chore: release v4.2.9.dev9

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

.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 }}

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

@@ -11,7 +11,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByDestinationResult,
+    CancelByOriginResult,
     ClearResult,
     EnqueueBatchResult,
     PruneResult,
@@ -107,18 +107,16 @@ async def cancel_by_batch_ids(
 
 
 @session_queue_router.put(
-    "/{queue_id}/cancel_by_destination",
-    operation_id="cancel_by_destination",
+    "/{queue_id}/cancel_by_origin",
+    operation_id="cancel_by_origin",
     responses={200: {"model": CancelByBatchIDsResult}},
 )
-async def cancel_by_destination(
+async def cancel_by_origin(
     queue_id: str = Path(description="The queue id to perform this operation on"),
-    destination: str = Query(description="The destination to cancel all queue items for"),
-) -> CancelByDestinationResult:
+    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_destination(
-        queue_id=queue_id, destination=destination
-    )
+    return ApiDependencies.invoker.services.session_queue.cancel_by_origin(queue_id=queue_id, origin=origin)
 
 
 @session_queue_router.put(

invokeai/app/invocations/compel.py

@@ -19,8 +19,7 @@ from invokeai.app.invocations.model import CLIPField
 from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.ti_utils import generate_ti_list
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoraPatcher
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
@@ -83,10 +82,9 @@ class CompelInvocation(BaseInvocation):
             # apply all patches while the model is on the target device
             text_encoder_info.model_on_device() as (cached_weights, text_encoder),
             tokenizer_info as tokenizer,
-            LoraPatcher.apply_lora_patches(
-                model=text_encoder,
-                patches=_lora_loader(),
-                prefix="lora_te_",
+            ModelPatcher.apply_lora_text_encoder(
+                text_encoder,
+                loras=_lora_loader(),
                 cached_weights=cached_weights,
             ),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
@@ -179,9 +177,9 @@ class SDXLPromptInvocationBase:
             # apply all patches while the model is on the target device
             text_encoder_info.model_on_device() as (cached_weights, text_encoder),
             tokenizer_info as tokenizer,
-            LoraPatcher.apply_lora_patches(
+            ModelPatcher.apply_lora(
                 text_encoder,
-                patches=_lora_loader(),
+                loras=_lora_loader(),
                 prefix=lora_prefix,
                 cached_weights=cached_weights,
             ),

invokeai/app/invocations/denoise_latents.py

@@ -36,8 +36,7 @@ from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoraPatcher
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType, ModelVariantType
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion import PipelineIntermediateState
@@ -186,7 +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,
     )
@@ -980,10 +979,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
             ModelPatcher.apply_freeu(unet, self.unet.freeu_config),
             SeamlessExt.static_patch_model(unet, self.unet.seamless_axes),  # FIXME
             # Apply the LoRA after unet has been moved to its target device for faster patching.
-            LoraPatcher.apply_lora_patches(
-                model=unet,
-                patches=_lora_loader(),
-                prefix="lora_unet_",
+            ModelPatcher.apply_lora_unet(
+                unet,
+                loras=_lora_loader(),
                 cached_weights=cached_weights,
             ),
         ):

invokeai/app/invocations/fields.py

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

invokeai/app/invocations/flux_denoise.py

@@ -1,267 +0,0 @@
-from typing import Callable, Iterator, Optional, Tuple
-
-import torch
-import torchvision.transforms as tv_transforms
-from torchvision.transforms.functional import resize as tv_resize
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import (
-    DenoiseMaskField,
-    FieldDescriptions,
-    FluxConditioningField,
-    Input,
-    InputField,
-    LatentsField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import TransformerField
-from invokeai.app.invocations.primitives import LatentsOutput
-from invokeai.app.services.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.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoraPatcher
-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.model_on_device() as (cached_weights, transformer),
-            # Apply the LoRA after transformer has been moved to its target device for faster patching.
-            LoraPatcher.apply_lora_patches(
-                model=transformer,
-                patches=self._lora_iterator(context),
-                prefix="",
-                cached_weights=cached_weights,
-            ),
-        ):
-            assert isinstance(transformer, Flux)
-
-            x = denoise(
-                model=transformer,
-                img=x,
-                img_ids=img_ids,
-                txt=t5_embeddings,
-                txt_ids=txt_ids,
-                vec=clip_embeddings,
-                timesteps=timesteps,
-                step_callback=self._build_step_callback(context),
-                guidance=self.guidance,
-                inpaint_extension=inpaint_extension,
-            )
-
-        x = unpack(x.float(), self.height, self.width)
-        return x
-
-    def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor) -> torch.Tensor | None:
-        """Prepare the inpaint mask.
-
-        - Loads the mask
-        - Resizes if necessary
-        - Casts to same device/dtype as latents
-        - Expands mask to the same shape as latents so that they line up after 'packing'
-
-        Args:
-            context (InvocationContext): The invocation context, for loading the inpaint mask.
-            latents (torch.Tensor): A latent image tensor. In 'unpacked' format. Used to determine the target shape,
-                device, and dtype for the inpaint mask.
-
-        Returns:
-            torch.Tensor | None: Inpaint mask.
-        """
-        if self.denoise_mask is None:
-            return None
-
-        mask = context.tensors.load(self.denoise_mask.mask_name)
-
-        _, _, latent_height, latent_width = latents.shape
-        mask = tv_resize(
-            img=mask,
-            size=[latent_height, latent_width],
-            interpolation=tv_transforms.InterpolationMode.BILINEAR,
-            antialias=False,
-        )
-
-        mask = mask.to(device=latents.device, dtype=latents.dtype)
-
-        # Expand the inpaint mask to the same shape as `latents` so that when we 'pack' `mask` it lines up with
-        # `latents`.
-        return mask.expand_as(latents)
-
-    def _lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
-        for lora in self.transformer.loras:
-            lora_info = context.models.load(lora.lora)
-            assert isinstance(lora_info.model, LoRAModelRaw)
-            yield (lora_info.model, lora.weight)
-            del lora_info
-
-    def _build_step_callback(self, context: InvocationContext) -> Callable[[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_lora_loader.py

