chore(ui): lint

Canvas dev build.

.github/workflows/build-container.yml

@@ -13,12 +13,6 @@ on:
     tags:
       - 'v*.*.*'
   workflow_dispatch:
-    inputs:
-      push-to-registry:
-        description: Push the built image to the container registry
-        required: false
-        type: boolean
-        default: false
 
 permissions:
   contents: write
@@ -56,15 +50,16 @@ jobs:
           df -h
 
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       - name: Docker meta
         id: meta
-        uses: docker/metadata-action@v5
+        uses: docker/metadata-action@v4
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           images: |
             ghcr.io/${{ github.repository }}
+            ${{ env.DOCKERHUB_REPOSITORY }}
           tags: |
             type=ref,event=branch
             type=ref,event=tag
@@ -77,33 +72,49 @@ jobs:
             suffix=-${{ matrix.gpu-driver }},onlatest=false
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
+        uses: docker/setup-qemu-action@v2
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
         with:
           platforms: ${{ env.PLATFORMS }}
 
       - name: Login to GitHub Container Registry
         if: github.event_name != 'pull_request'
-        uses: docker/login-action@v3
+        uses: docker/login-action@v2
         with:
           registry: ghcr.io
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
+      # - name: Login to Docker Hub
+      #   if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
+      #   uses: docker/login-action@v2
+      #   with:
+      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
+      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
+
       - name: Build container
         timeout-minutes: 40
         id: docker_build
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v4
         with:
           context: .
           file: docker/Dockerfile
           platforms: ${{ env.PLATFORMS }}
-          push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' || github.event.inputs.push-to-registry }}
+          push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' }}
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
           cache-from: |
             type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
             type=gha,scope=main-${{ matrix.gpu-driver }}
           cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
+
+      # - name: Docker Hub Description
+      #   if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
+      #   uses: peter-evans/dockerhub-description@v3
+      #   with:
+      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
+      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
+      #     repository: ${{ vars.DOCKERHUB_REPOSITORY }}
+      #     short-description: ${{ github.event.repository.description }}

.github/workflows/python-tests.yml

@@ -60,7 +60,7 @@ jobs:
             extra-index-url: 'https://download.pytorch.org/whl/cpu'
             github-env: $GITHUB_ENV
           - platform: macos-default
-            os: macOS-14
+            os: macOS-12
             github-env: $GITHUB_ENV
           - platform: windows-cpu
             os: windows-2022

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/denoise_latents.py

@@ -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

@@ -40,18 +40,14 @@ class UIType(str, Enum, metaclass=MetaEnum):
 
     # region Model Field Types
     MainModel = "MainModelField"
-    FluxMainModel = "FluxMainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
     VAEModel = "VAEModelField"
-    FluxVAEModel = "FluxVAEModelField"
     LoRAModel = "LoRAModelField"
     ControlNetModel = "ControlNetModelField"
     IPAdapterModel = "IPAdapterModelField"
     T2IAdapterModel = "T2IAdapterModelField"
-    T5EncoderModel = "T5EncoderModelField"
-    CLIPEmbedModel = "CLIPEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
     # endregion
 
@@ -129,17 +125,13 @@ class FieldDescriptions:
     negative_cond = "Negative conditioning tensor"
     noise = "Noise tensor"
     clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
-    t5_encoder = "T5 tokenizer and text encoder"
-    clip_embed_model = "CLIP Embed loader"
     unet = "UNet (scheduler, LoRAs)"
-    transformer = "Transformer"
     vae = "VAE"
     cond = "Conditioning tensor"
     controlnet_model = "ControlNet model to load"
     vae_model = "VAE model to load"
     lora_model = "LoRA model to load"
     main_model = "Main model (UNet, VAE, CLIP) to load"
-    flux_model = "Flux model (Transformer) to load"
     sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
@@ -181,7 +173,7 @@ 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"
@@ -239,12 +231,6 @@ class ColorField(BaseModel):
         return (self.r, self.g, self.b, self.a)
 
 
-class FluxConditioningField(BaseModel):
-    """A conditioning tensor primitive value"""
-
-    conditioning_name: str = Field(description="The name of conditioning tensor")
-
-
 class ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 

invokeai/app/invocations/flux_denoise.py

@@ -1,249 +0,0 @@
-from typing import Callable, Optional
-
-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.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.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
-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 as transformer:
-            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 _build_step_callback(self, context: InvocationContext) -> Callable[[PipelineIntermediateState], None]:
-        def step_callback(state: PipelineIntermediateState) -> None:
-            state.latents = unpack(state.latents.float(), self.height, self.width).squeeze()
-            context.util.flux_step_callback(state)
-
-        return step_callback

invokeai/app/invocations/flux_text_encoder.py

@@ -1,92 +0,0 @@
-from typing import Literal
-
-import torch
-from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
-from invokeai.app.invocations.model import CLIPField, T5EncoderField
-from invokeai.app.invocations.primitives import FluxConditioningOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.modules.conditioner import HFEncoder
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
-
-
-@invocation(
-    "flux_text_encoder",
-    title="FLUX Text Encoding",
-    tags=["prompt", "conditioning", "flux"],
-    category="conditioning",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class FluxTextEncoderInvocation(BaseInvocation):
-    """Encodes and preps a prompt for a flux image."""
-
-    clip: CLIPField = InputField(
-        title="CLIP",
-        description=FieldDescriptions.clip,
-        input=Input.Connection,
-    )
-    t5_encoder: T5EncoderField = InputField(
-        title="T5Encoder",
-        description=FieldDescriptions.t5_encoder,
-        input=Input.Connection,
-    )
-    t5_max_seq_len: Literal[256, 512] = InputField(
-        description="Max sequence length for the T5 encoder. Expected to be 256 for FLUX schnell models and 512 for FLUX dev models."
-    )
-    prompt: str = InputField(description="Text prompt to encode.")
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> FluxConditioningOutput:
-        # Note: The T5 and CLIP encoding are done in separate functions to ensure that all model references are locally
-        # scoped. This ensures that the T5 model can be freed and gc'd before loading the CLIP model (if necessary).
-        t5_embeddings = self._t5_encode(context)
-        clip_embeddings = self._clip_encode(context)
-        conditioning_data = ConditioningFieldData(
-            conditionings=[FLUXConditioningInfo(clip_embeds=clip_embeddings, t5_embeds=t5_embeddings)]
-        )
-
-        conditioning_name = context.conditioning.save(conditioning_data)
-        return FluxConditioningOutput.build(conditioning_name)
-
-    def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
-        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
-        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
-
-        prompt = [self.prompt]
-
-        with (
-            t5_text_encoder_info as t5_text_encoder,
-            t5_tokenizer_info as t5_tokenizer,
-        ):
-            assert isinstance(t5_text_encoder, T5EncoderModel)
-            assert isinstance(t5_tokenizer, T5Tokenizer)
-
-            t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, self.t5_max_seq_len)
-
-            prompt_embeds = t5_encoder(prompt)
-
-        assert isinstance(prompt_embeds, torch.Tensor)
-        return prompt_embeds
-
-    def _clip_encode(self, context: InvocationContext) -> torch.Tensor:
-        clip_tokenizer_info = context.models.load(self.clip.tokenizer)
-        clip_text_encoder_info = context.models.load(self.clip.text_encoder)
-
-        prompt = [self.prompt]
-
-        with (
-            clip_text_encoder_info as clip_text_encoder,
-            clip_tokenizer_info as clip_tokenizer,
-        ):
-            assert isinstance(clip_text_encoder, CLIPTextModel)
-            assert isinstance(clip_tokenizer, CLIPTokenizer)
-
-            clip_encoder = HFEncoder(clip_text_encoder, clip_tokenizer, True, 77)
-
-            pooled_prompt_embeds = clip_encoder(prompt)
-
-        assert isinstance(pooled_prompt_embeds, torch.Tensor)
-        return pooled_prompt_embeds

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

@@ -1,5 +1,5 @@
 import copy
-from typing import List, Literal, Optional
+from typing import List, Optional
 
 from pydantic import BaseModel, Field
 
@@ -13,14 +13,7 @@ from invokeai.app.invocations.baseinvocation import (
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.flux.util import max_seq_lengths
-from invokeai.backend.model_manager.config import (
-    AnyModelConfig,
-    BaseModelType,
-    CheckpointConfigBase,
-    ModelType,
-    SubModelType,
-)
+from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType, SubModelType
 
 
 class ModelIdentifierField(BaseModel):
@@ -67,15 +60,6 @@ class CLIPField(BaseModel):
     loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
-class TransformerField(BaseModel):
-    transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
-
-
-class T5EncoderField(BaseModel):
-    tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel")
-    text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
-
-
 class VAEField(BaseModel):
     vae: ModelIdentifierField = Field(description="Info to load vae submodel")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
@@ -138,78 +122,6 @@ class ModelIdentifierInvocation(BaseInvocation):
         return ModelIdentifierOutput(model=self.model)
 
 
-@invocation_output("flux_model_loader_output")
-class FluxModelLoaderOutput(BaseInvocationOutput):
-    """Flux base model loader output"""
-
-    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
-    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
-    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
-    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
-    max_seq_len: Literal[256, 512] = OutputField(
-        description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
-        title="Max Seq Length",
-    )
-
-
-@invocation(
-    "flux_model_loader",
-    title="Flux Main Model",
-    tags=["model", "flux"],
-    category="model",
-    version="1.0.4",
-    classification=Classification.Prototype,
-)
-class FluxModelLoaderInvocation(BaseInvocation):
-    """Loads a flux base model, outputting its submodels."""
-
-    model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.flux_model,
-        ui_type=UIType.FluxMainModel,
-        input=Input.Direct,
-    )
-
-    t5_encoder_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
-    )
-
-    clip_embed_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.clip_embed_model,
-        ui_type=UIType.CLIPEmbedModel,
-        input=Input.Direct,
-        title="CLIP Embed",
-    )
-
-    vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
-    )
-
-    def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
-        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
-            if not context.models.exists(key):
-                raise ValueError(f"Unknown model: {key}")
-
-        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
-        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
-
-        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-
-        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
-        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
-
-        transformer_config = context.models.get_config(transformer)
-        assert isinstance(transformer_config, CheckpointConfigBase)
-
-        return FluxModelLoaderOutput(
-            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),
-            max_seq_len=max_seq_lengths[transformer_config.config_path],
-        )
-
-
 @invocation(
     "main_model_loader",
     title="Main Model",

invokeai/app/invocations/primitives.py

@@ -12,7 +12,6 @@ from invokeai.app.invocations.fields import (
     ConditioningField,
     DenoiseMaskField,
     FieldDescriptions,
-    FluxConditioningField,
     ImageField,
     Input,
     InputField,
@@ -415,17 +414,6 @@ class MaskOutput(BaseInvocationOutput):
     height: int = OutputField(description="The height of the mask in pixels.")
 
 
-@invocation_output("flux_conditioning_output")
-class FluxConditioningOutput(BaseInvocationOutput):
-    """Base class for nodes that output a single conditioning tensor"""
-
-    conditioning: FluxConditioningField = OutputField(description=FieldDescriptions.cond)
-
-    @classmethod
-    def build(cls, conditioning_name: str) -> "FluxConditioningOutput":
-        return cls(conditioning=FluxConditioningField(conditioning_name=conditioning_name))
-
-
 @invocation_output("conditioning_output")
 class ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single conditioning tensor"""

invokeai/app/services/events/events_common.py

@@ -88,6 +88,7 @@ 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 batch")
 
 
 class InvocationEventBase(QueueItemEventBase):
@@ -95,8 +96,6 @@ 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 +113,7 @@ 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,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -147,6 +147,7 @@ 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,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -184,6 +185,7 @@ 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,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -216,6 +218,7 @@ 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,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -253,6 +256,7 @@ 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,
             session_id=queue_item.session_id,
             status=queue_item.status,
             error_type=queue_item.error_type,
@@ -279,12 +283,14 @@ 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/model_install/model_install_default.py

@@ -783,9 +783,8 @@ class ModelInstallService(ModelInstallServiceBase):
         # So what we do is to synthesize a folder named "sdxl-turbo_vae" here.
         if subfolder:
             top = Path(remote_files[0].path.parts[0])  # e.g. "sdxl-turbo/"
-            path_to_remove = top / subfolder  # sdxl-turbo/vae/
-            subfolder_rename = subfolder.name.replace("/", "_").replace("\\", "_")
-            path_to_add = Path(f"{top}_{subfolder_rename}")
+            path_to_remove = top / subfolder.parts[-1]  # sdxl-turbo/vae/
+            path_to_add = Path(f"{top}_{subfolder}")
         else:
             path_to_remove = Path(".")
             path_to_add = Path(".")

invokeai/app/services/model_records/model_records_base.py

@@ -77,7 +77,6 @@ class ModelRecordChanges(BaseModelExcludeNull):
     type: Optional[ModelType] = Field(description="Type of model", default=None)
     key: Optional[str] = Field(description="Database ID for this model", default=None)
     hash: Optional[str] = Field(description="hash of model file", default=None)
-    format: Optional[str] = Field(description="format of model file", default=None)
     trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
     default_settings: Optional[MainModelDefaultSettings | ControlAdapterDefaultSettings] = Field(
         description="Default settings for this model", default=None

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 +77,7 @@ 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 batch.")
     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 +196,7 @@ 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. ")
     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 +296,7 @@ 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")
     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 +331,12 @@ 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 +442,7 @@ class SessionQueueValueToInsert(NamedTuple):
     field_values: Optional[str]  # field_values json
     priority: int  # priority
     workflow: Optional[str]  # workflow json
+    origin: str | None
 
 
 ValuesToInsert: TypeAlias = list[SessionQueueValueToInsert]
@@ -453,6 +463,7 @@ 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
             )
         )
     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)
+                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 +578,8 @@ class SqliteSessionQueue(SessionQueueBase):
                     started_at,
                     session_id,
                     batch_id,
-                    queue_id
+                    queue_id,
+                    origin
                 FROM session_queue
                 WHERE queue_id = ?
             """
@@ -621,7 +659,7 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.acquire()
             self.__cursor.execute(
                 """--sql
-                SELECT status, count(*)
+                SELECT status, count(*), origin
                 FROM session_queue
                 WHERE
                   queue_id = ?
@@ -633,6 +671,7 @@ 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
         except Exception:
             self.__conn.rollback()
             raise
@@ -641,6 +680,7 @@ class SqliteSessionQueue(SessionQueueBase):
 
         return BatchStatus(
             batch_id=batch_id,
+            origin=origin,
             queue_id=queue_id,
             pending=counts.get("pending", 0),
             in_progress=counts.get("in_progress", 0),

invokeai/app/services/shared/invocation_context.py

@@ -14,7 +14,7 @@ from invokeai.app.services.image_records.image_records_common import ImageCatego
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.model_records.model_records_base import UnknownModelException
-from invokeai.app.util.step_callback import flux_step_callback, stable_diffusion_step_callback
+from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.model_manager.config import (
     AnyModel,
     AnyModelConfig,
@@ -557,24 +557,6 @@ class UtilInterface(InvocationContextInterface):
             is_canceled=self.is_canceled,
         )
 
-    def flux_step_callback(self, intermediate_state: PipelineIntermediateState) -> None:
-        """
-        The step callback emits a progress event with the current step, the total number of
-        steps, a preview image, and some other internal metadata.
-
-        This should be called after each denoising step.
-
-        Args:
-            intermediate_state: The intermediate state of the diffusion pipeline.
-        """
-
-        flux_step_callback(
-            context_data=self._data,
-            intermediate_state=intermediate_state,
-            events=self._services.events,
-            is_canceled=self.is_canceled,
-        )
-
 
 class InvocationContext:
     """Provides access to various services and data for the current invocation.

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 +1,31 @@
+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.
+        """
+
+        cursor.execute("ALTER TABLE session_queue ADD COLUMN origin 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.
+    """
+    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

@@ -1,326 +0,0 @@
-{
-  "name": "FLUX Text to Image",
-  "author": "InvokeAI",
-  "description": "A simple text-to-image workflow using FLUX dev or schnell models.",
-  "version": "1.0.4",
-  "contact": "",
-  "tags": "text2image, flux",
-  "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 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
-  "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": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "fieldName": "prompt"
-    },
-    {
-      "nodeId": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "fieldName": "num_steps"
-    }
-  ],
-  "meta": {
-    "version": "3.0.0",
-    "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",
-      "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": ""
-          },
-          "t5_encoder_model": {
-            "name": "t5_encoder_model",
-            "label": ""
-          },
-          "clip_embed_model": {
-            "name": "clip_embed_model",
-            "label": ""
-          },
-          "vae_model": {
-            "name": "vae_model",
-            "label": ""
-          }
-        }
-      },
-      "position": {
-        "x": 381.1882713063478,
-        "y": -95.89663532854017
-      }
-    },
-    {
-      "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"
-          }
-        }
-      },
-      "position": {
-        "x": 778.4899149328337,
-        "y": -100.36469216659502
-      }
-    },
-    {
-      "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": {
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-            "label": "",
-            "value": 2147483647
-          }
-        }
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-        "x": 800.9667463219505,
-        "y": 285.8297267547506
-      }
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-  "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"
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-      "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"
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-      "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"
-    },
-    {
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-      "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/util/step_callback.py

@@ -38,25 +38,6 @@ SD1_5_LATENT_RGB_FACTORS = [
     [-0.1307, -0.1874, -0.7445],  # L4
 ]
 
-FLUX_LATENT_RGB_FACTORS = [
-    [-0.0412, 0.0149, 0.0521],
-    [0.0056, 0.0291, 0.0768],
-    [0.0342, -0.0681, -0.0427],
-    [-0.0258, 0.0092, 0.0463],
-    [0.0863, 0.0784, 0.0547],
-    [-0.0017, 0.0402, 0.0158],
-    [0.0501, 0.1058, 0.1152],
-    [-0.0209, -0.0218, -0.0329],
-    [-0.0314, 0.0083, 0.0896],
-    [0.0851, 0.0665, -0.0472],
-    [-0.0534, 0.0238, -0.0024],
-    [0.0452, -0.0026, 0.0048],
-    [0.0892, 0.0831, 0.0881],
-    [-0.1117, -0.0304, -0.0789],
-    [0.0027, -0.0479, -0.0043],
-    [-0.1146, -0.0827, -0.0598],
-]
-
 
 def sample_to_lowres_estimated_image(
     samples: torch.Tensor, latent_rgb_factors: torch.Tensor, smooth_matrix: Optional[torch.Tensor] = None
@@ -113,32 +94,3 @@ def stable_diffusion_step_callback(
         intermediate_state,
         ProgressImage(dataURL=dataURL, width=width, height=height),
     )
-
-
-def flux_step_callback(
-    context_data: "InvocationContextData",
-    intermediate_state: PipelineIntermediateState,
-    events: "EventServiceBase",
-    is_canceled: Callable[[], bool],
-) -> None:
-    if is_canceled():
-        raise CanceledException
-    sample = intermediate_state.latents
-    latent_rgb_factors = torch.tensor(FLUX_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
-    latent_image_perm = sample.permute(1, 2, 0).to(dtype=sample.dtype, device=sample.device)
-    latent_image = latent_image_perm @ latent_rgb_factors
-    latents_ubyte = (
-        ((latent_image + 1) / 2).clamp(0, 1).mul(0xFF)  # change scale from -1..1 to 0..1  # to 0..255
-    ).to(device="cpu", dtype=torch.uint8)
-    image = Image.fromarray(latents_ubyte.cpu().numpy())
-    (width, height) = image.size
-    width *= 8
-    height *= 8
-    dataURL = image_to_dataURL(image, image_format="JPEG")
-
-    events.emit_invocation_denoise_progress(
-        context_data.queue_item,
-        context_data.invocation,
-        intermediate_state,
-        ProgressImage(dataURL=dataURL, width=width, height=height),
-    )

invokeai/backend/flux/denoise.py

@@ -1,56 +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
-from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
-
-
-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[[PipelineIntermediateState], None],
-    guidance: float,
-    inpaint_extension: InpaintExtension | None,
-):
-    step = 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,
-        )
-        preview_img = img - t_curr * pred
-        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(
-            PipelineIntermediateState(
-                step=step,
-                order=1,
-                total_steps=len(timesteps),
-                timestep=int(t_curr),
-                latents=preview_img,
-            ),
-        )
-        step += 1
-
-    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/math.py

