chore: release v4.2.9.dev9

I don't think it makes sense to merge control layers or regional guidance layers because they have additional state.

docs/help/FAQ.md

@@ -196,22 +196,6 @@ tips to reduce the problem:
     === "12GB VRAM GPU"
 
         This should be sufficient to generate larger images up to about 1280x1280.
-		
-## Checkpoint Models Load Slowly or Use Too Much RAM
-
-The difference between diffusers models (a folder containing multiple
-subfolders) and checkpoint models (a file ending with .safetensors or
-.ckpt) is that InvokeAI is able to load diffusers models into memory
-incrementally, while checkpoint models must be loaded all at
-once. With very large models, or systems with limited RAM, you may
-experience slowdowns and other memory-related issues when loading
-checkpoint models.
-
-To solve this, go to the Model Manager tab (the cube), select the
-checkpoint model that's giving you trouble, and press the "Convert"
-button in the upper right of your browser window. This will conver the
-checkpoint into a diffusers model, after which loading should be
-faster and less memory-intensive.
 
 ## Memory Leak (Linux)
 

invokeai/app/api/routers/model_manager.py

@@ -3,10 +3,8 @@
 
 import io
 import pathlib
-import shutil
 import traceback
 from copy import deepcopy
-from enum import Enum
 from tempfile import TemporaryDirectory
 from typing import List, Optional, Type
 
@@ -19,7 +17,6 @@ from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
 from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.services.config import get_config
 from invokeai.app.services.model_images.model_images_common import ModelImageFileNotFoundException
 from invokeai.app.services.model_install.model_install_common import ModelInstallJob
 from invokeai.app.services.model_records import (
@@ -34,7 +31,6 @@ from invokeai.backend.model_manager.config import (
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import CacheStats
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
 from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
 from invokeai.backend.model_manager.search import ModelSearch
@@ -54,13 +50,6 @@ class ModelsList(BaseModel):
     model_config = ConfigDict(use_enum_values=True)
 
 
-class CacheType(str, Enum):
-    """Cache type - one of vram or ram."""
-
-    RAM = "RAM"
-    VRAM = "VRAM"
-
-
 def add_cover_image_to_model_config(config: AnyModelConfig, dependencies: Type[ApiDependencies]) -> AnyModelConfig:
     """Add a cover image URL to a model configuration."""
     cover_image = dependencies.invoker.services.model_images.get_url(config.key)
@@ -808,83 +797,3 @@ async def get_starter_models() -> list[StarterModel]:
         model.dependencies = missing_deps
 
     return starter_models
-
-
-@model_manager_router.get(
-    "/model_cache",
-    operation_id="get_cache_size",
-    response_model=float,
-    summary="Get maximum size of model manager RAM or VRAM cache.",
-)
-async def get_cache_size(cache_type: CacheType = Query(description="The cache type", default=CacheType.RAM)) -> float:
-    """Return the current RAM or VRAM cache size setting (in GB)."""
-    cache = ApiDependencies.invoker.services.model_manager.load.ram_cache
-    value = 0.0
-    if cache_type == CacheType.RAM:
-        value = cache.max_cache_size
-    elif cache_type == CacheType.VRAM:
-        value = cache.max_vram_cache_size
-    return value
-
-
-@model_manager_router.put(
-    "/model_cache",
-    operation_id="set_cache_size",
-    response_model=float,
-    summary="Set maximum size of model manager RAM or VRAM cache, optionally writing new value out to invokeai.yaml config file.",
-)
-async def set_cache_size(
-    value: float = Query(description="The new value for the maximum cache size"),
-    cache_type: CacheType = Query(description="The cache type", default=CacheType.RAM),
-    persist: bool = Query(description="Write new value out to invokeai.yaml", default=False),
-) -> float:
-    """Set the current RAM or VRAM cache size setting (in GB). ."""
-    cache = ApiDependencies.invoker.services.model_manager.load.ram_cache
-    app_config = get_config()
-    # Record initial state.
-    vram_old = app_config.vram
-    ram_old = app_config.ram
-
-    # Prepare target state.
-    vram_new = vram_old
-    ram_new = ram_old
-    if cache_type == CacheType.RAM:
-        ram_new = value
-    elif cache_type == CacheType.VRAM:
-        vram_new = value
-    else:
-        raise ValueError(f"Unexpected {cache_type=}.")
-
-    config_path = app_config.config_file_path
-    new_config_path = config_path.with_suffix(".yaml.new")
-
-    try:
-        # Try to apply the target state.
-        cache.max_vram_cache_size = vram_new
-        cache.max_cache_size = ram_new
-        app_config.ram = ram_new
-        app_config.vram = vram_new
-        if persist:
-            app_config.write_file(new_config_path)
-            shutil.move(new_config_path, config_path)
-    except Exception as e:
-        # If there was a failure, restore the initial state.
-        cache.max_cache_size = ram_old
-        cache.max_vram_cache_size = vram_old
-        app_config.ram = ram_old
-        app_config.vram = vram_old
-
-        raise RuntimeError("Failed to update cache size") from e
-    return value
-
-
-@model_manager_router.get(
-    "/stats",
-    operation_id="get_stats",
-    response_model=Optional[CacheStats],
-    summary="Get model manager RAM cache performance statistics.",
-)
-async def get_stats() -> Optional[CacheStats]:
-    """Return performance statistics on the model manager's RAM cache. Will return null if no models have been loaded."""
-
-    return ApiDependencies.invoker.services.model_manager.load.ram_cache.stats

invokeai/app/api/routers/session_queue.py

@@ -11,6 +11,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
+    CancelByOriginResult,
     ClearResult,
     EnqueueBatchResult,
     PruneResult,
@@ -105,6 +106,19 @@ async def cancel_by_batch_ids(
     return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(queue_id=queue_id, batch_ids=batch_ids)
 
 
+@session_queue_router.put(
+    "/{queue_id}/cancel_by_origin",
+    operation_id="cancel_by_origin",
+    responses={200: {"model": CancelByBatchIDsResult}},
+)
+async def cancel_by_origin(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    origin: str = Query(description="The origin to cancel all queue items for"),
+) -> CancelByOriginResult:
+    """Immediately cancels all queue items with the given origin"""
+    return ApiDependencies.invoker.services.session_queue.cancel_by_origin(queue_id=queue_id, origin=origin)
+
+
 @session_queue_router.put(
     "/{queue_id}/clear",
     operation_id="clear",

invokeai/app/invocations/baseinvocation.py

@@ -20,7 +20,6 @@ from typing import (
     Type,
     TypeVar,
     Union,
-    cast,
 )
 
 import semver
@@ -80,7 +79,7 @@ class UIConfigBase(BaseModel):
     version: str = Field(
         description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".',
     )
-    node_pack: Optional[str] = Field(default=None, description="Whether or not this is a custom node")
+    node_pack: str = Field(description="The node pack that this node belongs to, will be 'invokeai' for built-in nodes")
     classification: Classification = Field(default=Classification.Stable, description="The node's classification")
 
     model_config = ConfigDict(
@@ -230,18 +229,16 @@ class BaseInvocation(ABC, BaseModel):
     @staticmethod
     def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseInvocation]) -> None:
         """Adds various UI-facing attributes to the invocation's OpenAPI schema."""
-        uiconfig = cast(UIConfigBase | None, getattr(model_class, "UIConfig", None))
-        if uiconfig is not None:
-            if uiconfig.title is not None:
-                schema["title"] = uiconfig.title
-            if uiconfig.tags is not None:
-                schema["tags"] = uiconfig.tags
-            if uiconfig.category is not None:
-                schema["category"] = uiconfig.category
-            if uiconfig.node_pack is not None:
-                schema["node_pack"] = uiconfig.node_pack
-            schema["classification"] = uiconfig.classification
-            schema["version"] = uiconfig.version
+        if title := model_class.UIConfig.title:
+            schema["title"] = title
+        if tags := model_class.UIConfig.tags:
+            schema["tags"] = tags
+        if category := model_class.UIConfig.category:
+            schema["category"] = category
+        if node_pack := model_class.UIConfig.node_pack:
+            schema["node_pack"] = node_pack
+        schema["classification"] = model_class.UIConfig.classification
+        schema["version"] = model_class.UIConfig.version
         if "required" not in schema or not isinstance(schema["required"], list):
             schema["required"] = []
         schema["class"] = "invocation"
@@ -312,7 +309,7 @@ class BaseInvocation(ABC, BaseModel):
         json_schema_extra={"field_kind": FieldKind.NodeAttribute},
     )
 
-    UIConfig: ClassVar[Type[UIConfigBase]]
+    UIConfig: ClassVar[UIConfigBase]
 
     model_config = ConfigDict(
         protected_namespaces=(),
@@ -441,30 +438,25 @@ def invocation(
         validate_fields(cls.model_fields, invocation_type)
 
         # Add OpenAPI schema extras
-        uiconfig_name = cls.__qualname__ + ".UIConfig"
-        if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconfig_name:
-            cls.UIConfig = type(uiconfig_name, (UIConfigBase,), {})
-        cls.UIConfig.title = title
-        cls.UIConfig.tags = tags
-        cls.UIConfig.category = category
-        cls.UIConfig.classification = classification
-
-        # Grab the node pack's name from the module name, if it's a custom node
-        is_custom_node = cls.__module__.rsplit(".", 1)[0] == "invokeai.app.invocations"
-        if is_custom_node:
-            cls.UIConfig.node_pack = cls.__module__.split(".")[0]
-        else:
-            cls.UIConfig.node_pack = None
+        uiconfig: dict[str, Any] = {}
+        uiconfig["title"] = title
+        uiconfig["tags"] = tags
+        uiconfig["category"] = category
+        uiconfig["classification"] = classification
+        # The node pack is the module name - will be "invokeai" for built-in nodes
+        uiconfig["node_pack"] = cls.__module__.split(".")[0]
 
         if version is not None:
             try:
                 semver.Version.parse(version)
             except ValueError as e:
                 raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
-            cls.UIConfig.version = version
+            uiconfig["version"] = version
         else:
             logger.warn(f'No version specified for node "{invocation_type}", using "1.0.0"')
-            cls.UIConfig.version = "1.0.0"
+            uiconfig["version"] = "1.0.0"
+
+        cls.UIConfig = UIConfigBase(**uiconfig)
 
         if use_cache is not None:
             cls.model_fields["use_cache"].default = use_cache

invokeai/app/invocations/compel.py

@@ -19,8 +19,8 @@ from invokeai.app.invocations.model import CLIPField
 from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.ti_utils import generate_ti_list
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.peft.lora import LoRAModelRaw
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
     ConditioningFieldData,

invokeai/app/invocations/denoise_latents.py

@@ -36,9 +36,9 @@ from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType, ModelVariantType
 from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.peft.lora import LoRAModelRaw
 from invokeai.backend.stable_diffusion import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext, DenoiseInputs
 from invokeai.backend.stable_diffusion.diffusers_pipeline import (
@@ -185,7 +185,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     )
     denoise_mask: Optional[DenoiseMaskField] = InputField(
         default=None,
-        description=FieldDescriptions.denoise_mask,
+        description=FieldDescriptions.mask,
         input=Input.Connection,
         ui_order=8,
     )

invokeai/app/invocations/fields.py

@@ -181,7 +181,7 @@ class FieldDescriptions:
     )
     num_1 = "The first number"
     num_2 = "The second number"
-    denoise_mask = "A mask of the region to apply the denoising process to."
+    mask = "The mask to use for the operation"
     board = "The board to save the image to"
     image = "The image to process"
     tile_size = "Tile size"

invokeai/app/invocations/flux_denoise.py

@@ -1,292 +0,0 @@
-from typing import Callable, Iterator, Optional, Tuple
-
-import torch
-import torchvision.transforms as tv_transforms
-from torchvision.transforms.functional import resize as tv_resize
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import (
-    DenoiseMaskField,
-    FieldDescriptions,
-    FluxConditioningField,
-    Input,
-    InputField,
-    LatentsField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import TransformerField
-from invokeai.app.invocations.primitives import LatentsOutput
-from invokeai.app.services.session_processor.session_processor_common import CanceledException
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.denoise import denoise
-from invokeai.backend.flux.inpaint_extension import InpaintExtension
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.sampling_utils import (
-    clip_timestep_schedule,
-    generate_img_ids,
-    get_noise,
-    get_schedule,
-    pack,
-    unpack,
-)
-from invokeai.backend.peft.lora import LoRAModelRaw
-from invokeai.backend.peft.peft_patcher import PeftPatcher
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
-from invokeai.backend.util.devices import TorchDevice
-
-
-@invocation(
-    "flux_denoise",
-    title="FLUX Denoise",
-    tags=["image", "flux"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Run denoising process with a FLUX transformer model."""
-
-    # If latents is provided, this means we are doing image-to-image.
-    latents: Optional[LatentsField] = InputField(
-        default=None,
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-    )
-    # denoise_mask is used for image-to-image inpainting. Only the masked region is modified.
-    denoise_mask: Optional[DenoiseMaskField] = InputField(
-        default=None,
-        description=FieldDescriptions.denoise_mask,
-        input=Input.Connection,
-    )
-    denoising_start: float = InputField(
-        default=0.0,
-        ge=0,
-        le=1,
-        description=FieldDescriptions.denoising_start,
-    )
-    denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
-    transformer: TransformerField = InputField(
-        description=FieldDescriptions.flux_model,
-        input=Input.Connection,
-        title="Transformer",
-    )
-    positive_text_conditioning: FluxConditioningField = InputField(
-        description=FieldDescriptions.positive_cond, input=Input.Connection
-    )
-    width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
-    height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
-    num_steps: int = InputField(
-        default=4, description="Number of diffusion steps. Recommended values are schnell: 4, dev: 50."
-    )
-    guidance: float = InputField(
-        default=4.0,
-        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
-    )
-    seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = self._run_diffusion(context)
-        latents = latents.detach().to("cpu")
-
-        name = context.tensors.save(tensor=latents)
-        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
-
-    def _run_diffusion(
-        self,
-        context: InvocationContext,
-    ):
-        inference_dtype = torch.bfloat16
-
-        # Load the conditioning data.
-        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
-        assert len(cond_data.conditionings) == 1
-        flux_conditioning = cond_data.conditionings[0]
-        assert isinstance(flux_conditioning, FLUXConditioningInfo)
-        flux_conditioning = flux_conditioning.to(dtype=inference_dtype)
-        t5_embeddings = flux_conditioning.t5_embeds
-        clip_embeddings = flux_conditioning.clip_embeds
-
-        # Load the input latents, if provided.
-        init_latents = context.tensors.load(self.latents.latents_name) if self.latents else None
-        if init_latents is not None:
-            init_latents = init_latents.to(device=TorchDevice.choose_torch_device(), dtype=inference_dtype)
-
-        # Prepare input noise.
-        noise = get_noise(
-            num_samples=1,
-            height=self.height,
-            width=self.width,
-            device=TorchDevice.choose_torch_device(),
-            dtype=inference_dtype,
-            seed=self.seed,
-        )
-
-        transformer_info = context.models.load(self.transformer.transformer)
-        is_schnell = "schnell" in transformer_info.config.config_path
-
-        # Calculate the timestep schedule.
-        image_seq_len = noise.shape[-1] * noise.shape[-2] // 4
-        timesteps = get_schedule(
-            num_steps=self.num_steps,
-            image_seq_len=image_seq_len,
-            shift=not is_schnell,
-        )
-
-        # Clip the timesteps schedule based on denoising_start and denoising_end.
-        timesteps = clip_timestep_schedule(timesteps, self.denoising_start, self.denoising_end)
-
-        # Prepare input latent image.
-        if init_latents is not None:
-            # If init_latents is provided, we are doing image-to-image.
-
-            if is_schnell:
-                context.logger.warning(
-                    "Running image-to-image with a FLUX schnell model. This is not recommended. The results are likely "
-                    "to be poor. Consider using a FLUX dev model instead."
-                )
-
-            # Noise the orig_latents by the appropriate amount for the first timestep.
-            t_0 = timesteps[0]
-            x = t_0 * noise + (1.0 - t_0) * init_latents
-        else:
-            # init_latents are not provided, so we are not doing image-to-image (i.e. we are starting from pure noise).
-            if self.denoising_start > 1e-5:
-                raise ValueError("denoising_start should be 0 when initial latents are not provided.")
-
-            x = noise
-
-        # If len(timesteps) == 1, then short-circuit. We are just noising the input latents, but not taking any
-        # denoising steps.
-        if len(timesteps) <= 1:
-            return x
-
-        inpaint_mask = self._prep_inpaint_mask(context, x)
-
-        b, _c, h, w = x.shape
-        img_ids = generate_img_ids(h=h, w=w, batch_size=b, device=x.device, dtype=x.dtype)
-
-        bs, t5_seq_len, _ = t5_embeddings.shape
-        txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
-
-        # Pack all latent tensors.
-        init_latents = pack(init_latents) if init_latents is not None else None
-        inpaint_mask = pack(inpaint_mask) if inpaint_mask is not None else None
-        noise = pack(noise)
-        x = pack(x)
-
-        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len correctly.
-        assert image_seq_len == x.shape[1]
-
-        # Prepare inpaint extension.
-        inpaint_extension: InpaintExtension | None = None
-        if inpaint_mask is not None:
-            assert init_latents is not None
-            inpaint_extension = InpaintExtension(
-                init_latents=init_latents,
-                inpaint_mask=inpaint_mask,
-                noise=noise,
-            )
-
-        with (
-            transformer_info.model_on_device() as (cached_weights, transformer),
-            # Apply the LoRA after transformer has been moved to its target device for faster patching.
-            PeftPatcher.apply_peft_patches(
-                model=transformer,
-                patches=self._lora_iterator(context),
-                prefix="",
-                cached_weights=cached_weights,
-            ),
-        ):
-            assert isinstance(transformer, Flux)
-
-            x = denoise(
-                model=transformer,
-                img=x,
-                img_ids=img_ids,
-                txt=t5_embeddings,
-                txt_ids=txt_ids,
-                vec=clip_embeddings,
-                timesteps=timesteps,
-                step_callback=self._build_step_callback(context),
-                guidance=self.guidance,
-                inpaint_extension=inpaint_extension,
-            )
-
-        x = unpack(x.float(), self.height, self.width)
-        return x
-
-    def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor) -> torch.Tensor | None:
-        """Prepare the inpaint mask.
-
-        - Loads the mask
-        - Resizes if necessary
-        - Casts to same device/dtype as latents
-        - Expands mask to the same shape as latents so that they line up after 'packing'
-
-        Args:
-            context (InvocationContext): The invocation context, for loading the inpaint mask.
-            latents (torch.Tensor): A latent image tensor. In 'unpacked' format. Used to determine the target shape,
-                device, and dtype for the inpaint mask.
-
-        Returns:
-            torch.Tensor | None: Inpaint mask.
-        """
-        if self.denoise_mask is None:
-            return None
-
-        mask = context.tensors.load(self.denoise_mask.mask_name)
-
-        _, _, latent_height, latent_width = latents.shape
-        mask = tv_resize(
-            img=mask,
-            size=[latent_height, latent_width],
-            interpolation=tv_transforms.InterpolationMode.BILINEAR,
-            antialias=False,
-        )
-
-        mask = mask.to(device=latents.device, dtype=latents.dtype)
-
-        # Expand the inpaint mask to the same shape as `latents` so that when we 'pack' `mask` it lines up with
-        # `latents`.
-        return mask.expand_as(latents)
-
-    def _lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
-        for lora in self.transformer.loras:
-            lora_info = context.models.load(lora.lora)
-            assert isinstance(lora_info.model, LoRAModelRaw)
-            yield (lora_info.model, lora.weight)
-            del lora_info
-
-    def _build_step_callback(self, context: InvocationContext) -> Callable[[], None]:
-        def step_callback() -> None:
-            if context.util.is_canceled():
-                raise CanceledException
-
-            # TODO: Make this look like the image before re-enabling
-            # latent_image = unpack(img.float(), self.height, self.width)
-            # latent_image = latent_image.squeeze()  # Remove unnecessary dimensions
-            # flattened_tensor = latent_image.reshape(-1)  # Flatten to shape [48*128*128]
-
-            # # Create a new tensor of the required shape [255, 255, 3]
-            # latent_image = flattened_tensor[: 255 * 255 * 3].reshape(255, 255, 3)  # Reshape to RGB format
-
-            # # Convert to a NumPy array and then to a PIL Image
-            # image = Image.fromarray(latent_image.cpu().numpy().astype(np.uint8))
-
-            # (width, height) = image.size
-            # width *= 8
-            # height *= 8
-
-            # dataURL = image_to_dataURL(image, image_format="JPEG")
-
-            # # TODO: move this whole function to invocation context to properly reference these variables
-            # context._services.events.emit_invocation_denoise_progress(
-            #     context._data.queue_item,
-            #     context._data.invocation,
-            #     state,
-            #     ProgressImage(dataURL=dataURL, width=width, height=height),
-            # )
-
-        return step_callback

invokeai/app/invocations/flux_lora_loader.py

@@ -1,53 +0,0 @@
-from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
-from invokeai.app.invocations.model import LoRAField, ModelIdentifierField, TransformerField
-from invokeai.app.services.shared.invocation_context import InvocationContext
-
-
-@invocation_output("flux_lora_loader_output")
-class FluxLoRALoaderOutput(BaseInvocationOutput):
-    """FLUX LoRA Loader Output"""
-
-    transformer: TransformerField = OutputField(
-        default=None, description=FieldDescriptions.transformer, title="FLUX Transformer"
-    )
-
-
-@invocation(
-    "flux_lora_loader",
-    title="FLUX LoRA",
-    tags=["lora", "model", "flux"],
-    category="model",
-    version="1.0.0",
-)
-class FluxLoRALoaderInvocation(BaseInvocation):
-    """Apply a LoRA model to a FLUX transformer."""
-
-    lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
-    )
-    weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
-    transformer: TransformerField = InputField(
-        description=FieldDescriptions.transformer,
-        input=Input.Connection,
-        title="FLUX Transformer",
-    )
-
-    def invoke(self, context: InvocationContext) -> FluxLoRALoaderOutput:
-        lora_key = self.lora.key
-
-        if not context.models.exists(lora_key):
-            raise ValueError(f"Unknown lora: {lora_key}!")
-
-        if any(lora.lora.key == lora_key for lora in self.transformer.loras):
-            raise Exception(f'LoRA "{lora_key}" already applied to transformer.')
-
-        transformer = self.transformer.model_copy(deep=True)
-        transformer.loras.append(
-            LoRAField(
-                lora=self.lora,
-                weight=self.weight,
-            )
-        )
-
-        return FluxLoRALoaderOutput(transformer=transformer)

invokeai/app/invocations/flux_text_to_image.py

@@ -0,0 +1,169 @@
+import torch
+from einops import rearrange
+from PIL import Image
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    FluxConditioningField,
+    Input,
+    InputField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import TransformerField, VAEField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.session_processor.session_processor_common import CanceledException
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.modules.autoencoder import AutoEncoder
+from invokeai.backend.flux.sampling import denoise, get_noise, get_schedule, prepare_latent_img_patches, unpack
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "flux_text_to_image",
+    title="FLUX Text to Image",
+    tags=["image", "flux"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Text-to-image generation using a FLUX model."""
+
+    transformer: TransformerField = InputField(
+        description=FieldDescriptions.flux_model,
+        input=Input.Connection,
+        title="Transformer",
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+    positive_text_conditioning: FluxConditioningField = InputField(
+        description=FieldDescriptions.positive_cond, input=Input.Connection
+    )
+    width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
+    height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
+    num_steps: int = InputField(
+        default=4, description="Number of diffusion steps. Recommend values are schnell: 4, dev: 50."
+    )
+    guidance: float = InputField(
+        default=4.0,
+        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
+    )
+    seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = self._run_diffusion(context)
+        image = self._run_vae_decoding(context, latents)
+        image_dto = context.images.save(image=image)
+        return ImageOutput.build(image_dto)
+
+    def _run_diffusion(
+        self,
+        context: InvocationContext,
+    ):
+        inference_dtype = torch.bfloat16
+
+        # Load the conditioning data.
+        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        flux_conditioning = cond_data.conditionings[0]
+        assert isinstance(flux_conditioning, FLUXConditioningInfo)
+        flux_conditioning = flux_conditioning.to(dtype=inference_dtype)
+        t5_embeddings = flux_conditioning.t5_embeds
+        clip_embeddings = flux_conditioning.clip_embeds
+
+        transformer_info = context.models.load(self.transformer.transformer)
+
+        # Prepare input noise.
+        x = get_noise(
+            num_samples=1,
+            height=self.height,
+            width=self.width,
+            device=TorchDevice.choose_torch_device(),
+            dtype=inference_dtype,
+            seed=self.seed,
+        )
+
+        x, img_ids = prepare_latent_img_patches(x)
+
+        is_schnell = "schnell" in transformer_info.config.config_path
+
+        timesteps = get_schedule(
+            num_steps=self.num_steps,
+            image_seq_len=x.shape[1],
+            shift=not is_schnell,
+        )
+
+        bs, t5_seq_len, _ = t5_embeddings.shape
+        txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
+
+        with transformer_info as transformer:
+            assert isinstance(transformer, Flux)
+
+            def step_callback() -> None:
+                if context.util.is_canceled():
+                    raise CanceledException
+
+                # TODO: Make this look like the image before re-enabling
+                # latent_image = unpack(img.float(), self.height, self.width)
+                # latent_image = latent_image.squeeze()  # Remove unnecessary dimensions
+                # flattened_tensor = latent_image.reshape(-1)  # Flatten to shape [48*128*128]
+
+                # # Create a new tensor of the required shape [255, 255, 3]
+                # latent_image = flattened_tensor[: 255 * 255 * 3].reshape(255, 255, 3)  # Reshape to RGB format
+
+                # # Convert to a NumPy array and then to a PIL Image
+                # image = Image.fromarray(latent_image.cpu().numpy().astype(np.uint8))
+
+                # (width, height) = image.size
+                # width *= 8
+                # height *= 8
+
+                # dataURL = image_to_dataURL(image, image_format="JPEG")
+
+                # # TODO: move this whole function to invocation context to properly reference these variables
+                # context._services.events.emit_invocation_denoise_progress(
+                #     context._data.queue_item,
+                #     context._data.invocation,
+                #     state,
+                #     ProgressImage(dataURL=dataURL, width=width, height=height),
+                # )
+
+            x = denoise(
+                model=transformer,
+                img=x,
+                img_ids=img_ids,
+                txt=t5_embeddings,
+                txt_ids=txt_ids,
+                vec=clip_embeddings,
+                timesteps=timesteps,
+                step_callback=step_callback,
+                guidance=self.guidance,
+            )
+
+        x = unpack(x.float(), self.height, self.width)
+
+        return x
+
+    def _run_vae_decoding(
+        self,
+        context: InvocationContext,
+        latents: torch.Tensor,
+    ) -> Image.Image:
+        vae_info = context.models.load(self.vae.vae)
+        with vae_info as vae:
+            assert isinstance(vae, AutoEncoder)
+            latents = latents.to(dtype=TorchDevice.choose_torch_dtype())
+            img = vae.decode(latents)
+
+        img = img.clamp(-1, 1)
+        img = rearrange(img[0], "c h w -> h w c")
+        img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
+
+        return img_pil

