider,{value:A,children:n})}O.displayName="ColorModeProvider";var te=["borders","breakpoints","colors","components","config","direction","fonts","fontSizes","fontWeights","letterSpacings","lineHeights","radii","shadows","sizes","space","styles","transition","zIndices"];function re(e){return V(e)?te.every(o=>Object.prototype.hasOwnProperty.call(e,o)):!1}function m(e){return typeof e=="function"}function oe(...e){return o=>e.reduce((n,r)=>r(n),o)}var ne=e=>function(...n){let r=[...n],t=n[n.length-1];return re(t)&&r.length>1?r=r.slice(0,r.length-1):t=e,oe(...r.map(i=>a=>m(i)?i(a):ae(a,i)))(t)},ie=ne(j);function ae(...e){return z({},...e,_)}function _(e,o,n,r){if((m(e)||m(o))&&Object.prototype.hasOwnProperty.call(r,n))return(...t)=>{const i=m(e)?e(...t):e,a=m(o)?o(...t):o;return z({},i,a,_)}}var q=l.createContext({getDocument(){return document},getWindow(){return window}});q.displayName="EnvironmentContext";function N(e){const{children:o,environment:n,disabled:r}=e,t=l.useRef(null),i=l.useMemo(()=>n||{getDocument:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument)!=null?u:document},getWindow:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument.defaultView)!=null?u:window}},[n]),a=!r||!n;return s.jsxs(q.Provider,{value:i,children:[o,a&&s.jsx("span",{id:"__chakra_env",hidden:!0,ref:t})]})}N.displayName="EnvironmentProvider";var se=e=>{const{children:o,colorModeManager:n,portalZIndex:r,resetScope:t,resetCSS:i=!0,theme:a={},environment:d,cssVarsRoot:u,disableEnvironment:p,disableGlobalStyle:y}=e,b=s.jsx(N,{environment:d,disabled:p,children:o});return s.jsx(D,{theme:a,cssVarsRoot:u,children:s.jsxs(O,{colorModeManager:n,options:a.config,children:[i?s.jsx(J,{scope:t}):s.jsx(B,{}),!y&&s.jsx(F,{}),r?s.jsx(G,{zIndex:r,children:b}):b]})})},le=e=>function({children:n,theme:r=e,toastOptions:t,...i}){return s.jsxs(se,{theme:r,...i,children:[s.jsx(W,{value:t==null?void 0:t.defaultOptions,children:n}),s.jsx(K,{...t})]})},de=le(j);const ue=()=>l.useMemo(()=>({colorScheme:"dark",fontFamily:"'Inter Variable', sans-serif",components:{ScrollArea:{defaultProps:{scrollbarSize:10},styles:{scrollbar:{"&:hover":{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}},thumb:{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}}}}}),[]),ce=L("@@invokeai-color-mode");function he({children:e}){const{i18n:o}=Y(),n=o.dir(),r=l.useMemo(()=>ie({...Z,direction:n}),[n]);l.useEffect(()=>{document.body.dir=n},[n]);const t=ue();return s.jsx(U,{theme:t,children:s.jsx(de,{theme:r,colorModeManager:ce,toastOptions:H,children:e})})}const ve=l.memo(he);export{ve as default};
+    `}),g={light:"chakra-ui-light",dark:"chakra-ui-dark"};function Q(e={}){const{preventTransition:o=!0}=e,n={setDataset:r=>{const t=o?n.preventTransition():void 0;document.documentElement.dataset.theme=r,document.documentElement.style.colorScheme=r,t==null||t()},setClassName(r){document.body.classList.add(r?g.dark:g.light),document.body.classList.remove(r?g.light:g.dark)},query(){return window.matchMedia("(prefers-color-scheme: dark)")},getSystemTheme(r){var t;return((t=n.query().matches)!=null?t:r==="dark")?"dark":"light"},addListener(r){const t=n.query(),i=a=>{r(a.matches?"dark":"light")};return typeof t.addListener=="function"?t.addListener(i):t.addEventListener("change",i),()=>{typeof t.removeListener=="function"?t.removeListener(i):t.removeEventListener("change",i)}},preventTransition(){const r=document.createElement("style");return r.appendChild(document.createTextNode("*{-webkit-transition:none!important;-moz-transition:none!important;-o-transition:none!important;-ms-transition:none!important;transition:none!important}")),document.head.appendChild(r),()=>{window.getComputedStyle(document.body),requestAnimationFrame(()=>{requestAnimationFrame(()=>{document.head.removeChild(r)})})}}};return n}var X="chakra-ui-color-mode";function L(e){return{ssr:!1,type:"localStorage",get(o){if(!(globalThis!=null&&globalThis.document))return o;let n;try{n=localStorage.getItem(e)||o}catch{}return n||o},set(o){try{localStorage.setItem(e,o)}catch{}}}}var ee=L(X),M=()=>{};function S(e,o){return e.type==="cookie"&&e.ssr?e.get(o):o}function O(e){const{value:o,children:n,options:{useSystemColorMode:r,initialColorMode:t,disableTransitionOnChange:i}={},colorModeManager:a=ee}=e,d=t==="dark"?"dark":"light",[u,p]=l.useState(()=>S(a,d)),[y,b]=l.useState(()=>S(a)),{getSystemTheme:w,setClassName:k,setDataset:x,addListener:$}=l.useMemo(()=>Q({preventTransition:i}),[i]),v=t==="system"&&!u?y:u,c=l.useCallback(h=>{const f=h==="system"?w():h;p(f),k(f==="dark"),x(f),a.set(f)},[a,w,k,x]);I(()=>{t==="system"&&b(w())},[]),l.useEffect(()=>{const h=a.get();if(h){c(h);return}if(t==="system"){c("system");return}c(d)},[a,d,t,c]);const C=l.useCallback(()=>{c(v==="dark"?"light":"dark")},[v,c]);l.useEffect(()=>{if(r)return $(c)},[r,$,c]);const A=l.useMemo(()=>({colorMode:o??v,toggleColorMode:o?M:C,setColorMode:o?M:c,forced:o!==void 0}),[v,C,c,o]);return s.jsx(R.Provider,{value:A,children:n})}O.displayName="ColorModeProvider";var te=["borders","breakpoints","colors","components","config","direction","fonts","fontSizes","fontWeights","letterSpacings","lineHeights","radii","shadows","sizes","space","styles","transition","zIndices"];function re(e){return V(e)?te.every(o=>Object.prototype.hasOwnProperty.call(e,o)):!1}function m(e){return typeof e=="function"}function oe(...e){return o=>e.reduce((n,r)=>r(n),o)}var ne=e=>function(...n){let r=[...n],t=n[n.length-1];return re(t)&&r.length>1?r=r.slice(0,r.length-1):t=e,oe(...r.map(i=>a=>m(i)?i(a):ae(a,i)))(t)},ie=ne(j);function ae(...e){return z({},...e,_)}function _(e,o,n,r){if((m(e)||m(o))&&Object.prototype.hasOwnProperty.call(r,n))return(...t)=>{const i=m(e)?e(...t):e,a=m(o)?o(...t):o;return z({},i,a,_)}}var q=l.createContext({getDocument(){return document},getWindow(){return window}});q.displayName="EnvironmentContext";function N(e){const{children:o,environment:n,disabled:r}=e,t=l.useRef(null),i=l.useMemo(()=>n||{getDocument:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument)!=null?u:document},getWindow:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument.defaultView)!=null?u:window}},[n]),a=!r||!n;return s.jsxs(q.Provider,{value:i,children:[o,a&&s.jsx("span",{id:"__chakra_env",hidden:!0,ref:t})]})}N.displayName="EnvironmentProvider";var se=e=>{const{children:o,colorModeManager:n,portalZIndex:r,resetScope:t,resetCSS:i=!0,theme:a={},environment:d,cssVarsRoot:u,disableEnvironment:p,disableGlobalStyle:y}=e,b=s.jsx(N,{environment:d,disabled:p,children:o});return s.jsx(D,{theme:a,cssVarsRoot:u,children:s.jsxs(O,{colorModeManager:n,options:a.config,children:[i?s.jsx(J,{scope:t}):s.jsx(B,{}),!y&&s.jsx(F,{}),r?s.jsx(G,{zIndex:r,children:b}):b]})})},le=e=>function({children:n,theme:r=e,toastOptions:t,...i}){return s.jsxs(se,{theme:r,...i,children:[s.jsx(W,{value:t==null?void 0:t.defaultOptions,children:n}),s.jsx(K,{...t})]})},de=le(j);const ue=()=>l.useMemo(()=>({colorScheme:"dark",fontFamily:"'Inter Variable', sans-serif",components:{ScrollArea:{defaultProps:{scrollbarSize:10},styles:{scrollbar:{"&:hover":{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}},thumb:{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}}}}}),[]),ce=L("@@invokeai-color-mode");function he({children:e}){const{i18n:o}=H(),n=o.dir(),r=l.useMemo(()=>ie({...U,direction:n}),[n]);l.useEffect(()=>{document.body.dir=n},[n]);const t=ue();return s.jsx(Z,{theme:t,children:s.jsx(de,{theme:r,colorModeManager:ce,toastOptions:Y,children:e})})}const ve=l.memo(he);export{ve as default};

invokeai/frontend/web/dist/index.html

@@ -3,6 +3,9 @@
   <head>
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate">
+    <meta http-equiv="Pragma" content="no-cache">
+    <meta http-equiv="Expires" content="0">
     <title>InvokeAI - A Stable Diffusion Toolkit</title>
     <link rel="shortcut icon" type="icon" href="./assets/favicon-0d253ced.ico" />
     <style>
@@ -12,7 +15,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-94062f76.js"></script>
+    <script type="module" crossorigin src="./assets/index-6f7e7659.js"></script>
   </head>
 