@@ -1,32 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-import torch
-from einops import rearrange
-from torch import Tensor
-
-
-def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
-    q, k = apply_rope(q, k, pe)
-
-    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
-    x = rearrange(x, "B H L D -> B L (H D)")
-
-    return x
-
-
-def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
-    assert dim % 2 == 0
-    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
-    omega = 1.0 / (theta**scale)
-    out = torch.einsum("...n,d->...nd", pos, omega)
-    out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
-    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
-    return out.float()
-
-
-def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
-    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
-    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
-    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
-    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
-    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)

invokeai/backend/flux/model.py

@@ -1,117 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-from dataclasses import dataclass
-
-import torch
-from torch import Tensor, nn
-
-from invokeai.backend.flux.modules.layers import (
-    DoubleStreamBlock,
-    EmbedND,
-    LastLayer,
-    MLPEmbedder,
-    SingleStreamBlock,
-    timestep_embedding,
-)
-
-
-@dataclass
-class FluxParams:
-    in_channels: int
-    vec_in_dim: int
-    context_in_dim: int
-    hidden_size: int
-    mlp_ratio: float
-    num_heads: int
-    depth: int
-    depth_single_blocks: int
-    axes_dim: list[int]
-    theta: int
-    qkv_bias: bool
-    guidance_embed: bool
-
-
-class Flux(nn.Module):
-    """
-    Transformer model for flow matching on sequences.
-    """
-
-    def __init__(self, params: FluxParams):
-        super().__init__()
-
-        self.params = params
-        self.in_channels = params.in_channels
-        self.out_channels = self.in_channels
-        if params.hidden_size % params.num_heads != 0:
-            raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
-        pe_dim = params.hidden_size // params.num_heads
-        if sum(params.axes_dim) != pe_dim:
-            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
-        self.hidden_size = params.hidden_size
-        self.num_heads = params.num_heads
-        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
-        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
-        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
-        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
-        self.guidance_in = (
-            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
-        )
-        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
-
-        self.double_blocks = nn.ModuleList(
-            [
-                DoubleStreamBlock(
-                    self.hidden_size,
-                    self.num_heads,
-                    mlp_ratio=params.mlp_ratio,
-                    qkv_bias=params.qkv_bias,
-                )
-                for _ in range(params.depth)
-            ]
-        )
-
-        self.single_blocks = nn.ModuleList(
-            [
-                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
-                for _ in range(params.depth_single_blocks)
-            ]
-        )
-
-        self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
-
-    def forward(
-        self,
-        img: Tensor,
-        img_ids: Tensor,
-        txt: Tensor,
-        txt_ids: Tensor,
-        timesteps: Tensor,
-        y: Tensor,
-        guidance: Tensor | None = None,
-    ) -> Tensor:
-        if img.ndim != 3 or txt.ndim != 3:
-            raise ValueError("Input img and txt tensors must have 3 dimensions.")
-
-        # running on sequences img
-        img = self.img_in(img)
-        vec = self.time_in(timestep_embedding(timesteps, 256))
-        if self.params.guidance_embed:
-            if guidance is None:
-                raise ValueError("Didn't get guidance strength for guidance distilled model.")
-            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
-        vec = vec + self.vector_in(y)
-        txt = self.txt_in(txt)
-
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        pe = self.pe_embedder(ids)
-
-        for block in self.double_blocks:
-            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
-
-        img = torch.cat((txt, img), 1)
-        for block in self.single_blocks:
-            img = block(img, vec=vec, pe=pe)
-        img = img[:, txt.shape[1] :, ...]
-
-        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
-        return img

invokeai/backend/flux/modules/autoencoder.py

@@ -1,324 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-from dataclasses import dataclass
-
-import torch
-from einops import rearrange
-from torch import Tensor, nn
-
-
-@dataclass
-class AutoEncoderParams:
-    resolution: int
-    in_channels: int
-    ch: int
-    out_ch: int
-    ch_mult: list[int]
-    num_res_blocks: int
-    z_channels: int
-    scale_factor: float
-    shift_factor: float
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels: int):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-        self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1)
-        self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1)
-        self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1)
-        self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1)
-
-    def attention(self, h_: Tensor) -> Tensor:
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        b, c, h, w = q.shape
-        q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous()
-        k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous()
-        v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous()
-        h_ = nn.functional.scaled_dot_product_attention(q, k, v)
-
-        return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
-
-    def forward(self, x: Tensor) -> Tensor:
-        return x + self.proj_out(self.attention(x))
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, in_channels: int, out_channels: int):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-
-        self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
-        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        if self.in_channels != self.out_channels:
-            self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, x):
-        h = x
-        h = self.norm1(h)
-        h = torch.nn.functional.silu(h)
-        h = self.conv1(h)
-
-        h = self.norm2(h)
-        h = torch.nn.functional.silu(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            x = self.nin_shortcut(x)
-
-        return x + h
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels: int):
-        super().__init__()
-        # no asymmetric padding in torch conv, must do it ourselves
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
-
-    def forward(self, x: Tensor):
-        pad = (0, 1, 0, 1)
-        x = nn.functional.pad(x, pad, mode="constant", value=0)
-        x = self.conv(x)
-        return x
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels: int):
-        super().__init__()
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x: Tensor):
-        x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        x = self.conv(x)
-        return x
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        resolution: int,
-        in_channels: int,
-        ch: int,
-        ch_mult: list[int],
-        num_res_blocks: int,
-        z_channels: int,
-    ):
-        super().__init__()
-        self.ch = ch
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        # downsampling
-        self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,) + tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
-        block_in = self.ch
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch * in_ch_mult[i_level]
-            block_out = ch * ch_mult[i_level]
-            for _ in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
-                block_in = block_out
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions - 1:
-                down.downsample = Downsample(block_in)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-
-        # end
-        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
-        self.conv_out = nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x: Tensor) -> Tensor:
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1])
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions - 1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h)
-        # end
-        h = self.norm_out(h)
-        h = torch.nn.functional.silu(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        ch: int,
-        out_ch: int,
-        ch_mult: list[int],
-        num_res_blocks: int,
-        in_channels: int,
-        resolution: int,
-        z_channels: int,
-    ):
-        super().__init__()
-        self.ch = ch
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.ffactor = 2 ** (self.num_resolutions - 1)
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        block_in = ch * ch_mult[self.num_resolutions - 1]
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.z_shape = (1, z_channels, curr_res, curr_res)
-
-        # z to block_in
-        self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch * ch_mult[i_level]
-            for _ in range(self.num_res_blocks + 1):
-                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
-                block_in = block_out
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in)
-                curr_res = curr_res * 2
-            self.up.insert(0, up)  # prepend to get consistent order
-
-        # end
-        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
-        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, z: Tensor) -> Tensor:
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.up[i_level].block[i_block](h)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = torch.nn.functional.silu(h)
-        h = self.conv_out(h)
-        return h
-
-
-class DiagonalGaussian(nn.Module):
-    def __init__(self, chunk_dim: int = 1):
-        super().__init__()
-        self.chunk_dim = chunk_dim
-
-    def forward(self, z: Tensor, sample: bool = True, generator: torch.Generator | None = None) -> Tensor:
-        mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim)
-        if 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)
-        else:
-            return mean
-
-
-class AutoEncoder(nn.Module):
-    def __init__(self, params: AutoEncoderParams):
-        super().__init__()
-        self.encoder = Encoder(
-            resolution=params.resolution,
-            in_channels=params.in_channels,
-            ch=params.ch,
-            ch_mult=params.ch_mult,
-            num_res_blocks=params.num_res_blocks,
-            z_channels=params.z_channels,
-        )
-        self.decoder = Decoder(
-            resolution=params.resolution,
-            in_channels=params.in_channels,
-            ch=params.ch,
-            out_ch=params.out_ch,
-            ch_mult=params.ch_mult,
-            num_res_blocks=params.num_res_blocks,
-            z_channels=params.z_channels,
-        )
-        self.reg = DiagonalGaussian()
-
-        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)
-        z = self.scale_factor * (z - self.shift_factor)
-        return z
-
-    def decode(self, z: Tensor) -> Tensor:
-        z = z / self.scale_factor + self.shift_factor
-        return self.decoder(z)
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.decode(self.encode(x))

invokeai/backend/flux/modules/conditioner.py

@@ -1,33 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-from torch import Tensor, nn
-from transformers import PreTrainedModel, PreTrainedTokenizer
-
-
-class HFEncoder(nn.Module):
-    def __init__(self, encoder: PreTrainedModel, tokenizer: PreTrainedTokenizer, is_clip: bool, max_length: int):
-        super().__init__()
-        self.max_length = max_length
-        self.is_clip = is_clip
-        self.output_key = "pooler_output" if self.is_clip else "last_hidden_state"
-        self.tokenizer = tokenizer
-        self.hf_module = encoder
-        self.hf_module = self.hf_module.eval().requires_grad_(False)
-
-    def forward(self, text: list[str]) -> Tensor:
-        batch_encoding = self.tokenizer(
-            text,
-            truncation=True,
-            max_length=self.max_length,
-            return_length=False,
-            return_overflowing_tokens=False,
-            padding="max_length",
-            return_tensors="pt",
-        )
-
-        outputs = self.hf_module(
-            input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
-            attention_mask=None,
-            output_hidden_states=False,
-        )
-        return outputs[self.output_key]

invokeai/backend/flux/modules/layers.py

@@ -1,253 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-import math
-from dataclasses import dataclass
-
-import torch
-from einops import rearrange
-from torch import Tensor, nn
-
-from invokeai.backend.flux.math import attention, rope
-
-
-class EmbedND(nn.Module):
-    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
-        super().__init__()
-        self.dim = dim
-        self.theta = theta
-        self.axes_dim = axes_dim
-
-    def forward(self, ids: Tensor) -> Tensor:
-        n_axes = ids.shape[-1]
-        emb = torch.cat(
-            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
-            dim=-3,
-        )
-
-        return emb.unsqueeze(1)
-
-
-def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
-    """
-    Create sinusoidal timestep embeddings.
-    :param t: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an (N, D) Tensor of positional embeddings.
-    """
-    t = time_factor * t
-    half = dim // 2
-    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(t.device)
-
-    args = t[:, None].float() * freqs[None]
-    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-    if dim % 2:
-        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    if torch.is_floating_point(t):
-        embedding = embedding.to(t)
-    return embedding
-
-
-class MLPEmbedder(nn.Module):
-    def __init__(self, in_dim: int, hidden_dim: int):
-        super().__init__()
-        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
-        self.silu = nn.SiLU()
-        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.out_layer(self.silu(self.in_layer(x)))
-
-
-class RMSNorm(torch.nn.Module):
-    def __init__(self, dim: int):
-        super().__init__()
-        self.scale = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x: Tensor):
-        x_dtype = x.dtype
-        x = x.float()
-        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
-        return (x * rrms).to(dtype=x_dtype) * self.scale
-
-
-class QKNorm(torch.nn.Module):
-    def __init__(self, dim: int):
-        super().__init__()
-        self.query_norm = RMSNorm(dim)
-        self.key_norm = RMSNorm(dim)
-
-    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
-        q = self.query_norm(q)
-        k = self.key_norm(k)
-        return q.to(v), k.to(v)
-
-
-class SelfAttention(nn.Module):
-    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.norm = QKNorm(head_dim)
-        self.proj = nn.Linear(dim, dim)
-
-    def forward(self, x: Tensor, pe: Tensor) -> Tensor:
-        qkv = self.qkv(x)
-        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        q, k = self.norm(q, k, v)
-        x = attention(q, k, v, pe=pe)
-        x = self.proj(x)
-        return x
-
-
-@dataclass
-class ModulationOut:
-    shift: Tensor
-    scale: Tensor
-    gate: Tensor
-
-
-class Modulation(nn.Module):
-    def __init__(self, dim: int, double: bool):
-        super().__init__()
-        self.is_double = double
-        self.multiplier = 6 if double else 3
-        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
-
-    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
-        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
-
-        return (
-            ModulationOut(*out[:3]),
-            ModulationOut(*out[3:]) if self.is_double else None,
-        )
-
-
-class DoubleStreamBlock(nn.Module):
-    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False):
-        super().__init__()
-
-        mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        self.num_heads = num_heads
-        self.hidden_size = hidden_size
-        self.img_mod = Modulation(hidden_size, double=True)
-        self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
-
-        self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.img_mlp = nn.Sequential(
-            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
-            nn.GELU(approximate="tanh"),
-            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
-        )
-
-        self.txt_mod = Modulation(hidden_size, double=True)
-        self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
-
-        self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.txt_mlp = nn.Sequential(
-            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
-            nn.GELU(approximate="tanh"),
-            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
-        )
-
-    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
-        img_mod1, img_mod2 = self.img_mod(vec)
-        txt_mod1, txt_mod2 = self.txt_mod(vec)
-
-        # prepare image for attention
-        img_modulated = self.img_norm1(img)
-        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
-        img_qkv = self.img_attn.qkv(img_modulated)
-        img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
-
-        # prepare txt for attention
-        txt_modulated = self.txt_norm1(txt)
-        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
-        txt_qkv = self.txt_attn.qkv(txt_modulated)
-        txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
-
-        # run actual attention
-        q = torch.cat((txt_q, img_q), dim=2)
-        k = torch.cat((txt_k, img_k), dim=2)
-        v = torch.cat((txt_v, img_v), dim=2)
-
-        attn = attention(q, k, v, pe=pe)
-        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
-
-        # calculate the img bloks
-        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
-        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
-
-        # calculate the txt bloks
-        txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
-        txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
-        return img, txt
-
-
-class SingleStreamBlock(nn.Module):
-    """
-    A DiT block with parallel linear layers as described in
-    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        mlp_ratio: float = 4.0,
-        qk_scale: float | None = None,
-    ):
-        super().__init__()
-        self.hidden_dim = hidden_size
-        self.num_heads = num_heads
-        head_dim = hidden_size // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        # qkv and mlp_in
-        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
-        # proj and mlp_out
-        self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
-
-        self.norm = QKNorm(head_dim)
-
-        self.hidden_size = hidden_size
-        self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-
-        self.mlp_act = nn.GELU(approximate="tanh")
-        self.modulation = Modulation(hidden_size, double=False)
-
-    def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
-        mod, _ = self.modulation(vec)
-        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
-        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
-
-        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        q, k = self.norm(q, k, v)
-
-        # compute attention
-        attn = attention(q, k, v, pe=pe)
-        # compute activation in mlp stream, cat again and run second linear layer
-        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
-        return x + mod.gate * output
-
-
-class LastLayer(nn.Module):
-    def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
-        super().__init__()
-        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
-        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
-
-    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
-        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
-        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
-        x = self.linear(x)
-        return x

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/flux/util.py

@@ -1,71 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-from dataclasses import dataclass
-from typing import Dict, Literal
-
-from invokeai.backend.flux.model import FluxParams
-from invokeai.backend.flux.modules.autoencoder import AutoEncoderParams
-
-
-@dataclass
-class ModelSpec:
-    params: FluxParams
-    ae_params: AutoEncoderParams
-    ckpt_path: str | None
-    ae_path: str | None
-    repo_id: str | None
-    repo_flow: str | None
-    repo_ae: str | None
-
-
-max_seq_lengths: Dict[str, Literal[256, 512]] = {
-    "flux-dev": 512,
-    "flux-schnell": 256,
-}
-
-
-ae_params = {
-    "flux": AutoEncoderParams(
-        resolution=256,
-        in_channels=3,
-        ch=128,
-        out_ch=3,
-        ch_mult=[1, 2, 4, 4],
-        num_res_blocks=2,
-        z_channels=16,
-        scale_factor=0.3611,
-        shift_factor=0.1159,
-    )
-}
-
-
-params = {
-    "flux-dev": FluxParams(
-        in_channels=64,
-        vec_in_dim=768,
-        context_in_dim=4096,
-        hidden_size=3072,
-        mlp_ratio=4.0,
-        num_heads=24,
-        depth=19,
-        depth_single_blocks=38,
-        axes_dim=[16, 56, 56],
-        theta=10_000,
-        qkv_bias=True,
-        guidance_embed=True,
-    ),
-    "flux-schnell": FluxParams(
-        in_channels=64,
-        vec_in_dim=768,
-        context_in_dim=4096,
-        hidden_size=3072,
-        mlp_ratio=4.0,
-        num_heads=24,
-        depth=19,
-        depth_single_blocks=38,
-        axes_dim=[16, 56, 56],
-        theta=10_000,
-        qkv_bias=True,
-        guidance_embed=False,
-    ),
-}

invokeai/backend/model_manager/config.py

@@ -52,7 +52,6 @@ class BaseModelType(str, Enum):
     StableDiffusion2 = "sd-2"
     StableDiffusionXL = "sdxl"
     StableDiffusionXLRefiner = "sdxl-refiner"
-    Flux = "flux"
     # Kandinsky2_1 = "kandinsky-2.1"
 
 
@@ -67,9 +66,7 @@ class ModelType(str, Enum):
     TextualInversion = "embedding"
     IPAdapter = "ip_adapter"
     CLIPVision = "clip_vision"
-    CLIPEmbed = "clip_embed"
     T2IAdapter = "t2i_adapter"
-    T5Encoder = "t5_encoder"
     SpandrelImageToImage = "spandrel_image_to_image"
 
 
@@ -77,7 +74,6 @@ class SubModelType(str, Enum):
     """Submodel type."""
 
     UNet = "unet"
-    Transformer = "transformer"
     TextEncoder = "text_encoder"
     TextEncoder2 = "text_encoder_2"
     Tokenizer = "tokenizer"
@@ -108,9 +104,6 @@ class ModelFormat(str, Enum):
     EmbeddingFile = "embedding_file"
     EmbeddingFolder = "embedding_folder"
     InvokeAI = "invokeai"
-    T5Encoder = "t5_encoder"
-    BnbQuantizedLlmInt8b = "bnb_quantized_int8b"
-    BnbQuantizednf4b = "bnb_quantized_nf4b"
 
 
 class SchedulerPredictionType(str, Enum):
@@ -193,9 +186,7 @@ class ModelConfigBase(BaseModel):
 class CheckpointConfigBase(ModelConfigBase):
     """Model config for checkpoint-style models."""
 
-    format: Literal[ModelFormat.Checkpoint, ModelFormat.BnbQuantizednf4b] = Field(
-        description="Format of the provided checkpoint model", default=ModelFormat.Checkpoint
-    )
+    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
     config_path: str = Field(description="path to the checkpoint model config file")
     converted_at: Optional[float] = Field(
         description="When this model was last converted to diffusers", default_factory=time.time
@@ -214,26 +205,6 @@ class LoRAConfigBase(ModelConfigBase):
     trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
 
 
-class T5EncoderConfigBase(ModelConfigBase):
-    type: Literal[ModelType.T5Encoder] = ModelType.T5Encoder
-
-
-class T5EncoderConfig(T5EncoderConfigBase):
-    format: Literal[ModelFormat.T5Encoder] = ModelFormat.T5Encoder
-
-    @staticmethod
-    def get_tag() -> Tag:
-        return Tag(f"{ModelType.T5Encoder.value}.{ModelFormat.T5Encoder.value}")
-
-
-class T5EncoderBnbQuantizedLlmInt8bConfig(T5EncoderConfigBase):
-    format: Literal[ModelFormat.BnbQuantizedLlmInt8b] = ModelFormat.BnbQuantizedLlmInt8b
-
-    @staticmethod
-    def get_tag() -> Tag:
-        return Tag(f"{ModelType.T5Encoder.value}.{ModelFormat.BnbQuantizedLlmInt8b.value}")
-
-
 class LoRALyCORISConfig(LoRAConfigBase):
     """Model config for LoRA/Lycoris models."""
 
@@ -258,6 +229,7 @@ class VAECheckpointConfig(CheckpointConfigBase):
     """Model config for standalone VAE models."""
 
     type: Literal[ModelType.VAE] = ModelType.VAE
+    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
 
     @staticmethod
     def get_tag() -> Tag:
@@ -296,6 +268,7 @@ class ControlNetCheckpointConfig(CheckpointConfigBase, ControlAdapterConfigBase)
     """Model config for ControlNet models (diffusers version)."""
 
     type: Literal[ModelType.ControlNet] = ModelType.ControlNet
+    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
 
     @staticmethod
     def get_tag() -> Tag:
@@ -344,21 +317,6 @@ class MainCheckpointConfig(CheckpointConfigBase, MainConfigBase):
         return Tag(f"{ModelType.Main.value}.{ModelFormat.Checkpoint.value}")
 
 
-class MainBnbQuantized4bCheckpointConfig(CheckpointConfigBase, MainConfigBase):
-    """Model config for main checkpoint models."""
-
-    prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
-    upcast_attention: bool = False
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.format = ModelFormat.BnbQuantizednf4b
-
-    @staticmethod
-    def get_tag() -> Tag:
-        return Tag(f"{ModelType.Main.value}.{ModelFormat.BnbQuantizednf4b.value}")
-
-
 class MainDiffusersConfig(DiffusersConfigBase, MainConfigBase):
     """Model config for main diffusers models."""
 