invokeai/app/invocations/flux_vae_decode.py

@@ -1,60 +0,0 @@
-import torch
-from einops import rearrange
-from PIL import Image
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    Input,
-    InputField,
-    LatentsField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import VAEField
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.modules.autoencoder import AutoEncoder
-from invokeai.backend.model_manager.load.load_base import LoadedModel
-from invokeai.backend.util.devices import TorchDevice
-
-
-@invocation(
-    "flux_vae_decode",
-    title="FLUX Latents to Image",
-    tags=["latents", "image", "vae", "l2i", "flux"],
-    category="latents",
-    version="1.0.0",
-)
-class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Generates an image from latents."""
-
-    latents: LatentsField = InputField(
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-    )
-    vae: VAEField = InputField(
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-    )
-
-    def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
-        with vae_info as vae:
-            assert isinstance(vae, AutoEncoder)
-            latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype())
-            img = vae.decode(latents)
-
-        img = img.clamp(-1, 1)
-        img = rearrange(img[0], "c h w -> h w c")  # noqa: F821
-        img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
-        return img_pil
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.tensors.load(self.latents.latents_name)
-        vae_info = context.models.load(self.vae.vae)
-        image = self._vae_decode(vae_info=vae_info, latents=latents)
-
-        TorchDevice.empty_cache()
-        image_dto = context.images.save(image=image)
-        return ImageOutput.build(image_dto)

invokeai/app/invocations/flux_vae_encode.py

@@ -1,67 +0,0 @@
-import einops
-import torch
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    ImageField,
-    Input,
-    InputField,
-)
-from invokeai.app.invocations.model import VAEField
-from invokeai.app.invocations.primitives import LatentsOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.modules.autoencoder import AutoEncoder
-from invokeai.backend.model_manager import LoadedModel
-from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
-from invokeai.backend.util.devices import TorchDevice
-
-
-@invocation(
-    "flux_vae_encode",
-    title="FLUX Image to Latents",
-    tags=["latents", "image", "vae", "i2l", "flux"],
-    category="latents",
-    version="1.0.0",
-)
-class FluxVaeEncodeInvocation(BaseInvocation):
-    """Encodes an image into latents."""
-
-    image: ImageField = InputField(
-        description="The image to encode.",
-    )
-    vae: VAEField = InputField(
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-    )
-
-    @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
-        # TODO(ryand): Expose seed parameter at the invocation level.
-        # TODO(ryand): Write a util function for generating random tensors that is consistent across devices / dtypes.
-        # There's a starting point in get_noise(...), but it needs to be extracted and generalized. This function
-        # should be used for VAE encode sampling.
-        generator = torch.Generator(device=TorchDevice.choose_torch_device()).manual_seed(0)
-        with vae_info as vae:
-            assert isinstance(vae, AutoEncoder)
-            image_tensor = image_tensor.to(
-                device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype()
-            )
-            latents = vae.encode(image_tensor, sample=True, generator=generator)
-            return latents
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        image = context.images.get_pil(self.image.image_name)
-
-        vae_info = context.models.load(self.vae.vae)
-
-        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
-        if image_tensor.dim() == 3:
-            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
-
-        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
-
-        latents = latents.to("cpu")
-        name = context.tensors.save(tensor=latents)
-        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)

invokeai/app/invocations/image.py

@@ -6,13 +6,19 @@ import cv2
 import numpy
 from PIL import Image, ImageChops, ImageFilter, ImageOps
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    Classification,
+    invocation,
+    invocation_output,
+)
 from invokeai.app.invocations.constants import IMAGE_MODES
 from invokeai.app.invocations.fields import (
     ColorField,
     FieldDescriptions,
     ImageField,
     InputField,
+    OutputField,
     WithBoard,
     WithMetadata,
 )
@@ -1007,3 +1013,62 @@ class MaskFromIDInvocation(BaseInvocation, WithMetadata, WithBoard):
         image_dto = context.images.save(image=mask, image_category=ImageCategory.MASK)
 
         return ImageOutput.build(image_dto)
+
+
+@invocation_output("canvas_v2_mask_and_crop_output")
+class CanvasV2MaskAndCropOutput(ImageOutput):
+    offset_x: int = OutputField(description="The x offset of the image, after cropping")
+    offset_y: int = OutputField(description="The y offset of the image, after cropping")
+
+
+@invocation(
+    "canvas_v2_mask_and_crop",
+    title="Canvas V2 Mask and Crop",
+    tags=["image", "mask", "id"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class CanvasV2MaskAndCropInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Handles Canvas V2 image output masking and cropping"""
+
+    source_image: ImageField | None = InputField(
+        default=None,
+        description="The source image onto which the masked generated image is pasted. If omitted, the masked generated image is returned with transparency.",
+    )
+    generated_image: ImageField = InputField(description="The image to apply the mask to")
+    mask: ImageField = InputField(description="The mask to apply")
+    mask_blur: int = InputField(default=0, ge=0, description="The amount to blur the mask by")
+
+    def _prepare_mask(self, mask: Image.Image) -> Image.Image:
+        mask_array = numpy.array(mask)
+        kernel = numpy.ones((self.mask_blur, self.mask_blur), numpy.uint8)
+        dilated_mask_array = cv2.erode(mask_array, kernel, iterations=3)
+        dilated_mask = Image.fromarray(dilated_mask_array)
+        if self.mask_blur > 0:
+            mask = dilated_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
+        return ImageOps.invert(mask.convert("L"))
+
+    def invoke(self, context: InvocationContext) -> CanvasV2MaskAndCropOutput:
+        mask = self._prepare_mask(context.images.get_pil(self.mask.image_name))
+
+        if self.source_image:
+            generated_image = context.images.get_pil(self.generated_image.image_name)
+            source_image = context.images.get_pil(self.source_image.image_name)
+            source_image.paste(generated_image, (0, 0), mask)
+            image_dto = context.images.save(image=source_image)
+        else:
+            generated_image = context.images.get_pil(self.generated_image.image_name)
+            generated_image.putalpha(mask)
+            image_dto = context.images.save(image=generated_image)
+
+        # bbox = image.getbbox()
+        # image = image.crop(bbox)
+
+        return CanvasV2MaskAndCropOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            offset_x=0,
+            offset_y=0,
+            width=image_dto.width,
+            height=image_dto.height,
+        )

invokeai/app/invocations/mask.py

@@ -126,7 +126,7 @@ class ImageMaskToTensorInvocation(BaseInvocation, WithMetadata):
     title="Tensor Mask to Image",
     tags=["mask"],
     category="mask",
-    version="1.1.0",
+    version="1.0.0",
 )
 class MaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Convert a mask tensor to an image."""
@@ -135,11 +135,6 @@ class MaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         mask = context.tensors.load(self.mask.tensor_name)
-
-        # Squeeze the channel dimension if it exists.
-        if mask.dim() == 3:
-            mask = mask.squeeze(0)
-
         # Ensure that the mask is binary.
         if mask.dtype != torch.bool:
             mask = mask > 0.5

invokeai/app/invocations/model.py

@@ -69,7 +69,6 @@ class CLIPField(BaseModel):
 
 class TransformerField(BaseModel):
     transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
-    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
 class T5EncoderField(BaseModel):
@@ -203,7 +202,7 @@ class FluxModelLoaderInvocation(BaseInvocation):
         assert isinstance(transformer_config, CheckpointConfigBase)
 
         return FluxModelLoaderOutput(
-            transformer=TransformerField(transformer=transformer, loras=[]),
+            transformer=TransformerField(transformer=transformer),
             clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
             t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
             vae=VAEField(vae=vae),

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -22,8 +22,8 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.model import UNetField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.peft.lora import LoRAModelRaw
 from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, PipelineIntermediateState
 from invokeai.backend.stable_diffusion.multi_diffusion_pipeline import (
     MultiDiffusionPipeline,

invokeai/app/services/events/events_common.py

@@ -88,6 +88,8 @@ class QueueItemEventBase(QueueEventBase):
 
     item_id: int = Field(description="The ID of the queue item")
     batch_id: str = Field(description="The ID of the queue batch")
+    origin: str | None = Field(default=None, description="The origin of the queue item")
+    destination: str | None = Field(default=None, description="The destination of the queue item")
 
 
 class InvocationEventBase(QueueItemEventBase):
@@ -95,8 +97,6 @@ class InvocationEventBase(QueueItemEventBase):
 
     session_id: str = Field(description="The ID of the session (aka graph execution state)")
     queue_id: str = Field(description="The ID of the queue")
-    item_id: int = Field(description="The ID of the queue item")
-    batch_id: str = Field(description="The ID of the queue batch")
     session_id: str = Field(description="The ID of the session (aka graph execution state)")
     invocation: AnyInvocation = Field(description="The ID of the invocation")
     invocation_source_id: str = Field(description="The ID of the prepared invocation's source node")
@@ -114,6 +114,8 @@ class InvocationStartedEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
+            origin=queue_item.origin,
+            destination=queue_item.destination,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -147,6 +149,8 @@ class InvocationDenoiseProgressEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
+            origin=queue_item.origin,
+            destination=queue_item.destination,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -184,6 +188,8 @@ class InvocationCompleteEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
+            origin=queue_item.origin,
+            destination=queue_item.destination,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -216,6 +222,8 @@ class InvocationErrorEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
+            origin=queue_item.origin,
+            destination=queue_item.destination,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -253,6 +261,8 @@ class QueueItemStatusChangedEvent(QueueItemEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
+            origin=queue_item.origin,
+            destination=queue_item.destination,
             session_id=queue_item.session_id,
             status=queue_item.status,
             error_type=queue_item.error_type,
@@ -279,12 +289,14 @@ class BatchEnqueuedEvent(QueueEventBase):
         description="The number of invocations initially requested to be enqueued (may be less than enqueued if queue was full)"
     )
     priority: int = Field(description="The priority of the batch")
+    origin: str | None = Field(default=None, description="The origin of the batch")
 
     @classmethod
     def build(cls, enqueue_result: EnqueueBatchResult) -> "BatchEnqueuedEvent":
         return cls(
             queue_id=enqueue_result.queue_id,
             batch_id=enqueue_result.batch.batch_id,
+            origin=enqueue_result.batch.origin,
             enqueued=enqueue_result.enqueued,
             requested=enqueue_result.requested,
             priority=enqueue_result.priority,

invokeai/app/services/model_install/model_install_common.py

@@ -103,7 +103,7 @@ class HFModelSource(StringLikeSource):
         if self.variant:
             base += f":{self.variant or ''}"
         if self.subfolder:
-            base += f"::{self.subfolder.as_posix()}"
+            base += f":{self.subfolder}"
         return base
 
 

invokeai/app/services/session_queue/session_queue_base.py

@@ -6,6 +6,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
+    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -95,6 +96,11 @@ class SessionQueueBase(ABC):
         """Cancels all queue items with matching batch IDs"""
         pass
 
+    @abstractmethod
+    def cancel_by_origin(self, queue_id: str, origin: str) -> CancelByOriginResult:
+        """Cancels all queue items with the given batch origin"""
+        pass
+
     @abstractmethod
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         """Cancels all queue items with matching queue ID"""

invokeai/app/services/session_queue/session_queue_common.py

@@ -77,6 +77,14 @@ BatchDataCollection: TypeAlias = list[list[BatchDatum]]
 
 class Batch(BaseModel):
     batch_id: str = Field(default_factory=uuid_string, description="The ID of the batch")
+    origin: str | None = Field(
+        default=None,
+        description="The origin of this queue item. This data is used by the frontend to determine how to handle results.",
+    )
+    destination: str | None = Field(
+        default=None,
+        description="The origin of this queue item. This data is used by the frontend to determine how to handle results",
+    )
     data: Optional[BatchDataCollection] = Field(default=None, description="The batch data collection.")
     graph: Graph = Field(description="The graph to initialize the session with")
     workflow: Optional[WorkflowWithoutID] = Field(
@@ -195,6 +203,14 @@ class SessionQueueItemWithoutGraph(BaseModel):
     status: QUEUE_ITEM_STATUS = Field(default="pending", description="The status of this queue item")
     priority: int = Field(default=0, description="The priority of this queue item")
     batch_id: str = Field(description="The ID of the batch associated with this queue item")
+    origin: str | None = Field(
+        default=None,
+        description="The origin of this queue item. This data is used by the frontend to determine how to handle results.",
+    )
+    destination: str | None = Field(
+        default=None,
+        description="The origin of this queue item. This data is used by the frontend to determine how to handle results",
+    )
     session_id: str = Field(
         description="The ID of the session associated with this queue item. The session doesn't exist in graph_executions until the queue item is executed."
     )
@@ -294,6 +310,8 @@ class SessionQueueStatus(BaseModel):
 class BatchStatus(BaseModel):
     queue_id: str = Field(..., description="The ID of the queue")
     batch_id: str = Field(..., description="The ID of the batch")
+    origin: str | None = Field(..., description="The origin of the batch")
+    destination: str | None = Field(..., description="The destination of the batch")
     pending: int = Field(..., description="Number of queue items with status 'pending'")
     in_progress: int = Field(..., description="Number of queue items with status 'in_progress'")
     completed: int = Field(..., description="Number of queue items with status 'complete'")
@@ -328,6 +346,12 @@ class CancelByBatchIDsResult(BaseModel):
     canceled: int = Field(..., description="Number of queue items canceled")
 
 
+class CancelByOriginResult(BaseModel):
+    """Result of canceling by list of batch ids"""
+
+    canceled: int = Field(..., description="Number of queue items canceled")
+
+
 class CancelByQueueIDResult(CancelByBatchIDsResult):
     """Result of canceling by queue id"""
 
@@ -433,6 +457,8 @@ class SessionQueueValueToInsert(NamedTuple):
     field_values: Optional[str]  # field_values json
     priority: int  # priority
     workflow: Optional[str]  # workflow json
+    origin: str | None
+    destination: str | None
 
 
 ValuesToInsert: TypeAlias = list[SessionQueueValueToInsert]
@@ -453,6 +479,8 @@ def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new
                 json.dumps(field_values, default=to_jsonable_python) if field_values else None,  # field_values (json)
                 priority,  # priority
                 json.dumps(workflow, default=to_jsonable_python) if workflow else None,  # workflow (json)
+                batch.origin,  # origin
+                batch.destination,  # destination
             )
         )
     return values_to_insert

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -10,6 +10,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
+    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -127,8 +128,8 @@ class SqliteSessionQueue(SessionQueueBase):
 
             self.__cursor.executemany(
                 """--sql
-                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow)
-                VALUES (?, ?, ?, ?, ?, ?, ?)
+                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin, destination)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
                 """,
                 values_to_insert,
             )
@@ -417,11 +418,7 @@ class SqliteSessionQueue(SessionQueueBase):
             )
             self.__conn.commit()
             if current_queue_item is not None and current_queue_item.batch_id in batch_ids:
-                batch_status = self.get_batch_status(queue_id=queue_id, batch_id=current_queue_item.batch_id)
-                queue_status = self.get_queue_status(queue_id=queue_id)
-                self.__invoker.services.events.emit_queue_item_status_changed(
-                    current_queue_item, batch_status, queue_status
-                )
+                self._set_queue_item_status(current_queue_item.item_id, "canceled")
         except Exception:
             self.__conn.rollback()
             raise
@@ -429,6 +426,46 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return CancelByBatchIDsResult(canceled=count)
 
+    def cancel_by_origin(self, queue_id: str, origin: str) -> CancelByOriginResult:
+        try:
+            current_queue_item = self.get_current(queue_id)
+            self.__lock.acquire()
+            where = """--sql
+                WHERE
+                  queue_id == ?
+                  AND origin == ?
+                  AND status != 'canceled'
+                  AND status != 'completed'
+                  AND status != 'failed'
+                """
+            params = (queue_id, origin)
+            self.__cursor.execute(
+                f"""--sql
+                SELECT COUNT(*)
+                FROM session_queue
+                {where};
+                """,
+                params,
+            )
+            count = self.__cursor.fetchone()[0]
+            self.__cursor.execute(
+                f"""--sql
+                UPDATE session_queue
+                SET status = 'canceled'
+                {where};
+                """,
+                params,
+            )
+            self.__conn.commit()
+            if current_queue_item is not None and current_queue_item.origin == origin:
+                self._set_queue_item_status(current_queue_item.item_id, "canceled")
+        except Exception:
+            self.__conn.rollback()
+            raise
+        finally:
+            self.__lock.release()
+        return CancelByOriginResult(canceled=count)
+
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         try:
             current_queue_item = self.get_current(queue_id)
@@ -541,7 +578,9 @@ class SqliteSessionQueue(SessionQueueBase):
                     started_at,
                     session_id,
                     batch_id,
-                    queue_id
+                    queue_id,
+                    origin,
+                    destination
                 FROM session_queue
                 WHERE queue_id = ?
             """
@@ -621,7 +660,7 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.acquire()
             self.__cursor.execute(
                 """--sql
-                SELECT status, count(*)
+                SELECT status, count(*), origin, destination
                 FROM session_queue
                 WHERE
                   queue_id = ?
@@ -633,6 +672,8 @@ class SqliteSessionQueue(SessionQueueBase):
             result = cast(list[sqlite3.Row], self.__cursor.fetchall())
             total = sum(row[1] for row in result)
             counts: dict[str, int] = {row[0]: row[1] for row in result}
+            origin = result[0]["origin"] if result else None
+            destination = result[0]["destination"] if result else None
         except Exception:
             self.__conn.rollback()
             raise
@@ -641,6 +682,8 @@ class SqliteSessionQueue(SessionQueueBase):
 
         return BatchStatus(
             batch_id=batch_id,
+            origin=origin,
+            destination=destination,
             queue_id=queue_id,
             pending=counts.get("pending", 0),
             in_progress=counts.get("in_progress", 0),

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

@@ -17,6 +17,7 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_11 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_12 import build_migration_12
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_13 import build_migration_13
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_14 import build_migration_14
+from invokeai.app.services.shared.sqlite_migrator.migrations.migration_15 import build_migration_15
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -51,6 +52,7 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_12(app_config=config))
     migrator.register_migration(build_migration_13())
     migrator.register_migration(build_migration_14())
+    migrator.register_migration(build_migration_15())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_15.py

@@ -0,0 +1,34 @@
+import sqlite3
+
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration15Callback:
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        self._add_origin_col(cursor)
+
+    def _add_origin_col(self, cursor: sqlite3.Cursor) -> None:
+        """
+        - Adds `origin` column to the session queue table.
+        - Adds `destination` column to the session queue table.
+        """
+
+        cursor.execute("ALTER TABLE session_queue ADD COLUMN origin TEXT;")
+        cursor.execute("ALTER TABLE session_queue ADD COLUMN destination TEXT;")
+
+
+def build_migration_15() -> Migration:
+    """
+    Build the migration from database version 14 to 15.
+
+    This migration does the following:
+        - Adds `origin` column to the session queue table.
+        - Adds `destination` column to the session queue table.
+    """
+    migration_15 = Migration(
+        from_version=14,
+        to_version=15,
+        callback=Migration15Callback(),
+    )
+
+    return migration_15

invokeai/app/services/workflow_records/default_workflows/FLUX Image to Image.json

@@ -1,407 +0,0 @@
-{
-  "name": "FLUX Image to Image",
-  "author": "InvokeAI",
-  "description": "A simple image-to-image workflow using a FLUX dev model. ",
-  "version": "1.0.4",
-  "contact": "",
-  "tags": "image2image, flux, image-to-image",
-  "notes": "Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend using FLUX dev models for image-to-image workflows. The image-to-image performance with FLUX schnell models is poor.",
-  "exposedFields": [
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "model"
-    },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "t5_encoder_model"
-    },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "clip_embed_model"
-    },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "vae_model"
-    },
-    {
-      "nodeId": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "fieldName": "denoising_start"
-    },
-    {
-      "nodeId": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "fieldName": "prompt"
-    },
-    {
-      "nodeId": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "fieldName": "num_steps"
-    }
-  ],
-  "meta": {
-    "version": "3.0.0",
-    "category": "default"
-  },
-  "nodes": [
-    {
-      "id": "2981a67c-480f-4237-9384-26b68dbf912b",
-      "type": "invocation",
-      "data": {
-        "id": "2981a67c-480f-4237-9384-26b68dbf912b",
-        "type": "flux_vae_encode",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": true,
-        "inputs": {
-          "image": {
-            "name": "image",
-            "label": "",
-            "value": {
-              "image_name": "8a5c62aa-9335-45d2-9c71-89af9fc1f8d4.png"
-            }
-          },
-          "vae": {
-            "name": "vae",
-            "label": ""
-          }
-        }
-      },
-      "position": {
-        "x": 732.7680166609682,
-        "y": -24.37398171806909
-      }
-    },
-    {
-      "id": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "type": "invocation",
-      "data": {
-        "id": "ace0258f-67d7-4eee-a218-6fff27065214",
-        "type": "flux_denoise",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": true,
-        "inputs": {
-          "board": {
-            "name": "board",
-            "label": ""
-          },
-          "metadata": {
-            "name": "metadata",
-            "label": ""
-          },
-          "latents": {
-            "name": "latents",
-            "label": ""
-          },
-          "denoise_mask": {
-            "name": "denoise_mask",
-            "label": ""
-          },
-          "denoising_start": {
-            "name": "denoising_start",
-            "label": "",
-            "value": 0.04
-          },
-          "denoising_end": {
-            "name": "denoising_end",
-            "label": "",
-            "value": 1
-          },
-          "transformer": {
-            "name": "transformer",
-            "label": ""
-          },
-          "positive_text_conditioning": {
-            "name": "positive_text_conditioning",
-            "label": ""
-          },
-          "width": {
-            "name": "width",
-            "label": "",
-            "value": 1024
-          },
-          "height": {
-            "name": "height",
-            "label": "",
-            "value": 1024
-          },
-          "num_steps": {
-            "name": "num_steps",
-            "label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
-            "value": 30
-          },
-          "guidance": {
-            "name": "guidance",
-            "label": "",
-            "value": 4
-          },
-          "seed": {
-            "name": "seed",
-            "label": "",
-            "value": 0
-          }
-        }
-      },
-      "position": {
-        "x": 1182.8836633018684,
-        "y": -251.38882958913183
-      }
-    },
-    {
-      "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "type": "invocation",
-      "data": {
-        "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-        "type": "flux_vae_decode",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": false,
-        "useCache": true,
-        "inputs": {
-          "board": {
-            "name": "board",
-            "label": ""
-          },
-          "metadata": {
-            "name": "metadata",
-            "label": ""
-          },
-          "latents": {
-            "name": "latents",
-            "label": ""
-          },
-          "vae": {
-            "name": "vae",
-            "label": ""
-          }
-        }
-      },
-      "position": {
-        "x": 1575.5797431839133,
-        "y": -209.00150975507415
-      }
-    },
-    {
-      "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "type": "invocation",
-      "data": {
-        "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-        "type": "flux_model_loader",
-        "version": "1.0.4",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": false,
-        "inputs": {
-          "model": {
-            "name": "model",
-            "label": "Model (dev variant recommended for Image-to-Image)"
-          },
-          "t5_encoder_model": {
-            "name": "t5_encoder_model",
-            "label": ""
-          },
-          "clip_embed_model": {
-            "name": "clip_embed_model",
-            "label": "",
-            "value": {
-              "key": "fa23a584-b623-415d-832a-21b5098ff1a1",
-              "hash": "blake3:17c19f0ef941c3b7609a9c94a659ca5364de0be364a91d4179f0e39ba17c3b70",
-              "name": "clip-vit-large-patch14",
-              "base": "any",
-              "type": "clip_embed"
-            }
-          },
-          "vae_model": {
-            "name": "vae_model",
-            "label": "",
-            "value": {
-              "key": "74fc82ba-c0a8-479d-a890-2126f82da758",
-              "hash": "blake3:ce21cb76364aa6e2421311cf4a4b5eb052a76c4f1cd207b50703d8978198a068",
-              "name": "FLUX.1-schnell_ae",
-              "base": "flux",
-              "type": "vae"
-            }
-          }
-        }
-      },
-      "position": {
-        "x": 328.1809894659957,
-        "y": -90.2241133566946
-      }
-    },
-    {
-      "id": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "type": "invocation",
-      "data": {
-        "id": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-        "type": "flux_text_encoder",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": true,
-        "inputs": {
-          "clip": {
-            "name": "clip",
-            "label": ""
-          },
-          "t5_encoder": {
-            "name": "t5_encoder",
-            "label": ""
-          },
-          "t5_max_seq_len": {
-            "name": "t5_max_seq_len",
-            "label": "T5 Max Seq Len",
-            "value": 256
-          },
-          "prompt": {
-            "name": "prompt",
-            "label": "",
-            "value": "a cat wearing a birthday hat"
-          }
-        }
-      },
-      "position": {
-        "x": 745.8823365057267,
-        "y": -299.60249175851914
-      }
-    },
-    {
-      "id": "4754c534-a5f3-4ad0-9382-7887985e668c",
-      "type": "invocation",
-      "data": {
-        "id": "4754c534-a5f3-4ad0-9382-7887985e668c",
-        "type": "rand_int",
-        "version": "1.0.1",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": false,
-        "inputs": {
-          "low": {
-            "name": "low",
-            "label": "",
-            "value": 0
-          },
-          "high": {
-            "name": "high",
-            "label": "",
-            "value": 2147483647
-          }
-        }
-      },
-      "position": {
-        "x": 725.834098928012,
-        "y": 496.2710031089931
-      }
-    }
-  ],
-  "edges": [
-    {
-      "id": "reactflow__edge-2981a67c-480f-4237-9384-26b68dbf912bheight-ace0258f-67d7-4eee-a218-6fff27065214height",
-      "type": "default",
-      "source": "2981a67c-480f-4237-9384-26b68dbf912b",
-      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "sourceHandle": "height",
-      "targetHandle": "height"
-    },
-    {
-      "id": "reactflow__edge-2981a67c-480f-4237-9384-26b68dbf912bwidth-ace0258f-67d7-4eee-a218-6fff27065214width",
-      "type": "default",
-      "source": "2981a67c-480f-4237-9384-26b68dbf912b",
-      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "sourceHandle": "width",
-      "targetHandle": "width"
-    },
-    {
-      "id": "reactflow__edge-2981a67c-480f-4237-9384-26b68dbf912blatents-ace0258f-67d7-4eee-a218-6fff27065214latents",
-      "type": "default",
-      "source": "2981a67c-480f-4237-9384-26b68dbf912b",
-      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "sourceHandle": "latents",
-      "targetHandle": "latents"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-2981a67c-480f-4237-9384-26b68dbf912bvae",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "2981a67c-480f-4237-9384-26b68dbf912b",
-      "sourceHandle": "vae",
-      "targetHandle": "vae"
-    },
-    {
-      "id": "reactflow__edge-ace0258f-67d7-4eee-a218-6fff27065214latents-7e5172eb-48c1-44db-a770-8fd83e1435d1latents",
-      "type": "default",
-      "source": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "sourceHandle": "latents",
-      "targetHandle": "latents"
-    },
-    {
-      "id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-ace0258f-67d7-4eee-a218-6fff27065214seed",
-      "type": "default",
-      "source": "4754c534-a5f3-4ad0-9382-7887985e668c",
-      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "sourceHandle": "value",
-      "targetHandle": "seed"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-ace0258f-67d7-4eee-a218-6fff27065214transformer",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "sourceHandle": "transformer",
-      "targetHandle": "transformer"
-    },
-    {
-      "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-ace0258f-67d7-4eee-a218-6fff27065214positive_text_conditioning",
-      "type": "default",
-      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "sourceHandle": "conditioning",
-      "targetHandle": "positive_text_conditioning"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-7e5172eb-48c1-44db-a770-8fd83e1435d1vae",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "sourceHandle": "vae",
-      "targetHandle": "vae"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90max_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "sourceHandle": "max_seq_len",
-      "targetHandle": "t5_max_seq_len"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90t5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "sourceHandle": "t5_encoder",
-      "targetHandle": "t5_encoder"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90clip-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cclip",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "sourceHandle": "clip",
-      "targetHandle": "clip"
-    }
-  ]
-}