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

invokeai/app/invocations/flux_text_to_image.py

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

invokeai/app/invocations/flux_vae_decode.py

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

invokeai/app/invocations/flux_vae_encode.py

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

invokeai/app/invocations/mask.py

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

invokeai/app/invocations/model.py

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

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -22,8 +22,8 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.model import UNetField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoraPatcher
+from invokeai.backend.lora import LoRAModelRaw
+from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, PipelineIntermediateState
 from invokeai.backend.stable_diffusion.multi_diffusion_pipeline import (
     MultiDiffusionPipeline,
@@ -204,11 +204,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
         # Load the UNet model.
         unet_info = context.models.load(self.unet.unet)
 
-        with (
-            ExitStack() as exit_stack,
-            unet_info as unet,
-            LoraPatcher.apply_lora_patches(model=unet, patches=_lora_loader(), prefix="lora_unet_"),
-        ):
+        with ExitStack() as exit_stack, unet_info as unet, ModelPatcher.apply_lora_unet(unet, _lora_loader()):
             assert isinstance(unet, UNet2DConditionModel)
             latents = latents.to(device=unet.device, dtype=unet.dtype)
             if noise is not None:

invokeai/app/services/model_install/model_install_common.py

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

invokeai/app/services/session_queue/session_queue_base.py

@@ -6,7 +6,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByDestinationResult,
+    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -97,8 +97,8 @@ class SessionQueueBase(ABC):
         pass
 
     @abstractmethod
-    def cancel_by_destination(self, queue_id: str, destination: str) -> CancelByDestinationResult:
-        """Cancels all queue items with the given batch destination"""
+    def cancel_by_origin(self, queue_id: str, origin: str) -> CancelByOriginResult:
+        """Cancels all queue items with the given batch origin"""
         pass
 
     @abstractmethod

invokeai/app/services/session_queue/session_queue_common.py

@@ -346,10 +346,10 @@ class CancelByBatchIDsResult(BaseModel):
     canceled: int = Field(..., description="Number of queue items canceled")
 
 
-class CancelByDestinationResult(CancelByBatchIDsResult):
-    """Result of canceling by a destination"""
+class CancelByOriginResult(BaseModel):
+    """Result of canceling by list of batch ids"""
 
-    pass
+    canceled: int = Field(..., description="Number of queue items canceled")
 
 
 class CancelByQueueIDResult(CancelByBatchIDsResult):

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -10,7 +10,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByDestinationResult,
+    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -426,19 +426,19 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return CancelByBatchIDsResult(canceled=count)
 
-    def cancel_by_destination(self, queue_id: str, destination: str) -> CancelByDestinationResult:
+    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 destination == ?
+                  AND origin == ?
                   AND status != 'canceled'
                   AND status != 'completed'
                   AND status != 'failed'
                 """
-            params = (queue_id, destination)
+            params = (queue_id, origin)
             self.__cursor.execute(
                 f"""--sql
                 SELECT COUNT(*)
@@ -457,14 +457,14 @@ class SqliteSessionQueue(SessionQueueBase):
                 params,
             )
             self.__conn.commit()
-            if current_queue_item is not None and current_queue_item.destination == destination:
+            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 CancelByDestinationResult(canceled=count)
+        return CancelByOriginResult(canceled=count)
 
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         try:

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

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 {
   "name": "FLUX Text to Image",
   "author": "InvokeAI",
-  "description": "A simple text-to-image workflow using FLUX dev or schnell models.",
+  "description": "A simple text-to-image workflow using FLUX dev or schnell models. Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
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@@ -11,25 +11,17 @@
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       "fieldName": "prompt"
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       "fieldName": "num_steps"
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-    },
-    {
-      "id": "reactflow__edge-4fe24f07-f906-4f55-ab2c-9beee56ef5bdlatents-7e5172eb-48c1-44db-a770-8fd83e1435d1latents",
-      "type": "default",
-      "source": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "sourceHandle": "latents",
-      "targetHandle": "latents"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-7e5172eb-48c1-44db-a770-8fd83e1435d1vae",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "sourceHandle": "vae",
-      "targetHandle": "vae"
-    },
     {
       "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90max_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
       "type": "default",
@@ -306,6 +208,14 @@
       "sourceHandle": "max_seq_len",
       "targetHandle": "t5_max_seq_len"
     },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-159bdf1b-79e7-4174-b86e-d40e646964c8vae",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "vae",
+      "targetHandle": "vae"
+    },
     {
       "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90t5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
       "type": "default",
@@ -321,6 +231,30 @@
       "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "sourceHandle": "clip",
       "targetHandle": "clip"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-159bdf1b-79e7-4174-b86e-d40e646964c8transformer",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "transformer",
+      "targetHandle": "transformer"
+    },
+    {
+      "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-159bdf1b-79e7-4174-b86e-d40e646964c8positive_text_conditioning",
+      "type": "default",
+      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "conditioning",
+      "targetHandle": "positive_text_conditioning"
+    },
+    {
+      "id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-159bdf1b-79e7-4174-b86e-d40e646964c8seed",
+      "type": "default",
+      "source": "4754c534-a5f3-4ad0-9382-7887985e668c",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "value",
+      "targetHandle": "seed"
     }
   ]
 }

invokeai/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/modules/autoencoder.py