@@ -392,17 +350,6 @@ class IPAdapterCheckpointConfig(IPAdapterBaseConfig):
         return Tag(f"{ModelType.IPAdapter.value}.{ModelFormat.Checkpoint.value}")
 
 
-class CLIPEmbedDiffusersConfig(DiffusersConfigBase):
-    """Model config for Clip Embeddings."""
-
-    type: Literal[ModelType.CLIPEmbed] = ModelType.CLIPEmbed
-    format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
-
-    @staticmethod
-    def get_tag() -> Tag:
-        return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}")
-
-
 class CLIPVisionDiffusersConfig(DiffusersConfigBase):
     """Model config for CLIPVision."""
 
@@ -461,15 +408,12 @@ AnyModelConfig = Annotated[
     Union[
         Annotated[MainDiffusersConfig, MainDiffusersConfig.get_tag()],
         Annotated[MainCheckpointConfig, MainCheckpointConfig.get_tag()],
-        Annotated[MainBnbQuantized4bCheckpointConfig, MainBnbQuantized4bCheckpointConfig.get_tag()],
         Annotated[VAEDiffusersConfig, VAEDiffusersConfig.get_tag()],
         Annotated[VAECheckpointConfig, VAECheckpointConfig.get_tag()],
         Annotated[ControlNetDiffusersConfig, ControlNetDiffusersConfig.get_tag()],
         Annotated[ControlNetCheckpointConfig, ControlNetCheckpointConfig.get_tag()],
         Annotated[LoRALyCORISConfig, LoRALyCORISConfig.get_tag()],
         Annotated[LoRADiffusersConfig, LoRADiffusersConfig.get_tag()],
-        Annotated[T5EncoderConfig, T5EncoderConfig.get_tag()],
-        Annotated[T5EncoderBnbQuantizedLlmInt8bConfig, T5EncoderBnbQuantizedLlmInt8bConfig.get_tag()],
         Annotated[TextualInversionFileConfig, TextualInversionFileConfig.get_tag()],
         Annotated[TextualInversionFolderConfig, TextualInversionFolderConfig.get_tag()],
         Annotated[IPAdapterInvokeAIConfig, IPAdapterInvokeAIConfig.get_tag()],
@@ -477,7 +421,6 @@ AnyModelConfig = Annotated[
         Annotated[T2IAdapterConfig, T2IAdapterConfig.get_tag()],
         Annotated[SpandrelImageToImageConfig, SpandrelImageToImageConfig.get_tag()],
         Annotated[CLIPVisionDiffusersConfig, CLIPVisionDiffusersConfig.get_tag()],
-        Annotated[CLIPEmbedDiffusersConfig, CLIPEmbedDiffusersConfig.get_tag()],
     ],
     Discriminator(get_model_discriminator_value),
 ]

invokeai/backend/model_manager/load/load_default.py

@@ -66,14 +66,12 @@ 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
 
         config.path = str(self._get_model_path(config))
-        self._ram_cache.make_room(self.get_size_fs(config, Path(config.path), submodel_type))
         loaded_model = self._load_model(config, submodel_type)
 
         self._ram_cache.put(
@@ -85,7 +83,7 @@ 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
@@ -210,6 +193,15 @@ class ModelCacheBase(ABC, Generic[T]):
         """
         pass
 
+    @abstractmethod
+    def exists(
+        self,
+        key: str,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> bool:
+        """Return true if the model identified by key and submodel_type is in the cache."""
+        pass
+
     @abstractmethod
     def cache_size(self) -> int:
         """Get the total size of the models currently cached."""

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

@@ -1,6 +1,22 @@
 # Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
 # TODO: Add Stalker's proper name to copyright
-""" """
+"""
+Manage a RAM cache of diffusion/transformer models for fast switching.
+They are moved between GPU VRAM and CPU RAM as necessary. If the cache
+grows larger than a preset maximum, then the least recently used
+model will be cleared and (re)loaded from disk when next needed.
+
+The cache returns context manager generators designed to load the
+model into the GPU within the context, and unload outside the
+context. Use like this:
+
+   cache = ModelCache(max_cache_size=7.5)
+   with cache.get_model('runwayml/stable-diffusion-1-5') as SD1,
+          cache.get_model('stabilityai/stable-diffusion-2') as SD2:
+       do_something_in_GPU(SD1,SD2)
+
+
+"""
 
 import gc
 import math
@@ -24,74 +40,53 @@ from invokeai.backend.model_manager.load.model_util import calc_model_size_by_da
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
-# Size of a GB in bytes.
-GB = 2**30
+# Maximum size of the cache, in gigs
+# Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
+DEFAULT_MAX_CACHE_SIZE = 6.0
+
+# amount of GPU memory to hold in reserve for use by generations (GB)
+DEFAULT_MAX_VRAM_CACHE_SIZE = 2.75
+
+# actual size of a gig
+GIG = 1073741824
 
 # Size of a MB in bytes.
 MB = 2**20
 
 
 class ModelCache(ModelCacheBase[AnyModel]):
-    """A cache for managing models in memory.
-
-    The cache is based on two levels of model storage:
-    - execution_device: The device where most models are executed (typically "cuda", "mps", or "cpu").
-    - storage_device: The device where models are offloaded when not in active use (typically "cpu").
-
-    The model cache is based on the following assumptions:
-    - storage_device_mem_size > execution_device_mem_size
-    - disk_to_storage_device_transfer_time >> storage_device_to_execution_device_transfer_time
-
-    A copy of all models in the cache is always kept on the storage_device. A subset of the models also have a copy on
-    the execution_device.
-
-    Models are moved between the storage_device and the execution_device as necessary. Cache size limits are enforced
-    on both the storage_device and the execution_device. The execution_device cache uses a smallest-first offload
-    policy. The storage_device cache uses a least-recently-used (LRU) offload policy.
-
-    Note: Neither of these offload policies has really been compared against alternatives. It's likely that different
-    policies would be better, although the optimal policies are likely heavily dependent on usage patterns and HW
-    configuration.
-
-    The cache returns context manager generators designed to load the model into the execution device (often GPU) within
-    the context, and unload outside the context.
-
-    Example usage:
-    ```
-    cache = ModelCache(max_cache_size=7.5, max_vram_cache_size=6.0)
-    with cache.get_model('runwayml/stable-diffusion-1-5') as SD1:
-        do_something_on_gpu(SD1)
-    ```
-    """
+    """Implementation of ModelCacheBase."""
 
     def __init__(
         self,
-        max_cache_size: float,
-        max_vram_cache_size: float,
+        max_cache_size: float = DEFAULT_MAX_CACHE_SIZE,
+        max_vram_cache_size: float = DEFAULT_MAX_VRAM_CACHE_SIZE,
         execution_device: torch.device = torch.device("cuda"),
         storage_device: torch.device = torch.device("cpu"),
         precision: torch.dtype = torch.float16,
+        sequential_offload: bool = False,
         lazy_offloading: bool = True,
+        sha_chunksize: int = 16777216,
         log_memory_usage: bool = False,
         logger: Optional[Logger] = None,
     ):
         """
         Initialize the model RAM cache.
 
-        :param max_cache_size: Maximum size of the storage_device cache in GBs.
-        :param max_vram_cache_size: Maximum size of the execution_device cache in GBs.
+        :param max_cache_size: Maximum size of the RAM cache [6.0 GB]
         :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 sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
         :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
+        self._precision: torch.dtype = precision
         self._max_cache_size: float = max_cache_size
         self._max_vram_cache_size: float = max_vram_cache_size
         self._execution_device: torch.device = execution_device
@@ -133,16 +128,6 @@ 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."""
@@ -160,6 +145,15 @@ class ModelCache(ModelCacheBase[AnyModel]):
             total += cache_record.size
         return total
 
+    def exists(
+        self,
+        key: str,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> bool:
+        """Return true if the model identified by key and submodel_type is in the cache."""
+        key = self._make_cache_key(key, submodel_type)
+        return key in self._cached_models
+
     def put(
         self,
         key: str,
@@ -209,7 +203,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         # more stats
         if self.stats:
             stats_name = stats_name or key
-            self.stats.cache_size = int(self._max_cache_size * GB)
+            self.stats.cache_size = int(self._max_cache_size * GIG)
             self.stats.high_watermark = max(self.stats.high_watermark, self.cache_size())
             self.stats.in_cache = len(self._cached_models)
             self.stats.loaded_model_sizes[stats_name] = max(
@@ -237,13 +231,10 @@ class ModelCache(ModelCacheBase[AnyModel]):
             return model_key
 
     def offload_unlocked_models(self, size_required: int) -> None:
-        """Offload models from the execution_device to make room for size_required.
-
-        :param size_required: The amount of space to clear in the execution_device cache, in bytes.
-        """
-        reserved = self._max_vram_cache_size * GB
+        """Move any unused models from VRAM."""
+        reserved = self._max_vram_cache_size * GIG
         vram_in_use = torch.cuda.memory_allocated() + size_required
-        self.logger.debug(f"{(vram_in_use/GB):.2f}GB VRAM needed for models; max allowed={(reserved/GB):.2f}GB")
+        self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM needed for models; max allowed={(reserved/GIG):.2f}GB")
         for _, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
             if vram_in_use <= reserved:
                 break
@@ -254,7 +245,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 cache_entry.loaded = False
                 vram_in_use = torch.cuda.memory_allocated() + size_required
                 self.logger.debug(
-                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GB):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GB):.2f}GB"
+                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GIG):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GIG):.2f}GB"
                 )
 
         TorchDevice.empty_cache()
@@ -312,7 +303,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         self.logger.debug(
             f"Moved model '{cache_entry.key}' from {source_device} to"
             f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s."
-            f"Estimated model size: {(cache_entry.size/GB):.3f} GB."
+            f"Estimated model size: {(cache_entry.size/GIG):.3f} GB."
             f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
         )
 
@@ -335,14 +326,14 @@ class ModelCache(ModelCacheBase[AnyModel]):
                     f"Moving model '{cache_entry.key}' from {source_device} to"
                     f" {target_device} caused an unexpected change in VRAM usage. The model's"
                     " estimated size may be incorrect. Estimated model size:"
-                    f" {(cache_entry.size/GB):.3f} GB.\n"
+                    f" {(cache_entry.size/GIG):.3f} GB.\n"
                     f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
                 )
 
     def print_cuda_stats(self) -> None:
         """Log CUDA diagnostics."""
-        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GB)
-        ram = "%4.2fG" % (self.cache_size() / GB)
+        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG)
+        ram = "%4.2fG" % (self.cache_size() / GIG)
 
         in_ram_models = 0
         in_vram_models = 0
@@ -362,20 +353,17 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 )
 
     def make_room(self, size: int) -> None:
-        """Make enough room in the cache to accommodate a new model of indicated size.
-
-        Note: This function deletes all of the cache's internal references to a model in order to free it. If there are
-        external references to the model, there's nothing that the cache can do about it, and those models will not be
-        garbage-collected.
-        """
+        """Make enough room in the cache to accommodate a new model of indicated size."""
+        # calculate how much memory this model will require
+        # multiplier = 2 if self.precision==torch.float32 else 1
         bytes_needed = size
-        maximum_size = self.max_cache_size * GB  # stored in GB, convert to bytes
+        maximum_size = self.max_cache_size * GIG  # stored in GB, convert to bytes
         current_size = self.cache_size()
 
         if current_size + bytes_needed > maximum_size:
             self.logger.debug(
-                f"Max cache size exceeded: {(current_size/GB):.2f}/{self.max_cache_size:.2f} GB, need an additional"
-                f" {(bytes_needed/GB):.2f} GB"
+                f"Max cache size exceeded: {(current_size/GIG):.2f}/{self.max_cache_size:.2f} GB, need an additional"
+                f" {(bytes_needed/GIG):.2f} GB"
             )
 
         self.logger.debug(f"Before making_room: cached_models={len(self._cached_models)}")
@@ -392,7 +380,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
 
             if not cache_entry.locked:
                 self.logger.debug(
-                    f"Removing {model_key} from RAM cache to free at least {(size/GB):.2f} GB (-{(cache_entry.size/GB):.2f} GB)"
+                    f"Removing {model_key} from RAM cache to free at least {(size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
                 )
                 current_size -= cache_entry.size
                 models_cleared += 1

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

@@ -1,246 +0,0 @@
-# Copyright (c) 2024, Brandon W. Rising and the InvokeAI Development Team
-"""Class for Flux model loading in InvokeAI."""
-
-from pathlib import Path
-from typing import Optional
-
-import accelerate
-import torch
-from safetensors.torch import load_file
-from transformers import AutoConfig, AutoModelForTextEncoding, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
-
-from invokeai.app.services.config.config_default import get_config
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.modules.autoencoder import AutoEncoder
-from invokeai.backend.flux.util import ae_params, params
-from invokeai.backend.model_manager import (
-    AnyModel,
-    AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
-)
-from invokeai.backend.model_manager.config import (
-    CheckpointConfigBase,
-    CLIPEmbedDiffusersConfig,
-    MainBnbQuantized4bCheckpointConfig,
-    MainCheckpointConfig,
-    T5EncoderBnbQuantizedLlmInt8bConfig,
-    T5EncoderConfig,
-    VAECheckpointConfig,
-)
-from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-from invokeai.backend.model_manager.util.model_util import (
-    convert_bundle_to_flux_transformer_checkpoint,
-)
-from invokeai.backend.util.silence_warnings import SilenceWarnings
-
-try:
-    from invokeai.backend.quantization.bnb_llm_int8 import quantize_model_llm_int8
-    from invokeai.backend.quantization.bnb_nf4 import quantize_model_nf4
-
-    bnb_available = True
-except ImportError:
-    bnb_available = False
-
-app_config = get_config()
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.VAE, format=ModelFormat.Checkpoint)
-class FluxVAELoader(ModelLoader):
-    """Class to load VAE models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, VAECheckpointConfig):
-            raise ValueError("Only VAECheckpointConfig models are currently supported here.")
-        model_path = Path(config.path)
-
-        with SilenceWarnings():
-            model = AutoEncoder(ae_params[config.config_path])
-            sd = load_file(model_path)
-            model.load_state_dict(sd, assign=True)
-            model.to(dtype=self._torch_dtype)
-
-        return model
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPEmbed, format=ModelFormat.Diffusers)
-class ClipCheckpointModel(ModelLoader):
-    """Class to load main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, CLIPEmbedDiffusersConfig):
-            raise ValueError("Only CLIPEmbedDiffusersConfig models are currently supported here.")
-
-        match submodel_type:
-            case SubModelType.Tokenizer:
-                return CLIPTokenizer.from_pretrained(Path(config.path) / "tokenizer")
-            case SubModelType.TextEncoder:
-                return CLIPTextModel.from_pretrained(Path(config.path) / "text_encoder")
-
-        raise ValueError(
-            f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
-        )
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.BnbQuantizedLlmInt8b)
-class BnbQuantizedLlmInt8bCheckpointModel(ModelLoader):
-    """Class to load main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, T5EncoderBnbQuantizedLlmInt8bConfig):
-            raise ValueError("Only T5EncoderBnbQuantizedLlmInt8bConfig models are currently supported here.")
-        if not bnb_available:
-            raise ImportError(
-                "The bnb modules are not available. Please install bitsandbytes if available on your platform."
-            )
-        match submodel_type:
-            case SubModelType.Tokenizer2:
-                return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2:
-                te2_model_path = Path(config.path) / "text_encoder_2"
-                model_config = AutoConfig.from_pretrained(te2_model_path)
-                with accelerate.init_empty_weights():
-                    model = AutoModelForTextEncoding.from_config(model_config)
-                    model = quantize_model_llm_int8(model, modules_to_not_convert=set())
-
-                state_dict_path = te2_model_path / "bnb_llm_int8_model.safetensors"
-                state_dict = load_file(state_dict_path)
-                self._load_state_dict_into_t5(model, state_dict)
-
-                return model
-
-        raise ValueError(
-            f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
-        )
-
-    @classmethod
-    def _load_state_dict_into_t5(cls, model: T5EncoderModel, state_dict: dict[str, torch.Tensor]):
-        # There is a shared reference to a single weight tensor in the model.
-        # Both "encoder.embed_tokens.weight" and "shared.weight" refer to the same tensor, so only the latter should
-        # be present in the state_dict.
-        missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False, assign=True)
-        assert len(unexpected_keys) == 0
-        assert set(missing_keys) == {"encoder.embed_tokens.weight"}
-        # Assert that the layers we expect to be shared are actually shared.
-        assert model.encoder.embed_tokens.weight is model.shared.weight
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.T5Encoder)
-class T5EncoderCheckpointModel(ModelLoader):
-    """Class to load main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, T5EncoderConfig):
-            raise ValueError("Only T5EncoderConfig models are currently supported here.")
-
-        match submodel_type:
-            case SubModelType.Tokenizer2:
-                return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2:
-                return T5EncoderModel.from_pretrained(Path(config.path) / "text_encoder_2")
-
-        raise ValueError(
-            f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
-        )
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.Main, format=ModelFormat.Checkpoint)
-class FluxCheckpointModel(ModelLoader):
-    """Class to load main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, CheckpointConfigBase):
-            raise ValueError("Only CheckpointConfigBase models are currently supported here.")
-
-        match submodel_type:
-            case SubModelType.Transformer:
-                return self._load_from_singlefile(config)
-
-        raise ValueError(
-            f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
-        )
-
-    def _load_from_singlefile(
-        self,
-        config: AnyModelConfig,
-    ) -> AnyModel:
-        assert isinstance(config, MainCheckpointConfig)
-        model_path = Path(config.path)
-
-        with SilenceWarnings():
-            model = Flux(params[config.config_path])
-            sd = load_file(model_path)
-            if "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale" in sd:
-                sd = convert_bundle_to_flux_transformer_checkpoint(sd)
-            new_sd_size = sum([ten.nelement() * torch.bfloat16.itemsize for ten in sd.values()])
-            self._ram_cache.make_room(new_sd_size)
-            for k in sd.keys():
-                # We need to cast to bfloat16 due to it being the only currently supported dtype for inference
-                sd[k] = sd[k].to(torch.bfloat16)
-            model.load_state_dict(sd, assign=True)
-        return model
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.Main, format=ModelFormat.BnbQuantizednf4b)
-class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
-    """Class to load main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, CheckpointConfigBase):
-            raise ValueError("Only CheckpointConfigBase models are currently supported here.")
-
-        match submodel_type:
-            case SubModelType.Transformer:
-                return self._load_from_singlefile(config)
-
-        raise ValueError(
-            f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
-        )
-
-    def _load_from_singlefile(
-        self,
-        config: AnyModelConfig,
-    ) -> AnyModel:
-        assert isinstance(config, MainBnbQuantized4bCheckpointConfig)
-        if not bnb_available:
-            raise ImportError(
-                "The bnb modules are not available. Please install bitsandbytes if available on your platform."
-            )
-        model_path = Path(config.path)
-
-        with SilenceWarnings():
-            with accelerate.init_empty_weights():
-                model = Flux(params[config.config_path])
-                model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=torch.bfloat16)
-            sd = load_file(model_path)
-            if "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale" in sd:
-                sd = convert_bundle_to_flux_transformer_checkpoint(sd)
-            model.load_state_dict(sd, assign=True)
-        return model

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

@@ -78,12 +78,7 @@ class GenericDiffusersLoader(ModelLoader):
 
     # TO DO: Add exception handling
     def _hf_definition_to_type(self, module: str, class_name: str) -> ModelMixin:  # fix with correct type
-        if module in [
-            "diffusers",
-            "transformers",
-            "invokeai.backend.quantization.fast_quantized_transformers_model",
-            "invokeai.backend.quantization.fast_quantized_diffusion_model",
-        ]:
+        if module in ["diffusers", "transformers"]:
             res_type = sys.modules[module]
         else:
             res_type = sys.modules["diffusers"].pipelines

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

@@ -36,18 +36,8 @@ VARIANT_TO_IN_CHANNEL_MAP = {
 }
 
 
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Main, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Main, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.Main, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(
-    base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Diffusers
-)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Main, format=ModelFormat.Checkpoint)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Main, format=ModelFormat.Checkpoint)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.Main, format=ModelFormat.Checkpoint)
-@ModelLoaderRegistry.register(
-    base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Checkpoint
-)
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Diffusers)
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Checkpoint)
 class StableDiffusionDiffusersModel(GenericDiffusersLoader):
     """Class to load main models."""
 