   <body dir="ltr">

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

@@ -697,7 +697,7 @@
         "noLoRAsAvailable": "No LoRAs available",
         "noMatchingLoRAs": "No matching LoRAs",
         "noMatchingModels": "No matching Models",
-        "noModelsAvailable": "No Modelss available",
+        "noModelsAvailable": "No models available",
         "selectLoRA": "Select a LoRA",
         "selectModel": "Select a Model"
     },

invokeai/frontend/web/index.html

@@ -3,6 +3,9 @@
   <head>
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate">
+    <meta http-equiv="Pragma" content="no-cache">
+    <meta http-equiv="Expires" content="0">
     <title>InvokeAI - A Stable Diffusion Toolkit</title>
     <link rel="shortcut icon" type="icon" href="favicon.ico" />
     <style>

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

@@ -697,7 +697,7 @@
         "noLoRAsAvailable": "No LoRAs available",
         "noMatchingLoRAs": "No matching LoRAs",
         "noMatchingModels": "No matching Models",
-        "noModelsAvailable": "No Modelss available",
+        "noModelsAvailable": "No models available",
         "selectLoRA": "Select a LoRA",
         "selectModel": "Select a Model"
     },

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

@@ -1,5 +1,8 @@
 import { logger } from 'app/logging/logger';
-import { controlNetRemoved } from 'features/controlNet/store/controlNetSlice';
+import {
+  controlNetRemoved,
+  ipAdapterModelChanged,
+} from 'features/controlNet/store/controlNetSlice';
 import { loraRemoved } from 'features/lora/store/loraSlice';
 import {
   modelChanged,
@@ -16,12 +19,14 @@ import {
 } from 'features/sdxl/store/sdxlSlice';
 import { forEach, some } from 'lodash-es';
 import {
+  ipAdapterModelsAdapter,
   mainModelsAdapter,
   modelsApi,
   vaeModelsAdapter,
 } from 'services/api/endpoints/models';
 import { TypeGuardFor } from 'services/api/types';
 import { startAppListening } from '..';
+import { zIPAdapterModel } from 'features/nodes/types/types';
 
 export const addModelsLoadedListener = () => {
   startAppListening({
@@ -234,6 +239,50 @@ export const addModelsLoadedListener = () => {
       });
     },
   });
+  startAppListening({
+    matcher: modelsApi.endpoints.getIPAdapterModels.matchFulfilled,
+    effect: async (action, { getState, dispatch }) => {
+      // ControlNet models loaded - need to remove missing ControlNets from state
+      const log = logger('models');
+      log.info(
+        { models: action.payload.entities },
+        `IP Adapter models loaded (${action.payload.ids.length})`
+      );
+
+      const { model } = getState().controlNet.ipAdapterInfo;
+
+      const isModelAvailable = some(
+        action.payload.entities,
+        (m) =>
+          m?.model_name === model?.model_name &&
+          m?.base_model === model?.base_model
+      );
+
+      if (isModelAvailable) {
+        return;
+      }
+
+      const firstModel = ipAdapterModelsAdapter
+        .getSelectors()
+        .selectAll(action.payload)[0];
+
+      if (!firstModel) {
+        dispatch(ipAdapterModelChanged(null));
+      }
+
+      const result = zIPAdapterModel.safeParse(firstModel);
+
+      if (!result.success) {
+        log.error(
+          { error: result.error.format() },
+          'Failed to parse IP Adapter model'
+        );
+        return;
+      }
+
+      dispatch(ipAdapterModelChanged(result.data));
+    },
+  });
   startAppListening({
     matcher: modelsApi.endpoints.getTextualInversionModels.matchFulfilled,
     effect: async (action) => {

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

@@ -8,6 +8,7 @@ import {
 } from 'features/gallery/store/gallerySlice';
 import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
 import { CANVAS_OUTPUT } from 'features/nodes/util/graphBuilders/constants';
+import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
 import { isImageOutput } from 'services/api/guards';
 import { imagesAdapter } from 'services/api/util';
@@ -70,11 +71,21 @@ export const addInvocationCompleteEventListener = () => {
             )
           );
 
+          // update the total images for the board
+          dispatch(
+            boardsApi.util.updateQueryData(
+              'getBoardImagesTotal',
+              imageDTO.board_id ?? 'none',
+              (draft) => {
+                // eslint-disable-next-line @typescript-eslint/no-unused-vars
+                draft.total += 1;
+              }
+            )
+          );
+
           dispatch(
             imagesApi.util.invalidateTags([
-              { type: 'BoardImagesTotal', id: imageDTO.board_id },
+              { type: 'Board', id: imageDTO.board_id ?? 'none' },
-              { type: 'BoardAssetsTotal', id: imageDTO.board_id },
-              { type: 'Board', id: imageDTO.board_id },
             ])
           );
 

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

@@ -5,8 +5,23 @@ import ParamIPAdapterFeatureToggle from './ParamIPAdapterFeatureToggle';
 import ParamIPAdapterImage from './ParamIPAdapterImage';
 import ParamIPAdapterModelSelect from './ParamIPAdapterModelSelect';
 import ParamIPAdapterWeight from './ParamIPAdapterWeight';
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from '../../../../app/store/store';
+import { defaultSelectorOptions } from '../../../../app/store/util/defaultMemoizeOptions';
+import { useAppSelector } from '../../../../app/store/storeHooks';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const { isIPAdapterEnabled } = state.controlNet;
+
+    return { isIPAdapterEnabled };
+  },
+  defaultSelectorOptions
+);
 
 const IPAdapterPanel = () => {
+  const { isIPAdapterEnabled } = useAppSelector(selector);
   return (
     <Flex
       sx={{
@@ -14,7 +29,6 @@ const IPAdapterPanel = () => {
         gap: 3,
         paddingInline: 3,
         paddingBlock: 2,
-        paddingBottom: 5,
         borderRadius: 'base',
         position: 'relative',
         bg: 'base.250',
@@ -24,11 +38,27 @@ const IPAdapterPanel = () => {
       }}
     >
       <ParamIPAdapterFeatureToggle />
-      <ParamIPAdapterImage />
+      {isIPAdapterEnabled && (
+        <>
           <ParamIPAdapterModelSelect />
+          <Flex gap="3">
+            <Flex
+              flexDirection="column"
+              sx={{
+                h: 28,
+                w: 'full',
+                gap: 4,
+                mb: 4,
+              }}
+            >
               <ParamIPAdapterWeight />
               <ParamIPAdapterBeginEnd />
             </Flex>
+            <ParamIPAdapterImage />
+          </Flex>
+        </>
+      )}
+    </Flex>
   );
 };
 

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

@@ -66,7 +66,8 @@ const ParamIPAdapterImage = () => {
       layerStyle="second"
       sx={{
         position: 'relative',
-        w: 'full',
+        h: 28,
+        w: 28,
         alignItems: 'center',
         justifyContent: 'center',
         aspectRatio: '1/1',

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

@@ -88,12 +88,16 @@ const ParamIPAdapterModelSelect = () => {
       className="nowheel nodrag"
       tooltip={selectedModel?.description}
       value={selectedModel?.id ?? null}
-      placeholder="Pick one"
+      placeholder={
-      error={!selectedModel}
+        data.length > 0
+          ? t('models.selectModel')
+          : t('models.noModelsAvailable')
+      }
+      error={!selectedModel && data.length > 0}
       data={data}
       onChange={handleValueChanged}
       sx={{ width: '100%' }}
-      disabled={!isEnabled}
+      disabled={!isEnabled || data.length === 0}
     />
   );
 };

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

@@ -53,8 +53,12 @@ export const isValidDrop = (
       }
 
       if (payloadType === 'IMAGE_DTOS') {
-        // TODO (multi-select)
+        // Assume all images are on the same board - this is true for the moment
-        return true;
+        const { imageDTOs } = active.data.current.payload;
+        const currentBoard = imageDTOs[0]?.board_id ?? 'none';
+        const destinationBoard = overData.context.boardId;
+
+        return currentBoard !== destinationBoard;
       }
 
       return false;
@@ -71,14 +75,17 @@ export const isValidDrop = (
       // Check if the image's board is the board we are dragging onto
       if (payloadType === 'IMAGE_DTO') {
         const { imageDTO } = active.data.current.payload;
-        const currentBoard = imageDTO.board_id;
+        const currentBoard = imageDTO.board_id ?? 'none';
 
         return currentBoard !== 'none';
       }
 
       if (payloadType === 'IMAGE_DTOS') {
-        // TODO (multi-select)
+        // Assume all images are on the same board - this is true for the moment
-        return true;
+        const { imageDTOs } = active.data.current.payload;
+        const currentBoard = imageDTOs[0]?.board_id ?? 'none';
+
+        return currentBoard !== 'none';
       }
 
       return false;

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

@@ -77,12 +77,12 @@ const GalleryBoard = ({
   const { data: imagesTotal } = useGetBoardImagesTotalQuery(board.board_id);
   const { data: assetsTotal } = useGetBoardAssetsTotalQuery(board.board_id);
   const tooltip = useMemo(() => {
-    if (!imagesTotal || !assetsTotal) {
+    if (imagesTotal?.total === undefined || assetsTotal?.total === undefined) {
       return undefined;
     }
-    return `${imagesTotal} image${
+    return `${imagesTotal.total} image${imagesTotal.total === 1 ? '' : 's'}, ${
-      imagesTotal > 1 ? 's' : ''
+      assetsTotal.total
-    }, ${assetsTotal} asset${assetsTotal > 1 ? 's' : ''}`;
+    } asset${assetsTotal.total === 1 ? '' : 's'}`;
   }, [assetsTotal, imagesTotal]);
 
   const { currentData: coverImage } = useGetImageDTOQuery(

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

@@ -1,4 +1,4 @@
-import { Box, Flex, Image, Text } from '@chakra-ui/react';
+import { Box, Flex, Image, Text, Tooltip } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
@@ -15,6 +15,10 @@ import { memo, useCallback, useMemo, useState } from 'react';
 import { useBoardName } from 'services/api/hooks/useBoardName';
 import AutoAddIcon from '../AutoAddIcon';
 import BoardContextMenu from '../BoardContextMenu';
+import {
+  useGetBoardAssetsTotalQuery,
+  useGetBoardImagesTotalQuery,
+} from 'services/api/endpoints/boards';
 