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

invokeai/backend/flux/sampling.py

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

invokeai/backend/flux/sampling_utils.py

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

invokeai/backend/lora.py

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

invokeai/backend/lora/conversions/flux_diffusers_lora_conversion_utils.py

@@ -1,210 +0,0 @@
-from typing import Dict
-
-import torch
-
-from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
-from invokeai.backend.lora.layers.lora_layer import LoRALayer
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-
-
-def is_state_dict_likely_in_flux_diffusers_format(state_dict: Dict[str, torch.Tensor]) -> bool:
-    """Checks if the provided state dict is likely in the Diffusers FLUX LoRA format.
-
-    This is intended to be a reasonably high-precision detector, but it is not guaranteed to have perfect precision. (A
-    perfect-precision detector would require checking all keys against a whitelist and verifying tensor shapes.)
-    """
-    # First, check that all keys end in "lora_A.weight" or "lora_B.weight" (i.e. are in PEFT format).
-    all_keys_in_peft_format = all(k.endswith(("lora_A.weight", "lora_B.weight")) for k in state_dict.keys())
-
-    # Next, check that this is likely a FLUX model by spot-checking a few keys.
-    expected_keys = [
-        "transformer.single_transformer_blocks.0.attn.to_q.lora_A.weight",
-        "transformer.single_transformer_blocks.0.attn.to_q.lora_B.weight",
-        "transformer.transformer_blocks.0.attn.add_q_proj.lora_A.weight",
-        "transformer.transformer_blocks.0.attn.add_q_proj.lora_B.weight",
-    ]
-    all_expected_keys_present = all(k in state_dict for k in expected_keys)
-
-    return all_keys_in_peft_format and all_expected_keys_present
-
-
-# TODO(ryand): What alpha should we use? 1.0? Rank of the LoRA?
-def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor], alpha: float = 1.0) -> LoRAModelRaw:  # pyright: ignore[reportRedeclaration] (state_dict is intentionally re-declared)
-    """Loads a state dict in the Diffusers FLUX LoRA format into a LoRAModelRaw object.
-
-    This function is based on:
-    https://github.com/huggingface/diffusers/blob/55ac421f7bb12fd00ccbef727be4dc2f3f920abb/scripts/convert_flux_to_diffusers.py
-    """
-    # Group keys by layer.
-    grouped_state_dict: dict[str, dict[str, torch.Tensor]] = _group_by_layer(state_dict)
-
-    # Remove the "transformer." prefix from all keys.
-    grouped_state_dict = {k.replace("transformer.", ""): v for k, v in grouped_state_dict.items()}
-
-    # Constants for FLUX.1
-    num_double_layers = 19
-    num_single_layers = 38
-    # inner_dim = 3072
-    # mlp_ratio = 4.0
-
-    layers: dict[str, AnyLoRALayer] = {}
-
-    def add_lora_layer_if_present(src_key: str, dst_key: str) -> None:
-        if src_key in grouped_state_dict:
-            src_layer_dict = grouped_state_dict.pop(src_key)
-            layers[dst_key] = LoRALayer(
-                values={
-                    "lora_down.weight": src_layer_dict.pop("lora_A.weight"),
-                    "lora_up.weight": src_layer_dict.pop("lora_B.weight"),
-                    "alpha": torch.tensor(alpha),
-                },
-            )
-            assert len(src_layer_dict) == 0
-
-    def add_qkv_lora_layer_if_present(src_keys: list[str], dst_qkv_key: str) -> None:
-        """Handle the Q, K, V matrices for a transformer block. We need special handling because the diffusers format
-        stores them in separate matrices, whereas the BFL format used internally by InvokeAI concatenates them.
-        """
-        # We expect that either all src keys are present or none of them are. Verify this.
-        keys_present = [key in grouped_state_dict for key in src_keys]
-        assert all(keys_present) or not any(keys_present)
-
-        # If none of the keys are present, return early.
-        if not any(keys_present):
-            return
-
-        src_layer_dicts = [grouped_state_dict.pop(key) for key in src_keys]
-        sub_layers: list[LoRALayerBase] = []
-        for src_layer_dict in src_layer_dicts:
-            sub_layers.append(
-                LoRALayer(
-                    values={
-                        "lora_down.weight": src_layer_dict.pop("lora_A.weight"),
-                        "lora_up.weight": src_layer_dict.pop("lora_B.weight"),
-                        "alpha": torch.tensor(alpha),
-                    },
-                )
-            )
-            assert len(src_layer_dict) == 0
-        layers[dst_qkv_key] = ConcatenatedLoRALayer(lora_layers=sub_layers, concat_axis=0)
-
-    # time_text_embed.timestep_embedder -> time_in.
-    add_lora_layer_if_present("time_text_embed.timestep_embedder.linear_1", "time_in.in_layer")
-    add_lora_layer_if_present("time_text_embed.timestep_embedder.linear_2", "time_in.out_layer")
-
-    # time_text_embed.text_embedder -> vector_in.
-    add_lora_layer_if_present("time_text_embed.text_embedder.linear_1", "vector_in.in_layer")
-    add_lora_layer_if_present("time_text_embed.text_embedder.linear_2", "vector_in.out_layer")
-
-    # time_text_embed.guidance_embedder -> guidance_in.
-    add_lora_layer_if_present("time_text_embed.guidance_embedder.linear_1", "guidance_in")
-    add_lora_layer_if_present("time_text_embed.guidance_embedder.linear_2", "guidance_in")
-
-    # context_embedder -> txt_in.
-    add_lora_layer_if_present("context_embedder", "txt_in")
-
-    # x_embedder -> img_in.
-    add_lora_layer_if_present("x_embedder", "img_in")
-
-    # Double transformer blocks.
-    for i in range(num_double_layers):
-        # norms.
-        add_lora_layer_if_present(f"transformer_blocks.{i}.norm1.linear", f"double_blocks.{i}.img_mod.lin")
-        add_lora_layer_if_present(f"transformer_blocks.{i}.norm1_context.linear", f"double_blocks.{i}.txt_mod.lin")
-
-        # Q, K, V
-        add_qkv_lora_layer_if_present(
-            [
-                f"transformer_blocks.{i}.attn.to_q",
-                f"transformer_blocks.{i}.attn.to_k",
-                f"transformer_blocks.{i}.attn.to_v",
-            ],
-            f"double_blocks.{i}.img_attn.qkv",
-        )
-        add_qkv_lora_layer_if_present(
-            [
-                f"transformer_blocks.{i}.attn.add_q_proj",
-                f"transformer_blocks.{i}.attn.add_k_proj",
-                f"transformer_blocks.{i}.attn.add_v_proj",
-            ],
-            f"double_blocks.{i}.txt_attn.qkv",
-        )
-
-        # ff img_mlp
-        add_lora_layer_if_present(
-            f"transformer_blocks.{i}.ff.net.0.proj",
-            f"double_blocks.{i}.img_mlp.0",
-        )
-        add_lora_layer_if_present(
-            f"transformer_blocks.{i}.ff.net.2",
-            f"double_blocks.{i}.img_mlp.2",
-        )
-
-        # ff txt_mlp
-        add_lora_layer_if_present(
-            f"transformer_blocks.{i}.ff_context.net.0.proj",
-            f"double_blocks.{i}.txt_mlp.0",
-        )
-        add_lora_layer_if_present(
-            f"transformer_blocks.{i}.ff_context.net.2",
-            f"double_blocks.{i}.txt_mlp.2",
-        )
-
-        # output projections.
-        add_lora_layer_if_present(
-            f"transformer_blocks.{i}.attn.to_out.0",
-            f"double_blocks.{i}.img_attn.proj",
-        )
-        add_lora_layer_if_present(
-            f"transformer_blocks.{i}.attn.to_add_out",
-            f"double_blocks.{i}.txt_attn.proj",
-        )
-
-    # Single transformer blocks.
-    for i in range(num_single_layers):
-        # norms
-        add_lora_layer_if_present(
-            f"single_transformer_blocks.{i}.norm.linear",
-            f"single_blocks.{i}.modulation.lin",
-        )
-
-        # Q, K, V, mlp
-        add_qkv_lora_layer_if_present(
-            [
-                f"single_transformer_blocks.{i}.attn.to_q",
-                f"single_transformer_blocks.{i}.attn.to_k",
-                f"single_transformer_blocks.{i}.attn.to_v",
-                f"single_transformer_blocks.{i}.proj_mlp",
-            ],
-            f"single_blocks.{i}.linear1",
-        )
-
-        # Output projections.
-        add_lora_layer_if_present(
-            f"single_transformer_blocks.{i}.proj_out",
-            f"single_blocks.{i}.linear2",
-        )
-
-    # Final layer.
-    add_lora_layer_if_present("proj_out", "final_layer.linear")
-
-    # Assert that all keys were processed.
-    assert len(grouped_state_dict) == 0
-
-    return LoRAModelRaw(layers=layers)
-
-
-def _group_by_layer(state_dict: Dict[str, torch.Tensor]) -> dict[str, dict[str, torch.Tensor]]:
-    """Groups the keys in the state dict by layer."""
-    layer_dict: dict[str, dict[str, torch.Tensor]] = {}
-    for key in state_dict:
-        # Split the 'lora_A.weight' or 'lora_B.weight' suffix from the layer name.
-        parts = key.rsplit(".", maxsplit=2)
-        layer_name = parts[0]
-        key_name = ".".join(parts[1:])
-        if layer_name not in layer_dict:
-            layer_dict[layer_name] = {}
-        layer_dict[layer_name][key_name] = state_dict[key]
-    return layer_dict