invokeai/backend/model_manager/load/model_util.py

@@ -9,7 +9,7 @@ from typing import Optional
 import torch
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
-from transformers import CLIPTokenizer, T5Tokenizer, T5TokenizerFast
+from transformers import CLIPTokenizer
 
 from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
@@ -50,17 +50,6 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
         ),
     ):
         return model.calc_size()
-    elif isinstance(
-        model,
-        (
-            T5TokenizerFast,
-            T5Tokenizer,
-        ),
-    ):
-        # HACK(ryand): len(model) just returns the vocabulary size, so this is blatantly wrong. It should be small
-        # relative to the text encoder that it's used with, so shouldn't matter too much, but we should fix this at some
-        # point.
-        return len(model)
     else:
         # TODO(ryand): Promote this from a log to an exception once we are confident that we are handling all of the
         # supported model types.

invokeai/backend/model_manager/probe.py

@@ -95,7 +95,6 @@ class ModelProbe(object):
     }
 
     CLASS2TYPE = {
-        "FluxPipeline": ModelType.Main,
         "StableDiffusionPipeline": ModelType.Main,
         "StableDiffusionInpaintPipeline": ModelType.Main,
         "StableDiffusionXLPipeline": ModelType.Main,
@@ -107,9 +106,6 @@ class ModelProbe(object):
         "ControlNetModel": ModelType.ControlNet,
         "CLIPVisionModelWithProjection": ModelType.CLIPVision,
         "T2IAdapter": ModelType.T2IAdapter,
-        "CLIPModel": ModelType.CLIPEmbed,
-        "CLIPTextModel": ModelType.CLIPEmbed,
-        "T5EncoderModel": ModelType.T5Encoder,
     }
 
     @classmethod
@@ -165,7 +161,7 @@ class ModelProbe(object):
         fields["description"] = (
             fields.get("description") or f"{fields['base'].value} {model_type.value} model {fields['name']}"
         )
-        fields["format"] = ModelFormat(fields.get("format")) if "format" in fields else probe.get_format()
+        fields["format"] = fields.get("format") or probe.get_format()
         fields["hash"] = fields.get("hash") or ModelHash(algorithm=hash_algo).hash(model_path)
 
         fields["default_settings"] = fields.get("default_settings")
@@ -180,10 +176,10 @@ class ModelProbe(object):
             fields["repo_variant"] = fields.get("repo_variant") or probe.get_repo_variant()
 
         # additional fields needed for main and controlnet models
-        if fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE] and fields["format"] in [
-            ModelFormat.Checkpoint,
-            ModelFormat.BnbQuantizednf4b,
-        ]:
+        if (
+            fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE]
+            and fields["format"] is ModelFormat.Checkpoint
+        ):
             ckpt_config_path = cls._get_checkpoint_config_path(
                 model_path,
                 model_type=fields["type"],
@@ -226,19 +222,7 @@ class ModelProbe(object):
         ckpt = ckpt.get("state_dict", ckpt)
 
         for key in [str(k) for k in ckpt.keys()]:
-            if key.startswith(
-                (
-                    "cond_stage_model.",
-                    "first_stage_model.",
-                    "model.diffusion_model.",
-                    # FLUX models in the official BFL format contain keys with the "double_blocks." prefix.
-                    "double_blocks.",
-                    # Some FLUX checkpoint files contain transformer keys prefixed with "model.diffusion_model".
-                    # This prefix is typically used to distinguish between multiple models bundled in a single file.
-                    "model.diffusion_model.double_blocks.",
-                )
-            ):
-                # Keys starting with double_blocks are associated with Flux models
+            if key.startswith(("cond_stage_model.", "first_stage_model.", "model.diffusion_model.")):
                 return ModelType.Main
             elif key.startswith(("encoder.conv_in", "decoder.conv_in")):
                 return ModelType.VAE
@@ -296,16 +280,9 @@ class ModelProbe(object):
         if (folder_path / "image_encoder.txt").exists():
             return ModelType.IPAdapter
 
-        config_path = None
-        for p in [
-            folder_path / "model_index.json",  # pipeline
-            folder_path / "config.json",  # most diffusers
-            folder_path / "text_encoder_2" / "config.json",  # T5 text encoder
-            folder_path / "text_encoder" / "config.json",  # T5 CLIP
-        ]:
-            if p.exists():
-                config_path = p
-                break
+        i = folder_path / "model_index.json"
+        c = folder_path / "config.json"
+        config_path = i if i.exists() else c if c.exists() else None
 
         if config_path:
             with open(config_path, "r") as file:
@@ -344,30 +321,10 @@ class ModelProbe(object):
             return possible_conf.absolute()
 
         if model_type is ModelType.Main:
-            if base_type == BaseModelType.Flux:
-                # TODO: Decide between dev/schnell
-                checkpoint = ModelProbe._scan_and_load_checkpoint(model_path)
-                state_dict = checkpoint.get("state_dict") or checkpoint
-                if (
-                    "guidance_in.out_layer.weight" in state_dict
-                    or "model.diffusion_model.guidance_in.out_layer.weight" in state_dict
-                ):
-                    # For flux, this is a key in invokeai.backend.flux.util.params
-                    #   Due to model type and format being the descriminator for model configs this
-                    #   is used rather than attempting to support flux with separate model types and format
-                    #   If changed in the future, please fix me
-                    config_file = "flux-dev"
-                else:
-                    # For flux, this is a key in invokeai.backend.flux.util.params
-                    #   Due to model type and format being the discriminator for model configs this
-                    #   is used rather than attempting to support flux with separate model types and format
-                    #   If changed in the future, please fix me
-                    config_file = "flux-schnell"
-            else:
-                config_file = LEGACY_CONFIGS[base_type][variant_type]
-                if isinstance(config_file, dict):  # need another tier for sd-2.x models
-                    config_file = config_file[prediction_type]
-                config_file = f"stable-diffusion/{config_file}"
+            config_file = LEGACY_CONFIGS[base_type][variant_type]
+            if isinstance(config_file, dict):  # need another tier for sd-2.x models
+                config_file = config_file[prediction_type]
+            config_file = f"stable-diffusion/{config_file}"
         elif model_type is ModelType.ControlNet:
             config_file = (
                 "controlnet/cldm_v15.yaml"
@@ -376,13 +333,7 @@ class ModelProbe(object):
             )
         elif model_type is ModelType.VAE:
             config_file = (
-                # For flux, this is a key in invokeai.backend.flux.util.ae_params
-                #   Due to model type and format being the descriminator for model configs this
-                #   is used rather than attempting to support flux with separate model types and format
-                #   If changed in the future, please fix me
-                "flux"
-                if base_type is BaseModelType.Flux
-                else "stable-diffusion/v1-inference.yaml"
+                "stable-diffusion/v1-inference.yaml"
                 if base_type is BaseModelType.StableDiffusion1
                 else "stable-diffusion/sd_xl_base.yaml"
                 if base_type is BaseModelType.StableDiffusionXL
@@ -465,18 +416,11 @@ class CheckpointProbeBase(ProbeBase):
         self.checkpoint = ModelProbe._scan_and_load_checkpoint(model_path)
 
     def get_format(self) -> ModelFormat:
-        state_dict = self.checkpoint.get("state_dict") or self.checkpoint
-        if (
-            "double_blocks.0.img_attn.proj.weight.quant_state.bitsandbytes__nf4" in state_dict
-            or "model.diffusion_model.double_blocks.0.img_attn.proj.weight.quant_state.bitsandbytes__nf4" in state_dict
-        ):
-            return ModelFormat.BnbQuantizednf4b
         return ModelFormat("checkpoint")
 
     def get_variant_type(self) -> ModelVariantType:
         model_type = ModelProbe.get_model_type_from_checkpoint(self.model_path, self.checkpoint)
-        base_type = self.get_base_type()
-        if model_type != ModelType.Main or base_type == BaseModelType.Flux:
+        if model_type != ModelType.Main:
             return ModelVariantType.Normal
         state_dict = self.checkpoint.get("state_dict") or self.checkpoint
         in_channels = state_dict["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
@@ -496,11 +440,6 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
         state_dict = self.checkpoint.get("state_dict") or checkpoint
-        if (
-            "double_blocks.0.img_attn.norm.key_norm.scale" in state_dict
-            or "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale" in state_dict
-        ):
-            return BaseModelType.Flux
         key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
         if key_name in state_dict and state_dict[key_name].shape[-1] == 768:
             return BaseModelType.StableDiffusion1
@@ -543,7 +482,6 @@ class VaeCheckpointProbe(CheckpointProbeBase):
             (r"xl", BaseModelType.StableDiffusionXL),
             (r"sd2", BaseModelType.StableDiffusion2),
             (r"vae", BaseModelType.StableDiffusion1),
-            (r"FLUX.1-schnell_ae", BaseModelType.Flux),
         ]:
             if re.search(regexp, self.model_path.name, re.IGNORECASE):
                 return basetype
@@ -775,30 +713,6 @@ class TextualInversionFolderProbe(FolderProbeBase):
         return TextualInversionCheckpointProbe(path).get_base_type()
 
 
-class T5EncoderFolderProbe(FolderProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        return BaseModelType.Any
-
-    def get_format(self) -> ModelFormat:
-        path = self.model_path / "text_encoder_2"
-        if (path / "model.safetensors.index.json").exists():
-            return ModelFormat.T5Encoder
-        files = list(path.glob("*.safetensors"))
-        if len(files) == 0:
-            raise InvalidModelConfigException(f"{self.model_path.as_posix()}: no .safetensors files found")
-
-        # shortcut: look for the quantization in the name
-        if any(x for x in files if "llm_int8" in x.as_posix()):
-            return ModelFormat.BnbQuantizedLlmInt8b
-
-        # more reliable path: probe contents for a 'SCB' key
-        ckpt = read_checkpoint_meta(files[0], scan=True)
-        if any("SCB" in x for x in ckpt.keys()):
-            return ModelFormat.BnbQuantizedLlmInt8b
-
-        raise InvalidModelConfigException(f"{self.model_path.as_posix()}: unknown model format")
-
-
 class ONNXFolderProbe(PipelineFolderProbe):
     def get_base_type(self) -> BaseModelType:
         # Due to the way the installer is set up, the configuration file for safetensors
@@ -891,11 +805,6 @@ class CLIPVisionFolderProbe(FolderProbeBase):
         return BaseModelType.Any
 
 
-class CLIPEmbedFolderProbe(FolderProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        return BaseModelType.Any
-
-
 class SpandrelImageToImageFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
         raise NotImplementedError()
@@ -926,10 +835,8 @@ ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.VAE, VaeFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.LoRA, LoRAFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInversionFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.T5Encoder, T5EncoderFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.IPAdapter, IPAdapterFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.CLIPEmbed, CLIPEmbedFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.CLIPVision, CLIPVisionFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.T2IAdapter, T2IAdapterFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.SpandrelImageToImage, SpandrelImageToImageFolderProbe)

invokeai/backend/model_manager/starter_models.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from pydantic import BaseModel
 
-from invokeai.backend.model_manager.config import BaseModelType, ModelFormat, ModelType
+from invokeai.backend.model_manager.config import BaseModelType, ModelType
 
 
 class StarterModelWithoutDependencies(BaseModel):
@@ -11,7 +11,6 @@ class StarterModelWithoutDependencies(BaseModel):
     name: str
     base: BaseModelType
     type: ModelType
-    format: Optional[ModelFormat] = None
     is_installed: bool = False
 
 
@@ -52,76 +51,10 @@ cyberrealistic_negative = StarterModel(
     type=ModelType.TextualInversion,
 )
 
-t5_base_encoder = StarterModel(
-    name="t5_base_encoder",
-    base=BaseModelType.Any,
-    source="InvokeAI/t5-v1_1-xxl::bfloat16",
-    description="T5-XXL text encoder (used in FLUX pipelines). ~8GB",
-    type=ModelType.T5Encoder,
-)
-
-t5_8b_quantized_encoder = StarterModel(
-    name="t5_bnb_int8_quantized_encoder",
-    base=BaseModelType.Any,
-    source="InvokeAI/t5-v1_1-xxl::bnb_llm_int8",
-    description="T5-XXL text encoder with bitsandbytes LLM.int8() quantization (used in FLUX pipelines). ~5GB",
-    type=ModelType.T5Encoder,
-    format=ModelFormat.BnbQuantizedLlmInt8b,
-)
-
-clip_l_encoder = StarterModel(
-    name="clip-vit-large-patch14",
-    base=BaseModelType.Any,
-    source="InvokeAI/clip-vit-large-patch14-text-encoder::bfloat16",
-    description="CLIP-L text encoder (used in FLUX pipelines). ~250MB",
-    type=ModelType.CLIPEmbed,
-)
-
-flux_vae = StarterModel(
-    name="FLUX.1-schnell_ae",
-    base=BaseModelType.Flux,
-    source="black-forest-labs/FLUX.1-schnell::ae.safetensors",
-    description="FLUX VAE compatible with both schnell and dev variants.",
-    type=ModelType.VAE,
-)
-
-
 # List of starter models, displayed on the frontend.
 # The order/sort of this list is not changed by the frontend - set it how you want it here.
 STARTER_MODELS: list[StarterModel] = [
     # region: Main
-    StarterModel(
-        name="FLUX Schnell (Quantized)",
-        base=BaseModelType.Flux,
-        source="InvokeAI/flux_schnell::transformer/bnb_nf4/flux1-schnell-bnb_nf4.safetensors",
-        description="FLUX schnell transformer quantized to bitsandbytes NF4 format. Total size with dependencies: ~12GB",
-        type=ModelType.Main,
-        dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
-    ),
-    StarterModel(
-        name="FLUX Dev (Quantized)",
-        base=BaseModelType.Flux,
-        source="InvokeAI/flux_dev::transformer/bnb_nf4/flux1-dev-bnb_nf4.safetensors",
-        description="FLUX dev transformer quantized to bitsandbytes NF4 format. Total size with dependencies: ~12GB",
-        type=ModelType.Main,
-        dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
-    ),
-    StarterModel(
-        name="FLUX Schnell",
-        base=BaseModelType.Flux,
-        source="InvokeAI/flux_schnell::transformer/base/flux1-schnell.safetensors",
-        description="FLUX schnell transformer in bfloat16. Total size with dependencies: ~33GB",
-        type=ModelType.Main,
-        dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
-    ),
-    StarterModel(
-        name="FLUX Dev",
-        base=BaseModelType.Flux,
-        source="InvokeAI/flux_dev::transformer/base/flux1-dev.safetensors",
-        description="FLUX dev transformer in bfloat16. Total size with dependencies: ~33GB",
-        type=ModelType.Main,
-        dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
-    ),
     StarterModel(
         name="CyberRealistic v4.1",
         base=BaseModelType.StableDiffusion1,
@@ -192,7 +125,6 @@ STARTER_MODELS: list[StarterModel] = [
     # endregion
     # region VAE
     sdxl_fp16_vae_fix,
-    flux_vae,
     # endregion
     # region LoRA
     StarterModel(
@@ -518,11 +450,6 @@ STARTER_MODELS: list[StarterModel] = [
         type=ModelType.SpandrelImageToImage,
     ),
     # endregion
-    # region TextEncoders
-    t5_base_encoder,
-    t5_8b_quantized_encoder,
-    clip_l_encoder,
-    # endregion
 ]
 
 assert len(STARTER_MODELS) == len({m.source for m in STARTER_MODELS}), "Duplicate starter models"

invokeai/backend/model_manager/util/model_util.py

@@ -133,29 +133,3 @@ def lora_token_vector_length(checkpoint: Dict[str, torch.Tensor]) -> Optional[in
             break
 
     return lora_token_vector_length
-
-
-def convert_bundle_to_flux_transformer_checkpoint(
-    transformer_state_dict: dict[str, torch.Tensor],
-) -> dict[str, torch.Tensor]:
-    original_state_dict: dict[str, torch.Tensor] = {}
-    keys_to_remove: list[str] = []
-
-    for k, v in transformer_state_dict.items():
-        if not k.startswith("model.diffusion_model"):
-            keys_to_remove.append(k)  # This can be removed in the future if we only want to delete transformer keys
-            continue
-        if k.endswith("scale"):
-            # Scale math must be done at bfloat16 due to our current flux model
-            # support limitations at inference time
-            v = v.to(dtype=torch.bfloat16)
-        new_key = k.replace("model.diffusion_model.", "")
-        original_state_dict[new_key] = v
-        keys_to_remove.append(k)
-
-    # Remove processed keys from the original dictionary, leaving others in case
-    # other model state dicts need to be pulled
-    for k in keys_to_remove:
-        del transformer_state_dict[k]
-
-    return original_state_dict

invokeai/backend/model_manager/util/select_hf_files.py

@@ -54,7 +54,6 @@ def filter_files(
                 "lora_weights.safetensors",
                 "weights.pb",
                 "onnx_data",
-                "spiece.model",  # Added for `black-forest-labs/FLUX.1-schnell`.
             )
         ):
             paths.append(file)
@@ -63,13 +62,13 @@ def filter_files(
         # downloading random checkpoints that might also be in the repo. However there is no guarantee
         # that a checkpoint doesn't contain "model" in its name, and no guarantee that future diffusers models
         # will adhere to this naming convention, so this is an area to be careful of.
-        elif re.search(r"model.*\.(safetensors|bin|onnx|xml|pth|pt|ckpt|msgpack)$", file.name):
+        elif re.search(r"model(\.[^.]+)?\.(safetensors|bin|onnx|xml|pth|pt|ckpt|msgpack)$", file.name):
             paths.append(file)
 
     # limit search to subfolder if requested
     if subfolder:
         subfolder = root / subfolder
-        paths = [x for x in paths if Path(subfolder) in x.parents]
+        paths = [x for x in paths if x.parent == Path(subfolder)]
 
     # _filter_by_variant uniquifies the paths and returns a set
     return sorted(_filter_by_variant(paths, variant))
@@ -98,9 +97,7 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
             if variant == ModelRepoVariant.Flax:
                 result.add(path)
 