 interface Props {
   isSelected: boolean;
@@ -41,6 +45,17 @@ 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);
   }, []);
@@ -74,6 +89,7 @@ const NoBoardBoard = memo(({ isSelected }: Props) => {
       >
         <BoardContextMenu board_id="none">
           {(ref) => (
+            <Tooltip label={tooltip} openDelay={1000} hasArrow>
               <Flex
                 ref={ref}
                 onClick={handleSelectBoard}
@@ -139,12 +155,16 @@ const NoBoardBoard = memo(({ isSelected }: Props) => {
                 >
                   {boardName}
                 </Flex>
-              <SelectionOverlay isSelected={isSelected} isHovered={isHovered} />
+                <SelectionOverlay
+                  isSelected={isSelected}
+                  isHovered={isHovered}
+                />
                 <IAIDroppable
                   data={droppableData}
                   dropLabel={<Text fontSize="md">Move</Text>}
                 />
               </Flex>
+            </Tooltip>
           )}
         </BoardContextMenu>
       </Flex>

invokeai/frontend/web/src/features/gallery/hooks/useMultiselect.ts

@@ -3,13 +3,12 @@ import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import { selectListImagesBaseQueryArgs } from 'features/gallery/store/gallerySelectors';
-import { uniq } from 'lodash-es';
 import { MouseEvent, useCallback, useMemo } from 'react';
 import { useListImagesQuery } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
-import { selectionChanged } from '../store/gallerySlice';
 import { imagesSelectors } from 'services/api/util';
 import { useFeatureStatus } from '../../system/hooks/useFeatureStatus';
+import { selectionChanged } from '../store/gallerySlice';
 
 const selector = createSelector(
   [stateSelector, selectListImagesBaseQueryArgs],
@@ -60,7 +59,7 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
           const start = Math.min(lastClickedIndex, currentClickedIndex);
           const end = Math.max(lastClickedIndex, currentClickedIndex);
           const imagesToSelect = imageDTOs.slice(start, end + 1);
-          dispatch(selectionChanged(uniq(selection.concat(imagesToSelect))));
+          dispatch(selectionChanged(selection.concat(imagesToSelect)));
         }
       } else if (e.ctrlKey || e.metaKey) {
         if (
@@ -73,7 +72,7 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
             )
           );
         } else {
-          dispatch(selectionChanged(uniq(selection.concat(imageDTO))));
+          dispatch(selectionChanged(selection.concat(imageDTO)));
         }
       } else {
         dispatch(selectionChanged([imageDTO]));

invokeai/frontend/web/src/features/gallery/hooks/useNextPrevImage.ts

@@ -20,7 +20,7 @@ export const nextPrevImageButtonsSelector = createSelector(
     const { data, status } =
       imagesApi.endpoints.listImages.select(baseQueryArgs)(state);
 
-    const { data: total } =
+    const { data: totalsData } =
       state.gallery.galleryView === 'images'
         ? boardsApi.endpoints.getBoardImagesTotal.select(
             baseQueryArgs.board_id ?? 'none'
@@ -34,7 +34,7 @@ export const nextPrevImageButtonsSelector = createSelector(
 
     const isFetching = status === 'pending';
 
-    if (!data || !lastSelectedImage || total === 0) {
+    if (!data || !lastSelectedImage || totalsData?.total === 0) {
       return {
         isFetching,
         queryArgs: baseQueryArgs,
@@ -74,7 +74,7 @@ export const nextPrevImageButtonsSelector = createSelector(
     return {
       loadedImagesCount: images.length,
       currentImageIndex,
-      areMoreImagesAvailable: (total ?? 0) > imagesLength,
+      areMoreImagesAvailable: (totalsData?.total ?? 0) > imagesLength,
       isFetching: status === 'pending',
       nextImage,
       prevImage,

invokeai/frontend/web/src/features/gallery/store/gallerySlice.ts

@@ -4,6 +4,7 @@ import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
 import { BoardId, GalleryState, GalleryView } from './types';
+import { uniqBy } from 'lodash-es';
 
 export const initialGalleryState: GalleryState = {
   selection: [],
@@ -24,7 +25,7 @@ export const gallerySlice = createSlice({
       state.selection = action.payload ? [action.payload] : [];
     },
     selectionChanged: (state, action: PayloadAction<ImageDTO[]>) => {
-      state.selection = action.payload;
+      state.selection = uniqBy(action.payload, (i) => i.image_name);
     },
     shouldAutoSwitchChanged: (state, action: PayloadAction<boolean>) => {
       state.shouldAutoSwitch = action.payload;

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

@@ -16,6 +16,7 @@ import SchedulerInputField from './inputs/SchedulerInputField';
 import StringInputField from './inputs/StringInputField';
 import VaeModelInputField from './inputs/VaeModelInputField';
 import IPAdapterModelInputField from './inputs/IPAdapterModelInputField';
+import T2IAdapterModelInputField from './inputs/T2IAdapterModelInputField';
 import BoardInputField from './inputs/BoardInputField';
 
 type InputFieldProps = {
@@ -188,6 +189,18 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
     );
   }
 
+  if (
+    field?.type === 'T2IAdapterModelField' &&
+    fieldTemplate?.type === 'T2IAdapterModelField'
+  ) {
+    return (
+      <T2IAdapterModelInputField
+        nodeId={nodeId}
+        field={field}
+        fieldTemplate={fieldTemplate}
+      />
+    );
+  }
   if (field?.type === 'ColorField' && fieldTemplate?.type === 'ColorField') {
     return (
       <ColorInputField

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/T2IAdapterInputField.tsx

@@ -0,0 +1,19 @@
+import {
+  T2IAdapterInputFieldTemplate,
+  T2IAdapterInputFieldValue,
+  T2IAdapterPolymorphicInputFieldTemplate,
+  T2IAdapterPolymorphicInputFieldValue,
+  FieldComponentProps,
+} from 'features/nodes/types/types';
+import { memo } from 'react';
+
+const T2IAdapterInputFieldComponent = (
+  _props: FieldComponentProps<
+    T2IAdapterInputFieldValue | T2IAdapterPolymorphicInputFieldValue,
+    T2IAdapterInputFieldTemplate | T2IAdapterPolymorphicInputFieldTemplate
+  >
+) => {
+  return null;
+};
+
+export default memo(T2IAdapterInputFieldComponent);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/T2IAdapterModelInputField.tsx

@@ -0,0 +1,100 @@
+import { SelectItem } from '@mantine/core';
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIMantineSelect from 'common/components/IAIMantineSelect';
+import { fieldT2IAdapterModelValueChanged } from 'features/nodes/store/nodesSlice';
+import {
+  T2IAdapterModelInputFieldTemplate,
+  T2IAdapterModelInputFieldValue,
+  FieldComponentProps,
+} from 'features/nodes/types/types';
+import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
+import { modelIdToT2IAdapterModelParam } from 'features/parameters/util/modelIdToT2IAdapterModelParam';
+import { forEach } from 'lodash-es';
+import { memo, useCallback, useMemo } from 'react';
+import { useGetT2IAdapterModelsQuery } from 'services/api/endpoints/models';
+
+const T2IAdapterModelInputFieldComponent = (
+  props: FieldComponentProps<
+    T2IAdapterModelInputFieldValue,
+    T2IAdapterModelInputFieldTemplate
+  >
+) => {
+  const { nodeId, field } = props;
+  const t2iAdapterModel = field.value;
+  const dispatch = useAppDispatch();
+
+  const { data: t2iAdapterModels } = useGetT2IAdapterModelsQuery();
+
+  // grab the full model entity from the RTK Query cache
+  const selectedModel = useMemo(
+    () =>
+      t2iAdapterModels?.entities[
+        `${t2iAdapterModel?.base_model}/t2i_adapter/${t2iAdapterModel?.model_name}`
+      ] ?? null,
+    [
+      t2iAdapterModel?.base_model,
+      t2iAdapterModel?.model_name,
+      t2iAdapterModels?.entities,
+    ]
+  );
+
+  const data = useMemo(() => {
+    if (!t2iAdapterModels) {
+      return [];
+    }
+
+    const data: SelectItem[] = [];
+
+    forEach(t2iAdapterModels.entities, (model, id) => {
+      if (!model) {
+        return;
+      }
+
+      data.push({
+        value: id,
+        label: model.model_name,
+        group: MODEL_TYPE_MAP[model.base_model],
+      });
+    });
+
+    return data;
+  }, [t2iAdapterModels]);
+
+  const handleValueChanged = useCallback(
+    (v: string | null) => {
+      if (!v) {
+        return;
+      }
+
+      const newT2IAdapterModel = modelIdToT2IAdapterModelParam(v);
+
+      if (!newT2IAdapterModel) {
+        return;
+      }
+
+      dispatch(
+        fieldT2IAdapterModelValueChanged({
+          nodeId,
+          fieldName: field.name,
+          value: newT2IAdapterModel,
+        })
+      );
+    },
+    [dispatch, field.name, nodeId]
+  );
+
+  return (
+    <IAIMantineSelect
+      className="nowheel nodrag"
+      tooltip={selectedModel?.description}
+      value={selectedModel?.id ?? null}
+      placeholder="Pick one"
+      error={!selectedModel}
+      data={data}
+      onChange={handleValueChanged}
+      sx={{ width: '100%' }}
+    />
+  );
+};
+
+export default memo(T2IAdapterModelInputFieldComponent);

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

@@ -55,6 +55,7 @@ import {
   SchedulerInputFieldValue,
   SDXLRefinerModelInputFieldValue,
   StringInputFieldValue,
+  T2IAdapterModelInputFieldValue,
   VaeModelInputFieldValue,
   Workflow,
 } from '../types/types';
@@ -645,6 +646,12 @@ const nodesSlice = createSlice({
     ) => {
       fieldValueReducer(state, action);
     },
+    fieldT2IAdapterModelValueChanged: (
+      state,
+      action: FieldValueAction<T2IAdapterModelInputFieldValue>
+    ) => {
+      fieldValueReducer(state, action);
+    },
     fieldEnumModelValueChanged: (
       state,
       action: FieldValueAction<EnumInputFieldValue>
@@ -1009,6 +1016,7 @@ export const {
   fieldEnumModelValueChanged,
   fieldImageValueChanged,
   fieldIPAdapterModelValueChanged,
+  fieldT2IAdapterModelValueChanged,
   fieldLabelChanged,
   fieldLoRAModelValueChanged,
   fieldMainModelValueChanged,