invokeai/backend/lora/conversions/flux_kohya_lora_conversion_utils.py

@@ -1,80 +0,0 @@
-import re
-from typing import Any, Dict, TypeVar
-
-import torch
-
-from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.lora.layers.utils import any_lora_layer_from_state_dict
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-
-# A regex pattern that matches all of the keys in the Kohya FLUX LoRA format.
-# Example keys:
-#   lora_unet_double_blocks_0_img_attn_proj.alpha
-#   lora_unet_double_blocks_0_img_attn_proj.lora_down.weight
-#   lora_unet_double_blocks_0_img_attn_proj.lora_up.weight
-FLUX_KOHYA_KEY_REGEX = (
-    r"lora_unet_(\w+_blocks)_(\d+)_(img_attn|img_mlp|img_mod|txt_attn|txt_mlp|txt_mod|linear1|linear2|modulation)_?(.*)"
-)
-
-
-def is_state_dict_likely_in_flux_kohya_format(state_dict: Dict[str, Any]) -> bool:
-    """Checks if the provided state dict is likely in the Kohya FLUX LoRA format.
-
-    This is intended to be a high-precision detector, but it is not guaranteed to have perfect precision. (A
-    perfect-precision detector would require checking all keys against a whitelist and verifying tensor shapes.)
-    """
-    return all(re.match(FLUX_KOHYA_KEY_REGEX, k) for k in state_dict.keys())
-
-
-def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -> LoRAModelRaw:
-    # Group keys by layer.
-    grouped_state_dict: dict[str, dict[str, torch.Tensor]] = {}
-    for key, value in state_dict.items():
-        layer_name, param_name = key.split(".", 1)
-        if layer_name not in grouped_state_dict:
-            grouped_state_dict[layer_name] = {}
-        grouped_state_dict[layer_name][param_name] = value
-
-    # Convert the state dict to the InvokeAI format.
-    grouped_state_dict = convert_flux_kohya_state_dict_to_invoke_format(grouped_state_dict)
-
-    # Create LoRA layers.
-    layers: dict[str, AnyLoRALayer] = {}
-    for layer_key, layer_state_dict in grouped_state_dict.items():
-        layers[layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
-
-    # Create and return the LoRAModelRaw.
-    return LoRAModelRaw(layers=layers)
-
-
-T = TypeVar("T")
-
-
-def convert_flux_kohya_state_dict_to_invoke_format(state_dict: Dict[str, T]) -> Dict[str, T]:
-    """Converts a state dict from the Kohya FLUX LoRA format to LoRA weight format used internally by InvokeAI.
-
-    Example key conversions:
-    "lora_unet_double_blocks_0_img_attn_proj" -> "double_blocks.0.img_attn.proj"
-    "lora_unet_double_blocks_0_img_attn_proj" -> "double_blocks.0.img_attn.proj"
-    "lora_unet_double_blocks_0_img_attn_proj" -> "double_blocks.0.img_attn.proj"
-    "lora_unet_double_blocks_0_img_attn_qkv" -> "double_blocks.0.img_attn.qkv"
-    "lora_unet_double_blocks_0_img_attn_qkv" -> "double_blocks.0.img.attn.qkv"
-    "lora_unet_double_blocks_0_img_attn_qkv" -> "double_blocks.0.img.attn.qkv"
-    """
-
-    def replace_func(match: re.Match[str]) -> str:
-        s = f"{match.group(1)}.{match.group(2)}.{match.group(3)}"
-        if match.group(4):
-            s += f".{match.group(4)}"
-        return s
-
-    converted_dict: dict[str, T] = {}
-    for k, v in state_dict.items():
-        match = re.match(FLUX_KOHYA_KEY_REGEX, k)
-        if match:
-            new_key = re.sub(FLUX_KOHYA_KEY_REGEX, replace_func, k)
-            converted_dict[new_key] = v
-        else:
-            raise ValueError(f"Key '{k}' does not match the expected pattern for FLUX LoRA weights.")
-
-    return converted_dict