-        # Note: '.model' was added to support:
-        # https://huggingface.co/black-forest-labs/FLUX.1-schnell/blob/768d12a373ed5cc9ef9a9dea7504dc09fcc14842/tokenizer_2/spiece.model
-        elif path.suffix in [".json", ".txt", ".model"]:
+        elif path.suffix in [".json", ".txt"]:
             result.add(path)
 
         elif variant in [
@@ -143,23 +140,6 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
             continue
 
     for candidate_list in subfolder_weights.values():
-        # Check if at least one of the files has the explicit fp16 variant.
-        at_least_one_fp16 = False
-        for candidate in candidate_list:
-            if len(candidate.path.suffixes) == 2 and candidate.path.suffixes[0] == ".fp16":
-                at_least_one_fp16 = True
-                break
-
-        if not at_least_one_fp16:
-            # If none of the candidates in this candidate_list have the explicit fp16 variant label, then this
-            # candidate_list probably doesn't adhere to the variant naming convention that we expected. In this case,
-            # we'll simply keep all the candidates. An example of a model that hits this case is
-            # `black-forest-labs/FLUX.1-schnell` (as of commit 012d2fd).
-            for candidate in candidate_list:
-                result.add(candidate.path)
-
-        # The candidate_list seems to have the expected variant naming convention. We'll select the highest scoring
-        # candidate.
         highest_score_candidate = max(candidate_list, key=lambda candidate: candidate.score)
         if highest_score_candidate:
             result.add(highest_score_candidate.path)

invokeai/backend/quantization/bnb_llm_int8.py

@@ -1,135 +0,0 @@
-import bitsandbytes as bnb
-import torch
-
-# This file contains utils for working with models that use bitsandbytes LLM.int8() quantization.
-# The utils in this file are partially inspired by:
-# https://github.com/Lightning-AI/pytorch-lightning/blob/1551a16b94f5234a4a78801098f64d0732ef5cb5/src/lightning/fabric/plugins/precision/bitsandbytes.py
-
-
-# NOTE(ryand): All of the custom state_dict manipulation logic in this file is pretty hacky. This could be made much
-# cleaner by re-implementing bnb.nn.Linear8bitLt with proper use of buffers and less magic. But, for now, we try to
-# stick close to the bitsandbytes classes to make interoperability easier with other models that might use bitsandbytes.
-
-
-class InvokeInt8Params(bnb.nn.Int8Params):
-    """We override cuda() to avoid re-quantizing the weights in the following cases:
-    - We loaded quantized weights from a state_dict on the cpu, and then moved the model to the gpu.
-    - We are moving the model back-and-forth between the cpu and gpu.
-    """
-
-    def cuda(self, device):
-        if self.has_fp16_weights:
-            return super().cuda(device)
-        elif self.CB is not None and self.SCB is not None:
-            self.data = self.data.cuda()
-            self.CB = self.data
-            self.SCB = self.SCB.cuda()
-        else:
-            # we store the 8-bit rows-major weight
-            # we convert this weight to the turning/ampere weight during the first inference pass
-            B = self.data.contiguous().half().cuda(device)
-            CB, CBt, SCB, SCBt, coo_tensorB = bnb.functional.double_quant(B)
-            del CBt
-            del SCBt
-            self.data = CB
-            self.CB = CB
-            self.SCB = SCB
-
-        return self
-
-
-class InvokeLinear8bitLt(bnb.nn.Linear8bitLt):
-    def _load_from_state_dict(
-        self,
-        state_dict: dict[str, torch.Tensor],
-        prefix: str,
-        local_metadata,
-        strict,
-        missing_keys,
-        unexpected_keys,
-        error_msgs,
-    ):
-        weight = state_dict.pop(prefix + "weight")
-        bias = state_dict.pop(prefix + "bias", None)
-
-        # See `bnb.nn.Linear8bitLt._save_to_state_dict()` for the serialization logic of SCB and weight_format.
-        scb = state_dict.pop(prefix + "SCB", None)
-
-        # Currently, we only support weight_format=0.
-        weight_format = state_dict.pop(prefix + "weight_format", None)
-        assert weight_format == 0
-
-        # TODO(ryand): Technically, we should be using `strict`, `missing_keys`, `unexpected_keys`, and `error_msgs`
-        # rather than raising an exception to correctly implement this API.
-        assert len(state_dict) == 0
-
-        if scb is not None:
-            # We are loading a pre-quantized state dict.
-            self.weight = InvokeInt8Params(
-                data=weight,
-                requires_grad=self.weight.requires_grad,
-                has_fp16_weights=False,
-                # Note: After quantization, CB is the same as weight.
-                CB=weight,
-                SCB=scb,
-            )
-            self.bias = bias if bias is None else torch.nn.Parameter(bias)
-        else:
-            # We are loading a non-quantized state dict.
-
-            # We could simply call the `super()._load_from_state_dict()` method here, but then we wouldn't be able to
-            # load from a state_dict into a model on the "meta" device. Attempting to load into a model on the "meta"
-            # device requires setting `assign=True`, doing this with the default `super()._load_from_state_dict()`
-            # implementation causes `Params4Bit` to be replaced by a `torch.nn.Parameter`. By initializing a new
-            # `Params4bit` object, we work around this issue. It's a bit hacky, but it gets the job done.
-            self.weight = InvokeInt8Params(
-                data=weight,
-                requires_grad=self.weight.requires_grad,
-                has_fp16_weights=False,
-                CB=None,
-                SCB=None,
-            )
-            self.bias = bias if bias is None else torch.nn.Parameter(bias)
-
-        # Reset the state. The persisted fields are based on the initialization behaviour in
-        # `bnb.nn.Linear8bitLt.__init__()`.
-        new_state = bnb.MatmulLtState()
-        new_state.threshold = self.state.threshold
-        new_state.has_fp16_weights = False
-        new_state.use_pool = self.state.use_pool
-        self.state = new_state
-
-
-def _convert_linear_layers_to_llm_8bit(
-    module: torch.nn.Module, ignore_modules: set[str], outlier_threshold: float, prefix: str = ""
-) -> None:
-    """Convert all linear layers in the module to bnb.nn.Linear8bitLt layers."""
-    for name, child in module.named_children():
-        fullname = f"{prefix}.{name}" if prefix else name
-        if isinstance(child, torch.nn.Linear) and not any(fullname.startswith(s) for s in ignore_modules):
-            has_bias = child.bias is not None
-            replacement = InvokeLinear8bitLt(
-                child.in_features,
-                child.out_features,
-                bias=has_bias,
-                has_fp16_weights=False,
-                threshold=outlier_threshold,
-            )
-            replacement.weight.data = child.weight.data
-            if has_bias:
-                replacement.bias.data = child.bias.data
-            replacement.requires_grad_(False)
-            module.__setattr__(name, replacement)
-        else:
-            _convert_linear_layers_to_llm_8bit(
-                child, ignore_modules, outlier_threshold=outlier_threshold, prefix=fullname
-            )
-
-
-def quantize_model_llm_int8(model: torch.nn.Module, modules_to_not_convert: set[str], outlier_threshold: float = 6.0):
-    """Apply bitsandbytes LLM.8bit() quantization to the model."""
-    _convert_linear_layers_to_llm_8bit(
-        module=model, ignore_modules=modules_to_not_convert, outlier_threshold=outlier_threshold
-    )
-
-    return model

invokeai/backend/quantization/bnb_nf4.py

@@ -1,156 +0,0 @@
-import bitsandbytes as bnb
-import torch
-
-# This file contains utils for working with models that use bitsandbytes NF4 quantization.
-# The utils in this file are partially inspired by:
-# https://github.com/Lightning-AI/pytorch-lightning/blob/1551a16b94f5234a4a78801098f64d0732ef5cb5/src/lightning/fabric/plugins/precision/bitsandbytes.py
-
-# NOTE(ryand): All of the custom state_dict manipulation logic in this file is pretty hacky. This could be made much
-# cleaner by re-implementing bnb.nn.LinearNF4 with proper use of buffers and less magic. But, for now, we try to stick
-# close to the bitsandbytes classes to make interoperability easier with other models that might use bitsandbytes.
-
-
-class InvokeLinearNF4(bnb.nn.LinearNF4):
-    """A class that extends `bnb.nn.LinearNF4` to add the following functionality:
-    - Ability to load Linear NF4 layers from a pre-quantized state_dict.
-    - Ability to load Linear NF4 layers from a state_dict when the model is on the "meta" device.
-    """
-
-    def _load_from_state_dict(
-        self,
-        state_dict: dict[str, torch.Tensor],
-        prefix: str,
-        local_metadata,
-        strict,
-        missing_keys,
-        unexpected_keys,
-        error_msgs,
-    ):
-        """This method is based on the logic in the bitsandbytes serialization unit tests for `Linear4bit`:
-        https://github.com/bitsandbytes-foundation/bitsandbytes/blob/6d714a5cce3db5bd7f577bc447becc7a92d5ccc7/tests/test_linear4bit.py#L52-L71
-        """
-        weight = state_dict.pop(prefix + "weight")
-        bias = state_dict.pop(prefix + "bias", None)
-        # We expect the remaining keys to be quant_state keys.
-        quant_state_sd = state_dict
-
-        # During serialization, the quant_state is stored as subkeys of "weight." (See
-        # `bnb.nn.LinearNF4._save_to_state_dict()`). We validate that they at least have the correct prefix.
-        # TODO(ryand): Technically, we should be using `strict`, `missing_keys`, `unexpected_keys`, and `error_msgs`
-        # rather than raising an exception to correctly implement this API.
-        assert all(k.startswith(prefix + "weight.") for k in quant_state_sd.keys())
-
-        if len(quant_state_sd) > 0:
-            # We are loading a pre-quantized state dict.
-            self.weight = bnb.nn.Params4bit.from_prequantized(
-                data=weight, quantized_stats=quant_state_sd, device=weight.device
-            )
-            self.bias = bias if bias is None else torch.nn.Parameter(bias, requires_grad=False)
-        else:
-            # We are loading a non-quantized state dict.
-
-            # We could simply call the `super()._load_from_state_dict()` method here, but then we wouldn't be able to
-            # load from a state_dict into a model on the "meta" device. Attempting to load into a model on the "meta"
-            # device requires setting `assign=True`, doing this with the default `super()._load_from_state_dict()`
-            # implementation causes `Params4Bit` to be replaced by a `torch.nn.Parameter`. By initializing a new
-            # `Params4bit` object, we work around this issue. It's a bit hacky, but it gets the job done.
-            self.weight = bnb.nn.Params4bit(
-                data=weight,
-                requires_grad=self.weight.requires_grad,
-                compress_statistics=self.weight.compress_statistics,
-                quant_type=self.weight.quant_type,
-                quant_storage=self.weight.quant_storage,
-                module=self,
-            )
-            self.bias = bias if bias is None else torch.nn.Parameter(bias)
-
-
-def _replace_param(
-    param: torch.nn.Parameter | bnb.nn.Params4bit,
-    data: torch.Tensor,
-) -> torch.nn.Parameter:
-    """A helper function to replace the data of a model parameter with new data in a way that allows replacing params on
-    the "meta" device.
-
-    Supports both `torch.nn.Parameter` and `bnb.nn.Params4bit` parameters.
-    """
-    if param.device.type == "meta":
-        # Doing `param.data = data` raises a RuntimeError if param.data was on the "meta" device, so we need to
-        # re-create the param instead of overwriting the data.
-        if isinstance(param, bnb.nn.Params4bit):
-            return bnb.nn.Params4bit(
-                data,
-                requires_grad=data.requires_grad,
-                quant_state=param.quant_state,
-                compress_statistics=param.compress_statistics,
-                quant_type=param.quant_type,
-            )
-        return torch.nn.Parameter(data, requires_grad=data.requires_grad)
-
-    param.data = data
-    return param
-
-
-def _convert_linear_layers_to_nf4(
-    module: torch.nn.Module,
-    ignore_modules: set[str],
-    compute_dtype: torch.dtype,
-    compress_statistics: bool = False,
-    prefix: str = "",
-) -> None:
-    """Convert all linear layers in the model to NF4 quantized linear layers.
-
-    Args:
-        module: All linear layers in this module will be converted.
-        ignore_modules: A set of module prefixes to ignore when converting linear layers.
-        compute_dtype: The dtype to use for computation in the quantized linear layers.
-        compress_statistics: Whether to enable nested quantization (aka double quantization) where the quantization
-           constants from the first quantization are quantized again.
-        prefix: The prefix of the current module in the model. Used to call this function recursively.
-    """
-    for name, child in module.named_children():
-        fullname = f"{prefix}.{name}" if prefix else name
-        if isinstance(child, torch.nn.Linear) and not any(fullname.startswith(s) for s in ignore_modules):
-            has_bias = child.bias is not None
-            replacement = InvokeLinearNF4(
-                child.in_features,
-                child.out_features,
-                bias=has_bias,
-                compute_dtype=compute_dtype,
-                compress_statistics=compress_statistics,
-            )
-            if has_bias:
-                replacement.bias = _replace_param(replacement.bias, child.bias.data)
-            replacement.weight = _replace_param(replacement.weight, child.weight.data)
-            replacement.requires_grad_(False)
-            module.__setattr__(name, replacement)
-        else:
-            _convert_linear_layers_to_nf4(child, ignore_modules, compute_dtype=compute_dtype, prefix=fullname)
-
-
-def quantize_model_nf4(model: torch.nn.Module, modules_to_not_convert: set[str], compute_dtype: torch.dtype):
-    """Apply bitsandbytes nf4 quantization to the model.
-
-    You likely want to call this function inside a `accelerate.init_empty_weights()` context.
-
-    Example usage:
-    ```
-    # Initialize the model from a config on the meta device.
-    with accelerate.init_empty_weights():
-        model = ModelClass.from_config(...)
-
-    # Add NF4 quantization linear layers to the model - still on the meta device.
-    with accelerate.init_empty_weights():
-        model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=torch.float16)
-
-    # Load a state_dict into the model. (Could be either a prequantized or non-quantized state_dict.)
-    model.load_state_dict(state_dict, strict=True, assign=True)
-
-    # Move the model to the "cuda" device. If the model was non-quantized, this is where the weight quantization takes
-    # place.
-    model.to("cuda")
-    ```
-    """
-    _convert_linear_layers_to_nf4(module=model, ignore_modules=modules_to_not_convert, compute_dtype=compute_dtype)
-
-    return model

invokeai/backend/quantization/scripts/load_flux_model_bnb_llm_int8.py

@@ -1,79 +0,0 @@
-from pathlib import Path
-
-import accelerate
-from safetensors.torch import load_file, save_file
-
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.util import params
-from invokeai.backend.quantization.bnb_llm_int8 import quantize_model_llm_int8
-from invokeai.backend.quantization.scripts.load_flux_model_bnb_nf4 import log_time
-
-
-def main():
-    """A script for quantizing a FLUX transformer model using the bitsandbytes LLM.int8() quantization method.
-
-    This script is primarily intended for reference. The script params (e.g. the model_path, modules_to_not_convert,
-    etc.) are hardcoded and would need to be modified for other use cases.
-    """
-    # Load the FLUX transformer model onto the meta device.
-    model_path = Path(
-        "/data/invokeai/models/.download_cache/https__huggingface.co_black-forest-labs_flux.1-schnell_resolve_main_flux1-schnell.safetensors/flux1-schnell.safetensors"
-    )
-
-    with log_time("Intialize FLUX transformer on meta device"):
-        # TODO(ryand): Determine if this is a schnell model or a dev model and load the appropriate config.
-        p = params["flux-schnell"]
-
-        # Initialize the model on the "meta" device.
-        with accelerate.init_empty_weights():
-            model = Flux(p)
-
-    # TODO(ryand): We may want to add some modules to not quantize here (e.g. the proj_out layer). See the accelerate
-    # `get_keys_to_not_convert(...)` function for a heuristic to determine which modules to not quantize.
-    modules_to_not_convert: set[str] = set()
-
-    model_int8_path = model_path.parent / "bnb_llm_int8.safetensors"
-    if model_int8_path.exists():
-        # The quantized model already exists, load it and return it.
-        print(f"A pre-quantized model already exists at '{model_int8_path}'. Attempting to load it...")
-
-        # Replace the linear layers with LLM.int8() quantized linear layers (still on the meta device).
-        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
-            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
-
-        with log_time("Load state dict into model"):
-            sd = load_file(model_int8_path)
-            model.load_state_dict(sd, strict=True, assign=True)
-
-        with log_time("Move model to cuda"):
-            model = model.to("cuda")
-
-        print(f"Successfully loaded pre-quantized model from '{model_int8_path}'.")
-
-    else:
-        # The quantized model does not exist, quantize the model and save it.
-        print(f"No pre-quantized model found at '{model_int8_path}'. Quantizing the model...")
-
-        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
-            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
-
-        with log_time("Load state dict into model"):
-            state_dict = load_file(model_path)
-            # TODO(ryand): Cast the state_dict to the appropriate dtype?
-            model.load_state_dict(state_dict, strict=True, assign=True)
-
-        with log_time("Move model to cuda and quantize"):
-            model = model.to("cuda")
-
-        with log_time("Save quantized model"):
-            model_int8_path.parent.mkdir(parents=True, exist_ok=True)
-            save_file(model.state_dict(), model_int8_path)
-
-        print(f"Successfully quantized and saved model to '{model_int8_path}'.")
-
-    assert isinstance(model, Flux)
-    return model
-
-
-if __name__ == "__main__":
-    main()

invokeai/backend/quantization/scripts/load_flux_model_bnb_nf4.py

@@ -1,96 +0,0 @@
-import time
-from contextlib import contextmanager
-from pathlib import Path
-
-import accelerate
-import torch
-from safetensors.torch import load_file, save_file
-
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.util import params
-from invokeai.backend.quantization.bnb_nf4 import quantize_model_nf4
-
-
-@contextmanager
-def log_time(name: str):
-    """Helper context manager to log the time taken by a block of code."""
-    start = time.time()
-    try:
-        yield None
-    finally:
-        end = time.time()
-        print(f"'{name}' took {end - start:.4f} secs")
-
-
-def main():
-    """A script for quantizing a FLUX transformer model using the bitsandbytes NF4 quantization method.
-
-    This script is primarily intended for reference. The script params (e.g. the model_path, modules_to_not_convert,
-    etc.) are hardcoded and would need to be modified for other use cases.
-    """
-    model_path = Path(
-        "/data/invokeai/models/.download_cache/https__huggingface.co_black-forest-labs_flux.1-schnell_resolve_main_flux1-schnell.safetensors/flux1-schnell.safetensors"
-    )
-
-    # inference_dtype = torch.bfloat16
-    with log_time("Intialize FLUX transformer on meta device"):
-        # TODO(ryand): Determine if this is a schnell model or a dev model and load the appropriate config.
-        p = params["flux-schnell"]
-
-        # Initialize the model on the "meta" device.
-        with accelerate.init_empty_weights():
-            model = Flux(p)
-
-    # TODO(ryand): We may want to add some modules to not quantize here (e.g. the proj_out layer). See the accelerate
-    # `get_keys_to_not_convert(...)` function for a heuristic to determine which modules to not quantize.
-    modules_to_not_convert: set[str] = set()
-
-    model_nf4_path = model_path.parent / "bnb_nf4.safetensors"
-    if model_nf4_path.exists():
-        # The quantized model already exists, load it and return it.
-        print(f"A pre-quantized model already exists at '{model_nf4_path}'. Attempting to load it...")
-
-        # Replace the linear layers with NF4 quantized linear layers (still on the meta device).
-        with log_time("Replace linear layers with NF4 layers"), accelerate.init_empty_weights():
-            model = quantize_model_nf4(
-                model, modules_to_not_convert=modules_to_not_convert, compute_dtype=torch.bfloat16
-            )
-
-        with log_time("Load state dict into model"):
-            state_dict = load_file(model_nf4_path)
-            model.load_state_dict(state_dict, strict=True, assign=True)
-
-        with log_time("Move model to cuda"):
-            model = model.to("cuda")
-
-        print(f"Successfully loaded pre-quantized model from '{model_nf4_path}'.")
-
-    else:
-        # The quantized model does not exist, quantize the model and save it.
-        print(f"No pre-quantized model found at '{model_nf4_path}'. Quantizing the model...")
-
-        with log_time("Replace linear layers with NF4 layers"), accelerate.init_empty_weights():
-            model = quantize_model_nf4(
-                model, modules_to_not_convert=modules_to_not_convert, compute_dtype=torch.bfloat16
-            )
-
-        with log_time("Load state dict into model"):
-            state_dict = load_file(model_path)
-            # TODO(ryand): Cast the state_dict to the appropriate dtype?
-            model.load_state_dict(state_dict, strict=True, assign=True)
-
-        with log_time("Move model to cuda and quantize"):
-            model = model.to("cuda")
-
-        with log_time("Save quantized model"):
-            model_nf4_path.parent.mkdir(parents=True, exist_ok=True)
-            save_file(model.state_dict(), model_nf4_path)
-
-        print(f"Successfully quantized and saved model to '{model_nf4_path}'.")
-
-    assert isinstance(model, Flux)
-    return model
-
-
-if __name__ == "__main__":
-    main()

invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py

@@ -1,92 +0,0 @@
-from pathlib import Path
-
-import accelerate
-from safetensors.torch import load_file, save_file
-from transformers import AutoConfig, AutoModelForTextEncoding, T5EncoderModel
-
-from invokeai.backend.quantization.bnb_llm_int8 import quantize_model_llm_int8
-from invokeai.backend.quantization.scripts.load_flux_model_bnb_nf4 import log_time
-
-
-def load_state_dict_into_t5(model: T5EncoderModel, state_dict: dict):
-    # There is a shared reference to a single weight tensor in the model.
-    # Both "encoder.embed_tokens.weight" and "shared.weight" refer to the same tensor, so only the latter should
-    # be present in the state_dict.
-    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False, assign=True)
-    assert len(unexpected_keys) == 0
-    assert set(missing_keys) == {"encoder.embed_tokens.weight"}
-    # Assert that the layers we expect to be shared are actually shared.
-    assert model.encoder.embed_tokens.weight is model.shared.weight
-
-
-def main():
-    """A script for quantizing a T5 text encoder model using the bitsandbytes LLM.int8() quantization method.
-
-    This script is primarily intended for reference. The script params (e.g. the model_path, modules_to_not_convert,
-    etc.) are hardcoded and would need to be modified for other use cases.
-    """
-    model_path = Path("/data/misc/text_encoder_2")
-
-    with log_time("Intialize T5 on meta device"):
-        model_config = AutoConfig.from_pretrained(model_path)
-        with accelerate.init_empty_weights():
-            model = AutoModelForTextEncoding.from_config(model_config)
-
-    # TODO(ryand): We may want to add some modules to not quantize here (e.g. the proj_out layer). See the accelerate
-    # `get_keys_to_not_convert(...)` function for a heuristic to determine which modules to not quantize.
-    modules_to_not_convert: set[str] = set()
-
-    model_int8_path = model_path / "bnb_llm_int8.safetensors"
-    if model_int8_path.exists():
-        # The quantized model already exists, load it and return it.
-        print(f"A pre-quantized model already exists at '{model_int8_path}'. Attempting to load it...")
-
-        # Replace the linear layers with LLM.int8() quantized linear layers (still on the meta device).
-        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
-            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
-
-        with log_time("Load state dict into model"):
-            sd = load_file(model_int8_path)
-            load_state_dict_into_t5(model, sd)
-
-        with log_time("Move model to cuda"):
-            model = model.to("cuda")
-
-        print(f"Successfully loaded pre-quantized model from '{model_int8_path}'.")
-
-    else:
-        # The quantized model does not exist, quantize the model and save it.
-        print(f"No pre-quantized model found at '{model_int8_path}'. Quantizing the model...")
-
-        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
-            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
-
-        with log_time("Load state dict into model"):
-            # Load sharded state dict.
-            files = list(model_path.glob("*.safetensors"))
-            state_dict = {}
-            for file in files:
-                sd = load_file(file)
-                state_dict.update(sd)
-            load_state_dict_into_t5(model, state_dict)
-
-        with log_time("Move model to cuda and quantize"):
-            model = model.to("cuda")
-
-        with log_time("Save quantized model"):
-            model_int8_path.parent.mkdir(parents=True, exist_ok=True)
-            state_dict = model.state_dict()
-            state_dict.pop("encoder.embed_tokens.weight")
-            save_file(state_dict, model_int8_path)
-            # This handling of shared weights could also be achieved with save_model(...), but then we'd lose control
-            # over which keys are kept. And, the corresponding load_model(...) function does not support assign=True.
-            # save_model(model, model_int8_path)
-
-        print(f"Successfully quantized and saved model to '{model_int8_path}'.")
-
-    assert isinstance(model, T5EncoderModel)
-    return model
-
-
-if __name__ == "__main__":
-    main()