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

@@ -31,6 +31,7 @@ export const COLLECTION_TYPES: FieldType[] = [
   'ConditioningCollection',
   'ControlCollection',
   'ColorCollection',
+  'T2IAdapterCollection',
 ];
 
 export const POLYMORPHIC_TYPES: FieldType[] = [
@@ -43,6 +44,7 @@ export const POLYMORPHIC_TYPES: FieldType[] = [
   'ConditioningPolymorphic',
   'ControlPolymorphic',
   'ColorPolymorphic',
+  'T2IAdapterPolymorphic',
 ];
 
 export const MODEL_TYPES: FieldType[] = [
@@ -57,6 +59,7 @@ export const MODEL_TYPES: FieldType[] = [
   'UNetField',
   'VaeField',
   'ClipField',
+  'T2IAdapterModelField',
 ];
 
 export const COLLECTION_MAP: FieldTypeMapWithNumber = {
@@ -70,6 +73,7 @@ export const COLLECTION_MAP: FieldTypeMapWithNumber = {
   ConditioningField: 'ConditioningCollection',
   ControlField: 'ControlCollection',
   ColorField: 'ColorCollection',
+  T2IAdapterField: 'T2IAdapterCollection',
 };
 export const isCollectionItemType = (
   itemType: string | undefined
@@ -87,6 +91,7 @@ export const SINGLE_TO_POLYMORPHIC_MAP: FieldTypeMapWithNumber = {
   ConditioningField: 'ConditioningPolymorphic',
   ControlField: 'ControlPolymorphic',
   ColorField: 'ColorPolymorphic',
+  T2IAdapterField: 'T2IAdapterPolymorphic',
 };
 
 export const POLYMORPHIC_TO_SINGLE_MAP: FieldTypeMap = {
@@ -99,6 +104,7 @@ export const POLYMORPHIC_TO_SINGLE_MAP: FieldTypeMap = {
   ConditioningPolymorphic: 'ConditioningField',
   ControlPolymorphic: 'ControlField',
   ColorPolymorphic: 'ColorField',
+  T2IAdapterPolymorphic: 'T2IAdapterField',
 };
 
 export const TYPES_WITH_INPUT_COMPONENTS: FieldType[] = [
@@ -123,6 +129,7 @@ export const TYPES_WITH_INPUT_COMPONENTS: FieldType[] = [
   'Scheduler',
   'IPAdapterModelField',
   'BoardField',
+  'T2IAdapterModelField',
 ];
 
 export const isPolymorphicItemType = (
@@ -272,7 +279,7 @@ export const FIELDS: Record<FieldType, FieldUIConfig> = {
     title: t('nodes.integerPolymorphic'),
   },
   IPAdapterField: {
-    color: 'green.300',
+    color: 'teal.500',
     description: 'IP-Adapter info passed between nodes.',
     title: 'IP-Adapter',
   },
@@ -341,6 +348,26 @@ export const FIELDS: Record<FieldType, FieldUIConfig> = {
     description: t('nodes.stringPolymorphicDescription'),
     title: t('nodes.stringPolymorphic'),
   },
+  T2IAdapterCollection: {
+    color: 'teal.500',
+    description: t('nodes.t2iAdapterCollectionDescription'),
+    title: t('nodes.t2iAdapterCollection'),
+  },
+  T2IAdapterField: {
+    color: 'teal.500',
+    description: t('nodes.t2iAdapterFieldDescription'),
+    title: t('nodes.t2iAdapterField'),
+  },
+  T2IAdapterModelField: {
+    color: 'teal.500',
+    description: 'TODO',
+    title: 'T2I-Adapter',
+  },
+  T2IAdapterPolymorphic: {
+    color: 'teal.500',
+    description: 'T2I-Adapter info passed between nodes.',
+    title: 'T2I-Adapter Polymorphic',
+  },
   UNetField: {
     color: 'red.500',
     description: t('nodes.uNetFieldDescription'),

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

@@ -114,6 +114,10 @@ export const zFieldType = z.enum([
   'string',
   'StringCollection',
   'StringPolymorphic',
+  'T2IAdapterCollection',
+  'T2IAdapterField',
+  'T2IAdapterModelField',
+  'T2IAdapterPolymorphic',
   'UNetField',
   'VaeField',
   'VaeModelField',
@@ -426,6 +430,48 @@ export type IPAdapterInputFieldValue = z.infer<
   typeof zIPAdapterInputFieldValue
 >;
 
+export const zT2IAdapterModel = zModelIdentifier;
+export type T2IAdapterModel = z.infer<typeof zT2IAdapterModel>;
+
+export const zT2IAdapterField = z.object({
+  image: zImageField,
+  t2i_adapter_model: zT2IAdapterModel,
+  weight: z.union([z.number(), z.array(z.number())]).optional(),
+  begin_step_percent: z.number().optional(),
+  end_step_percent: z.number().optional(),
+  resize_mode: z
+    .enum(['just_resize', 'crop_resize', 'fill_resize', 'just_resize_simple'])
+    .optional(),
+});
+export type T2IAdapterField = z.infer<typeof zT2IAdapterField>;
+
+export const zT2IAdapterInputFieldValue = zInputFieldValueBase.extend({
+  type: z.literal('T2IAdapterField'),
+  value: zT2IAdapterField.optional(),
+});
+export type T2IAdapterInputFieldValue = z.infer<
+  typeof zT2IAdapterInputFieldValue
+>;
+
+export const zT2IAdapterPolymorphicInputFieldValue =
+  zInputFieldValueBase.extend({
+    type: z.literal('T2IAdapterPolymorphic'),
+    value: z.union([zT2IAdapterField, z.array(zT2IAdapterField)]).optional(),
+  });
+export type T2IAdapterPolymorphicInputFieldValue = z.infer<
+  typeof zT2IAdapterPolymorphicInputFieldValue
+>;
+
+export const zT2IAdapterCollectionInputFieldValue = zInputFieldValueBase.extend(
+  {
+    type: z.literal('T2IAdapterCollection'),
+    value: z.array(zT2IAdapterField).optional(),
+  }
+);
+export type T2IAdapterCollectionInputFieldValue = z.infer<
+  typeof zT2IAdapterCollectionInputFieldValue
+>;
+
 export const zModelType = z.enum([
   'onnx',
   'main',
@@ -592,6 +638,17 @@ export type IPAdapterModelInputFieldValue = z.infer<
   typeof zIPAdapterModelInputFieldValue
 >;
 
+export const zT2IAdapterModelField = zModelIdentifier;
+export type T2IAdapterModelField = z.infer<typeof zT2IAdapterModelField>;
+
+export const zT2IAdapterModelInputFieldValue = zInputFieldValueBase.extend({
+  type: z.literal('T2IAdapterModelField'),
+  value: zT2IAdapterModelField.optional(),
+});
+export type T2IAdapterModelInputFieldValue = z.infer<
+  typeof zT2IAdapterModelInputFieldValue
+>;
+
 export const zCollectionInputFieldValue = zInputFieldValueBase.extend({
   type: z.literal('Collection'),
   value: z.array(z.any()).optional(), // TODO: should this field ever have a value?
@@ -688,6 +745,10 @@ export const zInputFieldValue = z.discriminatedUnion('type', [
   zStringCollectionInputFieldValue,
   zStringPolymorphicInputFieldValue,
   zStringInputFieldValue,
+  zT2IAdapterInputFieldValue,
+  zT2IAdapterModelInputFieldValue,
+  zT2IAdapterCollectionInputFieldValue,
+  zT2IAdapterPolymorphicInputFieldValue,
   zUNetInputFieldValue,
   zVaeInputFieldValue,
   zVaeModelInputFieldValue,
@@ -889,6 +950,24 @@ export type IPAdapterInputFieldTemplate = InputFieldTemplateBase & {
   type: 'IPAdapterField';
 };
 
+export type T2IAdapterInputFieldTemplate = InputFieldTemplateBase & {
+  default: undefined;
+  type: 'T2IAdapterField';
+};
+
+export type T2IAdapterCollectionInputFieldTemplate = InputFieldTemplateBase & {
+  default: undefined;
+  type: 'T2IAdapterCollection';
+  item_default?: T2IAdapterField;
+};
+
+export type T2IAdapterPolymorphicInputFieldTemplate = Omit<
+  T2IAdapterInputFieldTemplate,
+  'type'
+> & {
+  type: 'T2IAdapterPolymorphic';
+};
+
 export type EnumInputFieldTemplate = InputFieldTemplateBase & {
   default: string;
   type: 'enum';
@@ -931,6 +1010,11 @@ export type IPAdapterModelInputFieldTemplate = InputFieldTemplateBase & {
   type: 'IPAdapterModelField';
 };
 
+export type T2IAdapterModelInputFieldTemplate = InputFieldTemplateBase & {
+  default: string;
+  type: 'T2IAdapterModelField';
+};
+
 export type CollectionInputFieldTemplate = InputFieldTemplateBase & {
   default: [];
   type: 'Collection';
@@ -1016,6 +1100,10 @@ export type InputFieldTemplate =
   | StringCollectionInputFieldTemplate
   | StringPolymorphicInputFieldTemplate
   | StringInputFieldTemplate
+  | T2IAdapterInputFieldTemplate
+  | T2IAdapterCollectionInputFieldTemplate
+  | T2IAdapterModelInputFieldTemplate
+  | T2IAdapterPolymorphicInputFieldTemplate
   | UNetInputFieldTemplate
   | VaeInputFieldTemplate
   | VaeModelInputFieldTemplate;

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

@@ -62,6 +62,11 @@ import {
   ConditioningField,
   IPAdapterInputFieldTemplate,
   IPAdapterModelInputFieldTemplate,
+  T2IAdapterField,
+  T2IAdapterInputFieldTemplate,
+  T2IAdapterModelInputFieldTemplate,
+  T2IAdapterPolymorphicInputFieldTemplate,
+  T2IAdapterCollectionInputFieldTemplate,
   BoardInputFieldTemplate,
   InputFieldTemplate,
 } from '../types/types';
@@ -452,6 +457,19 @@ const buildIPAdapterModelInputFieldTemplate = ({
   return template;
 };
 