invokeai/backend/lora/conversions/sd_lora_conversion_utils.py

@@ -1,29 +0,0 @@
-from typing import Dict
-
-import torch
-
-from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.lora.layers.utils import any_lora_layer_from_state_dict
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-
-
-def lora_model_from_sd_state_dict(state_dict: Dict[str, torch.Tensor]) -> LoRAModelRaw:
-    grouped_state_dict: dict[str, dict[str, torch.Tensor]] = _group_state(state_dict)
-
-    layers: dict[str, AnyLoRALayer] = {}
-    for layer_key, values in grouped_state_dict.items():
-        layers[layer_key] = any_lora_layer_from_state_dict(values)
-
-    return LoRAModelRaw(layers=layers)
-
-
-def _group_state(state_dict: Dict[str, torch.Tensor]) -> Dict[str, Dict[str, torch.Tensor]]:
-    state_dict_groupped: Dict[str, Dict[str, torch.Tensor]] = {}
-
-    for key, value in state_dict.items():
-        stem, leaf = key.split(".", 1)
-        if stem not in state_dict_groupped:
-            state_dict_groupped[stem] = {}
-        state_dict_groupped[stem][leaf] = value
-
-    return state_dict_groupped

invokeai/backend/lora/conversions/sdxl_lora_conversion_utils.py

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

invokeai/backend/lora/layers/any_lora_layer.py

@@ -1,11 +0,0 @@
-from typing import Union
-
-from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
-from invokeai.backend.lora.layers.full_layer import FullLayer
-from invokeai.backend.lora.layers.ia3_layer import IA3Layer
-from invokeai.backend.lora.layers.loha_layer import LoHALayer
-from invokeai.backend.lora.layers.lokr_layer import LoKRLayer
-from invokeai.backend.lora.layers.lora_layer import LoRALayer
-from invokeai.backend.lora.layers.norm_layer import NormLayer
-
-AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer, NormLayer, ConcatenatedLoRALayer]

invokeai/backend/lora/layers/concatenated_lora_layer.py

@@ -1,46 +0,0 @@
-from typing import List, Optional
-
-import torch
-
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
-
-
-class ConcatenatedLoRALayer(LoRALayerBase):
-    """A LoRA layer that is composed of multiple LoRA layers concatenated along a specified axis.
-
-    This class was created to handle a special case with FLUX LoRA models. In the BFL FLUX model format, the attention
-    Q, K, V matrices are concatenated along the first dimension. In the diffusers LoRA format, the Q, K, V matrices are
-    stored as separate tensors. This class enables diffusers LoRA layers to be used in BFL FLUX models.
-    """
-
-    def __init__(self, lora_layers: List[LoRALayerBase], concat_axis: int = 0):
-        # Note: We pass values={} to the base class, because the values are handled by the individual LoRA layers.
-        super().__init__(values={})
-
-        self._lora_layers = lora_layers
-        self._concat_axis = concat_axis
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        # TODO(ryand): Currently, we pass orig_weight=None to the sub-layers. If we want to support sub-layers that
-        # require this value, we will need to implement chunking of the original weight tensor here.
-        layer_weights = [lora_layer.get_weight(None) for lora_layer in self._lora_layers]  # pyright: ignore[reportArgumentType]
-        return torch.cat(layer_weights, dim=self._concat_axis)
-
-    def get_bias(self, orig_bias: torch.Tensor) -> Optional[torch.Tensor]:
-        # TODO(ryand): Currently, we pass orig_bias=None to the sub-layers. If we want to support sub-layers that
-        # require this value, we will need to implement chunking of the original bias tensor here.
-        layer_biases = [lora_layer.get_bias(None) for lora_layer in self._lora_layers]  # pyright: ignore[reportArgumentType]
-        layer_bias_is_none = [layer_bias is None for layer_bias in layer_biases]
-        if any(layer_bias_is_none):
-            assert all(layer_bias_is_none)
-            return None
-
-        # Ignore the type error, because we have just verified that all layer biases are non-None.
-        return torch.cat(layer_biases, dim=self._concat_axis)
-
-    def calc_size(self) -> int:
-        return sum(lora_layer.calc_size() for lora_layer in self._lora_layers)
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        for lora_layer in self._lora_layers:
-            lora_layer.to(device=device, dtype=dtype)

invokeai/backend/lora/layers/full_layer.py

@@ -1,36 +0,0 @@
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
-
-
-class FullLayer(LoRALayerBase):
-    # bias handled in LoRALayerBase(calc_size, to)
-    # weight: torch.Tensor
-    # bias: Optional[torch.Tensor]
-
-    def __init__(
-        self,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(values)
-
-        self.weight = values["diff"]
-        self.bias = values.get("diff_b", None)
-
-        self.rank = None  # unscaled
-        self.check_keys(values, {"diff", "diff_b"})
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        return self.weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        model_size += self.weight.nelement() * self.weight.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        super().to(device=device, dtype=dtype)
-
-        self.weight = self.weight.to(device=device, dtype=dtype)