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -25,6 +25,11 @@ class BasicConditioningInfo:
         return self
 
 
+@dataclass
+class ConditioningFieldData:
+    conditionings: List[BasicConditioningInfo]
+
+
 @dataclass
 class SDXLConditioningInfo(BasicConditioningInfo):
     """SDXL text conditioning information produced by Compel."""
@@ -38,22 +43,6 @@ class SDXLConditioningInfo(BasicConditioningInfo):
         return super().to(device=device, dtype=dtype)
 
 
-@dataclass
-class FLUXConditioningInfo:
-    clip_embeds: torch.Tensor
-    t5_embeds: torch.Tensor
-
-    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
-        self.clip_embeds = self.clip_embeds.to(device=device, dtype=dtype)
-        self.t5_embeds = self.t5_embeds.to(device=device, dtype=dtype)
-        return self
-
-
-@dataclass
-class ConditioningFieldData:
-    conditionings: List[BasicConditioningInfo] | List[SDXLConditioningInfo] | List[FLUXConditioningInfo]
-
-
 @dataclass
 class IPAdapterConditioningInfo:
     cond_image_prompt_embeds: torch.Tensor

invokeai/backend/util/__init__.py

@@ -3,9 +3,10 @@ Initialization file for invokeai.backend.util
 """
 
 from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.backend.util.util import Chdir, directory_size
+from invokeai.backend.util.util import GIG, Chdir, directory_size
 
 __all__ = [
+    "GIG",
     "directory_size",
     "Chdir",
     "InvokeAILogger",

invokeai/backend/util/util.py

@@ -7,6 +7,9 @@ from pathlib import Path
 
 from PIL import Image
 
+# actual size of a gig
+GIG = 1073741824
+
 
 def slugify(value: str, allow_unicode: bool = False) -> str:
     """

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 .",
@@ -52,17 +52,17 @@
     }
   },
   "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",
     "compare-versions": "^6.1.1",
     "dateformat": "^5.0.3",
@@ -74,6 +74,8 @@
     "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",
@@ -88,7 +90,6 @@
     "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",
@@ -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/pnpm-lock.yaml

@@ -5,9 +5,6 @@ settings:
   excludeLinksFromLockfile: false
 
 dependencies:
-  '@chakra-ui/react-use-size':
-    specifier: ^2.1.0
-    version: 2.1.0(react@18.3.1)
   '@dagrejs/dagre':
     specifier: ^1.1.3
     version: 1.1.3
@@ -27,8 +24,8 @@ dependencies:
     specifier: ^5.0.20
     version: 5.0.20
   '@invoke-ai/ui-library':
-    specifier: ^0.0.29
-    version: 0.0.29(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1)
+    specifier: ^0.0.32
+    version: 0.0.32(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1)
   '@nanostores/react':
     specifier: ^0.7.3
     version: 0.7.3(nanostores@0.11.2)(react@18.3.1)
@@ -38,9 +35,12 @@ dependencies:
   '@roarr/browser-log-writer':
     specifier: ^1.3.0
     version: 1.3.0
+  async-mutex:
+    specifier: ^0.5.0
+    version: 0.5.0
   chakra-react-select:
     specifier: ^4.9.1
-    version: 4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.0)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1)
+    version: 4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.3)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1)
   compare-versions:
     specifier: ^6.1.1
     version: 6.1.1
@@ -71,6 +71,12 @@ dependencies:
   lodash-es:
     specifier: ^4.17.21
     version: 4.17.21
+  lru-cache:
+    specifier: ^11.0.0
+    version: 11.0.0
+  nanoid:
+    specifier: ^5.0.7
+    version: 5.0.7
   nanostores:
     specifier: ^0.11.2
     version: 0.11.2
@@ -113,9 +119,6 @@ dependencies:
   react-icons:
     specifier: ^5.2.1
     version: 5.2.1(react@18.3.1)
-  react-konva:
-    specifier: ^18.2.10
-    version: 18.2.10(konva@9.3.14)(react-dom@18.3.1)(react@18.3.1)
   react-redux:
     specifier: 9.1.2
     version: 9.1.2(@types/react@18.3.3)(react@18.3.1)(redux@5.0.1)
@@ -155,15 +158,15 @@ dependencies:
   socket.io-client:
     specifier: ^4.7.5
     version: 4.7.5
+  stable-hash:
+    specifier: ^0.0.4
+    version: 0.0.4
   use-debounce:
     specifier: ^10.0.2
     version: 10.0.2(react@18.3.1)
   use-device-pixel-ratio:
     specifier: ^1.1.2
     version: 1.1.2(react@18.3.1)
-  use-image:
-    specifier: ^1.1.1
-    version: 1.1.1(react-dom@18.3.1)(react@18.3.1)
   uuid:
     specifier: ^10.0.0
     version: 10.0.0
@@ -1749,6 +1752,13 @@ packages:
     dependencies:
       regenerator-runtime: 0.14.1
 
+  /@babel/runtime@7.25.4:
+    resolution: {integrity: sha512-DSgLeL/FNcpXuzav5wfYvHCGvynXkJbn3Zvc3823AEe9nPwW9IK4UoCSS5yGymmQzN0pCPvivtgS6/8U2kkm1w==}
+    engines: {node: '>=6.9.0'}
+    dependencies:
+      regenerator-runtime: 0.14.1
+    dev: false
+
   /@babel/template@7.24.0:
     resolution: {integrity: sha512-Bkf2q8lMB0AFpX0NFEqSbx1OkTHf0f+0j82mkw+ZpzBnkk7e9Ql0891vlfgi+kHwOk8tQjiQHpqh4LaSa0fKEA==}
     engines: {node: '>=6.9.0'}
@@ -1835,7 +1845,7 @@ packages:
       '@chakra-ui/react-use-controllable-state': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/transition': 2.1.0(framer-motion@10.18.0)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
@@ -1851,7 +1861,7 @@ packages:
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
       '@chakra-ui/spinner': 2.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1869,7 +1879,7 @@ packages:
       '@chakra-ui/react-children-utils': 2.0.6(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1882,7 +1892,7 @@ packages:
       '@chakra-ui/react-children-utils': 2.0.6(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1902,7 +1912,7 @@ packages:
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
       '@chakra-ui/spinner': 2.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1913,7 +1923,7 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1932,7 +1942,7 @@ packages:
       '@chakra-ui/react-use-safe-layout-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/visually-hidden': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@zag-js/focus-visible': 0.16.0
       react: 18.3.1
@@ -1955,7 +1965,7 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/icon': 3.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1974,7 +1984,7 @@ packages:
       '@chakra-ui/system': '>=2.0.0'
       react: '>=18'
     dependencies:
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1989,13 +1999,13 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/css-reset@2.3.0(@emotion/react@11.13.0)(react@18.3.1):
+  /@chakra-ui/css-reset@2.3.0(@emotion/react@11.13.3)(react@18.3.1):
     resolution: {integrity: sha512-cQwwBy5O0jzvl0K7PLTLgp8ijqLPKyuEMiDXwYzl95seD3AoeuoCLyzZcJtVqaUZ573PiBdAbY/IlZcwDOItWg==}
     peerDependencies:
       '@emotion/react': '>=10.0.35'
       react: '>=18'
     dependencies:
-      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2028,7 +2038,7 @@ packages:
       '@chakra-ui/react-use-safe-layout-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2059,7 +2069,7 @@ packages:
       '@chakra-ui/react-types': 2.0.7(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2082,7 +2092,7 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2093,7 +2103,7 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/icon': 3.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2105,7 +2115,7 @@ packages:
     dependencies:
       '@chakra-ui/react-use-safe-layout-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2120,7 +2130,7 @@ packages:
       '@chakra-ui/react-children-utils': 2.0.6(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2136,7 +2146,7 @@ packages:
       '@chakra-ui/react-children-utils': 2.0.6(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2161,7 +2171,7 @@ packages:
       '@chakra-ui/breakpoint-utils': 2.0.8
       '@chakra-ui/react-env': 3.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2186,7 +2196,7 @@ packages:
       '@chakra-ui/react-use-outside-click': 2.2.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/transition': 2.1.0(framer-motion@10.18.0)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
@@ -2213,7 +2223,7 @@ packages:
       '@chakra-ui/react-use-outside-click': 2.2.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/transition': 2.1.0(framer-motion@11.3.24)(react@18.3.1)
       framer-motion: 11.3.24(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
@@ -2234,7 +2244,7 @@ packages:
       '@chakra-ui/react-types': 2.0.7(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/transition': 2.1.0(framer-motion@10.18.0)(react@18.3.1)
       aria-hidden: 1.2.4
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
@@ -2263,7 +2273,7 @@ packages:
       '@chakra-ui/react-use-safe-layout-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2287,7 +2297,7 @@ packages:
       '@chakra-ui/react-use-controllable-state': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2309,7 +2319,7 @@ packages:
       '@chakra-ui/react-use-focus-on-pointer-down': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
     dev: false
@@ -2344,11 +2354,11 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/provider@2.4.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react-dom@18.3.1)(react@18.3.1):
+  /@chakra-ui/provider@2.4.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react-dom@18.3.1)(react@18.3.1):
     resolution: {integrity: sha512-w0Tef5ZCJK1mlJorcSjItCSbyvVuqpvyWdxZiVQmE6fvSJR83wZof42ux0+sfWD+I7rHSfj+f9nzhNaEWClysw==}
     peerDependencies:
       '@emotion/react': ^11.0.0
@@ -2356,13 +2366,13 @@ packages:
       react: '>=18'
       react-dom: '>=18'
     dependencies:
-      '@chakra-ui/css-reset': 2.3.0(@emotion/react@11.13.0)(react@18.3.1)
+      '@chakra-ui/css-reset': 2.3.0(@emotion/react@11.13.3)(react@18.3.1)
       '@chakra-ui/portal': 2.1.0(react-dom@18.3.1)(react@18.3.1)
       '@chakra-ui/react-env': 3.1.0(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/utils': 2.0.15
-      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
-      '@emotion/styled': 11.13.0(@emotion/react@11.13.0)(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.3)(react@18.3.1)
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
     dev: false
@@ -2378,7 +2388,7 @@ packages:
       '@chakra-ui/react-types': 2.0.7(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       '@zag-js/focus-visible': 0.16.0
       react: 18.3.1
     dev: false
@@ -2578,7 +2588,7 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/react@2.8.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(@types/react@18.3.3)(framer-motion@10.18.0)(react-dom@18.3.1)(react@18.3.1):
+  /@chakra-ui/react@2.8.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.3)(framer-motion@10.18.0)(react-dom@18.3.1)(react@18.3.1):
     resolution: {integrity: sha512-Hn0moyxxyCDKuR9ywYpqgX8dvjqwu9ArwpIb9wHNYjnODETjLwazgNIliCVBRcJvysGRiV51U2/JtJVrpeCjUQ==}
     peerDependencies:
       '@emotion/react': ^11.0.0
@@ -2597,7 +2607,7 @@ packages:
       '@chakra-ui/close-button': 2.1.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/control-box': 2.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/counter': 2.1.0(react@18.3.1)
-      '@chakra-ui/css-reset': 2.3.0(@emotion/react@11.13.0)(react@18.3.1)
+      '@chakra-ui/css-reset': 2.3.0(@emotion/react@11.13.3)(react@18.3.1)
       '@chakra-ui/editable': 3.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/focus-lock': 2.1.0(@types/react@18.3.3)(react@18.3.1)
       '@chakra-ui/form-control': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
@@ -2616,7 +2626,7 @@ packages:
       '@chakra-ui/popper': 3.1.0(react@18.3.1)
       '@chakra-ui/portal': 2.1.0(react-dom@18.3.1)(react@18.3.1)
       '@chakra-ui/progress': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/provider': 2.4.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react-dom@18.3.1)(react@18.3.1)
+      '@chakra-ui/provider': 2.4.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react-dom@18.3.1)(react@18.3.1)
       '@chakra-ui/radio': 2.1.2(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/react-env': 3.1.0(react@18.3.1)
       '@chakra-ui/select': 2.1.2(@chakra-ui/system@2.6.2)(react@18.3.1)
@@ -2628,7 +2638,7 @@ packages:
       '@chakra-ui/stepper': 2.3.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/styled-system': 2.9.2
       '@chakra-ui/switch': 2.1.2(@chakra-ui/system@2.6.2)(framer-motion@10.18.0)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/table': 2.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/tabs': 3.0.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/tag': 3.1.1(@chakra-ui/system@2.6.2)(react@18.3.1)
@@ -2640,8 +2650,8 @@ packages:
       '@chakra-ui/transition': 2.1.0(framer-motion@10.18.0)(react@18.3.1)
       '@chakra-ui/utils': 2.0.15
       '@chakra-ui/visually-hidden': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
-      '@emotion/styled': 11.13.0(@emotion/react@11.13.0)(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.3)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
@@ -2657,7 +2667,7 @@ packages:
     dependencies:
       '@chakra-ui/form-control': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2674,7 +2684,7 @@ packages:
       '@chakra-ui/media-query': 3.3.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/react-use-previous': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2684,7 +2694,7 @@ packages:
       '@chakra-ui/system': '>=2.0.0'
       react: '>=18'
     dependencies:
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2704,7 +2714,7 @@ packages:
       '@chakra-ui/react-use-pan-event': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-size': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2715,7 +2725,7 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2728,7 +2738,7 @@ packages:
       '@chakra-ui/icon': 3.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2741,7 +2751,7 @@ packages:
       '@chakra-ui/icon': 3.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2762,12 +2772,12 @@ packages:
     dependencies:
       '@chakra-ui/checkbox': 2.3.2(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/system@2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1):
+  /@chakra-ui/system@2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1):
     resolution: {integrity: sha512-EGtpoEjLrUu4W1fHD+a62XR+hzC5YfsWm+6lO0Kybcga3yYEij9beegO0jZgug27V+Rf7vns95VPVP6mFd/DEQ==}
     peerDependencies:
       '@emotion/react': ^11.0.0
@@ -2780,8 +2790,8 @@ packages:
       '@chakra-ui/styled-system': 2.9.2
       '@chakra-ui/theme-utils': 2.0.21
       '@chakra-ui/utils': 2.0.15
-      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
-      '@emotion/styled': 11.13.0(@emotion/react@11.13.0)(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.3)(react@18.3.1)
       react: 18.3.1
       react-fast-compare: 3.2.2
     dev: false
@@ -2794,7 +2804,7 @@ packages:
     dependencies:
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2813,7 +2823,7 @@ packages:
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-safe-layout-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2825,7 +2835,7 @@ packages:
     dependencies:
       '@chakra-ui/icon': 3.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2837,7 +2847,7 @@ packages:
     dependencies:
       '@chakra-ui/form-control': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2888,7 +2898,7 @@ packages:
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
       '@chakra-ui/styled-system': 2.9.2
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/theme': 3.3.1(@chakra-ui/styled-system@2.9.2)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
@@ -2911,7 +2921,7 @@ packages:
       '@chakra-ui/react-use-event-listener': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
@@ -2954,7 +2964,7 @@ packages:
       '@chakra-ui/system': '>=2.0.0'
       react: '>=18'
     dependencies:
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -3037,10 +3047,10 @@ packages:
     resolution: {integrity: sha512-y2WQb+oP8Jqvvclh8Q55gLUyb7UFvgv7eJfsj7td5TToBrIUtPay2kMrZi4xjq9qw2vD0ZR5fSho0yqoFgX7Rw==}
     dependencies:
       '@babel/helper-module-imports': 7.24.7
-      '@babel/runtime': 7.25.0
+      '@babel/runtime': 7.25.4
       '@emotion/hash': 0.9.2
       '@emotion/memoize': 0.9.0
-      '@emotion/serialize': 1.3.0
+      '@emotion/serialize': 1.3.1
       babel-plugin-macros: 3.1.0
       convert-source-map: 1.9.0
       escape-string-regexp: 4.0.0
@@ -3128,8 +3138,8 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@emotion/react@11.13.0(@types/react@18.3.3)(react@18.3.1):
-    resolution: {integrity: sha512-WkL+bw1REC2VNV1goQyfxjx1GYJkcc23CRQkXX+vZNLINyfI7o+uUn/rTGPt/xJ3bJHd5GcljgnxHf4wRw5VWQ==}
+  /@emotion/react@11.13.3(@types/react@18.3.3)(react@18.3.1):
+    resolution: {integrity: sha512-lIsdU6JNrmYfJ5EbUCf4xW1ovy5wKQ2CkPRM4xogziOxH1nXxBSjpC9YqbFAP7circxMfYp+6x676BqWcEiixg==}
     peerDependencies:
       '@types/react': '*'
       react: '>=16.8.0'
@@ -3137,10 +3147,10 @@ packages:
       '@types/react':
         optional: true
     dependencies:
-      '@babel/runtime': 7.25.0
+      '@babel/runtime': 7.25.4
       '@emotion/babel-plugin': 11.12.0
       '@emotion/cache': 11.13.1
-      '@emotion/serialize': 1.3.0
+      '@emotion/serialize': 1.3.1
       '@emotion/use-insertion-effect-with-fallbacks': 1.1.0(react@18.3.1)
       '@emotion/utils': 1.4.0
       '@emotion/weak-memoize': 0.4.0
@@ -3161,12 +3171,12 @@ packages:
       csstype: 3.1.3
     dev: false
 
-  /@emotion/serialize@1.3.0:
-    resolution: {integrity: sha512-jACuBa9SlYajnpIVXB+XOXnfJHyckDfe6fOpORIM6yhBDlqGuExvDdZYHDQGoDf3bZXGv7tNr+LpLjJqiEQ6EA==}
+  /@emotion/serialize@1.3.1:
+    resolution: {integrity: sha512-dEPNKzBPU+vFPGa+z3axPRn8XVDetYORmDC0wAiej+TNcOZE70ZMJa0X7JdeoM6q/nWTMZeLpN/fTnD9o8MQBA==}
     dependencies:
       '@emotion/hash': 0.9.2
       '@emotion/memoize': 0.9.0
-      '@emotion/unitless': 0.9.0
+      '@emotion/unitless': 0.10.0
       '@emotion/utils': 1.4.0
       csstype: 3.1.3
     dev: false
@@ -3179,7 +3189,7 @@ packages:
     resolution: {integrity: sha512-fTBW9/8r2w3dXWYM4HCB1Rdp8NLibOw2+XELH5m5+AkWiL/KqYX6dc0kKYlaYyKjrQ6ds33MCdMPEwgs2z1rqg==}
     dev: false
 
-  /@emotion/styled@11.13.0(@emotion/react@11.13.0)(@types/react@18.3.3)(react@18.3.1):
+  /@emotion/styled@11.13.0(@emotion/react@11.13.3)(@types/react@18.3.3)(react@18.3.1):
     resolution: {integrity: sha512-tkzkY7nQhW/zC4hztlwucpT8QEZ6eUzpXDRhww/Eej4tFfO0FxQYWRyg/c5CCXa4d/f174kqeXYjuQRnhzf6dA==}
     peerDependencies:
       '@emotion/react': ^11.0.0-rc.0
@@ -3189,11 +3199,11 @@ packages:
       '@types/react':
         optional: true
     dependencies:
-      '@babel/runtime': 7.25.0
+      '@babel/runtime': 7.25.4
       '@emotion/babel-plugin': 11.12.0
       '@emotion/is-prop-valid': 1.3.0
-      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
-      '@emotion/serialize': 1.3.0
+      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/serialize': 1.3.1
       '@emotion/use-insertion-effect-with-fallbacks': 1.1.0(react@18.3.1)
       '@emotion/utils': 1.4.0
       '@types/react': 18.3.3
@@ -3202,12 +3212,12 @@ packages:
       - supports-color
     dev: false
 