+const buildT2IAdapterModelInputFieldTemplate = ({
+  schemaObject,
+  baseField,
+}: BuildInputFieldArg): T2IAdapterModelInputFieldTemplate => {
+  const template: T2IAdapterModelInputFieldTemplate = {
+    ...baseField,
+    type: 'T2IAdapterModelField',
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
 const buildBoardInputFieldTemplate = ({
   schemaObject,
   baseField,
@@ -691,6 +709,46 @@ const buildIPAdapterInputFieldTemplate = ({
   return template;
 };
 
+const buildT2IAdapterInputFieldTemplate = ({
+  schemaObject,
+  baseField,
+}: BuildInputFieldArg): T2IAdapterInputFieldTemplate => {
+  const template: T2IAdapterInputFieldTemplate = {
+    ...baseField,
+    type: 'T2IAdapterField',
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
+const buildT2IAdapterPolymorphicInputFieldTemplate = ({
+  schemaObject,
+  baseField,
+}: BuildInputFieldArg): T2IAdapterPolymorphicInputFieldTemplate => {
+  const template: T2IAdapterPolymorphicInputFieldTemplate = {
+    ...baseField,
+    type: 'T2IAdapterPolymorphic',
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
+const buildT2IAdapterCollectionInputFieldTemplate = ({
+  schemaObject,
+  baseField,
+}: BuildInputFieldArg): T2IAdapterCollectionInputFieldTemplate => {
+  const template: T2IAdapterCollectionInputFieldTemplate = {
+    ...baseField,
+    type: 'T2IAdapterCollection',
+    default: schemaObject.default ?? [],
+    item_default: (schemaObject.item_default as T2IAdapterField) ?? undefined,
+  };
+
+  return template;
+};
+
 const buildEnumInputFieldTemplate = ({
   schemaObject,
   baseField,
@@ -910,6 +968,10 @@ const TEMPLATE_BUILDER_MAP: {
   string: buildStringInputFieldTemplate,
   StringCollection: buildStringCollectionInputFieldTemplate,
   StringPolymorphic: buildStringPolymorphicInputFieldTemplate,
+  T2IAdapterCollection: buildT2IAdapterCollectionInputFieldTemplate,
+  T2IAdapterField: buildT2IAdapterInputFieldTemplate,
+  T2IAdapterModelField: buildT2IAdapterModelInputFieldTemplate,
+  T2IAdapterPolymorphic: buildT2IAdapterPolymorphicInputFieldTemplate,
   UNetField: buildUNetInputFieldTemplate,
   VaeField: buildVaeInputFieldTemplate,
   VaeModelField: buildVaeModelInputFieldTemplate,

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

@@ -45,6 +45,10 @@ const FIELD_VALUE_FALLBACK_MAP: {
   string: '',
   StringCollection: [],
   StringPolymorphic: '',
+  T2IAdapterCollection: [],
+  T2IAdapterField: undefined,
+  T2IAdapterModelField: undefined,
+  T2IAdapterPolymorphic: undefined,
   UNetField: undefined,
   VaeField: undefined,
   VaeModelField: undefined,

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

@@ -340,6 +340,17 @@ export const zIPAdapterModel = z.object({
  * Type alias for model parameter, inferred from its zod schema
  */
 export type IPAdapterModelParam = z.infer<typeof zIPAdapterModel>;
+/**
+ * Zod schema for T2I-Adapter models
+ */
+export const zT2IAdapterModel = z.object({
+  model_name: z.string().min(1),
+  base_model: zBaseModel,
+});
+/**
+ * Type alias for model parameter, inferred from its zod schema
+ */
+export type T2IAdapterModelParam = z.infer<typeof zT2IAdapterModel>;
 /**
  * Zod schema for l2l strength parameter
  */

invokeai/frontend/web/src/features/parameters/util/modelIdToT2IAdapterModelParam.ts

@@ -0,0 +1,29 @@
+import { logger } from 'app/logging/logger';
+import { zT2IAdapterModel } from 'features/parameters/types/parameterSchemas';
+import { T2IAdapterModelField } from 'services/api/types';
+
+export const modelIdToT2IAdapterModelParam = (
+  t2iAdapterModelId: string
+): T2IAdapterModelField | undefined => {
+  const log = logger('models');
+  const [base_model, _model_type, model_name] = t2iAdapterModelId.split('/');
+
+  const result = zT2IAdapterModel.safeParse({
+    base_model,
+    model_name,
+  });
+
+  if (!result.success) {
+    log.error(
+      {
+        t2iAdapterModelId,
+        errors: result.error.format(),
+      },
+      'Failed to parse T2I-Adapter model id'
+    );
+
+    return;
+  }
+
+  return result.data;
+};

invokeai/frontend/web/src/features/ui/hooks/useCopyImageToClipboard.ts

@@ -25,6 +25,9 @@ export const useCopyImageToClipboard = () => {
       try {
         const getImageBlob = async () => {
           const response = await fetch(image_url);
+          if (!response.ok) {
+            throw new Error(`Problem retrieving image data`);
+          }
           return await response.blob();
         };
 

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

@@ -70,7 +70,7 @@ export const boardsApi = api.injectEndpoints({
       keepUnusedDataFor: 0,
     }),
 
-    getBoardImagesTotal: build.query<number, string | undefined>({
+    getBoardImagesTotal: build.query<{ total: number }, string | undefined>({
       query: (board_id) => ({
         url: getListImagesUrl({
           board_id: board_id ?? 'none',
@@ -85,11 +85,11 @@ export const boardsApi = api.injectEndpoints({
         { type: 'BoardImagesTotal', id: arg ?? 'none' },
       ],
       transformResponse: (response: OffsetPaginatedResults_ImageDTO_) => {
-        return response.total;
+        return { total: response.total };
       },
     }),
 
-    getBoardAssetsTotal: build.query<number, string | undefined>({
+    getBoardAssetsTotal: build.query<{ total: number }, string | undefined>({
       query: (board_id) => ({
         url: getListImagesUrl({
           board_id: board_id ?? 'none',
@@ -104,7 +104,7 @@ export const boardsApi = api.injectEndpoints({
         { type: 'BoardAssetsTotal', id: arg ?? 'none' },
       ],
       transformResponse: (response: OffsetPaginatedResults_ImageDTO_) => {
-        return response.total;
+        return { total: response.total };
       },
     }),
 

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

@@ -103,6 +103,9 @@ export const imagesApi = api.injectEndpoints({
       query: () => ({ url: getListImagesUrl({ is_intermediate: true }) }),
       providesTags: ['IntermediatesCount'],
       transformResponse: (response: OffsetPaginatedResults_ImageDTO_) => {
+        // TODO: This is storing a primitive value in the cache. `immer` cannot track state changes, so
+        // attempts to use manual cache updates on this value will fail. This should be changed into an
+        // object.
         return response.total;
       },
     }),
@@ -191,35 +194,51 @@ export const imagesApi = api.injectEndpoints({
         url: `images/i/${image_name}`,
         method: 'DELETE',
       }),
-      invalidatesTags: (result, error, { board_id }) => [
-        { type: 'BoardImagesTotal', id: board_id ?? 'none' },
-        { type: 'BoardAssetsTotal', id: board_id ?? 'none' },
-      ],
       async onQueryStarted(imageDTO, { dispatch, queryFulfilled }) {
         /**
          * Cache changes for `deleteImage`:
          * - NOT POSSIBLE: *remove* from getImageDTO
          * - $cache = [board_id|no_board]/[images|assets]
          * - *remove* from $cache
+         * - decrement the image's board's total
          */
 
         const { image_name, board_id } = imageDTO;
+        const isAsset = ASSETS_CATEGORIES.includes(imageDTO.image_category);
 
         const queryArg = {
           board_id: board_id ?? 'none',
           categories: getCategories(imageDTO),
         };
 
-        const patch = dispatch(
+        const patches: PatchCollection[] = [];
+
+        patches.push(
+          dispatch(
             imagesApi.util.updateQueryData('listImages', queryArg, (draft) => {
               imagesAdapter.removeOne(draft, image_name);
             })
+          )
         );
 
+        patches.push(
+          dispatch(
+            boardsApi.util.updateQueryData(
+              isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+              imageDTO.board_id ?? 'none',
+              (draft) => {
+                draft.total = Math.max(draft.total - 1, 0);
+              }
+            )
+          )
+        ); // decrement the image board's total
+
         try {
           await queryFulfilled;
         } catch {
+          patches.forEach((patch) => {
             patch.undo();
+          });
         }
       },
     }),
@@ -237,18 +256,11 @@ export const imagesApi = api.injectEndpoints({
           },
         };
       },
-      invalidatesTags: (result, error, { imageDTOs }) => {
-        // for now, assume bulk delete is all on one board
-        const boardId = imageDTOs[0]?.board_id;
-        return [
-          { type: 'BoardImagesTotal', id: boardId ?? 'none' },
-          { type: 'BoardAssetsTotal', id: boardId ?? 'none' },
-        ];
-      },
       async onQueryStarted({ imageDTOs }, { dispatch, queryFulfilled }) {
         /**
          * Cache changes for `deleteImages`:
          * - *remove* the deleted images from their boards
+         * - decrement the images' board's totals
          *
          * Unfortunately we cannot do an optimistic update here due to how immer handles patching
          * arrays. You have to undo *all* patches, else the entity adapter's `ids` array is borked.
@@ -279,6 +291,21 @@ export const imagesApi = api.injectEndpoints({
                   }
                 )
               );
+
+              const isAsset = ASSETS_CATEGORIES.includes(
+                imageDTO.image_category
+              );
+
+              // decrement the image board's total
+              dispatch(
+                boardsApi.util.updateQueryData(
+                  isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+                  imageDTO.board_id ?? 'none',
+                  (draft) => {
+                    draft.total = Math.max(draft.total - 1, 0);
+                  }
+                )
+              );
             }
           });
         } catch {
@@ -298,10 +325,6 @@ export const imagesApi = api.injectEndpoints({
         method: 'PATCH',
         body: { is_intermediate },
       }),
-      invalidatesTags: (result, error, { imageDTO }) => [
-        { type: 'BoardImagesTotal', id: imageDTO.board_id ?? 'none' },
-        { type: 'BoardAssetsTotal', id: imageDTO.board_id ?? 'none' },
-      ],
       async onQueryStarted(
         { imageDTO, is_intermediate },
         { dispatch, queryFulfilled, getState }
@@ -312,9 +335,11 @@ export const imagesApi = api.injectEndpoints({
          * - $cache = [board_id|no_board]/[images|assets]
          * - IF it is being changed to an intermediate:
          *    - remove from $cache
+         *    - decrement the image's board's total
          * - ELSE (it is being changed to a non-intermediate):
          *    - IF it eligible for insertion into existing $cache:
          *      - *upsert* to $cache
+         *    - increment the image's board's total
          */
 