invokeai/app/services/workflow_records/default_workflows/Flux Text to Image.json

@@ -1,7 +1,7 @@
 {
   "name": "FLUX Text to Image",
   "author": "InvokeAI",
-  "description": "A simple text-to-image workflow using FLUX dev or schnell models.",
+  "description": "A simple text-to-image workflow using FLUX dev or schnell models. Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
   "version": "1.0.4",
   "contact": "",
   "tags": "text2image, flux",
@@ -11,25 +11,17 @@
       "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
       "fieldName": "model"
     },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "t5_encoder_model"
-    },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "clip_embed_model"
-    },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "vae_model"
-    },
     {
       "nodeId": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "fieldName": "prompt"
     },
     {
-      "nodeId": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
+      "nodeId": "159bdf1b-79e7-4174-b86e-d40e646964c8",
       "fieldName": "num_steps"
+    },
+    {
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "fieldName": "t5_encoder_model"
     }
   ],
   "meta": {
@@ -37,121 +29,6 @@
     "category": "default"
   },
   "nodes": [
-    {
-      "id": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "type": "invocation",
-      "data": {
-        "id": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-        "type": "flux_denoise",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": true,
-        "inputs": {
-          "board": {
-            "name": "board",
-            "label": ""
-          },
-          "metadata": {
-            "name": "metadata",
-            "label": ""
-          },
-          "latents": {
-            "name": "latents",
-            "label": ""
-          },
-          "denoise_mask": {
-            "name": "denoise_mask",
-            "label": ""
-          },
-          "denoising_start": {
-            "name": "denoising_start",
-            "label": "",
-            "value": 0
-          },
-          "denoising_end": {
-            "name": "denoising_end",
-            "label": "",
-            "value": 1
-          },
-          "transformer": {
-            "name": "transformer",
-            "label": ""
-          },
-          "positive_text_conditioning": {
-            "name": "positive_text_conditioning",
-            "label": ""
-          },
-          "width": {
-            "name": "width",
-            "label": "",
-            "value": 1024
-          },
-          "height": {
-            "name": "height",
-            "label": "",
-            "value": 1024
-          },
-          "num_steps": {
-            "name": "num_steps",
-            "label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
-            "value": 30
-          },
-          "guidance": {
-            "name": "guidance",
-            "label": "",
-            "value": 4
-          },
-          "seed": {
-            "name": "seed",
-            "label": "",
-            "value": 0
-          }
-        }
-      },
-      "position": {
-        "x": 1186.1868226120378,
-        "y": -214.9459927686657
-      }
-    },
-    {
-      "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "type": "invocation",
-      "data": {
-        "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-        "type": "flux_vae_decode",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": false,
-        "useCache": true,
-        "inputs": {
-          "board": {
-            "name": "board",
-            "label": ""
-          },
-          "metadata": {
-            "name": "metadata",
-            "label": ""
-          },
-          "latents": {
-            "name": "latents",
-            "label": ""
-          },
-          "vae": {
-            "name": "vae",
-            "label": ""
-          }
-        }
-      },
-      "position": {
-        "x": 1575.5797431839133,
-        "y": -209.00150975507415
-      }
-    },
     {
       "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
       "type": "invocation",
@@ -222,8 +99,8 @@
         }
       },
       "position": {
-        "x": 778.4899149328337,
-        "y": -100.36469216659502
+        "x": 824.1970602278849,
+        "y": 146.98251001061735
       }
     },
     {
@@ -252,52 +129,77 @@
         }
       },
       "position": {
-        "x": 800.9667463219505,
-        "y": 285.8297267547506
+        "x": 822.9899179655476,
+        "y": 360.9657214885052
+      }
+    },
+    {
+      "id": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "type": "invocation",
+      "data": {
+        "id": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+        "type": "flux_text_to_image",
+        "version": "1.0.0",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": false,
+        "useCache": true,
+        "inputs": {
+          "board": {
+            "name": "board",
+            "label": ""
+          },
+          "metadata": {
+            "name": "metadata",
+            "label": ""
+          },
+          "transformer": {
+            "name": "transformer",
+            "label": ""
+          },
+          "vae": {
+            "name": "vae",
+            "label": ""
+          },
+          "positive_text_conditioning": {
+            "name": "positive_text_conditioning",
+            "label": ""
+          },
+          "width": {
+            "name": "width",
+            "label": "",
+            "value": 1024
+          },
+          "height": {
+            "name": "height",
+            "label": "",
+            "value": 1024
+          },
+          "num_steps": {
+            "name": "num_steps",
+            "label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
+            "value": 30
+          },
+          "guidance": {
+            "name": "guidance",
+            "label": "",
+            "value": 4
+          },
+          "seed": {
+            "name": "seed",
+            "label": "",
+            "value": 0
+          }
+        }
+      },
+      "position": {
+        "x": 1216.3900791301849,
+        "y": 5.500841807102248
       }
     }
   ],
   "edges": [
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-4fe24f07-f906-4f55-ab2c-9beee56ef5bdtransformer",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "sourceHandle": "transformer",
-      "targetHandle": "transformer"
-    },
-    {
-      "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-4fe24f07-f906-4f55-ab2c-9beee56ef5bdpositive_text_conditioning",
-      "type": "default",
-      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "sourceHandle": "conditioning",
-      "targetHandle": "positive_text_conditioning"
-    },
-    {
-      "id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-4fe24f07-f906-4f55-ab2c-9beee56ef5bdseed",
-      "type": "default",
-      "source": "4754c534-a5f3-4ad0-9382-7887985e668c",
-      "target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "sourceHandle": "value",
-      "targetHandle": "seed"
-    },
-    {
-      "id": "reactflow__edge-4fe24f07-f906-4f55-ab2c-9beee56ef5bdlatents-7e5172eb-48c1-44db-a770-8fd83e1435d1latents",
-      "type": "default",
-      "source": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "sourceHandle": "latents",
-      "targetHandle": "latents"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-7e5172eb-48c1-44db-a770-8fd83e1435d1vae",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "sourceHandle": "vae",
-      "targetHandle": "vae"
-    },
     {
       "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90max_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
       "type": "default",
@@ -306,6 +208,14 @@
       "sourceHandle": "max_seq_len",
       "targetHandle": "t5_max_seq_len"
     },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-159bdf1b-79e7-4174-b86e-d40e646964c8vae",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "vae",
+      "targetHandle": "vae"
+    },
     {
       "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90t5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
       "type": "default",
@@ -321,6 +231,30 @@
       "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "sourceHandle": "clip",
       "targetHandle": "clip"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-159bdf1b-79e7-4174-b86e-d40e646964c8transformer",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "transformer",
+      "targetHandle": "transformer"
+    },
+    {
+      "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-159bdf1b-79e7-4174-b86e-d40e646964c8positive_text_conditioning",
+      "type": "default",
+      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "conditioning",
+      "targetHandle": "positive_text_conditioning"
+    },
+    {
+      "id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-159bdf1b-79e7-4174-b86e-d40e646964c8seed",
+      "type": "default",
+      "source": "4754c534-a5f3-4ad0-9382-7887985e668c",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "value",
+      "targetHandle": "seed"
     }
   ]
 }

invokeai/backend/flux/denoise.py

@@ -1,45 +0,0 @@
-from typing import Callable
-
-import torch
-from tqdm import tqdm
-
-from invokeai.backend.flux.inpaint_extension import InpaintExtension
-from invokeai.backend.flux.model import Flux
-
-
-def denoise(
-    model: Flux,
-    # model input
-    img: torch.Tensor,
-    img_ids: torch.Tensor,
-    txt: torch.Tensor,
-    txt_ids: torch.Tensor,
-    vec: torch.Tensor,
-    # sampling parameters
-    timesteps: list[float],
-    step_callback: Callable[[], None],
-    guidance: float,
-    inpaint_extension: InpaintExtension | None,
-):
-    # guidance_vec is ignored for schnell.
-    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
-    for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
-        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
-        pred = model(
-            img=img,
-            img_ids=img_ids,
-            txt=txt,
-            txt_ids=txt_ids,
-            y=vec,
-            timesteps=t_vec,
-            guidance=guidance_vec,
-        )
-
-        img = img + (t_prev - t_curr) * pred
-
-        if inpaint_extension is not None:
-            img = inpaint_extension.merge_intermediate_latents_with_init_latents(img, t_prev)
-
-        step_callback()
-
-    return img

invokeai/backend/flux/inpaint_extension.py

@@ -1,35 +0,0 @@
-import torch
-
-
-class InpaintExtension:
-    """A class for managing inpainting with FLUX."""
-
-    def __init__(self, init_latents: torch.Tensor, inpaint_mask: torch.Tensor, noise: torch.Tensor):
-        """Initialize InpaintExtension.
-
-        Args:
-            init_latents (torch.Tensor): The initial latents (i.e. un-noised at timestep 0). In 'packed' format.
-            inpaint_mask (torch.Tensor): A mask specifying which elements to inpaint. Range [0, 1]. Values of 1 will be
-                re-generated. Values of 0 will remain unchanged. Values between 0 and 1 can be used to blend the
-                inpainted region with the background. In 'packed' format.
-            noise (torch.Tensor): The noise tensor used to noise the init_latents. In 'packed' format.
-        """
-        assert init_latents.shape == inpaint_mask.shape == noise.shape
-        self._init_latents = init_latents
-        self._inpaint_mask = inpaint_mask
-        self._noise = noise
-
-    def merge_intermediate_latents_with_init_latents(
-        self, intermediate_latents: torch.Tensor, timestep: float
-    ) -> torch.Tensor:
-        """Merge the intermediate latents with the initial latents for the current timestep using the inpaint mask. I.e.
-        update the intermediate latents to keep the regions that are not being inpainted on the correct noise
-        trajectory.
-
-        This function should be called after each denoising step.
-        """
-        # Noise the init latents for the current timestep.
-        noised_init_latents = self._noise * timestep + (1.0 - timestep) * self._init_latents
-
-        # Merge the intermediate latents with the noised_init_latents using the inpaint_mask.
-        return intermediate_latents * self._inpaint_mask + noised_init_latents * (1.0 - self._inpaint_mask)

invokeai/backend/flux/modules/autoencoder.py

@@ -258,17 +258,16 @@ class Decoder(nn.Module):
 
 
 class DiagonalGaussian(nn.Module):
-    def __init__(self, chunk_dim: int = 1):
+    def __init__(self, sample: bool = True, chunk_dim: int = 1):
         super().__init__()
+        self.sample = sample
         self.chunk_dim = chunk_dim
 
-    def forward(self, z: Tensor, sample: bool = True, generator: torch.Generator | None = None) -> Tensor:
+    def forward(self, z: Tensor) -> Tensor:
         mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim)
-        if sample:
+        if self.sample:
             std = torch.exp(0.5 * logvar)
-            # Unfortunately, torch.randn_like(...) does not accept a generator argument at the time of writing, so we
-            # have to use torch.randn(...) instead.
-            return mean + std * torch.randn(size=mean.size(), generator=generator, dtype=mean.dtype, device=mean.device)
+            return mean + std * torch.randn_like(mean)
         else:
             return mean
 
@@ -298,21 +297,8 @@ class AutoEncoder(nn.Module):
         self.scale_factor = params.scale_factor
         self.shift_factor = params.shift_factor
 
-    def encode(self, x: Tensor, sample: bool = True, generator: torch.Generator | None = None) -> Tensor:
-        """Run VAE encoding on input tensor x.
-
-        Args:
-            x (Tensor): Input image tensor. Shape: (batch_size, in_channels, height, width).
-            sample (bool, optional): If True, sample from the encoded distribution, else, return the distribution mean.
-                Defaults to True.
-            generator (torch.Generator | None, optional): Optional random number generator for reproducibility.
-                Defaults to None.
-
-        Returns:
-            Tensor: Encoded latent tensor. Shape: (batch_size, z_channels, latent_height, latent_width).
-        """
-
-        z = self.reg(self.encoder(x), sample=sample, generator=generator)
+    def encode(self, x: Tensor) -> Tensor:
+        z = self.reg(self.encoder(x))
         z = self.scale_factor * (z - self.shift_factor)
         return z
 

invokeai/backend/flux/sampling.py

@@ -0,0 +1,167 @@
+# Initially pulled from https://github.com/black-forest-labs/flux
+
+import math
+from typing import Callable
+
+import torch
+from einops import rearrange, repeat
+from torch import Tensor
+from tqdm import tqdm
+
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.modules.conditioner import HFEncoder
+
+
+def get_noise(
+    num_samples: int,
+    height: int,
+    width: int,
+    device: torch.device,
+    dtype: torch.dtype,
+    seed: int,
+):
+    # We always generate noise on the same device and dtype then cast to ensure consistency across devices/dtypes.
+    rand_device = "cpu"
+    rand_dtype = torch.float16
+    return torch.randn(
+        num_samples,
+        16,
+        # allow for packing
+        2 * math.ceil(height / 16),
+        2 * math.ceil(width / 16),
+        device=rand_device,
+        dtype=rand_dtype,
+        generator=torch.Generator(device=rand_device).manual_seed(seed),
+    ).to(device=device, dtype=dtype)
+
+
+def prepare(t5: HFEncoder, clip: HFEncoder, img: Tensor, prompt: str | list[str]) -> dict[str, Tensor]:
+    bs, c, h, w = img.shape
+    if bs == 1 and not isinstance(prompt, str):
+        bs = len(prompt)
+
+    img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+    if img.shape[0] == 1 and bs > 1:
+        img = repeat(img, "1 ... -> bs ...", bs=bs)
+
+    img_ids = torch.zeros(h // 2, w // 2, 3)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
+    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+    if isinstance(prompt, str):
+        prompt = [prompt]
+    txt = t5(prompt)
+    if txt.shape[0] == 1 and bs > 1:
+        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
+    txt_ids = torch.zeros(bs, txt.shape[1], 3)
+
+    vec = clip(prompt)
+    if vec.shape[0] == 1 and bs > 1:
+        vec = repeat(vec, "1 ... -> bs ...", bs=bs)
+
+    return {
+        "img": img,
+        "img_ids": img_ids.to(img.device),
+        "txt": txt.to(img.device),
+        "txt_ids": txt_ids.to(img.device),
+        "vec": vec.to(img.device),
+    }
+
+
+def time_shift(mu: float, sigma: float, t: Tensor):
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+
+def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
+    m = (y2 - y1) / (x2 - x1)
+    b = y1 - m * x1
+    return lambda x: m * x + b
+
+
+def get_schedule(
+    num_steps: int,
+    image_seq_len: int,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+    shift: bool = True,
+) -> list[float]:
+    # extra step for zero
+    timesteps = torch.linspace(1, 0, num_steps + 1)
+
+    # shifting the schedule to favor high timesteps for higher signal images
+    if shift:
+        # eastimate mu based on linear estimation between two points
+        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
+        timesteps = time_shift(mu, 1.0, timesteps)
+
+    return timesteps.tolist()
+
+
+def denoise(
+    model: Flux,
+    # model input
+    img: Tensor,
+    img_ids: Tensor,
+    txt: Tensor,
+    txt_ids: Tensor,
+    vec: Tensor,
+    # sampling parameters
+    timesteps: list[float],
+    step_callback: Callable[[], None],
+    guidance: float = 4.0,
+):
+    # guidance_vec is ignored for schnell.
+    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
+    for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
+        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
+        pred = model(
+            img=img,
+            img_ids=img_ids,
+            txt=txt,
+            txt_ids=txt_ids,
+            y=vec,
+            timesteps=t_vec,
+            guidance=guidance_vec,
+        )
+
+        img = img + (t_prev - t_curr) * pred
+        step_callback()
+
+    return img
+
+
+def unpack(x: Tensor, height: int, width: int) -> Tensor:
+    return rearrange(
+        x,
+        "b (h w) (c ph pw) -> b c (h ph) (w pw)",
+        h=math.ceil(height / 16),
+        w=math.ceil(width / 16),
+        ph=2,
+        pw=2,
+    )
+
+
+def prepare_latent_img_patches(latent_img: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """Convert an input image in latent space to patches for diffusion.
+
+    This implementation was extracted from:
+    https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/sampling.py#L32
+
+    Returns:
+        tuple[Tensor, Tensor]: (img, img_ids), as defined in the original flux repo.
+    """
+    bs, c, h, w = latent_img.shape
+
+    # Pixel unshuffle with a scale of 2, and flatten the height/width dimensions to get an array of patches.
+    img = rearrange(latent_img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+    if img.shape[0] == 1 and bs > 1:
+        img = repeat(img, "1 ... -> bs ...", bs=bs)
+
+    # Generate patch position ids.
+    img_ids = torch.zeros(h // 2, w // 2, 3, device=img.device, dtype=img.dtype)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device, dtype=img.dtype)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device, dtype=img.dtype)[None, :]
+    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+    return img, img_ids

invokeai/backend/flux/sampling_utils.py

@@ -1,135 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-import math
-from typing import Callable
-
-import torch
-from einops import rearrange, repeat
-
-
-def get_noise(
-    num_samples: int,
-    height: int,
-    width: int,
-    device: torch.device,
-    dtype: torch.dtype,
-    seed: int,
-):
-    # We always generate noise on the same device and dtype then cast to ensure consistency across devices/dtypes.
-    rand_device = "cpu"
-    rand_dtype = torch.float16
-    return torch.randn(
-        num_samples,
-        16,
-        # allow for packing
-        2 * math.ceil(height / 16),
-        2 * math.ceil(width / 16),
-        device=rand_device,
-        dtype=rand_dtype,
-        generator=torch.Generator(device=rand_device).manual_seed(seed),
-    ).to(device=device, dtype=dtype)
-
-
-def time_shift(mu: float, sigma: float, t: torch.Tensor) -> torch.Tensor:
-    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
-
-
-def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
-    m = (y2 - y1) / (x2 - x1)
-    b = y1 - m * x1
-    return lambda x: m * x + b
-
-
-def get_schedule(
-    num_steps: int,
-    image_seq_len: int,
-    base_shift: float = 0.5,
-    max_shift: float = 1.15,
-    shift: bool = True,
-) -> list[float]:
-    # extra step for zero
-    timesteps = torch.linspace(1, 0, num_steps + 1)
-
-    # shifting the schedule to favor high timesteps for higher signal images
-    if shift:
-        # estimate mu based on linear estimation between two points
-        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
-        timesteps = time_shift(mu, 1.0, timesteps)
-
-    return timesteps.tolist()
-
-
-def _find_last_index_ge_val(timesteps: list[float], val: float, eps: float = 1e-6) -> int:
-    """Find the last index in timesteps that is >= val.
-
-    We use epsilon-close equality to avoid potential floating point errors.
-    """
-    idx = len(list(filter(lambda t: t >= (val - eps), timesteps))) - 1
-    assert idx >= 0
-    return idx
-
-
-def clip_timestep_schedule(timesteps: list[float], denoising_start: float, denoising_end: float) -> list[float]:
-    """Clip the timestep schedule to the denoising range.
-
-    Args:
-        timesteps (list[float]): The original timestep schedule: [1.0, ..., 0.0].
-        denoising_start (float): A value in [0, 1] specifying the start of the denoising process. E.g. a value of 0.2
-            would mean that the denoising process start at the last timestep in the schedule >= 0.8.
-        denoising_end (float): A value in [0, 1] specifying the end of the denoising process. E.g. a value of 0.8 would
-            mean that the denoising process end at the last timestep in the schedule >= 0.2.
-
-    Returns:
-        list[float]: The clipped timestep schedule.
-    """
-    assert 0.0 <= denoising_start <= 1.0
-    assert 0.0 <= denoising_end <= 1.0
-    assert denoising_start <= denoising_end
-
-    t_start_val = 1.0 - denoising_start
-    t_end_val = 1.0 - denoising_end
-
-    t_start_idx = _find_last_index_ge_val(timesteps, t_start_val)
-    t_end_idx = _find_last_index_ge_val(timesteps, t_end_val)
-
-    clipped_timesteps = timesteps[t_start_idx : t_end_idx + 1]
-
-    return clipped_timesteps
-
-
-def unpack(x: torch.Tensor, height: int, width: int) -> torch.Tensor:
-    """Unpack flat array of patch embeddings to latent image."""
-    return rearrange(
-        x,
-        "b (h w) (c ph pw) -> b c (h ph) (w pw)",
-        h=math.ceil(height / 16),
-        w=math.ceil(width / 16),
-        ph=2,
-        pw=2,
-    )
-
-
-def pack(x: torch.Tensor) -> torch.Tensor:
-    """Pack latent image to flattented array of patch embeddings."""
-    # Pixel unshuffle with a scale of 2, and flatten the height/width dimensions to get an array of patches.
-    return rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
-
-
-def generate_img_ids(h: int, w: int, batch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
-    """Generate tensor of image position ids.
-
-    Args:
-        h (int): Height of image in latent space.
-        w (int): Width of image in latent space.
-        batch_size (int): Batch size.
-        device (torch.device): Device.
-        dtype (torch.dtype): dtype.
-
-    Returns:
-        torch.Tensor: Image position ids.
-    """
-    img_ids = torch.zeros(h // 2, w // 2, 3, device=device, dtype=dtype)
-    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=device, dtype=dtype)[:, None]
-    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=device, dtype=dtype)[None, :]
-    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size)
-    return img_ids