invokeai/backend/lora/layers/ia3_layer.py

@@ -1,41 +0,0 @@
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
-
-
-class IA3Layer(LoRALayerBase):
-    # weight: torch.Tensor
-    # on_input: torch.Tensor
-
-    def __init__(
-        self,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(values)
-
-        self.weight = values["weight"]
-        self.on_input = values["on_input"]
-
-        self.rank = None  # unscaled
-        self.check_keys(values, {"weight", "on_input"})
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        weight = self.weight
-        if not self.on_input:
-            weight = weight.reshape(-1, 1)
-        assert orig_weight is not None
-        return orig_weight * weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        model_size += self.weight.nelement() * self.weight.element_size()
-        model_size += self.on_input.nelement() * self.on_input.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None):
-        super().to(device=device, dtype=dtype)
-
-        self.weight = self.weight.to(device=device, dtype=dtype)
-        self.on_input = self.on_input.to(device=device, dtype=dtype)

invokeai/backend/lora/layers/loha_layer.py

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

invokeai/backend/lora/layers/lokr_layer.py

@@ -1,113 +0,0 @@
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
-
-
-class LoKRLayer(LoRALayerBase):
-    # w1: Optional[torch.Tensor] = None
-    # w1_a: Optional[torch.Tensor] = None
-    # w1_b: Optional[torch.Tensor] = None
-    # w2: Optional[torch.Tensor] = None
-    # w2_a: Optional[torch.Tensor] = None
-    # w2_b: Optional[torch.Tensor] = None
-    # t2: Optional[torch.Tensor] = None
-
-    def __init__(
-        self,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(values)
-
-        self.w1 = values.get("lokr_w1", None)
-        if self.w1 is None:
-            self.w1_a = values["lokr_w1_a"]
-            self.w1_b = values["lokr_w1_b"]
-        else:
-            self.w1_b = None
-            self.w1_a = None
-
-        self.w2 = values.get("lokr_w2", None)
-        if self.w2 is None:
-            self.w2_a = values["lokr_w2_a"]
-            self.w2_b = values["lokr_w2_b"]
-        else:
-            self.w2_a = None
-            self.w2_b = None
-
-        self.t2 = values.get("lokr_t2", None)
-
-        if self.w1_b is not None:
-            self.rank = self.w1_b.shape[0]
-        elif self.w2_b is not None:
-            self.rank = self.w2_b.shape[0]
-        else:
-            self.rank = None  # unscaled
-
-        self.check_keys(
-            values,
-            {
-                "lokr_w1",
-                "lokr_w1_a",
-                "lokr_w1_b",
-                "lokr_w2",
-                "lokr_w2_a",
-                "lokr_w2_b",
-                "lokr_t2",
-            },
-        )
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        w1: Optional[torch.Tensor] = self.w1
-        if w1 is None:
-            assert self.w1_a is not None
-            assert self.w1_b is not None
-            w1 = self.w1_a @ self.w1_b
-
-        w2 = self.w2
-        if w2 is None:
-            if self.t2 is None:
-                assert self.w2_a is not None
-                assert self.w2_b is not None
-                w2 = self.w2_a @ self.w2_b
-            else:
-                w2 = torch.einsum("i j k l, i p, j r -> p r k l", self.t2, self.w2_a, self.w2_b)
-
-        if len(w2.shape) == 4:
-            w1 = w1.unsqueeze(2).unsqueeze(2)
-        w2 = w2.contiguous()
-        assert w1 is not None
-        assert w2 is not None
-        weight = torch.kron(w1, w2)
-
-        return weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        for val in [self.w1, self.w1_a, self.w1_b, self.w2, self.w2_a, self.w2_b, self.t2]:
-            if val is not None:
-                model_size += val.nelement() * val.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        super().to(device=device, dtype=dtype)
-
-        if self.w1 is not None:
-            self.w1 = self.w1.to(device=device, dtype=dtype)
-        else:
-            assert self.w1_a is not None
-            assert self.w1_b is not None
-            self.w1_a = self.w1_a.to(device=device, dtype=dtype)
-            self.w1_b = self.w1_b.to(device=device, dtype=dtype)
-
-        if self.w2 is not None:
-            self.w2 = self.w2.to(device=device, dtype=dtype)
-        else:
-            assert self.w2_a is not None
-            assert self.w2_b is not None
-            self.w2_a = self.w2_a.to(device=device, dtype=dtype)
-            self.w2_b = self.w2_b.to(device=device, dtype=dtype)
-
-        if self.t2 is not None:
-            self.t2 = self.t2.to(device=device, dtype=dtype)

invokeai/backend/lora/layers/lora_layer.py

@@ -1,58 +0,0 @@
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
-
-
-# TODO: find and debug lora/locon with bias
-class LoRALayer(LoRALayerBase):
-    # up: torch.Tensor
-    # mid: Optional[torch.Tensor]
-    # down: torch.Tensor
-
-    def __init__(
-        self,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(values)
-
-        self.up = values["lora_up.weight"]
-        self.down = values["lora_down.weight"]
-        self.mid = values.get("lora_mid.weight", None)
-
-        self.rank = self.down.shape[0]
-        self.check_keys(
-            values,
-            {
-                "lora_up.weight",
-                "lora_down.weight",
-                "lora_mid.weight",
-            },
-        )
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        if self.mid is not None:
-            up = self.up.reshape(self.up.shape[0], self.up.shape[1])
-            down = self.down.reshape(self.down.shape[0], self.down.shape[1])
-            weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down)
-        else:
-            weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1)
-
-        return weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        for val in [self.up, self.mid, self.down]:
-            if val is not None:
-                model_size += val.nelement() * val.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        super().to(device=device, dtype=dtype)
-
-        self.up = self.up.to(device=device, dtype=dtype)
-        self.down = self.down.to(device=device, dtype=dtype)
-
-        if self.mid is not None:
-            self.mid = self.mid.to(device=device, dtype=dtype)