         // Store patches so we can undo if the query fails
@@ -335,6 +360,7 @@ export const imagesApi = api.injectEndpoints({
 
         // $cache = [board_id|no_board]/[images|assets]
         const categories = getCategories(imageDTO);
+        const isAsset = ASSETS_CATEGORIES.includes(imageDTO.image_category);
 
         if (is_intermediate) {
           // IF it is being changed to an intermediate:
@@ -350,8 +376,35 @@ export const imagesApi = api.injectEndpoints({
               )
             )
           );
+
+          // decrement the image board's total
+          patches.push(
+            dispatch(
+              boardsApi.util.updateQueryData(
+                isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+                imageDTO.board_id ?? 'none',
+                (draft) => {
+                  draft.total = Math.max(draft.total - 1, 0);
+                }
+              )
+            )
+          );
         } else {
           // ELSE (it is being changed to a non-intermediate):
+
+          // increment the image board's total
+          patches.push(
+            dispatch(
+              boardsApi.util.updateQueryData(
+                isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+                imageDTO.board_id ?? 'none',
+                (draft) => {
+                  draft.total += 1;
+                }
+              )
+            )
+          );
+
           const queryArgs = {
             board_id: imageDTO.board_id ?? 'none',
             categories,
@@ -361,9 +414,7 @@ export const imagesApi = api.injectEndpoints({
             getState()
           );
 
-          const { data: total } = IMAGE_CATEGORIES.includes(
+          const { data } = IMAGE_CATEGORIES.includes(imageDTO.image_category)
-            imageDTO.image_category
-          )
             ? boardsApi.endpoints.getBoardImagesTotal.select(
                 imageDTO.board_id ?? 'none'
               )(getState())
@@ -378,7 +429,8 @@ export const imagesApi = api.injectEndpoints({
           // - The image's `created_at` is within the range of the cached images
 
           const isCacheFullyPopulated =
-            currentCache.data && currentCache.data.ids.length >= (total ?? 0);
+            currentCache.data &&
+            currentCache.data.ids.length >= (data?.total ?? 0);
 
           const isInDateRange = getIsImageInDateRange(
             currentCache.data,
@@ -420,10 +472,6 @@ export const imagesApi = api.injectEndpoints({
         method: 'PATCH',
         body: { session_id },
       }),
-      invalidatesTags: (result, error, { imageDTO }) => [
-        { type: 'BoardImagesTotal', id: imageDTO.board_id ?? 'none' },
-        { type: 'BoardAssetsTotal', id: imageDTO.board_id ?? 'none' },
-      ],
       async onQueryStarted(
         { imageDTO, session_id },
         { dispatch, queryFulfilled }
@@ -473,6 +521,7 @@ export const imagesApi = api.injectEndpoints({
         if (images[0]) {
           const categories = getCategories(images[0]);
           const boardId = images[0].board_id;
+
           return [
             {
               type: 'ImageList',
@@ -481,6 +530,10 @@ export const imagesApi = api.injectEndpoints({
                 categories,
               }),
             },
+            {
+              type: 'Board',
+              id: boardId,
+            },
           ];
         }
         return [];
@@ -530,9 +583,7 @@ export const imagesApi = api.injectEndpoints({
               queryArgs
             )(getState());
 
-            const { data: previousTotal } = IMAGE_CATEGORIES.includes(
+            const { data } = IMAGE_CATEGORIES.includes(imageDTO.image_category)
-              imageDTO.image_category
-            )
               ? boardsApi.endpoints.getBoardImagesTotal.select(
                   boardId ?? 'none'
                 )(getState())
@@ -542,10 +593,10 @@ export const imagesApi = api.injectEndpoints({
 
             const isCacheFullyPopulated =
               currentCache.data &&
-              currentCache.data.ids.length >= (previousTotal ?? 0);
+              currentCache.data.ids.length >= (data?.total ?? 0);
 
             const isInDateRange =
-              (previousTotal || 0) >= IMAGE_LIMIT
+              (data?.total ?? 0) >= IMAGE_LIMIT
                 ? getIsImageInDateRange(currentCache.data, imageDTO)
                 : true;
 
@@ -595,6 +646,10 @@ export const imagesApi = api.injectEndpoints({
                 categories,
               }),
             },
+            {
+              type: 'Board',
+              id: boardId,
+            },
           ];
         }
         return [];
@@ -643,9 +698,7 @@ export const imagesApi = api.injectEndpoints({
               queryArgs
             )(getState());
 
-            const { data: previousTotal } = IMAGE_CATEGORIES.includes(
+            const { data } = IMAGE_CATEGORIES.includes(imageDTO.image_category)
-              imageDTO.image_category
-            )
               ? boardsApi.endpoints.getBoardImagesTotal.select(
                   boardId ?? 'none'
                 )(getState())
@@ -655,10 +708,10 @@ export const imagesApi = api.injectEndpoints({
 
             const isCacheFullyPopulated =
               currentCache.data &&
-              currentCache.data.ids.length >= (previousTotal ?? 0);
+              currentCache.data.ids.length >= (data?.total ?? 0);
 
             const isInDateRange =
-              (previousTotal || 0) >= IMAGE_LIMIT
+              (data?.total ?? 0) >= IMAGE_LIMIT
                 ? getIsImageInDateRange(currentCache.data, imageDTO)
                 : true;
 
@@ -727,6 +780,7 @@ export const imagesApi = api.injectEndpoints({
            *    - BAIL OUT
            * - *add* to `getImageDTO`
            * - *add* to no_board/assets
+           * - update the image's board's assets total
            */
 
           const { data: imageDTO } = await queryFulfilled;
@@ -761,11 +815,15 @@ export const imagesApi = api.injectEndpoints({
             )
           );
 
+          // increment new board's total
           dispatch(
-            imagesApi.util.invalidateTags([
+            boardsApi.util.updateQueryData(
-              { type: 'BoardImagesTotal', id: imageDTO.board_id ?? 'none' },
+              'getBoardAssetsTotal',
-              { type: 'BoardAssetsTotal', id: imageDTO.board_id ?? 'none' },
+              imageDTO.board_id ?? 'none',
-            ])
+              (draft) => {
+                draft.total += 1;
+              }
+            )
           );
         } catch {
           // query failed, no action needed
@@ -792,8 +850,6 @@ export const imagesApi = api.injectEndpoints({
             categories: ASSETS_CATEGORIES,
           }),
         },
-        { type: 'BoardImagesTotal', id: 'none' },
-        { type: 'BoardAssetsTotal', id: 'none' },
       ],
       async onQueryStarted(board_id, { dispatch, queryFulfilled }) {
         /**
@@ -806,6 +862,7 @@ export const imagesApi = api.injectEndpoints({
          *   have access to the deleted images DTOs - only the names, and a network request
          *   for all of a board's DTOs could be very large. Instead, we invalidate the 'No Board'
          *   cache.
+         * - set the board's totals to zero
          */
 
         try {
@@ -825,6 +882,28 @@ export const imagesApi = api.injectEndpoints({
             );
           });
 
+          // set the board's asset total to 0 (feels unnecessary since we are deleting it?)
+          dispatch(
+            boardsApi.util.updateQueryData(
+              'getBoardAssetsTotal',
+              board_id,
+              (draft) => {
+                draft.total = 0;
+              }
+            )
+          );
+
+          // set the board's images total to 0 (feels unnecessary since we are deleting it?)
+          dispatch(
+            boardsApi.util.updateQueryData(
+              'getBoardImagesTotal',
+              board_id,
+              (draft) => {
+                draft.total = 0;
+              }
+            )
+          );
+
           // update 'All Images' & 'All Assets' caches
           const queryArgsToUpdate = [
             {
@@ -881,8 +960,6 @@ export const imagesApi = api.injectEndpoints({
             categories: ASSETS_CATEGORIES,
           }),
         },
-        { type: 'BoardImagesTotal', id: 'none' },
-        { type: 'BoardAssetsTotal', id: 'none' },
       ],
       async onQueryStarted(board_id, { dispatch, queryFulfilled }) {
         /**
@@ -892,6 +969,7 @@ export const imagesApi = api.injectEndpoints({
          *   Instead, we rely on the UI to remove all components that use the deleted images.
          * - Remove every image in the 'All Images' cache that has the board_id
          * - Remove every image in the 'All Assets' cache that has the board_id
+         * - set the board's totals to zero
          */
 
         try {
@@ -919,6 +997,28 @@ export const imagesApi = api.injectEndpoints({
               )
             );
           });
+
+          // set the board's asset total to 0 (feels unnecessary since we are deleting it?)
+          dispatch(
+            boardsApi.util.updateQueryData(
+              'getBoardAssetsTotal',
+              board_id,
+              (draft) => {
+                draft.total = 0;
+              }
+            )
+          );
+
+          // set the board's images total to 0 (feels unnecessary since we are deleting it?)
+          dispatch(
+            boardsApi.util.updateQueryData(
+              'getBoardImagesTotal',
+              board_id,
+              (draft) => {
+                draft.total = 0;
+              }
+            )
+          );
         } catch {
           //no-op
         }
@@ -936,15 +1036,9 @@ export const imagesApi = api.injectEndpoints({
           body: { board_id, image_name },
         };
       },
-      invalidatesTags: (result, error, { board_id, imageDTO }) => [
+      invalidatesTags: (result, error, { board_id }) => [
         // refresh the board itself
         { type: 'Board', id: board_id },
-        // update old board totals
-        { type: 'BoardImagesTotal', id: board_id },
-        { type: 'BoardAssetsTotal', id: board_id },
-        // update new board totals
-        { type: 'BoardImagesTotal', id: imageDTO.board_id ?? 'none' },
-        { type: 'BoardAssetsTotal', id: imageDTO.board_id ?? 'none' },
       ],
       async onQueryStarted(
         { board_id, imageDTO },
@@ -961,11 +1055,13 @@ export const imagesApi = api.injectEndpoints({
          * - $cache = board_id/[images|assets]
          * - IF it eligible for insertion into existing $cache:
          *    - THEN *add* to $cache
+         * - decrement both old board's total
+         * - increment the new board's total
          */
 
         const patches: PatchCollection[] = [];
         const categories = getCategories(imageDTO);
-
+        const isAsset = ASSETS_CATEGORIES.includes(imageDTO.image_category);
         // *update* getImageDTO
         patches.push(
           dispatch(
@@ -996,6 +1092,32 @@ export const imagesApi = api.injectEndpoints({
             )
           );
 