invokeai/backend/lora.py

@@ -0,0 +1,672 @@
+# Copyright (c) 2024 The InvokeAI Development team
+"""LoRA model support."""
+
+import bisect
+from pathlib import Path
+from typing import Dict, List, Optional, Set, Tuple, Union
+
+import torch
+from safetensors.torch import load_file
+from typing_extensions import Self
+
+import invokeai.backend.util.logging as logger
+from invokeai.backend.model_manager import BaseModelType
+from invokeai.backend.raw_model import RawModel
+
+
+class LoRALayerBase:
+    # rank: Optional[int]
+    # alpha: Optional[float]
+    # bias: Optional[torch.Tensor]
+    # layer_key: str
+
+    # @property
+    # def scale(self):
+    #    return self.alpha / self.rank if (self.alpha and self.rank) else 1.0
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: Dict[str, torch.Tensor],
+    ):
+        if "alpha" in values:
+            self.alpha = values["alpha"].item()
+        else:
+            self.alpha = None
+
+        if "bias_indices" in values and "bias_values" in values and "bias_size" in values:
+            self.bias: Optional[torch.Tensor] = torch.sparse_coo_tensor(
+                values["bias_indices"],
+                values["bias_values"],
+                tuple(values["bias_size"]),
+            )
+
+        else:
+            self.bias = None
+
+        self.rank = None  # set in layer implementation
+        self.layer_key = layer_key
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError()
+
+    def get_bias(self, orig_bias: torch.Tensor) -> Optional[torch.Tensor]:
+        return self.bias
+
+    def get_parameters(self, orig_module: torch.nn.Module) -> Dict[str, torch.Tensor]:
+        params = {"weight": self.get_weight(orig_module.weight)}
+        bias = self.get_bias(orig_module.bias)
+        if bias is not None:
+            params["bias"] = bias
+        return params
+
+    def calc_size(self) -> int:
+        model_size = 0
+        for val in [self.bias]:
+            if val is not None:
+                model_size += val.nelement() * val.element_size()
+        return model_size
+
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
+        if self.bias is not None:
+            self.bias = self.bias.to(device=device, dtype=dtype)
+
+    def check_keys(self, values: Dict[str, torch.Tensor], known_keys: Set[str]):
+        """Log a warning if values contains unhandled keys."""
+        # {"alpha", "bias_indices", "bias_values", "bias_size"} are hard-coded, because they are handled by
+        # `LoRALayerBase`. Sub-classes should provide the known_keys that they handled.
+        all_known_keys = known_keys | {"alpha", "bias_indices", "bias_values", "bias_size"}
+        unknown_keys = set(values.keys()) - all_known_keys
+        if unknown_keys:
+            logger.warning(
+                f"Unexpected keys found in LoRA/LyCORIS layer, model might work incorrectly! Keys: {unknown_keys}"
+            )
+
+
+# TODO: find and debug lora/locon with bias
+class LoRALayer(LoRALayerBase):
+    # up: torch.Tensor
+    # mid: Optional[torch.Tensor]
+    # down: torch.Tensor
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: Dict[str, torch.Tensor],
+    ):
+        super().__init__(layer_key, values)
+
+        self.up = values["lora_up.weight"]
+        self.down = values["lora_down.weight"]
+        self.mid = values.get("lora_mid.weight", None)
+
+        self.rank = self.down.shape[0]
+        self.check_keys(
+            values,
+            {
+                "lora_up.weight",
+                "lora_down.weight",
+                "lora_mid.weight",
+            },
+        )
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        if self.mid is not None:
+            up = self.up.reshape(self.up.shape[0], self.up.shape[1])
+            down = self.down.reshape(self.down.shape[0], self.down.shape[1])
+            weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down)
+        else:
+            weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1)
+
+        return weight
+
+    def calc_size(self) -> int:
+        model_size = super().calc_size()
+        for val in [self.up, self.mid, self.down]:
+            if val is not None:
+                model_size += val.nelement() * val.element_size()
+        return model_size
+
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
+        super().to(device=device, dtype=dtype)
+
+        self.up = self.up.to(device=device, dtype=dtype)
+        self.down = self.down.to(device=device, dtype=dtype)
+
+        if self.mid is not None:
+            self.mid = self.mid.to(device=device, dtype=dtype)
+
+
+class LoHALayer(LoRALayerBase):
+    # w1_a: torch.Tensor
+    # w1_b: torch.Tensor
+    # w2_a: torch.Tensor
+    # w2_b: torch.Tensor
+    # t1: Optional[torch.Tensor] = None
+    # t2: Optional[torch.Tensor] = None
+
+    def __init__(self, layer_key: str, values: Dict[str, torch.Tensor]):
+        super().__init__(layer_key, values)
+
+        self.w1_a = values["hada_w1_a"]
+        self.w1_b = values["hada_w1_b"]
+        self.w2_a = values["hada_w2_a"]
+        self.w2_b = values["hada_w2_b"]
+        self.t1 = values.get("hada_t1", None)
+        self.t2 = values.get("hada_t2", None)
+
+        self.rank = self.w1_b.shape[0]
+        self.check_keys(
+            values,
+            {
+                "hada_w1_a",
+                "hada_w1_b",
+                "hada_w2_a",
+                "hada_w2_b",
+                "hada_t1",
+                "hada_t2",
+            },
+        )
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        if self.t1 is None:
+            weight: torch.Tensor = (self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b)
+
+        else:
+            rebuild1 = torch.einsum("i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a)
+            rebuild2 = torch.einsum("i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a)
+            weight = rebuild1 * rebuild2
+
+        return weight
+
+    def calc_size(self) -> int:
+        model_size = super().calc_size()
+        for val in [self.w1_a, self.w1_b, self.w2_a, self.w2_b, self.t1, self.t2]:
+            if val is not None:
+                model_size += val.nelement() * val.element_size()
+        return model_size
+
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
+        super().to(device=device, dtype=dtype)
+
+        self.w1_a = self.w1_a.to(device=device, dtype=dtype)
+        self.w1_b = self.w1_b.to(device=device, dtype=dtype)
+        if self.t1 is not None:
+            self.t1 = self.t1.to(device=device, dtype=dtype)
+
+        self.w2_a = self.w2_a.to(device=device, dtype=dtype)
+        self.w2_b = self.w2_b.to(device=device, dtype=dtype)
+        if self.t2 is not None:
+            self.t2 = self.t2.to(device=device, dtype=dtype)
+
+
+class LoKRLayer(LoRALayerBase):
+    # w1: Optional[torch.Tensor] = None
+    # w1_a: Optional[torch.Tensor] = None
+    # w1_b: Optional[torch.Tensor] = None
+    # w2: Optional[torch.Tensor] = None
+    # w2_a: Optional[torch.Tensor] = None
+    # w2_b: Optional[torch.Tensor] = None
+    # t2: Optional[torch.Tensor] = None
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: Dict[str, torch.Tensor],
+    ):
+        super().__init__(layer_key, values)
+
+        self.w1 = values.get("lokr_w1", None)
+        if self.w1 is None:
+            self.w1_a = values["lokr_w1_a"]
+            self.w1_b = values["lokr_w1_b"]
+        else:
+            self.w1_b = None
+            self.w1_a = None
+
+        self.w2 = values.get("lokr_w2", None)
+        if self.w2 is None:
+            self.w2_a = values["lokr_w2_a"]
+            self.w2_b = values["lokr_w2_b"]
+        else:
+            self.w2_a = None
+            self.w2_b = None
+
+        self.t2 = values.get("lokr_t2", None)
+
+        if self.w1_b is not None:
+            self.rank = self.w1_b.shape[0]
+        elif self.w2_b is not None:
+            self.rank = self.w2_b.shape[0]
+        else:
+            self.rank = None  # unscaled
+
+        self.check_keys(
+            values,
+            {
+                "lokr_w1",
+                "lokr_w1_a",
+                "lokr_w1_b",
+                "lokr_w2",
+                "lokr_w2_a",
+                "lokr_w2_b",
+                "lokr_t2",
+            },
+        )
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        w1: Optional[torch.Tensor] = self.w1
+        if w1 is None:
+            assert self.w1_a is not None
+            assert self.w1_b is not None
+            w1 = self.w1_a @ self.w1_b
+
+        w2 = self.w2
+        if w2 is None:
+            if self.t2 is None:
+                assert self.w2_a is not None
+                assert self.w2_b is not None
+                w2 = self.w2_a @ self.w2_b
+            else:
+                w2 = torch.einsum("i j k l, i p, j r -> p r k l", self.t2, self.w2_a, self.w2_b)
+
+        if len(w2.shape) == 4:
+            w1 = w1.unsqueeze(2).unsqueeze(2)
+        w2 = w2.contiguous()
+        assert w1 is not None
+        assert w2 is not None
+        weight = torch.kron(w1, w2)
+
+        return weight
+
+    def calc_size(self) -> int:
+        model_size = super().calc_size()
+        for val in [self.w1, self.w1_a, self.w1_b, self.w2, self.w2_a, self.w2_b, self.t2]:
+            if val is not None:
+                model_size += val.nelement() * val.element_size()
+        return model_size
+
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
+        super().to(device=device, dtype=dtype)
+
+        if self.w1 is not None:
+            self.w1 = self.w1.to(device=device, dtype=dtype)
+        else:
+            assert self.w1_a is not None
+            assert self.w1_b is not None
+            self.w1_a = self.w1_a.to(device=device, dtype=dtype)
+            self.w1_b = self.w1_b.to(device=device, dtype=dtype)
+
+        if self.w2 is not None:
+            self.w2 = self.w2.to(device=device, dtype=dtype)
+        else:
+            assert self.w2_a is not None
+            assert self.w2_b is not None
+            self.w2_a = self.w2_a.to(device=device, dtype=dtype)
+            self.w2_b = self.w2_b.to(device=device, dtype=dtype)
+
+        if self.t2 is not None:
+            self.t2 = self.t2.to(device=device, dtype=dtype)
+
+
+class FullLayer(LoRALayerBase):
+    # bias handled in LoRALayerBase(calc_size, to)
+    # weight: torch.Tensor
+    # bias: Optional[torch.Tensor]
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: Dict[str, torch.Tensor],
+    ):
+        super().__init__(layer_key, values)
+
+        self.weight = values["diff"]
+        self.bias = values.get("diff_b", None)
+
+        self.rank = None  # unscaled
+        self.check_keys(values, {"diff", "diff_b"})
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        return self.weight
+
+    def calc_size(self) -> int:
+        model_size = super().calc_size()
+        model_size += self.weight.nelement() * self.weight.element_size()
+        return model_size
+
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
+        super().to(device=device, dtype=dtype)
+
+        self.weight = self.weight.to(device=device, dtype=dtype)
+
+
+class IA3Layer(LoRALayerBase):
+    # weight: torch.Tensor
+    # on_input: torch.Tensor
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: Dict[str, torch.Tensor],
+    ):
+        super().__init__(layer_key, values)
+
+        self.weight = values["weight"]
+        self.on_input = values["on_input"]
+
+        self.rank = None  # unscaled
+        self.check_keys(values, {"weight", "on_input"})
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        weight = self.weight
+        if not self.on_input:
+            weight = weight.reshape(-1, 1)
+        assert orig_weight is not None
+        return orig_weight * weight
+
+    def calc_size(self) -> int:
+        model_size = super().calc_size()
+        model_size += self.weight.nelement() * self.weight.element_size()
+        model_size += self.on_input.nelement() * self.on_input.element_size()
+        return model_size
+
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None):
+        super().to(device=device, dtype=dtype)
+
+        self.weight = self.weight.to(device=device, dtype=dtype)
+        self.on_input = self.on_input.to(device=device, dtype=dtype)
+
+
+class NormLayer(LoRALayerBase):
+    # bias handled in LoRALayerBase(calc_size, to)
+    # weight: torch.Tensor
+    # bias: Optional[torch.Tensor]
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: Dict[str, torch.Tensor],
+    ):
+        super().__init__(layer_key, values)
+
+        self.weight = values["w_norm"]
+        self.bias = values.get("b_norm", None)
+
+        self.rank = None  # unscaled
+        self.check_keys(values, {"w_norm", "b_norm"})
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        return self.weight
+
+    def calc_size(self) -> int:
+        model_size = super().calc_size()
+        model_size += self.weight.nelement() * self.weight.element_size()
+        return model_size
+
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
+        super().to(device=device, dtype=dtype)
+
+        self.weight = self.weight.to(device=device, dtype=dtype)
+
+
+AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer, NormLayer]
+
+
+class LoRAModelRaw(RawModel):  # (torch.nn.Module):
+    _name: str
+    layers: Dict[str, AnyLoRALayer]
+
+    def __init__(
+        self,
+        name: str,
+        layers: Dict[str, AnyLoRALayer],
+    ):
+        self._name = name
+        self.layers = layers
+
+    @property
+    def name(self) -> str:
+        return self._name
+
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
+        # TODO: try revert if exception?
+        for _key, layer in self.layers.items():
+            layer.to(device=device, dtype=dtype)
+
+    def calc_size(self) -> int:
+        model_size = 0
+        for _, layer in self.layers.items():
+            model_size += layer.calc_size()
+        return model_size
+
+    @classmethod
+    def _convert_sdxl_keys_to_diffusers_format(cls, state_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+        """Convert the keys of an SDXL LoRA state_dict to diffusers format.
+
+        The input state_dict can be in either Stability AI format or diffusers format. If the state_dict is already in
+        diffusers format, then this function will have no effect.
+
+        This function is adapted from:
+        https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L385-L409
+
+        Args:
+            state_dict (Dict[str, Tensor]): The SDXL LoRA state_dict.
+
+        Raises:
+            ValueError: If state_dict contains an unrecognized key, or not all keys could be converted.
+
+        Returns:
+            Dict[str, Tensor]: The diffusers-format state_dict.
+        """
+        converted_count = 0  # The number of Stability AI keys converted to diffusers format.
+        not_converted_count = 0  # The number of keys that were not converted.
+
+        # Get a sorted list of Stability AI UNet keys so that we can efficiently search for keys with matching prefixes.
+        # For example, we want to efficiently find `input_blocks_4_1` in the list when searching for
+        # `input_blocks_4_1_proj_in`.
+        stability_unet_keys = list(SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP)
+        stability_unet_keys.sort()
+
+        new_state_dict = {}
+        for full_key, value in state_dict.items():
+            if full_key.startswith("lora_unet_"):
+                search_key = full_key.replace("lora_unet_", "")
+                # Use bisect to find the key in stability_unet_keys that *may* match the search_key's prefix.
+                position = bisect.bisect_right(stability_unet_keys, search_key)
+                map_key = stability_unet_keys[position - 1]
+                # Now, check if the map_key *actually* matches the search_key.
+                if search_key.startswith(map_key):
+                    new_key = full_key.replace(map_key, SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP[map_key])
+                    new_state_dict[new_key] = value
+                    converted_count += 1
+                else:
+                    new_state_dict[full_key] = value
+                    not_converted_count += 1
+            elif full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"):
+                # The CLIP text encoders have the same keys in both Stability AI and diffusers formats.
+                new_state_dict[full_key] = value
+                continue
+            else:
+                raise ValueError(f"Unrecognized SDXL LoRA key prefix: '{full_key}'.")
+
+        if converted_count > 0 and not_converted_count > 0:
+            raise ValueError(
+                f"The SDXL LoRA could only be partially converted to diffusers format. converted={converted_count},"
+                f" not_converted={not_converted_count}"
+            )
+
+        return new_state_dict
+
+    @classmethod
+    def from_checkpoint(
+        cls,
+        file_path: Union[str, Path],
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        base_model: Optional[BaseModelType] = None,
+    ) -> Self:
+        device = device or torch.device("cpu")
+        dtype = dtype or torch.float32
+
+        if isinstance(file_path, str):
+            file_path = Path(file_path)
+
+        model = cls(
+            name=file_path.stem,
+            layers={},
+        )
+
+        if file_path.suffix == ".safetensors":
+            sd = load_file(file_path.absolute().as_posix(), device="cpu")
+        else:
+            sd = torch.load(file_path, map_location="cpu")
+
+        state_dict = cls._group_state(sd)
+
+        if base_model == BaseModelType.StableDiffusionXL:
+            state_dict = cls._convert_sdxl_keys_to_diffusers_format(state_dict)
+
+        for layer_key, values in state_dict.items():
+            # Detect layers according to LyCORIS detection logic(`weight_list_det`)
+            # https://github.com/KohakuBlueleaf/LyCORIS/tree/8ad8000efb79e2b879054da8c9356e6143591bad/lycoris/modules
+
+            # lora and locon
+            if "lora_up.weight" in values:
+                layer: AnyLoRALayer = LoRALayer(layer_key, values)
+
+            # loha
+            elif "hada_w1_a" in values:
+                layer = LoHALayer(layer_key, values)
+
+            # lokr
+            elif "lokr_w1" in values or "lokr_w1_a" in values:
+                layer = LoKRLayer(layer_key, values)
+
+            # diff
+            elif "diff" in values:
+                layer = FullLayer(layer_key, values)
+
+            # ia3
+            elif "on_input" in values:
+                layer = IA3Layer(layer_key, values)
+
+            # norms
+            elif "w_norm" in values:
+                layer = NormLayer(layer_key, values)
+
+            else:
+                print(f">> Encountered unknown lora layer module in {model.name}: {layer_key} - {list(values.keys())}")
+                raise Exception("Unknown lora format!")
+
+            # lower memory consumption by removing already parsed layer values
+            state_dict[layer_key].clear()
+
+            layer.to(device=device, dtype=dtype)
+            model.layers[layer_key] = layer
+
+        return model
+
+    @staticmethod
+    def _group_state(state_dict: Dict[str, torch.Tensor]) -> Dict[str, Dict[str, torch.Tensor]]:
+        state_dict_groupped: Dict[str, Dict[str, torch.Tensor]] = {}
+
+        for key, value in state_dict.items():
+            stem, leaf = key.split(".", 1)
+            if stem not in state_dict_groupped:
+                state_dict_groupped[stem] = {}
+            state_dict_groupped[stem][leaf] = value
+
+        return state_dict_groupped
+
+
+# code from
+# https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L15C1-L97C32
+def make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
+    """Create a dict mapping state_dict keys from Stability AI SDXL format to diffusers SDXL format."""
+    unet_conversion_map_layer = []
+
+    for i in range(3):  # num_blocks is 3 in sdxl
+        # loop over downblocks/upblocks
+        for j in range(2):
+            # loop over resnets/attentions for downblocks
+            hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
+            sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
+            unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
+
+            if i < 3:
+                # no attention layers in down_blocks.3
+                hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
+                sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
+                unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
+
+        for j in range(3):
+            # loop over resnets/attentions for upblocks
+            hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
+            sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
+            unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
+
+            # if i > 0: commentout for sdxl
+            # no attention layers in up_blocks.0
+            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
+            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
+            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
+
+        if i < 3:
+            # no downsample in down_blocks.3
+            hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
+            sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
+            unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
+
+            # no upsample in up_blocks.3
+            hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+            sd_upsample_prefix = f"output_blocks.{3*i + 2}.{2}."  # change for sdxl
+            unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
+
+    hf_mid_atn_prefix = "mid_block.attentions.0."
+    sd_mid_atn_prefix = "middle_block.1."
+    unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
+
+    for j in range(2):
+        hf_mid_res_prefix = f"mid_block.resnets.{j}."
+        sd_mid_res_prefix = f"middle_block.{2*j}."
+        unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+    unet_conversion_map_resnet = [
+        # (stable-diffusion, HF Diffusers)
+        ("in_layers.0.", "norm1."),
+        ("in_layers.2.", "conv1."),
+        ("out_layers.0.", "norm2."),
+        ("out_layers.3.", "conv2."),
+        ("emb_layers.1.", "time_emb_proj."),
+        ("skip_connection.", "conv_shortcut."),
+    ]
+
+    unet_conversion_map = []
+    for sd, hf in unet_conversion_map_layer:
+        if "resnets" in hf:
+            for sd_res, hf_res in unet_conversion_map_resnet:
+                unet_conversion_map.append((sd + sd_res, hf + hf_res))
+        else:
+            unet_conversion_map.append((sd, hf))
+
+    for j in range(2):
+        hf_time_embed_prefix = f"time_embedding.linear_{j+1}."
+        sd_time_embed_prefix = f"time_embed.{j*2}."
+        unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix))
+
+    for j in range(2):
+        hf_label_embed_prefix = f"add_embedding.linear_{j+1}."
+        sd_label_embed_prefix = f"label_emb.0.{j*2}."
+        unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix))
+
+    unet_conversion_map.append(("input_blocks.0.0.", "conv_in."))
+    unet_conversion_map.append(("out.0.", "conv_norm_out."))
+    unet_conversion_map.append(("out.2.", "conv_out."))
+
+    return unet_conversion_map
+
+
+SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP = {
+    sd.rstrip(".").replace(".", "_"): hf.rstrip(".").replace(".", "_") for sd, hf in make_sdxl_unet_conversion_map()
+}

invokeai/backend/model_manager/load/load_default.py

@@ -66,9 +66,8 @@ class ModelLoader(ModelLoaderBase):
         return (model_base / config.path).resolve()
 
     def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> ModelLockerBase:
-        stats_name = ":".join([config.base, config.type, config.name, (submodel_type or "")])
         try:
-            return self._ram_cache.get(config.key, submodel_type, stats_name=stats_name)
+            return self._ram_cache.get(config.key, submodel_type)
         except IndexError:
             pass
 
@@ -85,7 +84,7 @@ class ModelLoader(ModelLoaderBase):
         return self._ram_cache.get(
             key=config.key,
             submodel_type=submodel_type,
-            stats_name=stats_name,
+            stats_name=":".join([config.base, config.type, config.name, (submodel_type or "")]),
         )
 
     def get_size_fs(

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

@@ -128,24 +128,7 @@ class ModelCacheBase(ABC, Generic[T]):
     @property
     @abstractmethod
     def max_cache_size(self) -> float:
-        """Return the maximum size the RAM cache can grow to."""
-        pass
-
-    @max_cache_size.setter
-    @abstractmethod
-    def max_cache_size(self, value: float) -> None:
-        """Set the cap on vram cache size."""
-
-    @property
-    @abstractmethod
-    def max_vram_cache_size(self) -> float:
-        """Return the maximum size the VRAM cache can grow to."""
-        pass
-
-    @max_vram_cache_size.setter
-    @abstractmethod
-    def max_vram_cache_size(self, value: float) -> float:
-        """Set the maximum size the VRAM cache can grow to."""
+        """Return true if the cache is configured to lazily offload models in VRAM."""
         pass
 
     @abstractmethod

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

@@ -70,7 +70,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
         max_vram_cache_size: float,
         execution_device: torch.device = torch.device("cuda"),
         storage_device: torch.device = torch.device("cpu"),
-        precision: torch.dtype = torch.float16,
         lazy_offloading: bool = True,
         log_memory_usage: bool = False,
         logger: Optional[Logger] = None,
@@ -82,13 +81,11 @@ class ModelCache(ModelCacheBase[AnyModel]):
         :param max_vram_cache_size: Maximum size of the execution_device cache in GBs.
         :param execution_device: Torch device to load active model into [torch.device('cuda')]
         :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
-        :param precision: Precision for loaded models [torch.float16]
-        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
+        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded.
         :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
             operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
             snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
             behaviour.
-        :param logger: InvokeAILogger to use (otherwise creates one)
         """
         # allow lazy offloading only when vram cache enabled
         self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
@@ -133,16 +130,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
         """Set the cap on cache size."""
         self._max_cache_size = value
 
-    @property
-    def max_vram_cache_size(self) -> float:
-        """Return the cap on vram cache size."""
-        return self._max_vram_cache_size
-
-    @max_vram_cache_size.setter
-    def max_vram_cache_size(self, value: float) -> None:
-        """Set the cap on vram cache size."""
-        self._max_vram_cache_size = value
-
     @property
     def stats(self) -> Optional[CacheStats]:
         """Return collected CacheStats object."""

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

@@ -5,10 +5,8 @@ from logging import Logger
 from pathlib import Path
 from typing import Optional
 
-import torch
-from safetensors.torch import load_file
-
 from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
@@ -20,11 +18,6 @@ from invokeai.backend.model_manager import (
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-from invokeai.backend.peft.conversions.flux_kohya_lora_conversion_utils import (
-    lora_model_from_flux_kohya_state_dict,
-)
-from invokeai.backend.peft.conversions.sd_lora_conversion_utils import lora_model_from_sd_state_dict
-from invokeai.backend.peft.conversions.sdxl_lora_conversion_utils import convert_sdxl_keys_to_diffusers_format
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.Diffusers)
@@ -52,28 +45,14 @@ class LoRALoader(ModelLoader):
             raise ValueError("There are no submodels in a LoRA model.")
         model_path = Path(config.path)
         assert self._model_base is not None
-
-        # Load the state dict from the model file.
-        if model_path.suffix == ".safetensors":
-            state_dict = load_file(model_path.absolute().as_posix(), device="cpu")
-        else:
-            state_dict = torch.load(model_path, map_location="cpu")
-
-        # Apply state_dict key conversions, if necessary.
-        if self._model_base == BaseModelType.StableDiffusionXL:
-            state_dict = convert_sdxl_keys_to_diffusers_format(state_dict)
-            model = lora_model_from_sd_state_dict(state_dict=state_dict)
-        elif self._model_base == BaseModelType.Flux:
-            model = lora_model_from_flux_kohya_state_dict(state_dict=state_dict)
-        elif self._model_base in [BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2]:
-            # Currently, we don't apply any conversions for SD1 and SD2 LoRA models.
-            model = lora_model_from_sd_state_dict(state_dict=state_dict)
-        else:
-            raise ValueError(f"Unsupported LoRA base model: {self._model_base}")
-
-        model.to(dtype=self._torch_dtype)
+        model = LoRAModelRaw.from_checkpoint(
+            file_path=model_path,
+            dtype=self._torch_dtype,
+            base_model=self._model_base,
+        )
         return model
 
+    # override
     def _get_model_path(self, config: AnyModelConfig) -> Path:
         # cheating a little - we remember this variable for using in the subsequent call to _load_model()
         self._model_base = config.base

invokeai/backend/model_manager/load/model_util.py

@@ -15,9 +15,9 @@ from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import D
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
 from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager.config import AnyModel
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
-from invokeai.backend.peft.lora import LoRAModelRaw
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
 from invokeai.backend.textual_inversion import TextualInversionModelRaw
 

invokeai/backend/model_manager/probe.py

@@ -26,7 +26,6 @@ from invokeai.backend.model_manager.config import (
     SchedulerPredictionType,
 )
 from invokeai.backend.model_manager.util.model_util import lora_token_vector_length, read_checkpoint_meta
-from invokeai.backend.peft.conversions.flux_kohya_lora_conversion_utils import is_state_dict_likely_in_flux_kohya_format
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
@@ -529,11 +528,9 @@ class LoRACheckpointProbe(CheckpointProbeBase):
         return ModelFormat("lycoris")
 
     def get_base_type(self) -> BaseModelType:
-        if is_state_dict_likely_in_flux_kohya_format(self.checkpoint):
-            return BaseModelType.Flux
+        checkpoint = self.checkpoint
+        token_vector_length = lora_token_vector_length(checkpoint)
 
-        # If we've gotten here, we assume that the model is a Stable Diffusion model.
-        token_vector_length = lora_token_vector_length(self.checkpoint)
         if token_vector_length == 768:
             return BaseModelType.StableDiffusion1
         elif token_vector_length == 1024:

invokeai/backend/model_patcher.py

@@ -13,10 +13,10 @@ from diffusers import OnnxRuntimeModel, UNet2DConditionModel
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
 from invokeai.app.shared.models import FreeUConfig
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import AnyModel
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
-from invokeai.backend.peft.lora import LoRAModelRaw
 from invokeai.backend.stable_diffusion.extensions.lora import LoRAExt
 from invokeai.backend.textual_inversion import TextualInversionManager, TextualInversionModelRaw
 from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage

invokeai/backend/peft/conversions/flux_kohya_lora_conversion_utils.py

@@ -1,84 +0,0 @@
-import re
-from typing import Any, Dict, TypeVar
-
-import torch
-
-from invokeai.backend.peft.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.peft.layers.utils import peft_layer_from_state_dict
-from invokeai.backend.peft.lora import LoRAModelRaw
-
-# A regex pattern that matches all of the keys in the Kohya FLUX LoRA format.
-# Example keys:
-#   lora_unet_double_blocks_0_img_attn_proj.alpha
-#   lora_unet_double_blocks_0_img_attn_proj.lora_down.weight
-#   lora_unet_double_blocks_0_img_attn_proj.lora_up.weight
-FLUX_KOHYA_KEY_REGEX = (
-    r"lora_unet_(\w+_blocks)_(\d+)_(img_attn|img_mlp|img_mod|txt_attn|txt_mlp|txt_mod|linear1|linear2|modulation)_?(.*)"
-)
-
-
-def is_state_dict_likely_in_flux_kohya_format(state_dict: Dict[str, Any]) -> bool:
-    """Checks if the provided state dict is likely in the Kohya FLUX LoRA format.
-
-    This is intended to be a high-precision detector, but it is not guaranteed to have perfect precision. (A
-    perfect-precision detector would require checking all keys against a whitelist and verifying tensor shapes.)
-    """
-    for k in state_dict.keys():
-        if not re.match(FLUX_KOHYA_KEY_REGEX, k):
-            return False
-    return True
-
-
-def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -> LoRAModelRaw:
-    # Group keys by layer.
-    grouped_state_dict: dict[str, dict[str, torch.Tensor]] = {}
-    for key, value in state_dict.items():
-        layer_name, param_name = key.split(".", 1)
-        if layer_name not in grouped_state_dict:
-            grouped_state_dict[layer_name] = {}
-        grouped_state_dict[layer_name][param_name] = value
-
-    # Convert the state dict to the InvokeAI format.
-    grouped_state_dict = convert_flux_kohya_state_dict_to_invoke_format(grouped_state_dict)
-
-    # Create LoRA layers.
-    layers: dict[str, AnyLoRALayer] = {}
-    for layer_key, layer_state_dict in grouped_state_dict.items():
-        layer = peft_layer_from_state_dict(layer_key, layer_state_dict)
-        layers[layer_key] = layer
-
-    # Create and return the LoRAModelRaw.
-    return LoRAModelRaw(layers=layers)
-
-
-T = TypeVar("T")
-
-
-def convert_flux_kohya_state_dict_to_invoke_format(state_dict: Dict[str, T]) -> Dict[str, T]:
-    """Converts a state dict from the Kohya FLUX LoRA format to LoRA weight format used internally by InvokeAI.
-
-    Example key conversions:
-    "lora_unet_double_blocks_0_img_attn_proj" -> "double_blocks.0.img_attn.proj"
-    "lora_unet_double_blocks_0_img_attn_proj" -> "double_blocks.0.img_attn.proj"
-    "lora_unet_double_blocks_0_img_attn_proj" -> "double_blocks.0.img_attn.proj"
-    "lora_unet_double_blocks_0_img_attn_qkv" -> "double_blocks.0.img_attn.qkv"
-    "lora_unet_double_blocks_0_img_attn_qkv" -> "double_blocks.0.img.attn.qkv"
-    "lora_unet_double_blocks_0_img_attn_qkv" -> "double_blocks.0.img.attn.qkv"
-    """
-
-    def replace_func(match: re.Match[str]) -> str:
-        s = f"{match.group(1)}.{match.group(2)}.{match.group(3)}"
-        if match.group(4):
-            s += f".{match.group(4)}"
-        return s
-
-    converted_dict: dict[str, T] = {}
-    for k, v in state_dict.items():
-        match = re.match(FLUX_KOHYA_KEY_REGEX, k)
-        if match:
-            new_key = re.sub(FLUX_KOHYA_KEY_REGEX, replace_func, k)
-            converted_dict[new_key] = v
-        else:
-            raise ValueError(f"Key '{k}' does not match the expected pattern for FLUX LoRA weights.")
-
-    return converted_dict

invokeai/backend/peft/conversions/sd_lora_conversion_utils.py

@@ -1,30 +0,0 @@
-from typing import Dict
-
-import torch
-
-from invokeai.backend.peft.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.peft.layers.utils import peft_layer_from_state_dict
-from invokeai.backend.peft.lora import LoRAModelRaw
-
-
-def lora_model_from_sd_state_dict(state_dict: Dict[str, torch.Tensor]) -> LoRAModelRaw:
-    grouped_state_dict: dict[str, dict[str, torch.Tensor]] = _group_state(state_dict)
-
-    layers: dict[str, AnyLoRALayer] = {}
-    for layer_key, values in grouped_state_dict.items():
-        layer = peft_layer_from_state_dict(layer_key, values)
-        layers[layer_key] = layer
-
-    return LoRAModelRaw(layers=layers)
-
-
-def _group_state(state_dict: Dict[str, torch.Tensor]) -> Dict[str, Dict[str, torch.Tensor]]:
-    state_dict_groupped: Dict[str, Dict[str, torch.Tensor]] = {}
-
-    for key, value in state_dict.items():
-        stem, leaf = key.split(".", 1)
-        if stem not in state_dict_groupped:
-            state_dict_groupped[stem] = {}
-        state_dict_groupped[stem][leaf] = value
-
-    return state_dict_groupped