invokeai/backend/lora/layers/lora_layer_base.py

@@ -1,71 +0,0 @@
-from typing import Dict, Optional, Set
-
-import torch
-
-import invokeai.backend.util.logging as logger
-
-
-class LoRALayerBase:
-    # rank: Optional[int]
-    # alpha: Optional[float]
-    # bias: Optional[torch.Tensor]
-
-    # @property
-    # def scale(self):
-    #    return self.alpha / self.rank if (self.alpha and self.rank) else 1.0
-
-    def __init__(
-        self,
-        values: Dict[str, torch.Tensor],
-    ):
-        if "alpha" in values:
-            self.alpha = values["alpha"].item()
-        else:
-            self.alpha = None
-
-        if "bias_indices" in values and "bias_values" in values and "bias_size" in values:
-            self.bias: Optional[torch.Tensor] = torch.sparse_coo_tensor(
-                values["bias_indices"],
-                values["bias_values"],
-                tuple(values["bias_size"]),
-            )
-
-        else:
-            self.bias = None
-
-        self.rank = None  # set in layer implementation
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        raise NotImplementedError()
-
-    def get_bias(self, orig_bias: torch.Tensor) -> Optional[torch.Tensor]:
-        return self.bias
-
-    def get_parameters(self, orig_module: torch.nn.Module) -> Dict[str, torch.Tensor]:
-        params = {"weight": self.get_weight(orig_module.weight)}
-        bias = self.get_bias(orig_module.bias)
-        if bias is not None:
-            params["bias"] = bias
-        return params
-
-    def calc_size(self) -> int:
-        model_size = 0
-        for val in [self.bias]:
-            if val is not None:
-                model_size += val.nelement() * val.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        if self.bias is not None:
-            self.bias = self.bias.to(device=device, dtype=dtype)
-
-    def check_keys(self, values: Dict[str, torch.Tensor], known_keys: Set[str]):
-        """Log a warning if values contains unhandled keys."""
-        # {"alpha", "bias_indices", "bias_values", "bias_size"} are hard-coded, because they are handled by
-        # `LoRALayerBase`. Sub-classes should provide the known_keys that they handled.
-        all_known_keys = known_keys | {"alpha", "bias_indices", "bias_values", "bias_size"}
-        unknown_keys = set(values.keys()) - all_known_keys
-        if unknown_keys:
-            logger.warning(
-                f"Unexpected keys found in LoRA/LyCORIS layer, model might work incorrectly! Keys: {unknown_keys}"
-            )

invokeai/backend/lora/layers/norm_layer.py

@@ -1,36 +0,0 @@
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
-
-
-class NormLayer(LoRALayerBase):
-    # bias handled in LoRALayerBase(calc_size, to)
-    # weight: torch.Tensor
-    # bias: Optional[torch.Tensor]
-
-    def __init__(
-        self,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(values)
-
-        self.weight = values["w_norm"]
-        self.bias = values.get("b_norm", None)
-
-        self.rank = None  # unscaled
-        self.check_keys(values, {"w_norm", "b_norm"})
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        return self.weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        model_size += self.weight.nelement() * self.weight.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        super().to(device=device, dtype=dtype)
-
-        self.weight = self.weight.to(device=device, dtype=dtype)

invokeai/backend/lora/layers/utils.py

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

invokeai/backend/lora/lora_model_raw.py

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

invokeai/backend/lora/lora_patcher.py

@@ -1,148 +0,0 @@
-from contextlib import contextmanager
-from typing import Dict, Iterable, Optional, Tuple
-
-import torch
-
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
-
-
-class LoraPatcher:
-    @staticmethod
-    @torch.no_grad()
-    @contextmanager
-    def apply_lora_patches(
-        model: torch.nn.Module,
-        patches: Iterable[Tuple[LoRAModelRaw, float]],
-        prefix: str,
-        cached_weights: Optional[Dict[str, torch.Tensor]] = None,
-    ):
-        """Apply one or more LoRA patches to a model within a context manager.
-
-        :param model: The model to patch.
-        :param loras: An iterator that returns tuples of LoRA patches and associated weights. An iterator is used so
-            that the LoRA patches do not need to be loaded into memory all at once.
-        :param prefix: The keys in the patches will be filtered to only include weights with this prefix.
-        :cached_weights: Read-only copy of the model's state dict in CPU, for efficient unpatching purposes.
-        """
-        original_weights = OriginalWeightsStorage(cached_weights)
-        try:
-            for patch, patch_weight in patches:
-                LoraPatcher.apply_lora_patch(
-                    model=model,
-                    prefix=prefix,
-                    patch=patch,
-                    patch_weight=patch_weight,
-                    original_weights=original_weights,
-                )
-                del patch
-
-            yield
-        finally:
-            for param_key, weight in original_weights.get_changed_weights():
-                model.get_parameter(param_key).copy_(weight)
-
-    @staticmethod
-    @torch.no_grad()
-    def apply_lora_patch(
-        model: torch.nn.Module,
-        prefix: str,
-        patch: LoRAModelRaw,
-        patch_weight: float,
-        original_weights: OriginalWeightsStorage,
-    ):
-        """
-        Apply a single LoRA patch to a model.
-        :param model: The model to patch.
-        :param patch: LoRA model to patch in.
-        :param patch_weight: LoRA patch weight.
-        :param prefix: A string prefix that precedes keys used in the LoRAs weight layers.
-        :param original_weights: Storage with original weights, filled by weights which lora patches, used for unpatching.
-        """
-
-        if patch_weight == 0:
-            return
-
-        # If the layer keys contain a dot, then they are not flattened, and can be directly used to access model
-        # submodules. If the layer keys do not contain a dot, then they are flattened, meaning that all '.' have been
-        # replaced with '_'. Non-flattened keys are preferred, because they allow submodules to be accessed directly
-        # without searching, but some legacy code still uses flattened keys.
-        layer_keys_are_flattened = "." not in next(iter(patch.layers.keys()))
-
-        prefix_len = len(prefix)
-
-        for layer_key, layer in patch.layers.items():
-            if not layer_key.startswith(prefix):
-                continue
-
-            module_key, module = LoraPatcher._get_submodule(
-                model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
-            )
-
-            # All of the LoRA weight calculations will be done on the same device as the module weight.
-            # (Performance will be best if this is a CUDA device.)
-            device = module.weight.device
-            dtype = module.weight.dtype
-
-            layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
-
-            # We intentionally move to the target device first, then cast. Experimentally, this was found to
-            # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
-            # same thing in a single call to '.to(...)'.
-            layer.to(device=device)
-            layer.to(dtype=torch.float32)
-
-            # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
-            # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
-            for param_name, lora_param_weight in layer.get_parameters(module).items():
-                param_key = module_key + "." + param_name
-                module_param = module.get_parameter(param_name)
-
-                # Save original weight
-                original_weights.save(param_key, module_param)
-
-                if module_param.shape != lora_param_weight.shape:
-                    lora_param_weight = lora_param_weight.reshape(module_param.shape)
-
-                lora_param_weight *= patch_weight * layer_scale
-                module_param += lora_param_weight.to(dtype=dtype)
-
-            layer.to(device=TorchDevice.CPU_DEVICE)
-
-    @staticmethod
-    def _get_submodule(
-        model: torch.nn.Module, layer_key: str, layer_key_is_flattened: bool
-    ) -> tuple[str, torch.nn.Module]:
-        """Get the submodule corresponding to the given layer key.
-        :param model: The model to search.
-        :param layer_key: The layer key to search for.
-        :param layer_key_is_flattened: Whether the layer key is flattened. If flattened, then all '.' have been replaced
-            with '_'. Non-flattened keys are preferred, because they allow submodules to be accessed directly without
-            searching, but some legacy code still uses flattened keys.
-        :return: A tuple containing the module key and the submodule.
-        """
-        if not layer_key_is_flattened:
-            return layer_key, model.get_submodule(layer_key)
-
-        # Handle flattened keys.
-        assert "." not in layer_key
-
-        module = model
-        module_key = ""
-        key_parts = layer_key.split("_")
-
-        submodule_name = key_parts.pop(0)
-
-        while len(key_parts) > 0:
-            try:
-                module = module.get_submodule(submodule_name)
-                module_key += "." + submodule_name
-                submodule_name = key_parts.pop(0)
-            except Exception:
-                submodule_name += "_" + key_parts.pop(0)
-
-        module = module.get_submodule(submodule_name)
-        module_key = (module_key + "." + submodule_name).lstrip(".")
-
-        return module_key, module