+          // decrement old board's total
+          patches.push(
+            dispatch(
+              boardsApi.util.updateQueryData(
+                isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+                imageDTO.board_id ?? 'none',
+                (draft) => {
+                  draft.total = Math.max(draft.total - 1, 0);
+                }
+              )
+            )
+          );
+
+          // increment new board's total
+          patches.push(
+            dispatch(
+              boardsApi.util.updateQueryData(
+                isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+                board_id ?? 'none',
+                (draft) => {
+                  draft.total += 1;
+                }
+              )
+            )
+          );
+
           // $cache = board_id/[images|assets]
           const queryArgs = { board_id: board_id ?? 'none', categories };
           const currentCache = imagesApi.endpoints.listImages.select(queryArgs)(
@@ -1008,9 +1130,7 @@ export const imagesApi = api.injectEndpoints({
           //    OR
           // - The image's `created_at` is within the range of the cached images
 
-          const { data: total } = IMAGE_CATEGORIES.includes(
+          const { data } = IMAGE_CATEGORIES.includes(imageDTO.image_category)
-            imageDTO.image_category
-          )
             ? boardsApi.endpoints.getBoardImagesTotal.select(
                 imageDTO.board_id ?? 'none'
               )(getState())
@@ -1019,7 +1139,8 @@ export const imagesApi = api.injectEndpoints({
               )(getState());
 
           const isCacheFullyPopulated =
-            currentCache.data && currentCache.data.ids.length >= (total ?? 0);
+            currentCache.data &&
+            currentCache.data.ids.length >= (data?.total ?? 0);
 
           const isInDateRange = getIsImageInDateRange(
             currentCache.data,
@@ -1063,12 +1184,6 @@ export const imagesApi = api.injectEndpoints({
         return [
           // invalidate the image's old board
           { type: 'Board', id: board_id ?? 'none' },
-          // update old board totals
-          { type: 'BoardImagesTotal', id: board_id ?? 'none' },
-          { type: 'BoardAssetsTotal', id: board_id ?? 'none' },
-          // update the no_board totals
-          { type: 'BoardImagesTotal', id: 'none' },
-          { type: 'BoardAssetsTotal', id: 'none' },
         ];
       },
       async onQueryStarted(
@@ -1082,10 +1197,13 @@ export const imagesApi = api.injectEndpoints({
          * - $cache = no_board/[images|assets]
          * - IF it eligible for insertion into existing $cache:
          *    - THEN *upsert* to $cache
+         * - decrement old board's total
+         * - increment the new board's total (no board)
          */
 
         const categories = getCategories(imageDTO);
         const patches: PatchCollection[] = [];
+        const isAsset = ASSETS_CATEGORIES.includes(imageDTO.image_category);
 
         // *update* getImageDTO
         patches.push(
@@ -1116,6 +1234,32 @@ export const imagesApi = api.injectEndpoints({
           )
         );
 
+        // decrement old board's total
+        patches.push(
+          dispatch(
+            boardsApi.util.updateQueryData(
+              isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+              imageDTO.board_id ?? 'none',
+              (draft) => {
+                draft.total = Math.max(draft.total - 1, 0);
+              }
+            )
+          )
+        );
+
+        // increment new board's total (no board)
+        patches.push(
+          dispatch(
+            boardsApi.util.updateQueryData(
+              isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+              'none',
+              (draft) => {
+                draft.total += 1;
+              }
+            )
+          )
+        );
+
         // $cache = no_board/[images|assets]
         const queryArgs = { board_id: 'none', categories };
         const currentCache = imagesApi.endpoints.listImages.select(queryArgs)(
@@ -1128,9 +1272,7 @@ export const imagesApi = api.injectEndpoints({
         //    OR
         // - The image's `created_at` is within the range of the cached images
 
-        const { data: total } = IMAGE_CATEGORIES.includes(
+        const { data } = IMAGE_CATEGORIES.includes(imageDTO.image_category)
-          imageDTO.image_category
-        )
           ? boardsApi.endpoints.getBoardImagesTotal.select(
               imageDTO.board_id ?? 'none'
             )(getState())
@@ -1139,7 +1281,8 @@ export const imagesApi = api.injectEndpoints({
             )(getState());
 
         const isCacheFullyPopulated =
-          currentCache.data && currentCache.data.ids.length >= (total ?? 0);
+          currentCache.data &&
+          currentCache.data.ids.length >= (data?.total ?? 0);
 
         const isInDateRange = getIsImageInDateRange(
           currentCache.data,
@@ -1183,21 +1326,10 @@ export const imagesApi = api.injectEndpoints({
           board_id,
         },
       }),
-      invalidatesTags: (result, error, { imageDTOs, board_id }) => {
+      invalidatesTags: (result, error, { board_id }) => {
-        //assume all images are being moved from one board for now
-        const oldBoardId = imageDTOs[0]?.board_id;
         return [
           // update the destination board
           { type: 'Board', id: board_id ?? 'none' },
-          // update new board totals
-          { type: 'BoardImagesTotal', id: board_id ?? 'none' },
-          { type: 'BoardAssetsTotal', id: board_id ?? 'none' },
-          // update old board totals
-          { type: 'BoardImagesTotal', id: oldBoardId ?? 'none' },
-          { type: 'BoardAssetsTotal', id: oldBoardId ?? 'none' },
-          // update the no_board totals
-          { type: 'BoardImagesTotal', id: 'none' },
-          { type: 'BoardAssetsTotal', id: 'none' },
         ];
       },
       async onQueryStarted(
@@ -1213,6 +1345,8 @@ export const imagesApi = api.injectEndpoints({
            * - *update* getImageDTO for each image
            * - *add* to board_id/[images|assets]
            * - *remove* from [old_board_id|no_board]/[images|assets]
+           * - decrement old board's totals for each image
+           * - increment new board's totals for each image
            */
 
           added_image_names.forEach((image_name) => {
@@ -1221,7 +1355,8 @@ export const imagesApi = api.injectEndpoints({
                 'getImageDTO',
                 image_name,
                 (draft) => {
-                  draft.board_id = new_board_id;
+                  draft.board_id =
+                    new_board_id === 'none' ? undefined : new_board_id;
                 }
               )
             );
@@ -1234,6 +1369,7 @@ export const imagesApi = api.injectEndpoints({
 
             const categories = getCategories(imageDTO);
             const old_board_id = imageDTO.board_id;
+            const isAsset = ASSETS_CATEGORIES.includes(imageDTO.image_category);
 
             // remove from the old board
             dispatch(
@@ -1246,6 +1382,28 @@ export const imagesApi = api.injectEndpoints({
               )
             );
 
+            // decrement old board's total
+            dispatch(
+              boardsApi.util.updateQueryData(
+                isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+                old_board_id ?? 'none',
+                (draft) => {
+                  draft.total = Math.max(draft.total - 1, 0);
+                }
+              )
+            );
+
+            // increment new board's total
+            dispatch(
+              boardsApi.util.updateQueryData(
+                isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+                new_board_id ?? 'none',
+                (draft) => {
+                  draft.total += 1;
+                }
+              )
+            );
+
             const queryArgs = {
               board_id: new_board_id,
               categories,
@@ -1255,9 +1413,7 @@ export const imagesApi = api.injectEndpoints({
               queryArgs
             )(getState());
 
-            const { data: previousTotal } = IMAGE_CATEGORIES.includes(
+            const { data } = IMAGE_CATEGORIES.includes(imageDTO.image_category)
-              imageDTO.image_category
-            )
               ? boardsApi.endpoints.getBoardImagesTotal.select(
                   new_board_id ?? 'none'
                 )(getState())
@@ -1267,10 +1423,10 @@ export const imagesApi = api.injectEndpoints({
 
             const isCacheFullyPopulated =
               currentCache.data &&
-              currentCache.data.ids.length >= (previousTotal ?? 0);
+              currentCache.data.ids.length >= (data?.total ?? 0);
 
             const isInDateRange =
-              (previousTotal || 0) >= IMAGE_LIMIT
+              (data?.total ?? 0) >= IMAGE_LIMIT
                 ? getIsImageInDateRange(currentCache.data, imageDTO)
                 : true;
 