-  /@emotion/unitless@0.8.1:
-    resolution: {integrity: sha512-KOEGMu6dmJZtpadb476IsZBclKvILjopjUii3V+7MnXIQCYh8W3NgNcgwo21n9LXZX6EDIKvqfjYxXebDwxKmQ==}
+  /@emotion/unitless@0.10.0:
+    resolution: {integrity: sha512-dFoMUuQA20zvtVTuxZww6OHoJYgrzfKM1t52mVySDJnMSEa08ruEvdYQbhvyu6soU+NeLVd3yKfTfT0NeV6qGg==}
     dev: false
 
-  /@emotion/unitless@0.9.0:
-    resolution: {integrity: sha512-TP6GgNZtmtFaFcsOgExdnfxLLpRDla4Q66tnenA9CktvVSdNKDvMVuUah4QvWPIpNjrWsGg3qeGo9a43QooGZQ==}
+  /@emotion/unitless@0.8.1:
+    resolution: {integrity: sha512-KOEGMu6dmJZtpadb476IsZBclKvILjopjUii3V+7MnXIQCYh8W3NgNcgwo21n9LXZX6EDIKvqfjYxXebDwxKmQ==}
     dev: false
 
   /@emotion/use-insertion-effect-with-fallbacks@1.0.1(react@18.3.1):
@@ -3561,8 +3571,8 @@ packages:
       prettier: 3.3.3
     dev: true
 
-  /@invoke-ai/ui-library@0.0.29(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-7SYOaiEEKk9iHk0hg2R2yVxiuV3I1x6bDEv0R3Y2tCH/Aq5XDG2tR+d7SQAPqf5+za3S+qfNFjbjl7GvEMwqmA==}
+  /@invoke-ai/ui-library@0.0.32(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-JxAoblrDu/cZ4ha9KO4ry5OWvyLUE1Dj28i+ciMaDNUpC/cN+IyiTbUBoFoPaoN5JP8Zpd/MYCcmF2qsziHDzg==}
     peerDependencies:
       '@fontsource-variable/inter': ^5.0.16
       react: ^18.2.0
@@ -3572,14 +3582,14 @@ packages:
       '@chakra-ui/icons': 2.1.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/layout': 2.3.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/portal': 2.1.0(react-dom@18.3.1)(react@18.3.1)
-      '@chakra-ui/react': 2.8.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(@types/react@18.3.3)(framer-motion@10.18.0)(react-dom@18.3.1)(react@18.3.1)
+      '@chakra-ui/react': 2.8.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.3)(framer-motion@10.18.0)(react-dom@18.3.1)(react@18.3.1)
       '@chakra-ui/styled-system': 2.9.2
       '@chakra-ui/theme-tools': 2.1.2(@chakra-ui/styled-system@2.9.2)
-      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
-      '@emotion/styled': 11.13.0(@emotion/react@11.13.0)(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.3)(react@18.3.1)
       '@fontsource-variable/inter': 5.0.20
       '@nanostores/react': 0.7.3(nanostores@0.11.2)(react@18.3.1)
-      chakra-react-select: 4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.0)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1)
+      chakra-react-select: 4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.3)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       lodash-es: 4.17.21
       nanostores: 0.11.2
@@ -5202,12 +5212,6 @@ packages:
       '@types/react': 18.3.3
     dev: true
 
-  /@types/react-reconciler@0.28.8:
-    resolution: {integrity: sha512-SN9c4kxXZonFhbX4hJrZy37yw9e7EIxcpHCxQv5JUS18wDE5ovkQKlqQEkufdJCCMfuI9BnjUJvhYeJ9x5Ra7g==}
-    dependencies:
-      '@types/react': 18.3.3
-    dev: false
-
   /@types/react-transition-group@4.4.10:
     resolution: {integrity: sha512-hT/+s0VQs2ojCX823m60m5f0sL5idt9SO6Tj6Dg+rdphGPIeJbJ6CxvBYkgkGKrYeDjvIpKTR38UzmtHJOGW3Q==}
     dependencies:
@@ -5777,7 +5781,7 @@ packages:
     resolution: {integrity: sha512-y+CcFFwelSXpLZk/7fMB2mUbGtX9lKycf1MWJ7CaTIERyitVlyQx6C+sxcROU2BAJ24OiZyK+8wj2i8AlBoS3A==}
     engines: {node: '>=10'}
     dependencies:
-      tslib: 2.6.3
+      tslib: 2.7.0
     dev: false
 
   /aria-query@5.3.0:
@@ -5903,6 +5907,12 @@ packages:
       tslib: 2.6.3
     dev: true
 
+  /async-mutex@0.5.0:
+    resolution: {integrity: sha512-1A94B18jkJ3DYq284ohPxoXbfTA5HsQ7/Mf4DEhcyLx3Bz27Rh59iScbB6EPiP+B+joue6YCxcMXSbFC1tZKwA==}
+    dependencies:
+      tslib: 2.6.3
+    dev: false
+
   /attr-accept@2.2.2:
     resolution: {integrity: sha512-7prDjvt9HmqiZ0cl5CRjtS84sEyhsHP2coDkaZKRKVfCDo9s7iw7ChVmar78Gu9pC4SoR/28wFu/G5JJhTnqEg==}
     engines: {node: '>=4'}
@@ -6123,7 +6133,7 @@ packages:
       type-detect: 4.0.8
     dev: true
 
-  /chakra-react-select@4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.0)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1):
+  /chakra-react-select@4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.3)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1):
     resolution: {integrity: sha512-jmgfN+S/wnTaCp3pW30GYDIZ5J8jWcT1gIbhpw6RdKV+atm/U4/sT+gaHOHHhRL8xeaYip+iI/m8MPGREkve0w==}
     peerDependencies:
       '@chakra-ui/form-control': ^2.0.0
@@ -6143,8 +6153,8 @@ packages:
       '@chakra-ui/media-query': 3.3.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/menu': 2.2.1(@chakra-ui/system@2.6.2)(framer-motion@11.3.24)(react@18.3.1)
       '@chakra-ui/spinner': 2.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
-      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
       react-select: 5.8.0(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1)
@@ -7569,7 +7579,7 @@ packages:
     resolution: {integrity: sha512-QFaHbhv9WPUeLYBDe/PAuLKJ4Dd9OPvKs9xZBr3yLXnUrDNaVXKu2baDBXe3naPY30hgHYSsf2JW4jzas2mDEQ==}
     engines: {node: '>=10'}
     dependencies:
-      tslib: 2.6.3
+      tslib: 2.7.0
     dev: false
 
   /for-each@0.3.3:
@@ -7608,7 +7618,7 @@ packages:
     dependencies:
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
-      tslib: 2.6.3
+      tslib: 2.7.0
     optionalDependencies:
       '@emotion/is-prop-valid': 0.8.8
     dev: false
@@ -8384,15 +8394,6 @@ packages:
       set-function-name: 2.0.2
     dev: true
 
-  /its-fine@1.2.5(react@18.3.1):
-    resolution: {integrity: sha512-fXtDA0X0t0eBYAGLVM5YsgJGsJ5jEmqZEPrGbzdf5awjv0xE7nqv3TVnvtUF060Tkes15DbDAKW/I48vsb6SyA==}
-    peerDependencies:
-      react: '>=18.0'
-    dependencies:
-      '@types/react-reconciler': 0.28.8
-      react: 18.3.1
-    dev: false
-
   /jackspeak@2.3.6:
     resolution: {integrity: sha512-N3yCS/NegsOBokc8GAdM8UcmfsKiSS8cipheD/nivzr700H+nsMOxJjQnvwOcRYVuFkdH0wGUvW2WbXGmrZGbQ==}
     engines: {node: '>=14'}
@@ -8710,6 +8711,11 @@ packages:
     engines: {node: 14 || >=16.14}
     dev: true
 
+  /lru-cache@11.0.0:
+    resolution: {integrity: sha512-Qv32eSV1RSCfhY3fpPE2GNZ8jgM9X7rdAfemLWqTUxwiyIC4jJ6Sy0fZ8H+oLWevO6i4/bizg7c8d8i6bxrzbA==}
+    engines: {node: 20 || >=22}
+    dev: false
+
   /lru-cache@5.1.1:
     resolution: {integrity: sha512-KpNARQA3Iwv+jTA0utUVVbrh+Jlrr1Fv0e56GGzAFOXN7dk/FviaDW8LHmK52DlcH4WP2n6gI8vN1aesBFgo9w==}
     dependencies:
@@ -8997,6 +9003,12 @@ packages:
     hasBin: true
     dev: true
 
+  /nanoid@5.0.7:
+    resolution: {integrity: sha512-oLxFY2gd2IqnjcYyOXD8XGCftpGtZP2AbHbOkthDkvRywH5ayNtPVy9YlOPcHckXzbLTCHpkb7FB+yuxKV13pQ==}
+    engines: {node: ^18 || >=20}
+    hasBin: true
+    dev: false
+
   /nanostores@0.11.2:
     resolution: {integrity: sha512-6bucNxMJA5rNV554WQl+MWGng0QVMzlRgpKTHHfIbVLrhQ+yRXBychV9ECGVuuUfCMQPjfIG9bj8oJFZ9hYP/Q==}
     engines: {node: ^18.0.0 || >=20.0.0}
@@ -9614,7 +9626,7 @@ packages:
     peerDependencies:
       react: ^15.3.0 || ^16.0.0 || ^17.0.0 || ^18.0.0
     dependencies:
-      '@babel/runtime': 7.25.0
+      '@babel/runtime': 7.25.4
       react: 18.3.1
     dev: false
 
@@ -9709,7 +9721,7 @@ packages:
       '@types/react':
         optional: true
     dependencies:
-      '@babel/runtime': 7.25.0
+      '@babel/runtime': 7.25.4
       '@types/react': 18.3.3
       focus-lock: 1.3.5
       prop-types: 15.8.1
@@ -9771,7 +9783,7 @@ packages:
       react-native:
         optional: true
     dependencies:
-      '@babel/runtime': 7.25.0
+      '@babel/runtime': 7.25.4
       html-parse-stringify: 3.0.1
       i18next: 23.12.2
       react: 18.3.1
@@ -9801,33 +9813,6 @@ packages:
     resolution: {integrity: sha512-/LLMVyas0ljjAtoYiPqYiL8VWXzUUdThrmU5+n20DZv+a+ClRoevUzw5JxU+Ieh5/c87ytoTBV9G1FiKfNJdmg==}
     dev: true
 
-  /react-konva@18.2.10(konva@9.3.14)(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-ohcX1BJINL43m4ynjZ24MxFI1syjBdrXhqVxYVDw2rKgr3yuS0x/6m1Y2Z4sl4T/gKhfreBx8KHisd0XC6OT1g==}
-    peerDependencies:
-      konva: ^8.0.1 || ^7.2.5 || ^9.0.0
-      react: '>=18.0.0'
-      react-dom: '>=18.0.0'
-    dependencies:
-      '@types/react-reconciler': 0.28.8
-      its-fine: 1.2.5(react@18.3.1)
-      konva: 9.3.14
-      react: 18.3.1
-      react-dom: 18.3.1(react@18.3.1)
-      react-reconciler: 0.29.2(react@18.3.1)
-      scheduler: 0.23.2
-    dev: false
-
-  /react-reconciler@0.29.2(react@18.3.1):
-    resolution: {integrity: sha512-zZQqIiYgDCTP/f1N/mAR10nJGrPD2ZR+jDSEsKWJHYC7Cm2wodlwbR3upZRdC3cjIjSlTLNVyO7Iu0Yy7t2AYg==}
-    engines: {node: '>=0.10.0'}
-    peerDependencies:
-      react: ^18.3.1
-    dependencies:
-      loose-envify: 1.4.0
-      react: 18.3.1
-      scheduler: 0.23.2
-    dev: false
-
   /react-redux@9.1.2(@types/react@18.3.3)(react@18.3.1)(redux@5.0.1):
     resolution: {integrity: sha512-0OA4dhM1W48l3uzmv6B7TXPCGmokUU4p1M44DGN2/D9a1FjVPukVjER1PcPX97jIg6aUeLq1XJo1IpfbgULn0w==}
     peerDependencies:
@@ -9860,7 +9845,7 @@ packages:
       '@types/react': 18.3.3
       react: 18.3.1
       react-style-singleton: 2.2.1(@types/react@18.3.3)(react@18.3.1)
-      tslib: 2.6.3
+      tslib: 2.7.0
     dev: false
 
   /react-remove-scroll@2.5.10(@types/react@18.3.3)(react@18.3.1):
@@ -9877,7 +9862,7 @@ packages:
       react: 18.3.1
       react-remove-scroll-bar: 2.3.6(@types/react@18.3.3)(react@18.3.1)
       react-style-singleton: 2.2.1(@types/react@18.3.3)(react@18.3.1)
-      tslib: 2.6.3
+      tslib: 2.7.0
       use-callback-ref: 1.3.2(@types/react@18.3.3)(react@18.3.1)
       use-sidecar: 1.1.2(@types/react@18.3.3)(react@18.3.1)
     dev: false
@@ -9927,7 +9912,7 @@ packages:
       get-nonce: 1.0.1
       invariant: 2.2.4
       react: 18.3.1
-      tslib: 2.6.3
+      tslib: 2.7.0
     dev: false
 
   /react-transition-group@4.4.5(react-dom@18.3.1)(react@18.3.1):
@@ -10625,6 +10610,10 @@ packages:
     resolution: {integrity: sha512-D9cPgkvLlV3t3IzL0D0YLvGA9Ahk4PcvVwUbN0dSGr1aP0Nrt4AEnTUbuGvquEC0mA64Gqt1fzirlRs5ibXx8g==}
     dev: true
 
+  /stable-hash@0.0.4:
+    resolution: {integrity: sha512-LjdcbuBeLcdETCrPn9i8AYAZ1eCtu4ECAWtP7UleOiZ9LzVxRzzUZEoZ8zB24nhkQnDWyET0I+3sWokSDS3E7g==}
+    dev: false
+
   /stack-generator@2.0.10:
     resolution: {integrity: sha512-mwnua/hkqM6pF4k8SnmZ2zfETsRUpWXREfA/goT8SLCV4iOFa4bzOX2nDipWAZFPTjLvQB82f5yaodMVhK0yJQ==}
     dependencies:
@@ -11063,6 +11052,10 @@ packages:
   /tslib@2.6.3:
     resolution: {integrity: sha512-xNvxJEOUiWPGhUuUdQgAJPKOOJfGnIyKySOc09XkKsgdUV/3E2zvwZYdejjmRgPCgcym1juLH3226yA7sEFJKQ==}
 
+  /tslib@2.7.0:
+    resolution: {integrity: sha512-gLXCKdN1/j47AiHiOkJN69hJmcbGTHI0ImLmbYLHykhgeN0jVGola9yVjFgzCUklsZQMW55o+dW7IXv3RCXDzA==}
+    dev: false
+
   /tsutils@3.21.0(typescript@5.5.4):
     resolution: {integrity: sha512-mHKK3iUXL+3UF6xL5k0PEhKRUBKPBCv/+RkEOpjRWxxx27KKRBmmA60A9pgOUvMi8GKhRMPEmjBRPzs2W7O1OA==}
     engines: {node: '>= 6'}
@@ -11310,7 +11303,7 @@ packages:
     dependencies:
       '@types/react': 18.3.3
       react: 18.3.1
-      tslib: 2.6.3
+      tslib: 2.7.0
     dev: false
 
   /use-debounce@10.0.2(react@18.3.1):
@@ -11330,16 +11323,6 @@ packages:
       react: 18.3.1
     dev: false
 
-  /use-image@1.1.1(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-n4YO2k8AJG/BcDtxmBx8Aa+47kxY5m335dJiCQA5tTeVU4XdhrhqR6wT0WISRXwdMEOv5CSjqekDZkEMiiWaYQ==}
-    peerDependencies:
-      react: '>=16.8.0'
-      react-dom: '>=16.8.0'
-    dependencies:
-      react: 18.3.1
-      react-dom: 18.3.1(react@18.3.1)
-    dev: false
-
   /use-isomorphic-layout-effect@1.1.2(@types/react@18.3.3)(react@18.3.1):
     resolution: {integrity: sha512-49L8yCO3iGT/ZF9QttjwLF/ZD9Iwto5LnH5LmEdk/6cFmXddqi2ulF0edxTwjj+7mqvpVVGQWvbXZdn32wRSHA==}
     peerDependencies:
@@ -11366,7 +11349,7 @@ packages:
       '@types/react': 18.3.3
       detect-node-es: 1.1.0
       react: 18.3.1
-      tslib: 2.6.3
+      tslib: 2.7.0
     dev: false
 
   /use-sync-external-store@1.2.0(react@18.3.1):

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

@@ -127,14 +127,7 @@
         "bulkDownloadRequestedDesc": "Dein Download wird vorbereitet. Dies kann ein paar Momente dauern.",
         "bulkDownloadRequestFailed": "Problem beim Download vorbereiten",
         "bulkDownloadFailed": "Download fehlgeschlagen",
-        "alwaysShowImageSizeBadge": "Zeige immer Bilder Größe Abzeichen",
-        "selectForCompare": "Zum Vergleichen auswählen",
-        "compareImage": "Bilder vergleichen",
-        "exitSearch": "Suche beenden",
-        "newestFirst": "Neueste zuerst",
-        "oldestFirst": "Älteste zuerst",
-        "openInViewer": "Im Viewer öffnen",
-        "swapImages": "Bilder tauschen"
+        "alwaysShowImageSizeBadge": "Zeige immer Bilder Größe Abzeichen"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tastenkürzel",
@@ -638,8 +631,7 @@
         "archived": "Archiviert",
         "noBoards": "Kein {boardType}} Ordner",
         "hideBoards": "Ordner verstecken",
-        "viewBoards": "Ordner ansehen",
-        "deletedPrivateBoardsCannotbeRestored": "Gelöschte Boards können nicht wiederhergestellt werden. Wenn Sie „Nur Board löschen“ wählen, werden die Bilder in einen privaten, nicht kategorisierten Status für den Ersteller des Bildes versetzt."
+        "viewBoards": "Ordner ansehen"
     },
     "controlnet": {
         "showAdvanced": "Zeige Erweitert",
@@ -789,9 +781,7 @@
         "batchFieldValues": "Stapelverarbeitungswerte",
         "batchQueued": "Stapelverarbeitung eingereiht",
         "graphQueued": "Graph eingereiht",
-        "graphFailedToQueue": "Fehler beim Einreihen des Graphen",
-        "generations_one": "Generation",
-        "generations_other": "Generationen"
+        "graphFailedToQueue": "Fehler beim Einreihen des Graphen"
     },
     "metadata": {
         "negativePrompt": "Negativ Beschreibung",
@@ -1156,10 +1146,5 @@
         "noMatchingTriggers": "Keine passenden Trigger",
         "addPromptTrigger": "Prompt-Trigger hinzufügen",
         "compatibleEmbeddings": "Kompatible Einbettungen"
-    },
-    "ui": {
-        "tabs": {
-            "queue": "Warteschlange"
-        }
     }
 }