invokeai/backend/peft/conversions/sdxl_lora_conversion_utils.py

@@ -1,154 +0,0 @@
-import bisect
-from typing import Dict, List, Tuple, TypeVar
-
-T = TypeVar("T")
-
-
-def convert_sdxl_keys_to_diffusers_format(state_dict: Dict[str, T]) -> dict[str, T]:
-    """Convert the keys of an SDXL LoRA state_dict to diffusers format.
-
-    The input state_dict can be in either Stability AI format or diffusers format. If the state_dict is already in
-    diffusers format, then this function will have no effect.
-
-    This function is adapted from:
-    https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L385-L409
-
-    Args:
-        state_dict (Dict[str, Tensor]): The SDXL LoRA state_dict.
-
-    Raises:
-        ValueError: If state_dict contains an unrecognized key, or not all keys could be converted.
-
-    Returns:
-        Dict[str, Tensor]: The diffusers-format state_dict.
-    """
-    converted_count = 0  # The number of Stability AI keys converted to diffusers format.
-    not_converted_count = 0  # The number of keys that were not converted.
-
-    # Get a sorted list of Stability AI UNet keys so that we can efficiently search for keys with matching prefixes.
-    # For example, we want to efficiently find `input_blocks_4_1` in the list when searching for
-    # `input_blocks_4_1_proj_in`.
-    stability_unet_keys = list(SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP)
-    stability_unet_keys.sort()
-
-    new_state_dict: dict[str, T] = {}
-    for full_key, value in state_dict.items():
-        if full_key.startswith("lora_unet_"):
-            search_key = full_key.replace("lora_unet_", "")
-            # Use bisect to find the key in stability_unet_keys that *may* match the search_key's prefix.
-            position = bisect.bisect_right(stability_unet_keys, search_key)
-            map_key = stability_unet_keys[position - 1]
-            # Now, check if the map_key *actually* matches the search_key.
-            if search_key.startswith(map_key):
-                new_key = full_key.replace(map_key, SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP[map_key])
-                new_state_dict[new_key] = value
-                converted_count += 1
-            else:
-                new_state_dict[full_key] = value
-                not_converted_count += 1
-        elif full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"):
-            # The CLIP text encoders have the same keys in both Stability AI and diffusers formats.
-            new_state_dict[full_key] = value
-            continue
-        else:
-            raise ValueError(f"Unrecognized SDXL LoRA key prefix: '{full_key}'.")
-
-    if converted_count > 0 and not_converted_count > 0:
-        raise ValueError(
-            f"The SDXL LoRA could only be partially converted to diffusers format. converted={converted_count},"
-            f" not_converted={not_converted_count}"
-        )
-
-    return new_state_dict
-
-
-# code from
-# https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L15C1-L97C32
-def _make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
-    """Create a dict mapping state_dict keys from Stability AI SDXL format to diffusers SDXL format."""
-    unet_conversion_map_layer: list[tuple[str, str]] = []
-
-    for i in range(3):  # num_blocks is 3 in sdxl
-        # loop over downblocks/upblocks
-        for j in range(2):
-            # loop over resnets/attentions for downblocks
-            hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
-            sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
-            unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
-
-            if i < 3:
-                # no attention layers in down_blocks.3
-                hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
-                sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
-                unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
-
-        for j in range(3):
-            # loop over resnets/attentions for upblocks
-            hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
-            sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
-            unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
-
-            # if i > 0: commentout for sdxl
-            # no attention layers in up_blocks.0
-            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
-            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
-            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
-
-        if i < 3:
-            # no downsample in down_blocks.3
-            hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
-            sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
-            unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
-
-            # no upsample in up_blocks.3
-            hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-            sd_upsample_prefix = f"output_blocks.{3*i + 2}.{2}."  # change for sdxl
-            unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
-
-    hf_mid_atn_prefix = "mid_block.attentions.0."
-    sd_mid_atn_prefix = "middle_block.1."
-    unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
-
-    for j in range(2):
-        hf_mid_res_prefix = f"mid_block.resnets.{j}."
-        sd_mid_res_prefix = f"middle_block.{2*j}."
-        unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-    unet_conversion_map_resnet = [
-        # (stable-diffusion, HF Diffusers)
-        ("in_layers.0.", "norm1."),
-        ("in_layers.2.", "conv1."),
-        ("out_layers.0.", "norm2."),
-        ("out_layers.3.", "conv2."),
-        ("emb_layers.1.", "time_emb_proj."),
-        ("skip_connection.", "conv_shortcut."),
-    ]
-
-    unet_conversion_map: list[tuple[str, str]] = []
-    for sd, hf in unet_conversion_map_layer:
-        if "resnets" in hf:
-            for sd_res, hf_res in unet_conversion_map_resnet:
-                unet_conversion_map.append((sd + sd_res, hf + hf_res))
-        else:
-            unet_conversion_map.append((sd, hf))
-
-    for j in range(2):
-        hf_time_embed_prefix = f"time_embedding.linear_{j+1}."
-        sd_time_embed_prefix = f"time_embed.{j*2}."
-        unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix))
-
-    for j in range(2):
-        hf_label_embed_prefix = f"add_embedding.linear_{j+1}."
-        sd_label_embed_prefix = f"label_emb.0.{j*2}."
-        unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix))
-
-    unet_conversion_map.append(("input_blocks.0.0.", "conv_in."))
-    unet_conversion_map.append(("out.0.", "conv_norm_out."))
-    unet_conversion_map.append(("out.2.", "conv_out."))
-
-    return unet_conversion_map
-
-
-SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP = {
-    sd.rstrip(".").replace(".", "_"): hf.rstrip(".").replace(".", "_") for sd, hf in _make_sdxl_unet_conversion_map()
-}

invokeai/backend/peft/layers/any_lora_layer.py

@@ -1,10 +0,0 @@
-from typing import Union
-
-from invokeai.backend.peft.layers.full_layer import FullLayer
-from invokeai.backend.peft.layers.ia3_layer import IA3Layer
-from invokeai.backend.peft.layers.loha_layer import LoHALayer
-from invokeai.backend.peft.layers.lokr_layer import LoKRLayer
-from invokeai.backend.peft.layers.lora_layer import LoRALayer
-from invokeai.backend.peft.layers.norm_layer import NormLayer
-
-AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer, NormLayer]

invokeai/backend/peft/layers/full_layer.py

@@ -1,37 +0,0 @@
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.peft.layers.lora_layer_base import LoRALayerBase
-
-
-class FullLayer(LoRALayerBase):
-    # bias handled in LoRALayerBase(calc_size, to)
-    # weight: torch.Tensor
-    # bias: Optional[torch.Tensor]
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(layer_key, values)
-
-        self.weight = values["diff"]
-        self.bias = values.get("diff_b", None)
-
-        self.rank = None  # unscaled
-        self.check_keys(values, {"diff", "diff_b"})
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        return self.weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        model_size += self.weight.nelement() * self.weight.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        super().to(device=device, dtype=dtype)
-
-        self.weight = self.weight.to(device=device, dtype=dtype)

invokeai/backend/peft/layers/ia3_layer.py

@@ -1,42 +0,0 @@
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.peft.layers.lora_layer_base import LoRALayerBase
-
-
-class IA3Layer(LoRALayerBase):
-    # weight: torch.Tensor
-    # on_input: torch.Tensor
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(layer_key, values)
-
-        self.weight = values["weight"]
-        self.on_input = values["on_input"]
-
-        self.rank = None  # unscaled
-        self.check_keys(values, {"weight", "on_input"})
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        weight = self.weight
-        if not self.on_input:
-            weight = weight.reshape(-1, 1)
-        assert orig_weight is not None
-        return orig_weight * weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        model_size += self.weight.nelement() * self.weight.element_size()
-        model_size += self.on_input.nelement() * self.on_input.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None):
-        super().to(device=device, dtype=dtype)
-
-        self.weight = self.weight.to(device=device, dtype=dtype)
-        self.on_input = self.on_input.to(device=device, dtype=dtype)

invokeai/backend/peft/layers/loha_layer.py

@@ -1,68 +0,0 @@
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.peft.layers.lora_layer_base import LoRALayerBase
-
-
-class LoHALayer(LoRALayerBase):
-    # w1_a: torch.Tensor
-    # w1_b: torch.Tensor
-    # w2_a: torch.Tensor
-    # w2_b: torch.Tensor
-    # t1: Optional[torch.Tensor] = None
-    # t2: Optional[torch.Tensor] = None
-
-    def __init__(self, layer_key: str, values: Dict[str, torch.Tensor]):
-        super().__init__(layer_key, values)
-
-        self.w1_a = values["hada_w1_a"]
-        self.w1_b = values["hada_w1_b"]
-        self.w2_a = values["hada_w2_a"]
-        self.w2_b = values["hada_w2_b"]
-        self.t1 = values.get("hada_t1", None)
-        self.t2 = values.get("hada_t2", None)
-
-        self.rank = self.w1_b.shape[0]
-        self.check_keys(
-            values,
-            {
-                "hada_w1_a",
-                "hada_w1_b",
-                "hada_w2_a",
-                "hada_w2_b",
-                "hada_t1",
-                "hada_t2",
-            },
-        )
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        if self.t1 is None:
-            weight: torch.Tensor = (self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b)
-
-        else:
-            rebuild1 = torch.einsum("i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a)
-            rebuild2 = torch.einsum("i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a)
-            weight = rebuild1 * rebuild2
-
-        return weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        for val in [self.w1_a, self.w1_b, self.w2_a, self.w2_b, self.t1, self.t2]:
-            if val is not None:
-                model_size += val.nelement() * val.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        super().to(device=device, dtype=dtype)
-
-        self.w1_a = self.w1_a.to(device=device, dtype=dtype)
-        self.w1_b = self.w1_b.to(device=device, dtype=dtype)
-        if self.t1 is not None:
-            self.t1 = self.t1.to(device=device, dtype=dtype)
-
-        self.w2_a = self.w2_a.to(device=device, dtype=dtype)
-        self.w2_b = self.w2_b.to(device=device, dtype=dtype)
-        if self.t2 is not None:
-            self.t2 = self.t2.to(device=device, dtype=dtype)

invokeai/backend/peft/layers/lokr_layer.py

@@ -1,114 +0,0 @@
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.peft.layers.lora_layer_base import LoRALayerBase
-
-
-class LoKRLayer(LoRALayerBase):
-    # w1: Optional[torch.Tensor] = None
-    # w1_a: Optional[torch.Tensor] = None
-    # w1_b: Optional[torch.Tensor] = None
-    # w2: Optional[torch.Tensor] = None
-    # w2_a: Optional[torch.Tensor] = None
-    # w2_b: Optional[torch.Tensor] = None
-    # t2: Optional[torch.Tensor] = None
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(layer_key, values)
-
-        self.w1 = values.get("lokr_w1", None)
-        if self.w1 is None:
-            self.w1_a = values["lokr_w1_a"]
-            self.w1_b = values["lokr_w1_b"]
-        else:
-            self.w1_b = None
-            self.w1_a = None
-
-        self.w2 = values.get("lokr_w2", None)
-        if self.w2 is None:
-            self.w2_a = values["lokr_w2_a"]
-            self.w2_b = values["lokr_w2_b"]
-        else:
-            self.w2_a = None
-            self.w2_b = None
-
-        self.t2 = values.get("lokr_t2", None)
-
-        if self.w1_b is not None:
-            self.rank = self.w1_b.shape[0]
-        elif self.w2_b is not None:
-            self.rank = self.w2_b.shape[0]
-        else:
-            self.rank = None  # unscaled
-
-        self.check_keys(
-            values,
-            {
-                "lokr_w1",
-                "lokr_w1_a",
-                "lokr_w1_b",
-                "lokr_w2",
-                "lokr_w2_a",
-                "lokr_w2_b",
-                "lokr_t2",
-            },
-        )
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        w1: Optional[torch.Tensor] = self.w1
-        if w1 is None:
-            assert self.w1_a is not None
-            assert self.w1_b is not None
-            w1 = self.w1_a @ self.w1_b
-
-        w2 = self.w2
-        if w2 is None:
-            if self.t2 is None:
-                assert self.w2_a is not None
-                assert self.w2_b is not None
-                w2 = self.w2_a @ self.w2_b
-            else:
-                w2 = torch.einsum("i j k l, i p, j r -> p r k l", self.t2, self.w2_a, self.w2_b)
-
-        if len(w2.shape) == 4:
-            w1 = w1.unsqueeze(2).unsqueeze(2)
-        w2 = w2.contiguous()
-        assert w1 is not None
-        assert w2 is not None
-        weight = torch.kron(w1, w2)
-
-        return weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        for val in [self.w1, self.w1_a, self.w1_b, self.w2, self.w2_a, self.w2_b, self.t2]:
-            if val is not None:
-                model_size += val.nelement() * val.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        super().to(device=device, dtype=dtype)
-
-        if self.w1 is not None:
-            self.w1 = self.w1.to(device=device, dtype=dtype)
-        else:
-            assert self.w1_a is not None
-            assert self.w1_b is not None
-            self.w1_a = self.w1_a.to(device=device, dtype=dtype)
-            self.w1_b = self.w1_b.to(device=device, dtype=dtype)
-
-        if self.w2 is not None:
-            self.w2 = self.w2.to(device=device, dtype=dtype)
-        else:
-            assert self.w2_a is not None
-            assert self.w2_b is not None
-            self.w2_a = self.w2_a.to(device=device, dtype=dtype)
-            self.w2_b = self.w2_b.to(device=device, dtype=dtype)
-
-        if self.t2 is not None:
-            self.t2 = self.t2.to(device=device, dtype=dtype)

invokeai/backend/peft/layers/lora_layer.py

@@ -1,59 +0,0 @@
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.peft.layers.lora_layer_base import LoRALayerBase
-
-
-# TODO: find and debug lora/locon with bias
-class LoRALayer(LoRALayerBase):
-    # up: torch.Tensor
-    # mid: Optional[torch.Tensor]
-    # down: torch.Tensor
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(layer_key, values)
-
-        self.up = values["lora_up.weight"]
-        self.down = values["lora_down.weight"]
-        self.mid = values.get("lora_mid.weight", None)
-
-        self.rank = self.down.shape[0]
-        self.check_keys(
-            values,
-            {
-                "lora_up.weight",
-                "lora_down.weight",
-                "lora_mid.weight",
-            },
-        )
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        if self.mid is not None:
-            up = self.up.reshape(self.up.shape[0], self.up.shape[1])
-            down = self.down.reshape(self.down.shape[0], self.down.shape[1])
-            weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down)
-        else:
-            weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1)
-
-        return weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        for val in [self.up, self.mid, self.down]:
-            if val is not None:
-                model_size += val.nelement() * val.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        super().to(device=device, dtype=dtype)
-
-        self.up = self.up.to(device=device, dtype=dtype)
-        self.down = self.down.to(device=device, dtype=dtype)
-
-        if self.mid is not None:
-            self.mid = self.mid.to(device=device, dtype=dtype)

invokeai/backend/peft/layers/lora_layer_base.py

@@ -1,74 +0,0 @@
-from typing import Dict, Optional, Set
-
-import torch
-
-import invokeai.backend.util.logging as logger
-
-
-class LoRALayerBase:
-    # rank: Optional[int]
-    # alpha: Optional[float]
-    # bias: Optional[torch.Tensor]
-    # layer_key: str
-
-    # @property
-    # def scale(self):
-    #    return self.alpha / self.rank if (self.alpha and self.rank) else 1.0
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: Dict[str, torch.Tensor],
-    ):
-        if "alpha" in values:
-            self.alpha = values["alpha"].item()
-        else:
-            self.alpha = None
-
-        if "bias_indices" in values and "bias_values" in values and "bias_size" in values:
-            self.bias: Optional[torch.Tensor] = torch.sparse_coo_tensor(
-                values["bias_indices"],
-                values["bias_values"],
-                tuple(values["bias_size"]),
-            )
-
-        else:
-            self.bias = None
-
-        self.rank = None  # set in layer implementation
-        self.layer_key = layer_key
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        raise NotImplementedError()
-
-    def get_bias(self, orig_bias: torch.Tensor) -> Optional[torch.Tensor]:
-        return self.bias
-
-    def get_parameters(self, orig_module: torch.nn.Module) -> Dict[str, torch.Tensor]:
-        params = {"weight": self.get_weight(orig_module.weight)}
-        bias = self.get_bias(orig_module.bias)
-        if bias is not None:
-            params["bias"] = bias
-        return params
-
-    def calc_size(self) -> int:
-        model_size = 0
-        for val in [self.bias]:
-            if val is not None:
-                model_size += val.nelement() * val.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        if self.bias is not None:
-            self.bias = self.bias.to(device=device, dtype=dtype)
-
-    def check_keys(self, values: Dict[str, torch.Tensor], known_keys: Set[str]):
-        """Log a warning if values contains unhandled keys."""
-        # {"alpha", "bias_indices", "bias_values", "bias_size"} are hard-coded, because they are handled by
-        # `LoRALayerBase`. Sub-classes should provide the known_keys that they handled.
-        all_known_keys = known_keys | {"alpha", "bias_indices", "bias_values", "bias_size"}
-        unknown_keys = set(values.keys()) - all_known_keys
-        if unknown_keys:
-            logger.warning(
-                f"Unexpected keys found in LoRA/LyCORIS layer, model might work incorrectly! Keys: {unknown_keys}"
-            )

invokeai/backend/peft/layers/norm_layer.py

@@ -1,37 +0,0 @@
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.peft.layers.lora_layer_base import LoRALayerBase
-
-
-class NormLayer(LoRALayerBase):
-    # bias handled in LoRALayerBase(calc_size, to)
-    # weight: torch.Tensor
-    # bias: Optional[torch.Tensor]
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(layer_key, values)
-
-        self.weight = values["w_norm"]
-        self.bias = values.get("b_norm", None)
-
-        self.rank = None  # unscaled
-        self.check_keys(values, {"w_norm", "b_norm"})
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        return self.weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        model_size += self.weight.nelement() * self.weight.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        super().to(device=device, dtype=dtype)
-
-        self.weight = self.weight.to(device=device, dtype=dtype)

invokeai/backend/peft/layers/utils.py

@@ -1,33 +0,0 @@
-from typing import Dict
-
-import torch
-
-from invokeai.backend.peft.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.peft.layers.full_layer import FullLayer
-from invokeai.backend.peft.layers.ia3_layer import IA3Layer
-from invokeai.backend.peft.layers.loha_layer import LoHALayer
-from invokeai.backend.peft.layers.lokr_layer import LoKRLayer
-from invokeai.backend.peft.layers.lora_layer import LoRALayer
-from invokeai.backend.peft.layers.norm_layer import NormLayer
-
-
-def peft_layer_from_state_dict(layer_key: str, state_dict: Dict[str, torch.Tensor]) -> AnyLoRALayer:
-    # Detect layers according to LyCORIS detection logic(`weight_list_det`)
-    # https://github.com/KohakuBlueleaf/LyCORIS/tree/8ad8000efb79e2b879054da8c9356e6143591bad/lycoris/modules
-
-    if "lora_up.weight" in state_dict:
-        # LoRA a.k.a LoCon
-        return LoRALayer(layer_key, state_dict)
-    elif "hada_w1_a" in state_dict:
-        return LoHALayer(layer_key, state_dict)
-    elif "lokr_w1" in state_dict or "lokr_w1_a" in state_dict:
-        return LoKRLayer(layer_key, state_dict)
-    elif "diff" in state_dict:
-        # Full a.k.a Diff
-        return FullLayer(layer_key, state_dict)
-    elif "on_input" in state_dict:
-        return IA3Layer(layer_key, state_dict)
-    elif "w_norm" in state_dict:
-        return NormLayer(layer_key, state_dict)
-    else:
-        raise ValueError(f"Unsupported lora format: {state_dict.keys()}")

invokeai/backend/peft/lora.py

@@ -1,22 +0,0 @@
-# Copyright (c) 2024 The InvokeAI Development team
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.peft.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.raw_model import RawModel
-
-
-class LoRAModelRaw(RawModel):  # (torch.nn.Module):
-    def __init__(self, layers: Dict[str, AnyLoRALayer]):
-        self.layers = layers
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        for _key, layer in self.layers.items():
-            layer.to(device=device, dtype=dtype)
-
-    def calc_size(self) -> int:
-        model_size = 0
-        for _, layer in self.layers.items():
-            model_size += layer.calc_size()
-        return model_size

invokeai/backend/peft/peft_patcher.py

@@ -1,102 +0,0 @@
-from contextlib import contextmanager
-from typing import Dict, Iterator, Optional, Tuple
-
-import torch
-
-from invokeai.backend.peft.lora import LoRAModelRaw
-from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
-
-
-class PeftPatcher:
-    @classmethod
-    @torch.no_grad()
-    @contextmanager
-    def apply_peft_patches(
-        cls,
-        model: torch.nn.Module,
-        patches: Iterator[Tuple[LoRAModelRaw, float]],
-        prefix: str,
-        cached_weights: Optional[Dict[str, torch.Tensor]] = None,
-    ):
-        """Apply one or more PEFT patches to a model.
-
-        :param model: The model to patch.
-        :param loras: An iterator that returns tuples of PEFT patches and associated weights. An iterator is used so
-            that the PEFT patches do not need to be loaded into memory all at once.
-        :param prefix: The keys in the patches will be filtered to only include weights with this prefix.
-        :cached_weights: Read-only copy of the model's state dict in CPU, for efficient unpatching purposes.
-        """
-        original_weights = OriginalWeightsStorage(cached_weights)
-        try:
-            for patch, patch_weight in patches:
-                cls._apply_peft_patch(
-                    model=model,
-                    prefix=prefix,
-                    patch=patch,
-                    patch_weight=patch_weight,
-                    original_weights=original_weights,
-                )
-
-            yield
-        finally:
-            for param_key, weight in original_weights.get_changed_weights():
-                model.get_parameter(param_key).copy_(weight)
-
-    @classmethod
-    @torch.no_grad()
-    def _apply_peft_patch(
-        cls,
-        model: torch.nn.Module,
-        prefix: str,
-        patch: LoRAModelRaw,
-        patch_weight: float,
-        original_weights: OriginalWeightsStorage,
-    ):
-        """
-        Apply one a LoRA to a model.
-        :param model: The model to patch.
-        :param patch: LoRA model to patch in.
-        :param patch_weight: LoRA patch weight.
-        :param prefix: A string prefix that precedes keys used in the LoRAs weight layers.
-        :param original_weights: Storage with original weights, filled by weights which lora patches, used for unpatching.
-        """
-
-        if patch_weight == 0:
-            return
-
-        for layer_key, layer in patch.layers.items():
-            if not layer_key.startswith(prefix):
-                continue
-
-            module = model.get_submodule(layer_key)
-
-            # All of the LoRA weight calculations will be done on the same device as the module weight.
-            # (Performance will be best if this is a CUDA device.)
-            device = module.weight.device
-            dtype = module.weight.dtype
-
-            layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
-
-            # We intentionally move to the target device first, then cast. Experimentally, this was found to
-            # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
-            # same thing in a single call to '.to(...)'.
-            layer.to(device=device)
-            layer.to(dtype=torch.float32)
-
-            # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
-            # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
-            for param_name, lora_param_weight in layer.get_parameters(module).items():
-                param_key = layer_key + "." + param_name
-                module_param = module.get_parameter(param_name)
-
-                # Save original weight
-                original_weights.save(param_key, module_param)
-
-                if module_param.shape != lora_param_weight.shape:
-                    lora_param_weight = lora_param_weight.reshape(module_param.shape)
-
-                lora_param_weight *= patch_weight * layer_scale
-                module_param += lora_param_weight.to(dtype=dtype)
-
-            layer.to(device=TorchDevice.CPU_DEVICE)

invokeai/backend/stable_diffusion/extensions/lora.py

@@ -12,7 +12,7 @@ from invokeai.backend.util.devices import TorchDevice
 if TYPE_CHECKING:
     from invokeai.app.invocations.model import ModelIdentifierField
     from invokeai.app.services.shared.invocation_context import InvocationContext
-    from invokeai.backend.peft.lora import LoRAModelRaw
+    from invokeai.backend.lora import LoRAModelRaw
     from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
 

invokeai/frontend/web/.eslintrc.js

@@ -12,6 +12,10 @@ module.exports = {
     'i18next/no-literal-string': 'error',
     // https://eslint.org/docs/latest/rules/no-console
     'no-console': 'error',
+    // https://eslint.org/docs/latest/rules/no-promise-executor-return
+    'no-promise-executor-return': 'error',
+    // https://eslint.org/docs/latest/rules/require-await
+    'require-await': 'error',
   },
   overrides: [
     /**

invokeai/frontend/web/.storybook/ReduxInit.tsx

@@ -1,5 +1,5 @@
 import { PropsWithChildren, memo, useEffect } from 'react';
-import { modelChanged } from '../src/features/parameters/store/generationSlice';
+import { modelChanged } from '../src/features/controlLayers/store/paramsSlice';
 import { useAppDispatch } from '../src/app/store/storeHooks';
 import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
 /**
@@ -10,7 +10,9 @@ export const ReduxInit = memo((props: PropsWithChildren) => {
   const dispatch = useAppDispatch();
   useGlobalModifiersInit();
   useEffect(() => {
-    dispatch(modelChanged({ key: 'test_model', hash: 'some_hash', name: 'some name', base: 'sd-1', type: 'main' }));
+    dispatch(
+      modelChanged({ model: { key: 'test_model', hash: 'some_hash', name: 'some name', base: 'sd-1', type: 'main' } })
+    );
   }, []);
 
   return props.children;

invokeai/frontend/web/knip.ts

@@ -9,6 +9,8 @@ const config: KnipConfig = {
     'src/services/api/schema.ts',
     'src/features/nodes/types/v1/**',
     'src/features/nodes/types/v2/**',
+    // TODO(psyche): maybe we can clean up these utils after canvas v2 release
+    'src/features/controlLayers/konva/util.ts',
   ],
   ignoreBinaries: ['only-allow'],
   paths: {

invokeai/frontend/web/package.json

@@ -24,7 +24,7 @@
     "build": "pnpm run lint && vite build",
     "typegen": "node scripts/typegen.js",
     "preview": "vite preview",
-    "lint:knip": "knip",
+    "lint:knip": "knip --tags=-knipignore",
     "lint:dpdm": "dpdm --no-warning --no-tree --transform --exit-code circular:1 src/main.tsx",
     "lint:eslint": "eslint --max-warnings=0 .",
     "lint:prettier": "prettier --check .",
@@ -52,18 +52,19 @@
     }
   },
   "dependencies": {
-    "@chakra-ui/react-use-size": "^2.1.0",
     "@dagrejs/dagre": "^1.1.3",
     "@dagrejs/graphlib": "^2.2.3",
     "@dnd-kit/core": "^6.1.0",
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
     "@fontsource-variable/inter": "^5.0.20",
-    "@invoke-ai/ui-library": "^0.0.29",
+    "@invoke-ai/ui-library": "^0.0.32",
     "@nanostores/react": "^0.7.3",
     "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",
+    "async-mutex": "^0.5.0",
     "chakra-react-select": "^4.9.1",
+    "cmdk": "^1.0.0",
     "compare-versions": "^6.1.1",
     "dateformat": "^5.0.3",
     "fracturedjsonjs": "^4.0.2",
@@ -74,6 +75,8 @@
     "jsondiffpatch": "^0.6.0",
     "konva": "^9.3.14",
     "lodash-es": "^4.17.21",
+    "lru-cache": "^11.0.0",
+    "nanoid": "^5.0.7",
     "nanostores": "^0.11.2",
     "new-github-issue-url": "^1.0.0",
     "overlayscrollbars": "^2.10.0",
@@ -88,10 +91,8 @@
     "react-hotkeys-hook": "4.5.0",
     "react-i18next": "^14.1.3",
     "react-icons": "^5.2.1",
-    "react-konva": "^18.2.10",
     "react-redux": "9.1.2",
     "react-resizable-panels": "^2.0.23",
-    "react-select": "5.8.0",
     "react-use": "^17.5.1",
     "react-virtuoso": "^4.9.0",
     "reactflow": "^11.11.4",
@@ -102,9 +103,9 @@
     "roarr": "^7.21.1",
     "serialize-error": "^11.0.3",
     "socket.io-client": "^4.7.5",
+    "stable-hash": "^0.0.4",
     "use-debounce": "^10.0.2",
     "use-device-pixel-ratio": "^1.1.2",
-    "use-image": "^1.1.1",
     "uuid": "^10.0.0",
     "zod": "^3.23.8",
     "zod-validation-error": "^3.3.1"