@@ -1310,10 +1466,7 @@ export const imagesApi = api.injectEndpoints({
       }),
       invalidatesTags: (result, error, { imageDTOs }) => {
         const touchedBoardIds: string[] = [];
-        const tags: ApiTagDescription[] = [
+        const tags: ApiTagDescription[] = [];
-          { type: 'BoardImagesTotal', id: 'none' },
-          { type: 'BoardAssetsTotal', id: 'none' },
-        ];
 
         result?.removed_image_names.forEach((image_name) => {
           const board_id = imageDTOs.find((i) => i.image_name === image_name)
@@ -1324,8 +1477,6 @@ export const imagesApi = api.injectEndpoints({
           }
 
           tags.push({ type: 'Board', id: board_id });
-          tags.push({ type: 'BoardImagesTotal', id: board_id });
-          tags.push({ type: 'BoardAssetsTotal', id: board_id });
         });
 
         return tags;
@@ -1343,6 +1494,8 @@ export const imagesApi = api.injectEndpoints({
            * - *update* getImageDTO for each image
            * - *remove* from old_board_id/[images|assets]
            * - *add* to no_board/[images|assets]
+           * - decrement old board's totals for each image
+           * - increment new board's (no board) totals for each image
            */
 
           removed_image_names.forEach((image_name) => {
@@ -1363,6 +1516,7 @@ export const imagesApi = api.injectEndpoints({
             }
 
             const categories = getCategories(imageDTO);
+            const isAsset = ASSETS_CATEGORIES.includes(imageDTO.image_category);
 
             // remove from the old board
             dispatch(
@@ -1375,6 +1529,28 @@ export const imagesApi = api.injectEndpoints({
               )
             );
 
+            // decrement old board's total
+            dispatch(
+              boardsApi.util.updateQueryData(
+                isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+                imageDTO.board_id ?? 'none',
+                (draft) => {
+                  draft.total = Math.max(draft.total - 1, 0);
+                }
+              )
+            );
+
+            // increment new board's total (no board)
+            dispatch(
+              boardsApi.util.updateQueryData(
+                isAsset ? 'getBoardAssetsTotal' : 'getBoardImagesTotal',
+                'none',
+                (draft) => {
+                  draft.total += 1;
+                }
+              )
+            );
+
             // add to `no_board`
             const queryArgs = {
               board_id: 'none',
@@ -1385,9 +1561,7 @@ export const imagesApi = api.injectEndpoints({
               queryArgs
             )(getState());
 
-            const { data: total } = IMAGE_CATEGORIES.includes(
+            const { data } = IMAGE_CATEGORIES.includes(imageDTO.image_category)
-              imageDTO.image_category
-            )
               ? boardsApi.endpoints.getBoardImagesTotal.select(
                   imageDTO.board_id ?? 'none'
                 )(getState())
@@ -1396,10 +1570,11 @@ export const imagesApi = api.injectEndpoints({
                 )(getState());
 
             const isCacheFullyPopulated =
-              currentCache.data && currentCache.data.ids.length >= (total ?? 0);
+              currentCache.data &&
+              currentCache.data.ids.length >= (data?.total ?? 0);
 
             const isInDateRange =
-              (total || 0) >= IMAGE_LIMIT
+              (data?.total ?? 0) >= IMAGE_LIMIT
                 ? getIsImageInDateRange(currentCache.data, imageDTO)
                 : true;
 

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

@@ -6,6 +6,7 @@ import {
   CheckpointModelConfig,
   ControlNetModelConfig,
   IPAdapterModelConfig,
+  T2IAdapterModelConfig,
   DiffusersModelConfig,
   ImportModelConfig,
   LoRAModelConfig,
@@ -41,6 +42,10 @@ export type IPAdapterModelConfigEntity = IPAdapterModelConfig & {
   id: string;
 };
 
+export type T2IAdapterModelConfigEntity = T2IAdapterModelConfig & {
+  id: string;
+};
+
 export type TextualInversionModelConfigEntity = TextualInversionModelConfig & {
   id: string;
 };
@@ -53,6 +58,7 @@ type AnyModelConfigEntity =
   | LoRAModelConfigEntity
   | ControlNetModelConfigEntity
   | IPAdapterModelConfigEntity
+  | T2IAdapterModelConfigEntity
   | TextualInversionModelConfigEntity
   | VaeModelConfigEntity;
 
@@ -145,6 +151,10 @@ export const ipAdapterModelsAdapter =
   createEntityAdapter<IPAdapterModelConfigEntity>({
     sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
   });
+export const t2iAdapterModelsAdapter =
+  createEntityAdapter<T2IAdapterModelConfigEntity>({
+    sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
+  });
 export const textualInversionModelsAdapter =
   createEntityAdapter<TextualInversionModelConfigEntity>({
     sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
@@ -470,6 +480,37 @@ export const modelsApi = api.injectEndpoints({
         );
       },
     }),
+    getT2IAdapterModels: build.query<
+      EntityState<T2IAdapterModelConfigEntity>,
+      void
+    >({
+      query: () => ({ url: 'models/', params: { model_type: 't2i_adapter' } }),
+      providesTags: (result) => {
+        const tags: ApiTagDescription[] = [
+          { type: 'T2IAdapterModel', id: LIST_TAG },
+        ];
+
+        if (result) {
+          tags.push(
+            ...result.ids.map((id) => ({
+              type: 'T2IAdapterModel' as const,
+              id,
+            }))
+          );
+        }
+
+        return tags;
+      },
+      transformResponse: (response: { models: T2IAdapterModelConfig[] }) => {
+        const entities = createModelEntities<T2IAdapterModelConfigEntity>(
+          response.models
+        );
+        return t2iAdapterModelsAdapter.setAll(
+          t2iAdapterModelsAdapter.getInitialState(),
+          entities
+        );
+      },
+    }),
     getVaeModels: build.query<EntityState<VaeModelConfigEntity>, void>({
       query: () => ({ url: 'models/', params: { model_type: 'vae' } }),
       providesTags: (result) => {
@@ -567,6 +608,7 @@ export const {
   useGetOnnxModelsQuery,
   useGetControlNetModelsQuery,
   useGetIPAdapterModelsQuery,
+  useGetT2IAdapterModelsQuery,
   useGetLoRAModelsQuery,
   useGetTextualInversionModelsQuery,
   useGetVaeModelsQuery,

invokeai/frontend/web/src/services/api/hooks/useBoardTotal.ts

@@ -13,7 +13,7 @@ export const useBoardTotal = (board_id: BoardId) => {
   const { data: totalAssets } = useGetBoardAssetsTotalQuery(board_id);
 
   const currentViewTotal = useMemo(
-    () => (galleryView === 'images' ? totalImages : totalAssets),
+    () => (galleryView === 'images' ? totalImages?.total : totalAssets?.total),
     [galleryView, totalAssets, totalImages]
   );
 

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

@@ -67,6 +67,7 @@ export type VAEModelField = s['VAEModelField'];
 export type LoRAModelField = s['LoRAModelField'];
 export type ControlNetModelField = s['ControlNetModelField'];
 export type IPAdapterModelField = s['IPAdapterModelField'];
+export type T2IAdapterModelField = s['T2IAdapterModelField'];
 export type ModelsList = s['ModelsList'];
 export type ControlField = s['ControlField'];
 export type IPAdapterField = s['IPAdapterField'];
@@ -83,6 +84,9 @@ export type ControlNetModelConfig =
   | ControlNetModelDiffusersConfig;
 export type IPAdapterModelInvokeAIConfig = s['IPAdapterModelInvokeAIConfig'];
 export type IPAdapterModelConfig = IPAdapterModelInvokeAIConfig;
+export type T2IAdapterModelDiffusersConfig =
+  s['T2IAdapterModelDiffusersConfig'];
+export type T2IAdapterModelConfig = T2IAdapterModelDiffusersConfig;
 export type TextualInversionModelConfig = s['TextualInversionModelConfig'];
 export type DiffusersModelConfig =
   | s['StableDiffusion1ModelDiffusersConfig']
@@ -99,6 +103,7 @@ export type AnyModelConfig =
   | VaeModelConfig
   | ControlNetModelConfig
   | IPAdapterModelConfig
+  | T2IAdapterModelConfig
   | TextualInversionModelConfig
   | MainModelConfig
   | OnnxModelConfig;

pyproject.toml

@@ -161,16 +161,16 @@ version = { attr = "invokeai.version.__version__" }
 [tool.setuptools.packages.find]
 "where" = ["."]
 "include" = [
-    "invokeai.assets.web*","invokeai.version*",
+    "invokeai.assets.fonts*","invokeai.version*",
     "invokeai.generator*","invokeai.backend*",
     "invokeai.frontend*", "invokeai.frontend.web.dist*",
     "invokeai.frontend.web.static*",
     "invokeai.configs*",
-    "invokeai.app*","ldm*",
+    "invokeai.app*",
 ]
 
 [tool.setuptools.package-data]
-"invokeai.assets.web" = ["**.png","**.js","**.woff2","**.css"]
+"invokeai.assets.fonts" = ["**/*.ttf"]
 "invokeai.backend" = ["**.png"]
 "invokeai.configs" = ["*.example", "**/*.yaml", "*.txt"]
 "invokeai.frontend.web.dist" = ["**"]

tests/app/util/test_controlnet_utils.py

@@ -0,0 +1,42 @@
+import numpy as np
+import pytest
+from PIL import Image
+
+from invokeai.app.util.controlnet_utils import prepare_control_image
+
+
+@pytest.mark.parametrize("num_channels", [1, 2, 3])
+def test_prepare_control_image_num_channels(num_channels):
+    """Test that the `num_channels` parameter is applied correctly in prepare_control_image(...)."""
+    np_image = np.zeros((256, 256, 3), dtype=np.uint8)
+    pil_image = Image.fromarray(np_image)
+
+    torch_image = prepare_control_image(
+        image=pil_image,
+        width=256,
+        height=256,
+        num_channels=num_channels,
+        device="cpu",
+        do_classifier_free_guidance=False,
+    )
+
+    assert torch_image.shape == (1, num_channels, 256, 256)
+
+
+@pytest.mark.parametrize("num_channels", [0, 4])
+def test_prepare_control_image_num_channels_too_large(num_channels):
+    """Test that an exception is raised in prepare_control_image(...) if the `num_channels` parameter is out of the
+    supported range.
+    """
+    np_image = np.zeros((256, 256, 3), dtype=np.uint8)
+    pil_image = Image.fromarray(np_image)
+
+    with pytest.raises(ValueError):
+        _ = prepare_control_image(
+            image=pil_image,
+            width=256,
+            height=256,
+            num_channels=num_channels,
+            device="cpu",
+            do_classifier_free_guidance=False,
+        )