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",
@@ -325,6 +327,10 @@
         "canceled": "Canceled",
         "completedIn": "Completed in",
         "batch": "Batch",
+        "origin": "Origin",
+        "originCanvas": "Canvas",
+        "originWorkflows": "Workflows",
+        "originOther": "Other",
         "batchFieldValues": "Batch Field Values",
         "item": "Item",
         "session": "Session",
@@ -696,8 +702,6 @@
         "availableModels": "Available Models",
         "baseModel": "Base Model",
         "cancel": "Cancel",
-        "clipEmbed": "CLIP Embed",
-        "clipVision": "CLIP Vision",
         "config": "Config",
         "convert": "Convert",
         "convertingModelBegin": "Converting Model. Please wait.",
@@ -785,16 +789,13 @@
         "settings": "Settings",
         "simpleModelPlaceholder": "URL or path to a local file or diffusers folder",
         "source": "Source",
-        "spandrelImageToImage": "Image to Image (Spandrel)",
         "starterModels": "Starter Models",
-        "starterModelsInModelManager": "Starter Models can be found in Model Manager",
         "syncModels": "Sync Models",
         "textualInversions": "Textual Inversions",
         "triggerPhrases": "Trigger Phrases",
         "loraTriggerPhrases": "LoRA Trigger Phrases",
         "mainModelTriggerPhrases": "Main Model Trigger Phrases",
         "typePhraseHere": "Type phrase here",
-        "t5Encoder": "T5 Encoder",
         "upcastAttention": "Upcast Attention",
         "uploadImage": "Upload Image",
         "urlOrLocalPath": "URL or Local Path",
@@ -1100,7 +1101,6 @@
         "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 +1567,7 @@
         "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",
@@ -1645,16 +1645,32 @@
         "storeNotInitialized": "Store is not initialized"
     },
     "controlLayers": {
+        "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",
         "deleteAll": "Delete 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",
@@ -1663,21 +1679,83 @@
         "addPositivePrompt": "Add $t(common.positivePrompt)",
         "addNegativePrompt": "Add $t(common.negativePrompt)",
         "addIPAdapter": "Add $t(common.ipAdapter)",
-        "regionalGuidance": "Regional Guidance",
         "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
+        "raster": "Raster",
+        "rasterLayer_one": "Raster Layer",
+        "controlLayer_one": "Control Layer",
+        "inpaintMask_one": "Inpaint Mask",
+        "regionalGuidance_one": "Regional Guidance",
+        "ipAdapter_one": "IP Adapter",
+        "rasterLayer_other": "Raster Layers",
+        "controlLayer_other": "Control Layers",
+        "inpaintMask_other": "Inpaint Masks",
+        "regionalGuidance_other": "Regional Guidance",
+        "ipAdapter_other": "IP Adapters",
         "opacity": "Opacity",
+        "regionalGuidance_withCount_hidden": "Regional Guidance ({{count}} hidden)",
+        "controlAdapters_withCount_hidden": "Control Adapters ({{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}})",
+        "controlAdapters_withCount_visible": "Control Adapters ({{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) $t(unifiedCanvas.layer)",
+        "layer": "Layer",
         "opacityFilter": "Opacity Filter",
         "clearProcessor": "Clear Processor",
         "resetProcessor": "Reset Processor to Defaults",
         "noLayersAdded": "No Layers Added",
         "layers_one": "Layer",
-        "layers_other": "Layers"
+        "layers_other": "Layers",
+        "objects_zero": "empty",
+        "objects_one": "{{count}} object",
+        "objects_other": "{{count}} objects",
+        "convertToControlLayer": "Convert to Control Layer",
+        "convertToRasterLayer": "Convert to Raster Layer",
+        "transparency": "Transparency",
+        "enableTransparencyEffect": "Enable Transparency Effect",
+        "disableTransparencyEffect": "Disable Transparency Effect",
+        "hidingType": "Hiding {{type}}",
+        "showingType": "Showing {{type}}",
+        "dynamicGrid": "Dynamic Grid",
+        "logDebugInfo": "Log Debug Info",
+        "fill": {
+            "fillStyle": "Fill Style",
+            "solid": "Solid",
+            "grid": "Grid",
+            "crosshatch": "Crosshatch",
+            "vertical": "Vertical",
+            "horizontal": "Horizontal",
+            "diagonal": "Diagonal"
+        },
+        "tool": {
+            "brush": "Brush",
+            "eraser": "Eraser",
+            "rectangle": "Rectangle",
+            "bbox": "Bbox",
+            "move": "Move",
+            "view": "View",
+            "transform": "Transform",
+            "colorPicker": "Color Picker"
+        },
+        "filter": {
+            "filter": "Filter",
+            "filters": "Filters",
+            "filterType": "Filter Type",
+            "preview": "Preview",
+            "apply": "Apply",
+            "cancel": "Cancel"
+        }
     },
     "upscaling": {
         "upscale": "Upscale",
@@ -1765,5 +1843,30 @@
             "upscaling": "Upscaling",
             "upscalingTab": "$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/public/locales/es.json

@@ -86,15 +86,15 @@
         "loadMore": "Cargar más",
         "noImagesInGallery": "No hay imágenes para mostrar",
         "deleteImage_one": "Eliminar Imagen",
-        "deleteImage_many": "Eliminar {{count}} Imágenes",
-        "deleteImage_other": "Eliminar {{count}} Imágenes",
+        "deleteImage_many": "",
+        "deleteImage_other": "",
         "deleteImagePermanent": "Las imágenes eliminadas no se pueden restaurar.",
         "assets": "Activos",
         "autoAssignBoardOnClick": "Asignación automática de tableros al hacer clic"
     },
     "hotkeys": {
         "keyboardShortcuts": "Atajos de teclado",
-        "appHotkeys": "Atajos de aplicación",
+        "appHotkeys": "Atajos de applicación",
         "generalHotkeys": "Atajos generales",
         "galleryHotkeys": "Atajos de galería",
         "unifiedCanvasHotkeys": "Atajos de lienzo unificado",
@@ -535,7 +535,7 @@
         "bottomMessage": "Al eliminar este panel y las imágenes que contiene, se restablecerán las funciones que los estén utilizando actualmente.",
         "deleteBoardAndImages": "Borrar el panel y las imágenes",
         "loading": "Cargando...",
-        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar. Al Seleccionar 'Borrar Solo el Panel' transferirá las imágenes a un estado sin categorizar.",
+        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar",
         "move": "Mover",
         "menuItemAutoAdd": "Agregar automáticamente a este panel",
         "searchBoard": "Buscando paneles…",
@@ -549,13 +549,7 @@
         "imagesWithCount_other": "{{count}} imágenes",
         "assetsWithCount_one": "{{count}} activo",
         "assetsWithCount_many": "{{count}} activos",
-        "assetsWithCount_other": "{{count}} activos",
-        "hideBoards": "Ocultar Paneles",
-        "addPrivateBoard": "Agregar un tablero privado",
-        "addSharedBoard": "Agregar Panel Compartido",
-        "boards": "Paneles",
-        "archiveBoard": "Archivar Panel",
-        "archived": "Archivado"
+        "assetsWithCount_other": "{{count}} activos"
     },
     "accordions": {
         "compositing": {

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

@@ -496,9 +496,7 @@
         "main": "Principali",
         "noModelsInstalledDesc1": "Installa i modelli con",
         "ipAdapters": "Adattatori IP",
-        "noMatchingModels": "Nessun modello corrispondente",
-        "starterModelsInModelManager": "I modelli iniziali possono essere trovati in Gestione Modelli",
-        "spandrelImageToImage": "Immagine a immagine (Spandrel)"
+        "noMatchingModels": "Nessun modello corrispondente"
     },
     "parameters": {
         "images": "Immagini",
@@ -512,7 +510,7 @@
         "perlinNoise": "Rumore Perlin",
         "type": "Tipo",
         "strength": "Forza",
-        "upscaling": "Amplia",
+        "upscaling": "Ampliamento",
         "scale": "Scala",
         "imageFit": "Adatta l'immagine iniziale alle dimensioni di output",
         "scaleBeforeProcessing": "Scala prima dell'elaborazione",
@@ -595,7 +593,7 @@
         "globalPositivePromptPlaceholder": "Prompt positivo globale",
         "globalNegativePromptPlaceholder": "Prompt negativo globale",
         "processImage": "Elabora Immagine",
-        "sendToUpscale": "Invia a Amplia",
+        "sendToUpscale": "Invia a Ampliare",
         "postProcessing": "Post-elaborazione (Shift + U)"
     },
     "settings": {
@@ -1422,7 +1420,7 @@
         "paramUpscaleMethod": {
             "heading": "Metodo di ampliamento",
             "paragraphs": [
-                "Metodo utilizzato per ampliare l'immagine per la correzione ad alta risoluzione."
+                "Metodo utilizzato per eseguire l'ampliamento dell'immagine per la correzione ad alta risoluzione."
             ]
         },
         "patchmatchDownScaleSize": {
@@ -1530,7 +1528,7 @@
         },
         "upscaleModel": {
             "paragraphs": [
-                "Il modello di ampliamento, scala l'immagine alle dimensioni di uscita prima di aggiungere i dettagli. È possibile utilizzare qualsiasi modello di ampliamento supportato, ma alcuni sono specializzati per diversi tipi di immagini, come foto o disegni al tratto."
+                "Il modello di ampliamento (Upscale), scala l'immagine alle dimensioni di uscita prima di aggiungere i dettagli. È possibile utilizzare qualsiasi modello di ampliamento supportato, ma alcuni sono specializzati per diversi tipi di immagini, come foto o disegni al tratto."
             ],
             "heading": "Modello di ampliamento"
         },
@@ -1722,27 +1720,26 @@
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
             "queue": "Coda",
             "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
-            "upscaling": "Amplia",
+            "upscaling": "Ampliamento",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
         }
     },
     "upscaling": {
         "creativity": "Creatività",
         "structure": "Struttura",
-        "upscaleModel": "Modello di ampliamento",
+        "upscaleModel": "Modello di Ampliamento",
         "scale": "Scala",
         "missingModelsWarning": "Visita <LinkComponent>Gestione modelli</LinkComponent> per installare i modelli richiesti:",
         "mainModelDesc": "Modello principale (architettura SD1.5 o SDXL)",
         "tileControlNetModelDesc": "Modello Tile ControlNet per l'architettura del modello principale scelto",
-        "upscaleModelDesc": "Modello per l'ampliamento (immagine a immagine)",
+        "upscaleModelDesc": "Modello per l'ampliamento (da immagine a immagine)",
         "missingUpscaleInitialImage": "Immagine iniziale mancante per l'ampliamento",
         "missingUpscaleModel": "Modello per l’ampliamento mancante",
         "missingTileControlNetModel": "Nessun modello ControlNet Tile valido installato",
         "postProcessingModel": "Modello di post-elaborazione",
         "postProcessingMissingModelWarning": "Visita <LinkComponent>Gestione modelli</LinkComponent> per installare un modello di post-elaborazione (da immagine a immagine).",
         "exceedsMaxSize": "Le impostazioni di ampliamento superano il limite massimo delle dimensioni",
-        "exceedsMaxSizeDetails": "Il limite massimo di ampliamento è {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixel. Prova un'immagine più piccola o diminuisci la scala selezionata.",
-        "upscale": "Amplia"
+        "exceedsMaxSizeDetails": "Il limite massimo di ampliamento è {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixel. Prova un'immagine più piccola o diminuisci la scala selezionata."
     },
     "upsell": {
         "inviteTeammates": "Invita collaboratori",
@@ -1792,7 +1789,6 @@
         "positivePromptColumn": "'prompt' o 'positive_prompt'",
         "noTemplates": "Nessun modello",
         "acceptedColumnsKeys": "Colonne/chiavi accettate:",
-        "templateActions": "Azioni modello",
-        "promptTemplateCleared": "Modello di prompt cancellato"
+        "templateActions": "Azioni modello"
     }
 }

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

@@ -501,8 +501,7 @@
         "noModelsInstalled": "Нет установленных моделей",
         "noModelsInstalledDesc1": "Установите модели с помощью",
         "noMatchingModels": "Нет подходящих моделей",
-        "ipAdapters": "IP адаптеры",
-        "starterModelsInModelManager": "Стартовые модели можно найти в Менеджере моделей"
+        "ipAdapters": "IP адаптеры"
     },
     "parameters": {
         "images": "Изображения",
@@ -1759,8 +1758,7 @@
         "postProcessingModel": "Модель постобработки",
         "tileControlNetModelDesc": "Модель ControlNet для выбранной архитектуры основной модели",
         "missingModelsWarning": "Зайдите в <LinkComponent>Менеджер моделей</LinkComponent> чтоб установить необходимые модели:",
-        "postProcessingMissingModelWarning": "Посетите <LinkComponent>Менеджер моделей</LinkComponent>, чтобы установить модель постобработки (img2img).",
-        "upscale": "Увеличить"
+        "postProcessingMissingModelWarning": "Посетите <LinkComponent>Менеджер моделей</LinkComponent>, чтобы установить модель постобработки (img2img)."
     },
     "stylePresets": {
         "noMatchingTemplates": "Нет подходящих шаблонов",
@@ -1806,8 +1804,7 @@
         "noTemplates": "Нет шаблонов",
         "promptTemplatesDesc2": "Используйте строку-заполнитель <Pre>{{placeholder}}</Pre>, чтобы указать место, куда должен быть включен ваш запрос в шаблоне.",
         "searchByName": "Поиск по имени",
-        "shared": "Общий",
-        "promptTemplateCleared": "Шаблон запроса создан"
+        "shared": "Общий"
     },
     "upsell": {
         "inviteTeammates": "Пригласите членов команды",

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

@@ -154,8 +154,7 @@
         "displaySearch": "显示搜索",
         "stretchToFit": "拉伸以适应",
         "exitCompare": "退出对比",
-        "compareHelp1": "在点击图库中的图片或使用箭头键切换比较图片时，请按住<Kbd>Alt</Kbd> 键。",
-        "go": "运行"
+        "compareHelp1": "在点击图库中的图片或使用箭头键切换比较图片时，请按住<Kbd>Alt</Kbd> 键。"
     },
     "hotkeys": {
         "keyboardShortcuts": "快捷键",
@@ -495,9 +494,7 @@
         "huggingFacePlaceholder": "所有者或模型名称",
         "huggingFaceRepoID": "HuggingFace仓库ID",
         "loraTriggerPhrases": "LoRA 触发词",
-        "ipAdapters": "IP适配器",
-        "spandrelImageToImage": "图生图(Spandrel)",
-        "starterModelsInModelManager": "您可以在模型管理器中找到初始模型"
+        "ipAdapters": "IP适配器"
     },
     "parameters": {
         "images": "图像",
@@ -698,9 +695,7 @@
         "outOfMemoryErrorDesc": "您当前的生成设置已超出系统处理能力.请调整设置后再次尝试.",
         "parametersSet": "参数已恢复",
         "errorCopied": "错误信息已复制",
-        "modelImportCanceled": "模型导入已取消",
-        "importFailed": "导入失败",
-        "importSuccessful": "导入成功"
+        "modelImportCanceled": "模型导入已取消"
     },
     "unifiedCanvas": {
         "layer": "图层",
@@ -1710,55 +1705,12 @@
         "missingModelsWarning": "请访问<LinkComponent>模型管理器</LinkComponent> 安装所需的模型：",
         "mainModelDesc": "主模型（SD1.5或SDXL架构）",
         "exceedsMaxSize": "放大设置超出了最大尺寸限制",
-        "exceedsMaxSizeDetails": "最大放大限制是 {{maxUpscaleDimension}}x{{maxUpscaleDimension}} 像素.请尝试一个较小的图像或减少您的缩放选择.",
-        "upscale": "放大"
+        "exceedsMaxSizeDetails": "最大放大限制是 {{maxUpscaleDimension}}x{{maxUpscaleDimension}} 像素.请尝试一个较小的图像或减少您的缩放选择."
     },
     "upsell": {
         "inviteTeammates": "邀请团队成员",
         "professional": "专业",
         "professionalUpsell": "可在 Invoke 的专业版中使用.点击此处或访问 invoke.com/pricing 了解更多详情.",
         "shareAccess": "共享访问权限"
-    },
-    "stylePresets": {
-        "positivePrompt": "正向提示词",
-        "preview": "预览",
-        "deleteImage": "删除图像",
-        "deleteTemplate": "删除模版",
-        "deleteTemplate2": "您确定要删除这个模板吗？请注意，删除后无法恢复.",
-        "importTemplates": "导入提示模板，支持CSV或JSON格式",
-        "insertPlaceholder": "插入一个占位符",
-        "myTemplates": "我的模版",
-        "name": "名称",
-        "type": "类型",
-        "unableToDeleteTemplate": "无法删除提示模板",
-        "updatePromptTemplate": "更新提示词模版",
-        "exportPromptTemplates": "导出我的提示模板为CSV格式",
-        "exportDownloaded": "导出已下载",
-        "noMatchingTemplates": "无匹配的模版",
-        "promptTemplatesDesc1": "提示模板可以帮助您在编写提示时添加预设的文本内容.",
-        "promptTemplatesDesc3": "如果您没有使用占位符，那么模板的内容将会被添加到您提示的末尾.",
-        "searchByName": "按名称搜索",
-        "shared": "已分享",
-        "sharedTemplates": "已分享的模版",
-        "templateActions": "模版操作",
-        "templateDeleted": "提示模版已删除",
-        "toggleViewMode": "切换显示模式",
-        "uploadImage": "上传图像",
-        "active": "激活",
-        "choosePromptTemplate": "选择提示词模板",
-        "clearTemplateSelection": "清除模版选择",
-        "copyTemplate": "拷贝模版",
-        "createPromptTemplate": "创建提示词模版",
-        "defaultTemplates": "默认模版",
-        "editTemplate": "编辑模版",
-        "exportFailed": "无法生成并下载CSV文件",
-        "flatten": "将选定的模板内容合并到当前提示中",
-        "negativePrompt": "反向提示词",
-        "promptTemplateCleared": "提示模板已清除",
-        "useForTemplate": "用于提示词模版",
-        "viewList": "预览模版列表",
-        "viewModeTooltip": "这是您的提示在当前选定的模板下的预览效果。如需编辑提示，请直接在文本框中点击进行修改.",
-        "noTemplates": "无模版",
-        "private": "私密"
     }
 }

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 +14,13 @@ 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 { 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 { 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';
@@ -40,17 +41,10 @@ interface Props {
     action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
   };
   selectedWorkflowId?: string;
-  selectedStylePresetId?: string;
-  destination?: InvokeTabName | undefined;
+  destination?: TabName | undefined;
 }
 
-const App = ({
-  config = DEFAULT_CONFIG,
-  selectedImage,
-  selectedWorkflowId,
-  selectedStylePresetId,
-  destination,
-}: Props) => {
+const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, destination }: Props) => {
   const language = useAppSelector(languageSelector);
   const logger = useLogger('system');
   const dispatch = useAppDispatch();
@@ -89,12 +83,6 @@ const App = ({
     }
   }, [selectedWorkflowId, getAndLoadWorkflow]);
 
-  useEffect(() => {
-    if (selectedStylePresetId) {
-      dispatch(activeStylePresetIdChanged(selectedStylePresetId));
-    }
-  }, [dispatch, selectedStylePresetId]);
-
   useEffect(() => {
     if (destination) {
       dispatch(setActiveTab(destination));
@@ -107,6 +95,7 @@ const App = ({
 
   useStarterModelsToast();
   useSyncQueueStatus();
+  useScopeFocusWatcher();
 
   return (
     <ErrorBoundary onReset={handleReset} FallbackComponent={AppErrorBoundaryFallback}>
@@ -119,7 +108,7 @@ const App = ({
         {...dropzone.getRootProps()}
       >
         <input {...dropzone.getInputProps()} />
-        <InvokeTabs />
+        <AppContent />
         <AnimatePresence>
           {dropzone.isDragActive && isHandlingUpload && (
             <ImageUploadOverlay dropzone={dropzone} setIsHandlingUpload={setIsHandlingUpload} />
@@ -130,6 +119,7 @@ const App = ({
       <ChangeBoardModal />
       <DynamicPromptsModal />
       <StylePresetModal />
+      <ClearQueueConfirmationsAlertDialog />
       <PreselectedImage selectedImage={selectedImage} />
     </ErrorBoundary>
   );

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';
@@ -45,8 +45,7 @@ interface Props extends PropsWithChildren {
     action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
   };
   selectedWorkflowId?: string;
-  selectedStylePresetId?: string;
-  destination?: InvokeTabName;
+  destination?: TabName;
   customStarUi?: CustomStarUi;
   socketOptions?: Partial<ManagerOptions & SocketOptions>;
   isDebugging?: boolean;
@@ -67,7 +66,6 @@ const InvokeAIUI = ({
   queueId,
   selectedImage,
   selectedWorkflowId,
-  selectedStylePresetId,
   destination,
   customStarUi,
   socketOptions,
@@ -229,7 +227,6 @@ const InvokeAIUI = ({
                 config={config}
                 selectedImage={selectedImage}
                 selectedWorkflowId={selectedWorkflowId}
-                selectedStylePresetId={selectedStylePresetId}
                 destination={destination}
               />
             </AppDndContext>

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

@@ -3,27 +3,34 @@ import { useAppSelector } from 'app/store/storeHooks';
 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((s) => s.system.logLevel);
+  const logNamespaces = useAppSelector((s) => s.system.logNamespaces);
+  const logIsEnabled = useAppSelector((s) => s.system.logIsEnabled);
 
   // 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/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 +1,36 @@
-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/canvasV2Slice';
+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 +39,6 @@ export const addCommitStagingAreaImageListener = (startAppListening: AppStartLis
             status: 'success',
           });
         }
-        dispatch(canvasBatchIdsReset());
       } catch {
         log.error('Failed to cancel canvas batches');
         toast({
@@ -47,4 +49,26 @@ 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 } = state.canvasV2.present.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: true }));
+      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

@@ -1,7 +1,4 @@
 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 { getImageUsage } from 'features/deleteImageModal/store/selectors';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
 import { imagesApi } from 'services/api/endpoints/images';
@@ -9,39 +6,22 @@ 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 { nodes, canvasV2 } = getState();
+
       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.present, canvasV2.present, 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
-      }
-    },
-  });
-};