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

@@ -80,6 +80,7 @@
         "aboutDesc": "Using Invoke for work? Check out:",
         "aboutHeading": "Own Your Creative Power",
         "accept": "Accept",
+        "apply": "Apply",
         "add": "Add",
         "advanced": "Advanced",
         "ai": "ai",
@@ -115,6 +116,7 @@
         "githubLabel": "Github",
         "goTo": "Go to",
         "hotkeysLabel": "Hotkeys",
+        "loadingImage": "Loading Image",
         "imageFailedToLoad": "Unable to Load Image",
         "img2img": "Image To Image",
         "inpaint": "inpaint",
@@ -162,10 +164,10 @@
         "alpha": "Alpha",
         "selected": "Selected",
         "tab": "Tab",
-        "viewing": "Viewing",
-        "viewingDesc": "Review images in a large gallery view",
-        "editing": "Editing",
-        "editingDesc": "Edit on the Control Layers canvas",
+        "view": "View",
+        "viewDesc": "Review images in a large gallery view",
+        "edit": "Edit",
+        "editDesc": "Edit on the Canvas",
         "comparing": "Comparing",
         "comparingDesc": "Comparing two images",
         "enabled": "Enabled",
@@ -325,6 +327,14 @@
         "canceled": "Canceled",
         "completedIn": "Completed in",
         "batch": "Batch",
+        "origin": "Origin",
+        "destination": "Destination",
+        "upscaling": "Upscaling",
+        "canvas": "Canvas",
+        "generation": "Generation",
+        "workflows": "Workflows",
+        "other": "Other",
+        "gallery": "Gallery",
         "batchFieldValues": "Batch Field Values",
         "item": "Item",
         "session": "Session",
@@ -1100,7 +1110,6 @@
         "confirmOnDelete": "Confirm On Delete",
         "developer": "Developer",
         "displayInProgress": "Display Progress Images",
-        "enableImageDebugging": "Enable Image Debugging",
         "enableInformationalPopovers": "Enable Informational Popovers",
         "informationalPopoversDisabled": "Informational Popovers Disabled",
         "informationalPopoversDisabledDesc": "Informational popovers have been disabled. Enable them in Settings.",
@@ -1567,7 +1576,7 @@
         "copyToClipboard": "Copy to Clipboard",
         "cursorPosition": "Cursor Position",
         "darkenOutsideSelection": "Darken Outside Selection",
-        "discardAll": "Discard All",
+        "discardAll": "Discard All & Cancel Pending Generations",
         "discardCurrent": "Discard Current",
         "downloadAsImage": "Download As Image",
         "enableMask": "Enable Mask",
@@ -1645,39 +1654,143 @@
         "storeNotInitialized": "Store is not initialized"
     },
     "controlLayers": {
-        "deleteAll": "Delete All",
+        "saveCanvasToGallery": "Save Canvas To Gallery",
+        "saveBboxToGallery": "Save Bbox To Gallery",
+        "savedToGalleryOk": "Saved to Gallery",
+        "savedToGalleryError": "Error saving to gallery",
+        "mergeVisible": "Merge Visible",
+        "mergeVisibleOk": "Merged visible layers",
+        "mergeVisibleError": "Error merging visible layers",
+        "clearHistory": "Clear History",
+        "generateMode": "Generate",
+        "generateModeDesc": "Create individual images. Generated images are added directly to the gallery.",
+        "composeMode": "Compose",
+        "composeModeDesc": "Compose your work iterative. Generated images are added back to the canvas.",
+        "autoSave": "Auto-save to Gallery",
+        "resetCanvas": "Reset Canvas",
+        "resetAll": "Reset All",
+        "clearCaches": "Clear Caches",
+        "recalculateRects": "Recalculate Rects",
+        "clipToBbox": "Clip Strokes to Bbox",
         "addLayer": "Add Layer",
+        "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
         "moveToBack": "Move to Back",
         "moveForward": "Move Forward",
         "moveBackward": "Move Backward",
         "brushSize": "Brush Size",
+        "width": "Width",
+        "zoom": "Zoom",
+        "resetView": "Reset View",
         "controlLayers": "Control Layers",
         "globalMaskOpacity": "Global Mask Opacity",
         "autoNegative": "Auto Negative",
+        "enableAutoNegative": "Enable Auto Negative",
+        "disableAutoNegative": "Disable Auto Negative",
         "deletePrompt": "Delete Prompt",
         "resetRegion": "Reset Region",
         "debugLayers": "Debug Layers",
+        "showHUD": "Show HUD",
         "rectangle": "Rectangle",
-        "maskPreviewColor": "Mask Preview Color",
+        "maskFill": "Mask Fill",
         "addPositivePrompt": "Add $t(common.positivePrompt)",
         "addNegativePrompt": "Add $t(common.negativePrompt)",
         "addIPAdapter": "Add $t(common.ipAdapter)",
-        "regionalGuidance": "Regional Guidance",
+        "addRasterLayer": "Add $t(controlLayers.rasterLayer)",
+        "addControlLayer": "Add $t(controlLayers.controlLayer)",
+        "addInpaintMask": "Add $t(controlLayers.inpaintMask)",
+        "addRegionalGuidance": "Add $t(controlLayers.regionalGuidance)",
         "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
+        "raster": "Raster",
+        "rasterLayer": "Raster Layer",
+        "controlLayer": "Control Layer",
+        "inpaintMask": "Inpaint Mask",
+        "regionalGuidance": "Regional Guidance",
+        "ipAdapter": "IP Adapter",
+        "sendToGallery": "Send To Gallery",
+        "sendToGalleryDesc": "Generations will be sent to the gallery.",
+        "sendToCanvas": "Send To Canvas",
+        "sendToCanvasDesc": "Generations will be staged onto the canvas.",
+        "rasterLayer_withCount_one": "$t(controlLayers.rasterLayer)",
+        "controlLayer_withCount_one": "$t(controlLayers.controlLayer)",
+        "inpaintMask_withCount_one": "$t(controlLayers.inpaintMask)",
+        "regionalGuidance_withCount_one": "$t(controlLayers.regionalGuidance)",
+        "ipAdapter_withCount_one": "$t(controlLayers.ipAdapter)",
+        "rasterLayer_withCount_other": "Raster Layers",
+        "controlLayer_withCount_other": "Control Layers",
+        "inpaintMask_withCount_other": "Inpaint Masks",
+        "regionalGuidance_withCount_other": "Regional Guidance",
+        "ipAdapter_withCount_other": "IP Adapters",
         "opacity": "Opacity",
+        "regionalGuidance_withCount_hidden": "Regional Guidance ({{count}} hidden)",
+        "controlLayers_withCount_hidden": "Control Layers ({{count}} hidden)",
+        "rasterLayers_withCount_hidden": "Raster Layers ({{count}} hidden)",
+        "ipAdapters_withCount_hidden": "IP Adapters ({{count}} hidden)",
+        "inpaintMasks_withCount_hidden": "Inpaint Masks ({{count}} hidden)",
+        "regionalGuidance_withCount_visible": "Regional Guidance ({{count}})",
+        "controlLayers_withCount_visible": "Control Layers ({{count}})",
+        "rasterLayers_withCount_visible": "Raster Layers ({{count}})",
+        "ipAdapters_withCount_visible": "IP Adapters ({{count}})",
+        "inpaintMasks_withCount_visible": "Inpaint Masks ({{count}})",
         "globalControlAdapter": "Global $t(controlnet.controlAdapter_one)",
         "globalControlAdapterLayer": "Global $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
         "globalIPAdapter": "Global $t(common.ipAdapter)",
         "globalIPAdapterLayer": "Global $t(common.ipAdapter) $t(unifiedCanvas.layer)",
         "globalInitialImage": "Global Initial Image",
         "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) $t(unifiedCanvas.layer)",
+        "layer": "Layer",
         "opacityFilter": "Opacity Filter",
         "clearProcessor": "Clear Processor",
         "resetProcessor": "Reset Processor to Defaults",
         "noLayersAdded": "No Layers Added",
         "layers_one": "Layer",
-        "layers_other": "Layers"
+        "layers_other": "Layers",
+        "objects_zero": "empty",
+        "objects_one": "{{count}} object",
+        "objects_other": "{{count}} objects",
+        "convertToControlLayer": "Convert to Control Layer",
+        "convertToRasterLayer": "Convert to Raster Layer",
+        "transparency": "Transparency",
+        "enableTransparencyEffect": "Enable Transparency Effect",
+        "disableTransparencyEffect": "Disable Transparency Effect",
+        "hidingType": "Hiding {{type}}",
+        "showingType": "Showing {{type}}",
+        "dynamicGrid": "Dynamic Grid",
+        "logDebugInfo": "Log Debug Info",
+        "locked": "Locked",
+        "unlocked": "Unlocked",
+        "deleteSelected": "Delete Selected",
+        "deleteAll": "Delete All",
+        "flipHorizontal": "Flip Horizontal",
+        "flipVertical": "Flip Vertical",
+        "fill": {
+            "fillColor": "Fill Color",
+            "fillStyle": "Fill Style",
+            "solid": "Solid",
+            "grid": "Grid",
+            "crosshatch": "Crosshatch",
+            "vertical": "Vertical",
+            "horizontal": "Horizontal",
+            "diagonal": "Diagonal"
+        },
+        "tool": {
+            "brush": "Brush",
+            "eraser": "Eraser",
+            "rectangle": "Rectangle",
+            "bbox": "Bbox",
+            "move": "Move",
+            "view": "View",
+            "transform": "Transform",
+            "colorPicker": "Color Picker"
+        },
+        "filter": {
+            "filter": "Filter",
+            "filters": "Filters",
+            "filterType": "Filter Type",
+            "preview": "Preview",
+            "apply": "Apply",
+            "cancel": "Cancel"
+        }
     },
     "upscaling": {
         "upscale": "Upscale",
@@ -1765,5 +1878,30 @@
             "upscaling": "Upscaling",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
         }
+    },
+    "system": {
+        "enableLogging": "Enable Logging",
+        "logLevel": {
+            "logLevel": "Log Level",
+            "trace": "Trace",
+            "debug": "Debug",
+            "info": "Info",
+            "warn": "Warn",
+            "error": "Error",
+            "fatal": "Fatal"
+        },
+        "logNamespaces": {
+            "logNamespaces": "Log Namespaces",
+            "gallery": "Gallery",
+            "models": "Models",
+            "config": "Config",
+            "canvas": "Canvas",
+            "generation": "Generation",
+            "workflows": "Workflows",
+            "system": "System",
+            "events": "Events",
+            "queue": "Queue",
+            "metadata": "Metadata"
+        }
     }
 }

invokeai/frontend/web/scripts/typegen.js

@@ -38,7 +38,7 @@ async function generateTypes(schema) {
   process.stdout.write(`\nOK!\r\n`);
 }
 
-async function main() {
+function main() {
   const encoding = 'utf-8';
 
   if (process.stdin.isTTY) {

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

@@ -6,6 +6,7 @@ import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/ap
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import type { PartialAppConfig } from 'app/types/invokeai';
 import ImageUploadOverlay from 'common/components/ImageUploadOverlay';
+import { useScopeFocusWatcher } from 'common/hooks/interactionScopes';
 import { useClearStorage } from 'common/hooks/useClearStorage';
 import { useFullscreenDropzone } from 'common/hooks/useFullscreenDropzone';
 import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
@@ -13,13 +14,16 @@ import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardMo
 import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
 import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicPromptsPreviewModal';
 import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
+import { ClearQueueConfirmationsAlertDialog } from 'features/queue/components/ClearQueueConfirmationAlertDialog';
 import { StylePresetModal } from 'features/stylePresets/components/StylePresetForm/StylePresetModal';
 import { activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
+import RefreshAfterResetModal from 'features/system/components/SettingsModal/RefreshAfterResetModal';
+import SettingsModal from 'features/system/components/SettingsModal/SettingsModal';
 import { configChanged } from 'features/system/store/configSlice';
-import { languageSelector } from 'features/system/store/systemSelectors';
-import InvokeTabs from 'features/ui/components/InvokeTabs';
-import type { InvokeTabName } from 'features/ui/store/tabMap';
+import { selectLanguage } from 'features/system/store/systemSelectors';
+import { AppContent } from 'features/ui/components/AppContent';
 import { setActiveTab } from 'features/ui/store/uiSlice';
+import type { TabName } from 'features/ui/store/uiTypes';
 import { useGetAndLoadLibraryWorkflow } from 'features/workflowLibrary/hooks/useGetAndLoadLibraryWorkflow';
 import { AnimatePresence } from 'framer-motion';
 import i18n from 'i18n';
@@ -41,7 +45,7 @@ interface Props {
   };
   selectedWorkflowId?: string;
   selectedStylePresetId?: string;
-  destination?: InvokeTabName | undefined;
+  destination?: TabName;
 }
 
 const App = ({
@@ -51,7 +55,7 @@ const App = ({
   selectedStylePresetId,
   destination,
 }: Props) => {
-  const language = useAppSelector(languageSelector);
+  const language = useAppSelector(selectLanguage);
   const logger = useLogger('system');
   const dispatch = useAppDispatch();
   const clearStorage = useClearStorage();
@@ -107,6 +111,7 @@ const App = ({
 
   useStarterModelsToast();
   useSyncQueueStatus();
+  useScopeFocusWatcher();
 
   return (
     <ErrorBoundary onReset={handleReset} FallbackComponent={AppErrorBoundaryFallback}>
@@ -119,7 +124,7 @@ const App = ({
         {...dropzone.getRootProps()}
       >
         <input {...dropzone.getInputProps()} />
-        <InvokeTabs />
+        <AppContent />
         <AnimatePresence>
           {dropzone.isDragActive && isHandlingUpload && (
             <ImageUploadOverlay dropzone={dropzone} setIsHandlingUpload={setIsHandlingUpload} />
@@ -130,7 +135,10 @@ const App = ({
       <ChangeBoardModal />
       <DynamicPromptsModal />
       <StylePresetModal />
+      <ClearQueueConfirmationsAlertDialog />
       <PreselectedImage selectedImage={selectedImage} />
+      <SettingsModal />
+      <RefreshAfterResetModal />
     </ErrorBoundary>
   );
 };

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

@@ -1,5 +1,7 @@
 import { Button, Flex, Heading, Image, Link, Text } from '@invoke-ai/ui-library';
+import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
+import { selectConfigSlice } from 'features/system/store/configSlice';
 import { toast } from 'features/toast/toast';
 import newGithubIssueUrl from 'new-github-issue-url';
 import InvokeLogoYellow from 'public/assets/images/invoke-symbol-ylw-lrg.svg';
@@ -13,9 +15,11 @@ type Props = {
   resetErrorBoundary: () => void;
 };
 
+const selectIsLocal = createSelector(selectConfigSlice, (config) => config.isLocal);
+
 const AppErrorBoundaryFallback = ({ error, resetErrorBoundary }: Props) => {
   const { t } = useTranslation();
-  const isLocal = useAppSelector((s) => s.config.isLocal);
+  const isLocal = useAppSelector(selectIsLocal);
 
   const handleCopy = useCallback(() => {
     const text = JSON.stringify(serializeError(error), null, 2);

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

@@ -19,7 +19,7 @@ import type { PartialAppConfig } from 'app/types/invokeai';
 import Loading from 'common/components/Loading/Loading';
 import AppDndContext from 'features/dnd/components/AppDndContext';
 import type { WorkflowCategory } from 'features/nodes/types/workflow';
-import type { InvokeTabName } from 'features/ui/store/tabMap';
+import type { TabName } from 'features/ui/store/uiTypes';
 import type { PropsWithChildren, ReactNode } from 'react';
 import React, { lazy, memo, useEffect, useMemo } from 'react';
 import { Provider } from 'react-redux';
@@ -46,7 +46,7 @@ interface Props extends PropsWithChildren {
   };
   selectedWorkflowId?: string;
   selectedStylePresetId?: string;
-  destination?: InvokeTabName;
+  destination?: TabName;
   customStarUi?: CustomStarUi;
   socketOptions?: Partial<ManagerOptions & SocketOptions>;
   isDebugging?: boolean;

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

@@ -2,7 +2,7 @@ import { useStore } from '@nanostores/react';
 import { $authToken } from 'app/store/nanostores/authToken';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
 import { $isDebugging } from 'app/store/nanostores/isDebugging';
-import { useAppDispatch } from 'app/store/storeHooks';
+import { useAppStore } from 'app/store/nanostores/store';
 import type { MapStore } from 'nanostores';
 import { atom, map } from 'nanostores';
 import { useEffect, useMemo } from 'react';
@@ -18,14 +18,19 @@ declare global {
   }
 }
 
+export type AppSocket = Socket<ServerToClientEvents, ClientToServerEvents>;
+
+export const $socket = atom<AppSocket | null>(null);
 export const $socketOptions = map<Partial<ManagerOptions & SocketOptions>>({});
+
 const $isSocketInitialized = atom<boolean>(false);
+export const $isConnected = atom<boolean>(false);
 
 /**
  * Initializes the socket.io connection and sets up event listeners.
  */
 export const useSocketIO = () => {
-  const dispatch = useAppDispatch();
+  const { dispatch, getState } = useAppStore();
   const baseUrl = useStore($baseUrl);
   const authToken = useStore($authToken);
   const addlSocketOptions = useStore($socketOptions);
@@ -61,8 +66,9 @@ export const useSocketIO = () => {
       return;
     }
 
-    const socket: Socket<ServerToClientEvents, ClientToServerEvents> = io(socketUrl, socketOptions);
-    setEventListeners({ dispatch, socket });
+    const socket: AppSocket = io(socketUrl, socketOptions);
+    $socket.set(socket);
+    setEventListeners({ socket, dispatch, getState, setIsConnected: $isConnected.set });
     socket.connect();
 
     if ($isDebugging.get() || import.meta.env.MODE === 'development') {
@@ -84,5 +90,5 @@ export const useSocketIO = () => {
       socket.disconnect();
       $isSocketInitialized.set(false);
     };
-  }, [dispatch, socketOptions, socketUrl]);
+  }, [dispatch, getState, socketOptions, socketUrl]);
 };

invokeai/frontend/web/src/app/logging/logger.ts

@@ -15,21 +15,21 @@ export const BASE_CONTEXT = {};
 
 export const $logger = atom<Logger>(Roarr.child(BASE_CONTEXT));
 
-export type LoggerNamespace =
-  | 'images'
-  | 'models'
-  | 'config'
-  | 'canvas'
-  | 'generation'
-  | 'nodes'
-  | 'system'
-  | 'socketio'
-  | 'session'
-  | 'queue'
-  | 'dnd'
-  | 'controlLayers';
+export const zLogNamespace = z.enum([
+  'canvas',
+  'config',
+  'events',
+  'gallery',
+  'generation',
+  'metadata',
+  'models',
+  'system',
+  'queue',
+  'workflows',
+]);
+export type LogNamespace = z.infer<typeof zLogNamespace>;
 
-export const logger = (namespace: LoggerNamespace) => $logger.get().child({ namespace });
+export const logger = (namespace: LogNamespace) => $logger.get().child({ namespace });
 
 export const zLogLevel = z.enum(['trace', 'debug', 'info', 'warn', 'error', 'fatal']);
 export type LogLevel = z.infer<typeof zLogLevel>;

invokeai/frontend/web/src/app/logging/useLogger.ts

@@ -1,29 +1,41 @@
 import { createLogWriter } from '@roarr/browser-log-writer';
 import { useAppSelector } from 'app/store/storeHooks';
+import {
+  selectSystemLogIsEnabled,
+  selectSystemLogLevel,
+  selectSystemLogNamespaces,
+} from 'features/system/store/systemSlice';
 import { useEffect, useMemo } from 'react';
 import { ROARR, Roarr } from 'roarr';
 
-import type { LoggerNamespace } from './logger';
+import type { LogNamespace } from './logger';
 import { $logger, BASE_CONTEXT, LOG_LEVEL_MAP, logger } from './logger';
 
-export const useLogger = (namespace: LoggerNamespace) => {
-  const consoleLogLevel = useAppSelector((s) => s.system.consoleLogLevel);
-  const shouldLogToConsole = useAppSelector((s) => s.system.shouldLogToConsole);
+export const useLogger = (namespace: LogNamespace) => {
+  const logLevel = useAppSelector(selectSystemLogLevel);
+  const logNamespaces = useAppSelector(selectSystemLogNamespaces);
+  const logIsEnabled = useAppSelector(selectSystemLogIsEnabled);
 
   // The provided Roarr browser log writer uses localStorage to config logging to console
   useEffect(() => {
-    if (shouldLogToConsole) {
+    if (logIsEnabled) {
       // Enable console log output
       localStorage.setItem('ROARR_LOG', 'true');
 
       // Use a filter to show only logs of the given level
-      localStorage.setItem('ROARR_FILTER', `context.logLevel:>=${LOG_LEVEL_MAP[consoleLogLevel]}`);
+      let filter = `context.logLevel:>=${LOG_LEVEL_MAP[logLevel]}`;
+      if (logNamespaces.length > 0) {
+        filter += ` AND (${logNamespaces.map((ns) => `context.namespace:${ns}`).join(' OR ')})`;
+      } else {
+        filter += ' AND context.namespace:undefined';
+      }
+      localStorage.setItem('ROARR_FILTER', filter);
     } else {
       // Disable console log output
       localStorage.setItem('ROARR_LOG', 'false');
     }
     ROARR.write = createLogWriter();
-  }, [consoleLogLevel, shouldLogToConsole]);
+  }, [logLevel, logIsEnabled, logNamespaces]);
 
   // Update the module-scoped logger context as needed
   useEffect(() => {

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

@@ -1,7 +1,7 @@
 import { createAction } from '@reduxjs/toolkit';
-import type { InvokeTabName } from 'features/ui/store/tabMap';
+import type { TabName } from 'features/ui/store/uiTypes';
 
 export const enqueueRequested = createAction<{
-  tabName: InvokeTabName;
+  tabName: TabName;
   prepend: boolean;
 }>('app/enqueueRequested');

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

@@ -1,2 +1,3 @@
 export const STORAGE_PREFIX = '@@invokeai-';
 export const EMPTY_ARRAY = [];
+export const EMPTY_OBJECT = {};

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

@@ -1,5 +1,6 @@
 import { createDraftSafeSelectorCreator, createSelectorCreator, lruMemoize } from '@reduxjs/toolkit';
 import type { GetSelectorsOptions } from '@reduxjs/toolkit/dist/entities/state_selectors';
+import type { RootState } from 'app/store/store';
 import { isEqual } from 'lodash-es';
 
 /**
@@ -19,3 +20,5 @@ export const getSelectorsOptions: GetSelectorsOptions = {
     argsMemoize: lruMemoize,
   }),
 };
+
+export const createMemoizedAppSelector = createMemoizedSelector.withTypes<RootState>();

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

@@ -1,5 +1,4 @@
 import { logger } from 'app/logging/logger';
-import { parseify } from 'common/util/serialize';
 import { PersistError, RehydrateError } from 'redux-remember';
 import { serializeError } from 'serialize-error';
 
@@ -41,6 +40,6 @@ export const errorHandler = (err: PersistError | RehydrateError) => {
   } else if (err instanceof RehydrateError) {
     log.error({ error: serializeError(err) }, 'Problem rehydrating state');
   } else {
-    log.error({ error: parseify(err) }, 'Problem in persistence layer');
+    log.error({ error: serializeError(err) }, 'Problem in persistence layer');
   }
 };

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

@@ -1,9 +1,7 @@
 import type { UnknownAction } from '@reduxjs/toolkit';
-import { deepClone } from 'common/util/deepClone';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { Graph } from 'services/api/types';
-import { socketGeneratorProgress } from 'services/events/actions';
 
 export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
   if (isAnyGraphBuilt(action)) {
@@ -24,13 +22,5 @@ export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
     };
   }
 
-  if (socketGeneratorProgress.match(action)) {
-    const sanitized = deepClone(action);
-    if (sanitized.payload.data.progress_image) {
-      sanitized.payload.data.progress_image.dataURL = '<Progress image omitted>';
-    }
-    return sanitized;
-  }
-
   return action;
 };

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

@@ -1,7 +1,7 @@
 import type { TypedStartListening } from '@reduxjs/toolkit';
 import { createListenerMiddleware } from '@reduxjs/toolkit';
 import { addAdHocPostProcessingRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/addAdHocPostProcessingRequestedListener';
-import { addCommitStagingAreaImageListener } from 'app/store/middleware/listenerMiddleware/listeners/addCommitStagingAreaImageListener';
+import { addStagingListeners } from 'app/store/middleware/listenerMiddleware/listeners/addCommitStagingAreaImageListener';
 import { addAnyEnqueuedListener } from 'app/store/middleware/listenerMiddleware/listeners/anyEnqueued';
 import { addAppConfigReceivedListener } from 'app/store/middleware/listenerMiddleware/listeners/appConfigReceived';
 import { addAppStartedListener } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
@@ -9,17 +9,6 @@ import { addBatchEnqueuedListener } from 'app/store/middleware/listenerMiddlewar
 import { addDeleteBoardAndImagesFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted';
 import { addBoardIdSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/boardIdSelected';
 import { addBulkDownloadListeners } from 'app/store/middleware/listenerMiddleware/listeners/bulkDownload';
-import { addCanvasCopiedToClipboardListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasCopiedToClipboard';
-import { addCanvasDownloadedAsImageListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasDownloadedAsImage';
-import { addCanvasImageToControlNetListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasImageToControlNet';
-import { addCanvasMaskSavedToGalleryListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMaskSavedToGallery';
-import { addCanvasMaskToControlNetListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMaskToControlNet';
-import { addCanvasMergedListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMerged';
-import { addCanvasSavedToGalleryListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery';
-import { addControlAdapterPreprocessor } from 'app/store/middleware/listenerMiddleware/listeners/controlAdapterPreprocessor';
-import { addControlNetAutoProcessListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess';
-import { addControlNetImageProcessedListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed';
-import { addEnqueueRequestedCanvasListener } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedCanvas';
 import { addEnqueueRequestedLinear } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedLinear';
 import { addEnqueueRequestedNodes } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedNodes';
 import { addGalleryImageClickedListener } from 'app/store/middleware/listenerMiddleware/listeners/galleryImageClicked';
@@ -37,16 +26,7 @@ import { addModelSelectedListener } from 'app/store/middleware/listenerMiddlewar
 import { addModelsLoadedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelsLoaded';
 import { addDynamicPromptsListener } from 'app/store/middleware/listenerMiddleware/listeners/promptChanged';
 import { addSetDefaultSettingsListener } from 'app/store/middleware/listenerMiddleware/listeners/setDefaultSettings';
-import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected';
-import { addSocketDisconnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketDisconnected';
-import { addGeneratorProgressEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketGeneratorProgress';
-import { addInvocationCompleteEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete';
-import { addInvocationErrorEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationError';
-import { addInvocationStartedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationStarted';
-import { addModelInstallEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketModelInstall';
-import { addModelLoadEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketModelLoad';
-import { addSocketQueueItemStatusChangedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketQueueItemStatusChanged';
-import { addStagingAreaImageSavedListener } from 'app/store/middleware/listenerMiddleware/listeners/stagingAreaImageSaved';
+import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketConnected';
 import { addUpdateAllNodesRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/updateAllNodesRequested';
 import { addWorkflowLoadRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/workflowLoadRequested';
 import type { AppDispatch, RootState } from 'app/store/store';
@@ -83,7 +63,6 @@ addGalleryImageClickedListener(startAppListening);
 addGalleryOffsetChangedListener(startAppListening);
 
 // User Invoked
-addEnqueueRequestedCanvasListener(startAppListening);
 addEnqueueRequestedNodes(startAppListening);
 addEnqueueRequestedLinear(startAppListening);
 addEnqueueRequestedUpscale(startAppListening);
@@ -91,31 +70,22 @@ addAnyEnqueuedListener(startAppListening);
 addBatchEnqueuedListener(startAppListening);
 
 // Canvas actions
-addCanvasSavedToGalleryListener(startAppListening);
-addCanvasMaskSavedToGalleryListener(startAppListening);
-addCanvasImageToControlNetListener(startAppListening);
-addCanvasMaskToControlNetListener(startAppListening);
-addCanvasDownloadedAsImageListener(startAppListening);
-addCanvasCopiedToClipboardListener(startAppListening);
-addCanvasMergedListener(startAppListening);
-addStagingAreaImageSavedListener(startAppListening);
-addCommitStagingAreaImageListener(startAppListening);
+// addCanvasSavedToGalleryListener(startAppListening);
+// addCanvasMaskSavedToGalleryListener(startAppListening);
+// addCanvasImageToControlNetListener(startAppListening);
+// addCanvasMaskToControlNetListener(startAppListening);
+// addCanvasDownloadedAsImageListener(startAppListening);
+// addCanvasCopiedToClipboardListener(startAppListening);
+// addCanvasMergedListener(startAppListening);
+// addStagingAreaImageSavedListener(startAppListening);
+// addCommitStagingAreaImageListener(startAppListening);
+addStagingListeners(startAppListening);
 
 // Socket.IO
-addGeneratorProgressEventListener(startAppListening);
-addInvocationCompleteEventListener(startAppListening);
-addInvocationErrorEventListener(startAppListening);
-addInvocationStartedEventListener(startAppListening);
 addSocketConnectedEventListener(startAppListening);
-addSocketDisconnectedEventListener(startAppListening);
-addModelLoadEventListener(startAppListening);
-addModelInstallEventListener(startAppListening);
-addSocketQueueItemStatusChangedEventListener(startAppListening);
-addBulkDownloadListeners(startAppListening);
 
-// ControlNet
-addControlNetImageProcessedListener(startAppListening);
-addControlNetAutoProcessListener(startAppListening);
+// Gallery bulk download
+addBulkDownloadListeners(startAppListening);
 
 // Boards
 addImageAddedToBoardFulfilledListener(startAppListening);
@@ -148,4 +118,4 @@ addAdHocPostProcessingRequestedListener(startAppListening);
 addDynamicPromptsListener(startAppListening);
 
 addSetDefaultSettingsListener(startAppListening);
-addControlAdapterPreprocessor(startAppListening);
+// addControlAdapterPreprocessor(startAppListening);

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

@@ -1,21 +1,21 @@
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { parseify } from 'common/util/serialize';
+import type { SerializableObject } from 'common/types';
 import { buildAdHocPostProcessingGraph } from 'features/nodes/util/graph/buildAdHocPostProcessingGraph';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { BatchConfig, ImageDTO } from 'services/api/types';
 
+const log = logger('queue');
+
 export const adHocPostProcessingRequested = createAction<{ imageDTO: ImageDTO }>(`upscaling/postProcessingRequested`);
 
 export const addAdHocPostProcessingRequestedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: adHocPostProcessingRequested,
     effect: async (action, { dispatch, getState }) => {
-      const log = logger('session');
-
       const { imageDTO } = action.payload;
       const state = getState();
 
@@ -39,9 +39,9 @@ export const addAdHocPostProcessingRequestedListener = (startAppListening: AppSt
 
         const enqueueResult = await req.unwrap();
         req.reset();
-        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
+        log.debug({ enqueueResult } as SerializableObject, t('queue.graphQueued'));
       } catch (error) {
-        log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
+        log.error({ enqueueBatchArg } as SerializableObject, t('queue.graphFailedToQueue'));
 
         if (error instanceof Object && 'status' in error && error.status === 403) {
           return;

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

@@ -23,7 +23,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
    */
   startAppListening({
     matcher: matchAnyBoardDeleted,
-    effect: async (action, { dispatch, getState }) => {
+    effect: (action, { dispatch, getState }) => {
       const state = getState();
       const deletedBoardId = action.meta.arg.originalArgs;
       const { autoAddBoardId, selectedBoardId } = state.gallery;
@@ -44,7 +44,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
   // If we archived a board, it may end up hidden. If it's selected or the auto-add board, we should reset those.
   startAppListening({
     matcher: boardsApi.endpoints.updateBoard.matchFulfilled,
-    effect: async (action, { dispatch, getState }) => {
+    effect: (action, { dispatch, getState }) => {
       const state = getState();
       const { shouldShowArchivedBoards } = state.gallery;
 
@@ -61,7 +61,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
   // When we hide archived boards, if the selected or the auto-add board is archived, we should reset those.
   startAppListening({
     actionCreator: shouldShowArchivedBoardsChanged,
-    effect: async (action, { dispatch, getState }) => {
+    effect: (action, { dispatch, getState }) => {
       const shouldShowArchivedBoards = action.payload;
 
       // We only need to take action if we have just hidden archived boards.
@@ -100,7 +100,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
    */
   startAppListening({
     matcher: boardsApi.endpoints.listAllBoards.matchFulfilled,
-    effect: async (action, { dispatch, getState }) => {
+    effect: (action, { dispatch, getState }) => {
       const boards = action.payload;
       const state = getState();
       const { selectedBoardId, autoAddBoardId } = state.gallery;

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

@@ -1,33 +1,37 @@
-import { isAnyOf } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import {
-  canvasBatchIdsReset,
-  commitStagingAreaImage,
-  discardStagedImages,
-  resetCanvas,
-  setInitialCanvasImage,
-} from 'features/canvas/store/canvasSlice';
+  sessionStagingAreaImageAccepted,
+  sessionStagingAreaReset,
+} from 'features/controlLayers/store/canvasSessionSlice';
+import { rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
+import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
+import type { CanvasRasterLayerState } from 'features/controlLayers/store/types';
+import { imageDTOToImageObject } from 'features/controlLayers/store/types';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
+import { $lastCanvasProgressEvent } from 'services/events/setEventListeners';
+import { assert } from 'tsafe';
 
-const matcher = isAnyOf(commitStagingAreaImage, discardStagedImages, resetCanvas, setInitialCanvasImage);
+const log = logger('canvas');
 
-export const addCommitStagingAreaImageListener = (startAppListening: AppStartListening) => {
+export const addStagingListeners = (startAppListening: AppStartListening) => {
   startAppListening({
-    matcher,
-    effect: async (_, { dispatch, getState }) => {
-      const log = logger('canvas');
-      const state = getState();
-      const { batchIds } = state.canvas;
-
+    actionCreator: sessionStagingAreaReset,
+    effect: async (_, { dispatch }) => {
       try {
         const req = dispatch(
-          queueApi.endpoints.cancelByBatchIds.initiate({ batch_ids: batchIds }, { fixedCacheKey: 'cancelByBatchIds' })
+          queueApi.endpoints.cancelByBatchOrigin.initiate(
+            { origin: 'canvas' },
+            { fixedCacheKey: 'cancelByBatchOrigin' }
+          )
         );
         const { canceled } = await req.unwrap();
         req.reset();
+
+        $lastCanvasProgressEvent.set(null);
+
         if (canceled > 0) {
           log.debug(`Canceled ${canceled} canvas batches`);
           toast({
@@ -36,7 +40,6 @@ export const addCommitStagingAreaImageListener = (startAppListening: AppStartLis
             status: 'success',
           });
         }
-        dispatch(canvasBatchIdsReset());
       } catch {
         log.error('Failed to cancel canvas batches');
         toast({
@@ -47,4 +50,26 @@ export const addCommitStagingAreaImageListener = (startAppListening: AppStartLis
       }
     },
   });
+
+  startAppListening({
+    actionCreator: sessionStagingAreaImageAccepted,
+    effect: (action, api) => {
+      const { index } = action.payload;
+      const state = api.getState();
+      const stagingAreaImage = state.canvasSession.stagedImages[index];
+
+      assert(stagingAreaImage, 'No staged image found to accept');
+      const { x, y } = selectCanvasSlice(state).bbox.rect;
+
+      const { imageDTO, offsetX, offsetY } = stagingAreaImage;
+      const imageObject = imageDTOToImageObject(imageDTO);
+      const overrides: Partial<CanvasRasterLayerState> = {
+        position: { x: x + offsetX, y: y + offsetY },
+        objects: [imageObject],
+      };
+
+      api.dispatch(rasterLayerAdded({ overrides, isSelected: false }));
+      api.dispatch(sessionStagingAreaReset());
+    },
+  });
 };

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

@@ -4,7 +4,7 @@ import { queueApi, selectQueueStatus } from 'services/api/endpoints/queue';
 export const addAnyEnqueuedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: queueApi.endpoints.enqueueBatch.matchFulfilled,
-    effect: async (_, { dispatch, getState }) => {
+    effect: (_, { dispatch, getState }) => {
       const { data } = selectQueueStatus(getState());
 
       if (!data || data.processor.is_started) {

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

@@ -1,14 +1,14 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { setInfillMethod } from 'features/parameters/store/generationSlice';
+import { setInfillMethod } from 'features/controlLayers/store/paramsSlice';
 import { shouldUseNSFWCheckerChanged, shouldUseWatermarkerChanged } from 'features/system/store/systemSlice';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 
 export const addAppConfigReceivedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: appInfoApi.endpoints.getAppConfig.matchFulfilled,
-    effect: async (action, { getState, dispatch }) => {
+    effect: (action, { getState, dispatch }) => {
       const { infill_methods = [], nsfw_methods = [], watermarking_methods = [] } = action.payload;
-      const infillMethod = getState().generation.infillMethod;
+      const infillMethod = getState().params.infillMethod;
 
       if (!infill_methods.includes(infillMethod)) {
         // if there is no infill method, set it to the first one

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

@@ -6,7 +6,7 @@ export const appStarted = createAction('app/appStarted');
 export const addAppStartedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: appStarted,
-    effect: async (action, { unsubscribe, cancelActiveListeners }) => {
+    effect: (action, { unsubscribe, cancelActiveListeners }) => {
       // this should only run once
       cancelActiveListeners();
       unsubscribe();

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

@@ -1,27 +1,30 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { parseify } from 'common/util/serialize';
+import type { SerializableObject } from 'common/types';
 import { zPydanticValidationError } from 'features/system/store/zodSchemas';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { truncate, upperFirst } from 'lodash-es';
+import { serializeError } from 'serialize-error';
 import { queueApi } from 'services/api/endpoints/queue';
 
+const log = logger('queue');
+
 export const addBatchEnqueuedListener = (startAppListening: AppStartListening) => {
   // success
   startAppListening({
     matcher: queueApi.endpoints.enqueueBatch.matchFulfilled,
-    effect: async (action) => {
-      const response = action.payload;
+    effect: (action) => {
+      const enqueueResult = action.payload;
       const arg = action.meta.arg.originalArgs;
-      logger('queue').debug({ enqueueResult: parseify(response) }, 'Batch enqueued');
+      log.debug({ enqueueResult } as SerializableObject, 'Batch enqueued');
 
       toast({
         id: 'QUEUE_BATCH_SUCCEEDED',
         title: t('queue.batchQueued'),
         status: 'success',
         description: t('queue.batchQueuedDesc', {
-          count: response.enqueued,
+          count: enqueueResult.enqueued,
           direction: arg.prepend ? t('queue.front') : t('queue.back'),
         }),
       });
@@ -31,9 +34,9 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
   // error
   startAppListening({
     matcher: queueApi.endpoints.enqueueBatch.matchRejected,
-    effect: async (action) => {
+    effect: (action) => {
       const response = action.payload;
-      const arg = action.meta.arg.originalArgs;
+      const batchConfig = action.meta.arg.originalArgs;
 
       if (!response) {
         toast({
@@ -42,7 +45,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
           status: 'error',
           description: t('common.unknownError'),
         });
-        logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));
+        log.error({ batchConfig } as SerializableObject, t('queue.batchFailedToQueue'));
         return;
       }
 
@@ -68,7 +71,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
           description: t('common.unknownError'),
         });
       }
-      logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));
+      log.error({ batchConfig, error: serializeError(response) } as SerializableObject, t('queue.batchFailedToQueue'));
     },
   });
 };

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

@@ -1,47 +1,31 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { resetCanvas } from 'features/canvas/store/canvasSlice';
-import { controlAdaptersReset } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { allLayersDeleted } from 'features/controlLayers/store/controlLayersSlice';
+import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import { getImageUsage } from 'features/deleteImageModal/store/selectors';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
+import { selectNodesSlice } from 'features/nodes/store/selectors';
 import { imagesApi } from 'services/api/endpoints/images';
 
 export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.deleteBoardAndImages.matchFulfilled,
-    effect: async (action, { dispatch, getState }) => {
+    effect: (action, { dispatch, getState }) => {
       const { deleted_images } = action.payload;
 
       // Remove all deleted images from the UI
 
-      let wasCanvasReset = false;
       let wasNodeEditorReset = false;
-      let wereControlAdaptersReset = false;
-      let wereControlLayersReset = false;
 
-      const { canvas, nodes, controlAdapters, controlLayers } = getState();
+      const state = getState();
+      const nodes = selectNodesSlice(state);
+      const canvas = selectCanvasSlice(state);
+
       deleted_images.forEach((image_name) => {
-        const imageUsage = getImageUsage(canvas, nodes.present, controlAdapters, controlLayers.present, image_name);
-
-        if (imageUsage.isCanvasImage && !wasCanvasReset) {
-          dispatch(resetCanvas());
-          wasCanvasReset = true;
-        }
+        const imageUsage = getImageUsage(nodes, canvas, image_name);
 
         if (imageUsage.isNodesImage && !wasNodeEditorReset) {
           dispatch(nodeEditorReset());
           wasNodeEditorReset = true;
         }
-
-        if (imageUsage.isControlImage && !wereControlAdaptersReset) {
-          dispatch(controlAdaptersReset());
-          wereControlAdaptersReset = true;
-        }
-
-        if (imageUsage.isControlLayerImage && !wereControlLayersReset) {
-          dispatch(allLayersDeleted());
-          wereControlLayersReset = true;
-        }
       });
     },
   });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/bulkDownload.tsx

@@ -1,21 +1,15 @@
-import { ExternalLink } from '@invoke-ai/ui-library';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
-import {
-  socketBulkDownloadComplete,
-  socketBulkDownloadError,
-  socketBulkDownloadStarted,
-} from 'services/events/actions';
 
-const log = logger('images');
+const log = logger('gallery');
 
 export const addBulkDownloadListeners = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.bulkDownloadImages.matchFulfilled,
-    effect: async (action) => {
+    effect: (action) => {
       log.debug(action.payload, 'Bulk download requested');
 
       // If we have an item name, we are processing the bulk download locally and should use it as the toast id to
@@ -33,7 +27,7 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
 
   startAppListening({
     matcher: imagesApi.endpoints.bulkDownloadImages.matchRejected,
-    effect: async () => {
+    effect: () => {
       log.debug('Bulk download request failed');
 
       // There isn't any toast to update if we get this event.
@@ -44,55 +38,4 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
       });
     },
   });
-
-  startAppListening({
-    actionCreator: socketBulkDownloadStarted,
-    effect: async (action) => {
-      // This should always happen immediately after the bulk download request, so we don't need to show a toast here.
-      log.debug(action.payload.data, 'Bulk download preparation started');
-    },
-  });
-
-  startAppListening({
-    actionCreator: socketBulkDownloadComplete,
-    effect: async (action) => {
-      log.debug(action.payload.data, 'Bulk download preparation completed');
-
-      const { bulk_download_item_name } = action.payload.data;
-
-      // TODO(psyche): This URL may break in in some environments (e.g. Nvidia workbench) but we need to test it first
-      const url = `/api/v1/images/download/${bulk_download_item_name}`;
-
-      toast({
-        id: bulk_download_item_name,
-        title: t('gallery.bulkDownloadReady', 'Download ready'),
-        status: 'success',
-        description: (
-          <ExternalLink
-            label={t('gallery.clickToDownload', 'Click here to download')}
-            href={url}
-            download={bulk_download_item_name}
-          />
-        ),
-        duration: null,
-      });
-    },
-  });
-
-  startAppListening({
-    actionCreator: socketBulkDownloadError,
-    effect: async (action) => {
-      log.debug(action.payload.data, 'Bulk download preparation failed');
-
-      const { bulk_download_item_name } = action.payload.data;
-
-      toast({
-        id: bulk_download_item_name,
-        title: t('gallery.bulkDownloadFailed'),
-        status: 'error',
-        description: action.payload.data.error,
-        duration: null,
-      });
-    },
-  });
 };

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

@@ -1,38 +0,0 @@
-import { $logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { canvasCopiedToClipboard } from 'features/canvas/store/actions';
-import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
-import { copyBlobToClipboard } from 'features/system/util/copyBlobToClipboard';
-import { toast } from 'features/toast/toast';
-import { t } from 'i18next';
-
-export const addCanvasCopiedToClipboardListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: canvasCopiedToClipboard,
-    effect: async (action, { getState }) => {
-      const moduleLog = $logger.get().child({ namespace: 'canvasCopiedToClipboardListener' });
-      const state = getState();
-
-      try {
-        const blob = getBaseLayerBlob(state);
-
-        copyBlobToClipboard(blob);
-      } catch (err) {
-        moduleLog.error(String(err));
-        toast({
-          id: 'CANVAS_COPY_FAILED',
-          title: t('toast.problemCopyingCanvas'),
-          description: t('toast.problemCopyingCanvasDesc'),
-          status: 'error',
-        });
-        return;
-      }
-
-      toast({
-        id: 'CANVAS_COPY_SUCCEEDED',
-        title: t('toast.canvasCopiedClipboard'),
-        status: 'success',
-      });
-    },
-  });
-};

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

@@ -1,34 +0,0 @@
-import { $logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { canvasDownloadedAsImage } from 'features/canvas/store/actions';
-import { downloadBlob } from 'features/canvas/util/downloadBlob';
-import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
-import { toast } from 'features/toast/toast';
-import { t } from 'i18next';
-
-export const addCanvasDownloadedAsImageListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: canvasDownloadedAsImage,
-    effect: async (action, { getState }) => {
-      const moduleLog = $logger.get().child({ namespace: 'canvasSavedToGalleryListener' });
-      const state = getState();
-
-      let blob;
-      try {
-        blob = await getBaseLayerBlob(state);
-      } catch (err) {
-        moduleLog.error(String(err));
-        toast({
-          id: 'CANVAS_DOWNLOAD_FAILED',
-          title: t('toast.problemDownloadingCanvas'),
-          description: t('toast.problemDownloadingCanvasDesc'),
-          status: 'error',
-        });
-        return;
-      }
-
-      downloadBlob(blob, 'canvas.png');
-      toast({ id: 'CANVAS_DOWNLOAD_SUCCEEDED', title: t('toast.canvasDownloaded'), status: 'success' });
-    },
-  });
-};

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

@@ -1,60 +0,0 @@
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { canvasImageToControlAdapter } from 'features/canvas/store/actions';
-import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
-import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { toast } from 'features/toast/toast';
-import { t } from 'i18next';
-import { imagesApi } from 'services/api/endpoints/images';
-
-export const addCanvasImageToControlNetListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: canvasImageToControlAdapter,
-    effect: async (action, { dispatch, getState }) => {
-      const log = logger('canvas');
-      const state = getState();
-      const { id } = action.payload;
-
-      let blob: Blob;
-      try {
-        blob = await getBaseLayerBlob(state, true);
-      } catch (err) {
-        log.error(String(err));
-        toast({
-          id: 'PROBLEM_SAVING_CANVAS',
-          title: t('toast.problemSavingCanvas'),
-          description: t('toast.problemSavingCanvasDesc'),
-          status: 'error',
-        });
-        return;
-      }
-
-      const { autoAddBoardId } = state.gallery;
-
-      const imageDTO = await dispatch(
-        imagesApi.endpoints.uploadImage.initiate({
-          file: new File([blob], 'savedCanvas.png', {
-            type: 'image/png',
-          }),
-          image_category: 'control',
-          is_intermediate: true,
-          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-          crop_visible: false,
-          postUploadAction: {
-            type: 'TOAST',
-            title: t('toast.canvasSentControlnetAssets'),
-          },
-        })
-      ).unwrap();
-
-      const { image_name } = imageDTO;
-
-      dispatch(
-        controlAdapterImageChanged({
-          id,
-          controlImage: image_name,
-        })
-      );
-    },
-  });
-};

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

@@ -1,60 +0,0 @@
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { canvasMaskSavedToGallery } from 'features/canvas/store/actions';
-import { getCanvasData } from 'features/canvas/util/getCanvasData';
-import { toast } from 'features/toast/toast';
-import { t } from 'i18next';
-import { imagesApi } from 'services/api/endpoints/images';
-
-export const addCanvasMaskSavedToGalleryListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: canvasMaskSavedToGallery,
-    effect: async (action, { dispatch, getState }) => {
-      const log = logger('canvas');
-      const state = getState();
-
-      const canvasBlobsAndImageData = await getCanvasData(
-        state.canvas.layerState,
-        state.canvas.boundingBoxCoordinates,
-        state.canvas.boundingBoxDimensions,
-        state.canvas.isMaskEnabled,
-        state.canvas.shouldPreserveMaskedArea
-      );
-
-      if (!canvasBlobsAndImageData) {
-        return;
-      }
-
-      const { maskBlob } = canvasBlobsAndImageData;
-
-      if (!maskBlob) {
-        log.error('Problem getting mask layer blob');
-        toast({
-          id: 'PROBLEM_SAVING_MASK',
-          title: t('toast.problemSavingMask'),
-          description: t('toast.problemSavingMaskDesc'),
-          status: 'error',
-        });
-        return;
-      }
-
-      const { autoAddBoardId } = state.gallery;
-
-      dispatch(
-        imagesApi.endpoints.uploadImage.initiate({
-          file: new File([maskBlob], 'canvasMaskImage.png', {
-            type: 'image/png',
-          }),
-          image_category: 'mask',
-          is_intermediate: false,
-          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-          crop_visible: true,
-          postUploadAction: {
-            type: 'TOAST',
-            title: t('toast.maskSavedAssets'),
-          },
-        })
-      );
-    },
-  });
-};

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

@@ -1,70 +0,0 @@
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { canvasMaskToControlAdapter } from 'features/canvas/store/actions';
-import { getCanvasData } from 'features/canvas/util/getCanvasData';
-import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { toast } from 'features/toast/toast';
-import { t } from 'i18next';
-import { imagesApi } from 'services/api/endpoints/images';
-
-export const addCanvasMaskToControlNetListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: canvasMaskToControlAdapter,
-    effect: async (action, { dispatch, getState }) => {
-      const log = logger('canvas');
-      const state = getState();
-      const { id } = action.payload;
-      const canvasBlobsAndImageData = await getCanvasData(
-        state.canvas.layerState,
-        state.canvas.boundingBoxCoordinates,
-        state.canvas.boundingBoxDimensions,
-        state.canvas.isMaskEnabled,
-        state.canvas.shouldPreserveMaskedArea
-      );
-
-      if (!canvasBlobsAndImageData) {
-        return;
-      }
-
-      const { maskBlob } = canvasBlobsAndImageData;
-
-      if (!maskBlob) {
-        log.error('Problem getting mask layer blob');
-        toast({
-          id: 'PROBLEM_IMPORTING_MASK',
-          title: t('toast.problemImportingMask'),
-          description: t('toast.problemImportingMaskDesc'),
-          status: 'error',
-        });
-        return;
-      }
-
-      const { autoAddBoardId } = state.gallery;
-
-      const imageDTO = await dispatch(
-        imagesApi.endpoints.uploadImage.initiate({
-          file: new File([maskBlob], 'canvasMaskImage.png', {
-            type: 'image/png',
-          }),
-          image_category: 'mask',
-          is_intermediate: true,
-          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-          crop_visible: false,
-          postUploadAction: {
-            type: 'TOAST',
-            title: t('toast.maskSentControlnetAssets'),
-          },
-        })
-      ).unwrap();
-
-      const { image_name } = imageDTO;
-
-      dispatch(
-        controlAdapterImageChanged({
-          id,
-          controlImage: image_name,
-        })
-      );
-    },
-  });
-};

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

@@ -1,73 +0,0 @@
-import { $logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { canvasMerged } from 'features/canvas/store/actions';
-import { $canvasBaseLayer } from 'features/canvas/store/canvasNanostore';
-import { setMergedCanvas } from 'features/canvas/store/canvasSlice';
-import { getFullBaseLayerBlob } from 'features/canvas/util/getFullBaseLayerBlob';
-import { toast } from 'features/toast/toast';
-import { t } from 'i18next';
-import { imagesApi } from 'services/api/endpoints/images';
-
-export const addCanvasMergedListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: canvasMerged,
-    effect: async (action, { dispatch }) => {
-      const moduleLog = $logger.get().child({ namespace: 'canvasCopiedToClipboardListener' });
-      const blob = await getFullBaseLayerBlob();
-
-      if (!blob) {
-        moduleLog.error('Problem getting base layer blob');
-        toast({
-          id: 'PROBLEM_MERGING_CANVAS',
-          title: t('toast.problemMergingCanvas'),
-          description: t('toast.problemMergingCanvasDesc'),
-          status: 'error',
-        });
-        return;
-      }
-
-      const canvasBaseLayer = $canvasBaseLayer.get();
-
-      if (!canvasBaseLayer) {
-        moduleLog.error('Problem getting canvas base layer');
-        toast({
-          id: 'PROBLEM_MERGING_CANVAS',
-          title: t('toast.problemMergingCanvas'),
-          description: t('toast.problemMergingCanvasDesc'),
-          status: 'error',
-        });
-        return;
-      }
-
-      const baseLayerRect = canvasBaseLayer.getClientRect({
-        relativeTo: canvasBaseLayer.getParent() ?? undefined,
-      });
-
-      const imageDTO = await dispatch(
-        imagesApi.endpoints.uploadImage.initiate({
-          file: new File([blob], 'mergedCanvas.png', {
-            type: 'image/png',
-          }),
-          image_category: 'general',
-          is_intermediate: true,
-          postUploadAction: {
-            type: 'TOAST',
-            title: t('toast.canvasMerged'),
-          },
-        })
-      ).unwrap();
-
-      // TODO: I can't figure out how to do the type narrowing in the `take()` so just brute forcing it here
-      const { image_name } = imageDTO;
-
-      dispatch(
-        setMergedCanvas({
-          kind: 'image',
-          layer: 'base',
-          imageName: image_name,
-          ...baseLayerRect,
-        })
-      );
-    },
-  });
-};

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

@@ -1,53 +0,0 @@
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { parseify } from 'common/util/serialize';
-import { canvasSavedToGallery } from 'features/canvas/store/actions';
-import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
-import { toast } from 'features/toast/toast';
-import { t } from 'i18next';
-import { imagesApi } from 'services/api/endpoints/images';
-
-export const addCanvasSavedToGalleryListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: canvasSavedToGallery,
-    effect: async (action, { dispatch, getState }) => {
-      const log = logger('canvas');
-      const state = getState();
-
-      let blob;
-      try {
-        blob = await getBaseLayerBlob(state);
-      } catch (err) {
-        log.error(String(err));
-        toast({
-          id: 'CANVAS_SAVE_FAILED',
-          title: t('toast.problemSavingCanvas'),
-          description: t('toast.problemSavingCanvasDesc'),
-          status: 'error',
-        });
-        return;
-      }
-
-      const { autoAddBoardId } = state.gallery;
-
-      dispatch(
-        imagesApi.endpoints.uploadImage.initiate({
-          file: new File([blob], 'savedCanvas.png', {
-            type: 'image/png',
-          }),
-          image_category: 'general',
-          is_intermediate: false,
-          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-          crop_visible: true,
-          postUploadAction: {
-            type: 'TOAST',
-            title: t('toast.canvasSavedGallery'),
-          },
-          metadata: {
-            _canvas_objects: parseify(state.canvas.layerState.objects),
-          },
-        })
-      );
-    },
-  });
-};

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

@@ -1,194 +0,0 @@
-import { isAnyOf } from '@reduxjs/toolkit';
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { AppDispatch } from 'app/store/store';
-import { parseify } from 'common/util/serialize';
-import {
-  caLayerImageChanged,
-  caLayerModelChanged,
-  caLayerProcessedImageChanged,
-  caLayerProcessorConfigChanged,
-  caLayerProcessorPendingBatchIdChanged,
-  caLayerRecalled,
-  isControlAdapterLayer,
-} from 'features/controlLayers/store/controlLayersSlice';
-import { CA_PROCESSOR_DATA } from 'features/controlLayers/util/controlAdapters';
-import { toast } from 'features/toast/toast';
-import { t } from 'i18next';
-import { isEqual } from 'lodash-es';
-import { getImageDTO } from 'services/api/endpoints/images';
-import { queueApi } from 'services/api/endpoints/queue';
-import type { BatchConfig } from 'services/api/types';
-import { socketInvocationComplete } from 'services/events/actions';
-import { assert } from 'tsafe';
-
-const matcher = isAnyOf(
-  caLayerImageChanged,
-  caLayerProcessedImageChanged,
-  caLayerProcessorConfigChanged,
-  caLayerModelChanged,
-  caLayerRecalled
-);
-
-const DEBOUNCE_MS = 300;
-const log = logger('session');
-
-/**
- * Simple helper to cancel a batch and reset the pending batch ID
- */
-const cancelProcessorBatch = async (dispatch: AppDispatch, layerId: string, batchId: string) => {
-  const req = dispatch(queueApi.endpoints.cancelByBatchIds.initiate({ batch_ids: [batchId] }));
-  log.trace({ batchId }, 'Cancelling existing preprocessor batch');
-  try {
-    await req.unwrap();
-  } catch {
-    // no-op
-  } finally {
-    req.reset();
-    // Always reset the pending batch ID - the cancel req could fail if the batch doesn't exist
-    dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
-  }
-};
-
-export const addControlAdapterPreprocessor = (startAppListening: AppStartListening) => {
-  startAppListening({
-    matcher,
-    effect: async (action, { dispatch, getState, getOriginalState, cancelActiveListeners, delay, take, signal }) => {
-      const layerId = caLayerRecalled.match(action) ? action.payload.id : action.payload.layerId;
-      const state = getState();
-      const originalState = getOriginalState();
-
-      // Cancel any in-progress instances of this listener
-      cancelActiveListeners();
-      log.trace('Control Layer CA auto-process triggered');
-
-      // Delay before starting actual work
-      await delay(DEBOUNCE_MS);
-
-      const layer = state.controlLayers.present.layers.filter(isControlAdapterLayer).find((l) => l.id === layerId);
-
-      if (!layer) {
-        return;
-      }
-
-      // We should only process if the processor settings or image have changed
-      const originalLayer = originalState.controlLayers.present.layers
-        .filter(isControlAdapterLayer)
-        .find((l) => l.id === layerId);
-      const originalImage = originalLayer?.controlAdapter.image;
-      const originalConfig = originalLayer?.controlAdapter.processorConfig;
-
-      const image = layer.controlAdapter.image;
-      const processedImage = layer.controlAdapter.processedImage;
-      const config = layer.controlAdapter.processorConfig;
-
-      if (isEqual(config, originalConfig) && isEqual(image, originalImage) && processedImage) {
-        // Neither config nor image have changed, we can bail
-        return;
-      }
-
-      if (!image || !config) {
-        // - If we have no image, we have nothing to process
-        // - If we have no processor config, we have nothing to process
-        // Clear the processed image and bail
-        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO: null }));
-        return;
-      }
-
-      // At this point, the user has stopped fiddling with the processor settings and there is a processor selected.
-
-      // If there is a pending processor batch, cancel it.
-      if (layer.controlAdapter.processorPendingBatchId) {
-        cancelProcessorBatch(dispatch, layerId, layer.controlAdapter.processorPendingBatchId);
-      }
-
-      // TODO(psyche): I can't get TS to be happy, it thinkgs `config` is `never` but it should be inferred from the generic... I'll just cast it for now
-      const processorNode = CA_PROCESSOR_DATA[config.type].buildNode(image, config as never);
-      const enqueueBatchArg: BatchConfig = {
-        prepend: true,
-        batch: {
-          graph: {
-            nodes: {
-              [processorNode.id]: {
-                ...processorNode,
-                // Control images are always intermediate - do not save to gallery
-                is_intermediate: true,
-              },
-            },
-            edges: [],
-          },
-          runs: 1,
-        },
-      };
-
-      // Kick off the processor batch
-      const req = dispatch(
-        queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
-          fixedCacheKey: 'enqueueBatch',
-        })
-      );
-
-      try {
-        const enqueueResult = await req.unwrap();
-        // TODO(psyche): Update the pydantic models, pretty sure we will _always_ have a batch_id here, but the model says it's optional
-        assert(enqueueResult.batch.batch_id, 'Batch ID not returned from queue');
-        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: enqueueResult.batch.batch_id }));
-        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
-
-        // Wait for the processor node to complete
-        const [invocationCompleteAction] = await take(
-          (action): action is ReturnType<typeof socketInvocationComplete> =>
-            socketInvocationComplete.match(action) &&
-            action.payload.data.batch_id === enqueueResult.batch.batch_id &&
-            action.payload.data.invocation_source_id === processorNode.id
-        );
-
-        // We still have to check the output type
-        assert(
-          invocationCompleteAction.payload.data.result.type === 'image_output',
-          `Processor did not return an image output, got: ${invocationCompleteAction.payload.data.result}`
-        );
-        const { image_name } = invocationCompleteAction.payload.data.result.image;
-
-        const imageDTO = await getImageDTO(image_name);
-        assert(imageDTO, "Failed to fetch processor output's image DTO");
-
-        // Whew! We made it. Update the layer with the processed image
-        log.debug({ layerId, imageDTO }, 'ControlNet image processed');
-        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO }));
-        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
-      } catch (error) {
-        if (signal.aborted) {
-          // The listener was canceled - we need to cancel the pending processor batch, if there is one (could have changed by now).
-          const pendingBatchId = getState()
-            .controlLayers.present.layers.filter(isControlAdapterLayer)
-            .find((l) => l.id === layerId)?.controlAdapter.processorPendingBatchId;
-          if (pendingBatchId) {
-            cancelProcessorBatch(dispatch, layerId, pendingBatchId);
-          }
-          log.trace('Control Adapter preprocessor cancelled');
-        } else {
-          // Some other error condition...
-          log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
-
-          if (error instanceof Object) {
-            if ('data' in error && 'status' in error) {
-              if (error.status === 403) {
-                dispatch(caLayerImageChanged({ layerId, imageDTO: null }));
-                return;
-              }
-            }
-          }
-
-          toast({
-            id: 'GRAPH_QUEUE_FAILED',
-            title: t('queue.graphFailedToQueue'),
-            status: 'error',
-          });
-        }
-      } finally {
-        req.reset();
-      }
-    },
-  });
-};

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

@@ -1,85 +0,0 @@
-import type { AnyListenerPredicate } from '@reduxjs/toolkit';
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { RootState } from 'app/store/store';
-import { controlAdapterImageProcessed } from 'features/controlAdapters/store/actions';
-import {
-  controlAdapterAutoConfigToggled,
-  controlAdapterImageChanged,
-  controlAdapterModelChanged,
-  controlAdapterProcessorParamsChanged,
-  controlAdapterProcessortTypeChanged,
-  selectControlAdapterById,
-} from 'features/controlAdapters/store/controlAdaptersSlice';
-import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
-
-type AnyControlAdapterParamChangeAction =
-  | ReturnType<typeof controlAdapterProcessorParamsChanged>
-  | ReturnType<typeof controlAdapterModelChanged>
-  | ReturnType<typeof controlAdapterImageChanged>
-  | ReturnType<typeof controlAdapterProcessortTypeChanged>
-  | ReturnType<typeof controlAdapterAutoConfigToggled>;
-
-const predicate: AnyListenerPredicate<RootState> = (action, state, prevState) => {
-  const isActionMatched =
-    controlAdapterProcessorParamsChanged.match(action) ||
-    controlAdapterModelChanged.match(action) ||
-    controlAdapterImageChanged.match(action) ||
-    controlAdapterProcessortTypeChanged.match(action) ||
-    controlAdapterAutoConfigToggled.match(action);
-
-  if (!isActionMatched) {
-    return false;
-  }
-
-  const { id } = action.payload;
-  const prevCA = selectControlAdapterById(prevState.controlAdapters, id);
-  const ca = selectControlAdapterById(state.controlAdapters, id);
-  if (!prevCA || !isControlNetOrT2IAdapter(prevCA) || !ca || !isControlNetOrT2IAdapter(ca)) {
-    return false;
-  }
-
-  if (controlAdapterAutoConfigToggled.match(action)) {
-    // do not process if the user just disabled auto-config
-    if (prevCA.shouldAutoConfig === true) {
-      return false;
-    }
-  }
-
-  const { controlImage, processorType, shouldAutoConfig } = ca;
-  if (controlAdapterModelChanged.match(action) && !shouldAutoConfig) {
-    // do not process if the action is a model change but the processor settings are dirty
-    return false;
-  }
-
-  const isProcessorSelected = processorType !== 'none';
-
-  const hasControlImage = Boolean(controlImage);
-
-  return isProcessorSelected && hasControlImage;
-};
-
-const DEBOUNCE_MS = 300;
-
-/**
- * Listener that automatically processes a ControlNet image when its processor parameters are changed.
- *
- * The network request is debounced.
- */
-export const addControlNetAutoProcessListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    predicate,
-    effect: async (action, { dispatch, cancelActiveListeners, delay }) => {
-      const log = logger('session');
-      const { id } = (action as AnyControlAdapterParamChangeAction).payload;
-
-      // Cancel any in-progress instances of this listener
-      cancelActiveListeners();
-      log.trace('ControlNet auto-process triggered');
-      // Delay before starting actual work
-      await delay(DEBOUNCE_MS);
-
-      dispatch(controlAdapterImageProcessed({ id }));
-    },
-  });
-};

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

@@ -1,118 +0,0 @@
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { parseify } from 'common/util/serialize';
-import { controlAdapterImageProcessed } from 'features/controlAdapters/store/actions';
-import {
-  controlAdapterImageChanged,
-  controlAdapterProcessedImageChanged,
-  pendingControlImagesCleared,
-  selectControlAdapterById,
-} from 'features/controlAdapters/store/controlAdaptersSlice';
-import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
-import { toast } from 'features/toast/toast';
-import { t } from 'i18next';
-import { imagesApi } from 'services/api/endpoints/images';
-import { queueApi } from 'services/api/endpoints/queue';
-import type { BatchConfig, ImageDTO } from 'services/api/types';
-import { socketInvocationComplete } from 'services/events/actions';
-
-export const addControlNetImageProcessedListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: controlAdapterImageProcessed,
-    effect: async (action, { dispatch, getState, take }) => {
-      const log = logger('session');
-      const { id } = action.payload;
-      const ca = selectControlAdapterById(getState().controlAdapters, id);
-
-      if (!ca?.controlImage || !isControlNetOrT2IAdapter(ca)) {
-        log.error('Unable to process ControlNet image');
-        return;
-      }
-
-      if (ca.processorType === 'none' || ca.processorNode.type === 'none') {
-        return;
-      }
-
-      // ControlNet one-off procressing graph is just the processor node, no edges.
-      // Also we need to grab the image.
-
-      const nodeId = ca.processorNode.id;
-      const enqueueBatchArg: BatchConfig = {
-        prepend: true,
-        batch: {
-          graph: {
-            nodes: {
-              [ca.processorNode.id]: {
-                ...ca.processorNode,
-                is_intermediate: true,
-                use_cache: false,
-                image: { image_name: ca.controlImage },
-              },
-            },
-            edges: [],
-          },
-          runs: 1,
-        },
-      };
-
-      try {
-        const req = dispatch(
-          queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
-            fixedCacheKey: 'enqueueBatch',
-          })
-        );
-        const enqueueResult = await req.unwrap();
-        req.reset();
-        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
-
-        const [invocationCompleteAction] = await take(
-          (action): action is ReturnType<typeof socketInvocationComplete> =>
-            socketInvocationComplete.match(action) &&
-            action.payload.data.batch_id === enqueueResult.batch.batch_id &&
-            action.payload.data.invocation_source_id === nodeId
-        );
-
-        // We still have to check the output type
-        if (invocationCompleteAction.payload.data.result.type === 'image_output') {
-          const { image_name } = invocationCompleteAction.payload.data.result.image;
-
-          // Wait for the ImageDTO to be received
-          const [{ payload }] = await take(
-            (action) =>
-              imagesApi.endpoints.getImageDTO.matchFulfilled(action) && action.payload.image_name === image_name
-          );
-
-          const processedControlImage = payload as ImageDTO;
-
-          log.debug({ controlNetId: action.payload, processedControlImage }, 'ControlNet image processed');
-
-          // Update the processed image in the store
-          dispatch(
-            controlAdapterProcessedImageChanged({
-              id,
-              processedControlImage: processedControlImage.image_name,
-            })
-          );
-        }
-      } catch (error) {
-        log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
-
-        if (error instanceof Object) {
-          if ('data' in error && 'status' in error) {
-            if (error.status === 403) {
-              dispatch(pendingControlImagesCleared());
-              dispatch(controlAdapterImageChanged({ id, controlImage: null }));
-              return;
-            }
-          }
-        }
-
-        toast({
-          id: 'GRAPH_QUEUE_FAILED',
-          title: t('queue.graphFailedToQueue'),
-          status: 'error',
-        });
-      }
-    },
-  });
-};

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

@@ -1,144 +0,0 @@
-import { logger } from 'app/logging/logger';
-import { enqueueRequested } from 'app/store/actions';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
-import { parseify } from 'common/util/serialize';
-import { canvasBatchIdAdded, stagingAreaInitialized } from 'features/canvas/store/canvasSlice';
-import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
-import { getCanvasData } from 'features/canvas/util/getCanvasData';
-import { getCanvasGenerationMode } from 'features/canvas/util/getCanvasGenerationMode';
-import { canvasGraphBuilt } from 'features/nodes/store/actions';
-import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildCanvasGraph } from 'features/nodes/util/graph/canvas/buildCanvasGraph';
-import { imagesApi } from 'services/api/endpoints/images';
-import { queueApi } from 'services/api/endpoints/queue';
-import type { ImageDTO } from 'services/api/types';
-
-/**
- * This listener is responsible invoking the canvas. This involves a number of steps:
- *
- * 1. Generate image blobs from the canvas layers
- * 2. Determine the generation mode from the layers (txt2img, img2img, inpaint)
- * 3. Build the canvas graph
- * 4. Create the session with the graph
- * 5. Upload the init image if necessary
- * 6. Upload the mask image if necessary
- * 7. Update the init and mask images with the session ID
- * 8. Initialize the staging area if not yet initialized
- * 9. Dispatch the sessionReadyToInvoke action to invoke the session
- */
-export const addEnqueueRequestedCanvasListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    predicate: (action): action is ReturnType<typeof enqueueRequested> =>
-      enqueueRequested.match(action) && action.payload.tabName === 'canvas',
-    effect: async (action, { getState, dispatch }) => {
-      const log = logger('queue');
-      const { prepend } = action.payload;
-      const state = getState();
-
-      const { layerState, boundingBoxCoordinates, boundingBoxDimensions, isMaskEnabled, shouldPreserveMaskedArea } =
-        state.canvas;
-
-      // Build canvas blobs
-      const canvasBlobsAndImageData = await getCanvasData(
-        layerState,
-        boundingBoxCoordinates,
-        boundingBoxDimensions,
-        isMaskEnabled,
-        shouldPreserveMaskedArea
-      );
-
-      if (!canvasBlobsAndImageData) {
-        log.error('Unable to create canvas data');
-        return;
-      }
-
-      const { baseBlob, baseImageData, maskBlob, maskImageData } = canvasBlobsAndImageData;
-
-      // Determine the generation mode
-      const generationMode = getCanvasGenerationMode(baseImageData, maskImageData);
-
-      if (state.system.enableImageDebugging) {
-        const baseDataURL = await blobToDataURL(baseBlob);
-        const maskDataURL = await blobToDataURL(maskBlob);
-        openBase64ImageInTab([
-          { base64: maskDataURL, caption: 'mask b64' },
-          { base64: baseDataURL, caption: 'image b64' },
-        ]);
-      }
-
-      log.debug(`Generation mode: ${generationMode}`);
-
-      // Temp placeholders for the init and mask images
-      let canvasInitImage: ImageDTO | undefined;
-      let canvasMaskImage: ImageDTO | undefined;
-
-      // For img2img and inpaint/outpaint, we need to upload the init images
-      if (['img2img', 'inpaint', 'outpaint'].includes(generationMode)) {
-        // upload the image, saving the request id
-        canvasInitImage = await dispatch(
-          imagesApi.endpoints.uploadImage.initiate({
-            file: new File([baseBlob], 'canvasInitImage.png', {
-              type: 'image/png',
-            }),
-            image_category: 'general',
-            is_intermediate: true,
-          })
-        ).unwrap();
-      }
-
-      // For inpaint/outpaint, we also need to upload the mask layer
-      if (['inpaint', 'outpaint'].includes(generationMode)) {
-        // upload the image, saving the request id
-        canvasMaskImage = await dispatch(
-          imagesApi.endpoints.uploadImage.initiate({
-            file: new File([maskBlob], 'canvasMaskImage.png', {
-              type: 'image/png',
-            }),
-            image_category: 'mask',
-            is_intermediate: true,
-          })
-        ).unwrap();
-      }
-
-      const graph = await buildCanvasGraph(state, generationMode, canvasInitImage, canvasMaskImage);
-
-      log.debug({ graph: parseify(graph) }, `Canvas graph built`);
-
-      // currently this action is just listened to for logging
-      dispatch(canvasGraphBuilt(graph));
-
-      const batchConfig = prepareLinearUIBatch(state, graph, prepend);
-
-      try {
-        const req = dispatch(
-          queueApi.endpoints.enqueueBatch.initiate(batchConfig, {
-            fixedCacheKey: 'enqueueBatch',
-          })
-        );
-
-        const enqueueResult = await req.unwrap();
-        req.reset();
-
-        const batchId = enqueueResult.batch.batch_id as string; // we know the is a string, backend provides it
-
-        // Prep the canvas staging area if it is not yet initialized
-        if (!state.canvas.layerState.stagingArea.boundingBox) {
-          dispatch(
-            stagingAreaInitialized({
-              boundingBox: {
-                ...state.canvas.boundingBoxCoordinates,
-                ...state.canvas.boundingBoxDimensions,
-              },
-            })
-          );
-        }
-
-        // Associate the session with the canvas session ID
-        dispatch(canvasBatchIdAdded(batchId));
-      } catch {
-        // no-op
-      }
-    },
-  });
-};

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

@@ -1,10 +1,21 @@
+import { logger } from 'app/logging/logger';
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
+import type { SerializableObject } from 'common/types';
+import type { Result } from 'common/util/result';
+import { isErr, withResult, withResultAsync } from 'common/util/result';
+import { $canvasManager } from 'features/controlLayers/konva/CanvasManager';
+import { sessionStagingAreaReset, sessionStartedStaging } from 'features/controlLayers/store/canvasSessionSlice';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildGenerationTabGraph } from 'features/nodes/util/graph/generation/buildGenerationTabGraph';
-import { buildGenerationTabSDXLGraph } from 'features/nodes/util/graph/generation/buildGenerationTabSDXLGraph';
+import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph';
+import { buildSDXLGraph } from 'features/nodes/util/graph/generation/buildSDXLGraph';
+import type { Graph } from 'features/nodes/util/graph/generation/Graph';
+import { serializeError } from 'serialize-error';
 import { queueApi } from 'services/api/endpoints/queue';
+import type { Invocation } from 'services/api/types';
+import { assert } from 'tsafe';
+
+const log = logger('generation');
 
 export const addEnqueueRequestedLinear = (startAppListening: AppStartListening) => {
   startAppListening({
@@ -12,33 +23,81 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
       enqueueRequested.match(action) && action.payload.tabName === 'generation',
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
-      const { shouldShowProgressInViewer } = state.ui;
-      const model = state.generation.model;
+      const model = state.params.model;
       const { prepend } = action.payload;
 
-      let graph;
+      const manager = $canvasManager.get();
+      assert(manager, 'No model found in state');
 
-      if (model?.base === 'sdxl') {
-        graph = await buildGenerationTabSDXLGraph(state);
-      } else {
-        graph = await buildGenerationTabGraph(state);
+      let didStartStaging = false;
+
+      if (!state.canvasSession.isStaging && state.canvasSession.sendToCanvas) {
+        dispatch(sessionStartedStaging());
+        didStartStaging = true;
       }
 
-      const batchConfig = prepareLinearUIBatch(state, graph, prepend);
+      const abortStaging = () => {
+        if (didStartStaging && getState().canvasSession.isStaging) {
+          dispatch(sessionStagingAreaReset());
+        }
+      };
+
+      let buildGraphResult: Result<
+        { g: Graph; noise: Invocation<'noise'>; posCond: Invocation<'compel' | 'sdxl_compel_prompt'> },
+        Error
+      >;
+
+      assert(model, 'No model found in state');
+      const base = model.base;
+
+      switch (base) {
+        case 'sdxl':
+          buildGraphResult = await withResultAsync(() => buildSDXLGraph(state, manager));
+          break;
+        case 'sd-1':
+        case `sd-2`:
+          buildGraphResult = await withResultAsync(() => buildSD1Graph(state, manager));
+          break;
+        default:
+          assert(false, `No graph builders for base ${base}`);
+      }
+
+      if (isErr(buildGraphResult)) {
+        log.error({ error: serializeError(buildGraphResult.error) }, 'Failed to build graph');
+        abortStaging();
+        return;
+      }
+
+      const { g, noise, posCond } = buildGraphResult.value;
+
+      const destination = state.canvasSession.sendToCanvas ? 'canvas' : 'gallery';
+
+      const prepareBatchResult = withResult(() =>
+        prepareLinearUIBatch(state, g, prepend, noise, posCond, 'generation', destination)
+      );
+
+      if (isErr(prepareBatchResult)) {
+        log.error({ error: serializeError(prepareBatchResult.error) }, 'Failed to prepare batch');
+        abortStaging();
+        return;
+      }
 
       const req = dispatch(
-        queueApi.endpoints.enqueueBatch.initiate(batchConfig, {
+        queueApi.endpoints.enqueueBatch.initiate(prepareBatchResult.value, {
           fixedCacheKey: 'enqueueBatch',
         })
       );
-      try {
-        await req.unwrap();
-        if (shouldShowProgressInViewer) {
-          dispatch(isImageViewerOpenChanged(true));
-        }
-      } finally {
-        req.reset();
+      req.reset();
+
+      const enqueueResult = await withResultAsync(() => req.unwrap());
+
+      if (isErr(enqueueResult)) {
+        log.error({ error: serializeError(enqueueResult.error) }, 'Failed to enqueue batch');
+        abortStaging();
+        return;
       }
+
+      log.debug({ batchConfig: prepareBatchResult.value } as SerializableObject, 'Enqueued batch');
     },
   });
 };

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

@@ -1,5 +1,6 @@
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { selectNodesSlice } from 'features/nodes/store/selectors';
 import { buildNodesGraph } from 'features/nodes/util/graph/buildNodesGraph';
 import { buildWorkflowWithValidation } from 'features/nodes/util/workflow/buildWorkflow';
 import { queueApi } from 'services/api/endpoints/queue';
@@ -11,12 +12,12 @@ export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) =
       enqueueRequested.match(action) && action.payload.tabName === 'workflows',
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
-      const { nodes, edges } = state.nodes.present;
+      const nodes = selectNodesSlice(state);
       const workflow = state.workflow;
-      const graph = buildNodesGraph(state.nodes.present);
+      const graph = buildNodesGraph(nodes);
       const builtWorkflow = buildWorkflowWithValidation({
-        nodes,
-        edges,
+        nodes: nodes.nodes,
+        edges: nodes.edges,
         workflow,
       });
 
@@ -29,7 +30,9 @@ export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) =
         batch: {
           graph,
           workflow: builtWorkflow,
-          runs: state.generation.iterations,
+          runs: state.params.iterations,
+          origin: 'workflows',
+          destination: 'gallery',
         },
         prepend: action.payload.prepend,
       };

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

@@ -14,9 +14,9 @@ export const addEnqueueRequestedUpscale = (startAppListening: AppStartListening)
       const { shouldShowProgressInViewer } = state.ui;
       const { prepend } = action.payload;
 
-      const graph = await buildMultidiffusionUpscaleGraph(state);
+      const { g, noise, posCond } = await buildMultidiffusionUpscaleGraph(state);
 
-      const batchConfig = prepareLinearUIBatch(state, graph, prepend);
+      const batchConfig = prepareLinearUIBatch(state, g, prepend, noise, posCond, 'upscaling', 'gallery');
 
       const req = dispatch(
         queueApi.endpoints.enqueueBatch.initiate(batchConfig, {

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

@@ -27,7 +27,7 @@ export const galleryImageClicked = createAction<{
 export const addGalleryImageClickedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: galleryImageClicked,
-    effect: async (action, { dispatch, getState }) => {
+    effect: (action, { dispatch, getState }) => {
       const { imageDTO, shiftKey, ctrlKey, metaKey, altKey } = action.payload;
       const state = getState();
       const queryArgs = selectListImagesQueryArgs(state);

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

@@ -1,24 +1,27 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { SerializableObject } from 'common/types';
 import { parseify } from 'common/util/serialize';
 import { $templates } from 'features/nodes/store/nodesSlice';
 import { parseSchema } from 'features/nodes/util/schema/parseSchema';
 import { size } from 'lodash-es';
+import { serializeError } from 'serialize-error';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 
+const log = logger('system');
+
 export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: appInfoApi.endpoints.getOpenAPISchema.matchFulfilled,
     effect: (action, { getState }) => {
-      const log = logger('system');
       const schemaJSON = action.payload;
 
-      log.debug({ schemaJSON: parseify(schemaJSON) }, 'Received OpenAPI schema');
+      log.debug({ schemaJSON: parseify(schemaJSON) } as SerializableObject, 'Received OpenAPI schema');
       const { nodesAllowlist, nodesDenylist } = getState().config;
 
       const nodeTemplates = parseSchema(schemaJSON, nodesAllowlist, nodesDenylist);
 
-      log.debug({ nodeTemplates: parseify(nodeTemplates) }, `Built ${size(nodeTemplates)} node templates`);
+      log.debug({ nodeTemplates } as SerializableObject, `Built ${size(nodeTemplates)} node templates`);
 
       $templates.set(nodeTemplates);
     },
@@ -30,8 +33,7 @@ export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening
       // If action.meta.condition === true, the request was canceled/skipped because another request was in flight or
       // the value was already in the cache. We don't want to log these errors.
       if (!action.meta.condition) {
-        const log = logger('system');
-        log.error({ error: parseify(action.error) }, 'Problem retrieving OpenAPI Schema');
+        log.error({ error: serializeError(action.error) }, 'Problem retrieving OpenAPI Schema');
       }
     },
   });