invokeai/backend/model_manager/load/load_default.py

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

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

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

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

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

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

@@ -32,9 +32,6 @@ from invokeai.backend.model_manager.config import (
 )
 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:
@@ -193,13 +190,6 @@ class FluxCheckpointModel(ModelLoader):
         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
 
@@ -240,7 +230,5 @@ class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
                 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/lora.py

@@ -5,18 +5,8 @@ from logging import Logger
 from pathlib import Path
 from typing import Optional
 
-import torch
-from safetensors.torch import load_file
-
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils import (
-    lora_model_from_flux_diffusers_state_dict,
-)
-from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import (
-    lora_model_from_flux_kohya_state_dict,
-)
-from invokeai.backend.lora.conversions.sd_lora_conversion_utils import lora_model_from_sd_state_dict
-from invokeai.backend.lora.conversions.sdxl_lora_conversion_utils import convert_sdxl_keys_to_diffusers_format
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
@@ -55,33 +45,14 @@ class LoRALoader(ModelLoader):
             raise ValueError("There are no submodels in a LoRA model.")
         model_path = Path(config.path)
         assert self._model_base is not None
-
-        # Load the state dict from the model file.
-        if model_path.suffix == ".safetensors":
-            state_dict = load_file(model_path.absolute().as_posix(), device="cpu")
-        else:
-            state_dict = torch.load(model_path, map_location="cpu")
-
-        # Apply state_dict key conversions, if necessary.
-        if self._model_base == BaseModelType.StableDiffusionXL:
-            state_dict = convert_sdxl_keys_to_diffusers_format(state_dict)
-            model = lora_model_from_sd_state_dict(state_dict=state_dict)
-        elif self._model_base == BaseModelType.Flux:
-            if config.format == ModelFormat.Diffusers:
-                model = lora_model_from_flux_diffusers_state_dict(state_dict=state_dict)
-            elif config.format == ModelFormat.LyCORIS:
-                model = lora_model_from_flux_kohya_state_dict(state_dict=state_dict)
-            else:
-                raise ValueError(f"LoRA model is in unsupported FLUX format: {config.format}")
-        elif self._model_base in [BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2]:
-            # Currently, we don't apply any conversions for SD1 and SD2 LoRA models.
-            model = lora_model_from_sd_state_dict(state_dict=state_dict)
-        else:
-            raise ValueError(f"Unsupported LoRA base model: {self._model_base}")
-
-        model.to(dtype=self._torch_dtype)
+        model = LoRAModelRaw.from_checkpoint(
+            file_path=model_path,
+            dtype=self._torch_dtype,
+            base_model=self._model_base,
+        )
         return model
 
+    # override
     def _get_model_path(self, config: AnyModelConfig) -> Path:
         # cheating a little - we remember this variable for using in the subsequent call to _load_model()
         self._model_base = config.base

invokeai/backend/model_manager/load/model_util.py

@@ -15,7 +15,7 @@ from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import D
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
 from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager.config import AnyModel
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel

invokeai/backend/model_manager/probe.py

@@ -10,10 +10,6 @@ from picklescan.scanner import scan_file_path
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.util.misc import uuid_string
-from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils import (
-    is_state_dict_likely_in_flux_diffusers_format,
-)
-from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import is_state_dict_likely_in_flux_kohya_format
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
@@ -112,8 +108,6 @@ class ModelProbe(object):
         "CLIPVisionModelWithProjection": ModelType.CLIPVision,
         "T2IAdapter": ModelType.T2IAdapter,
         "CLIPModel": ModelType.CLIPEmbed,
-        "CLIPTextModel": ModelType.CLIPEmbed,
-        "T5EncoderModel": ModelType.T5Encoder,
     }
 
     @classmethod
@@ -230,27 +224,14 @@ 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.",
-                )
-            ):
+            if key.startswith(("cond_stage_model.", "first_stage_model.", "model.diffusion_model.", "double_blocks.")):
                 # Keys starting with double_blocks are associated with Flux models
                 return ModelType.Main
             elif key.startswith(("encoder.conv_in", "decoder.conv_in")):
                 return ModelType.VAE
             elif key.startswith(("lora_te_", "lora_unet_")):
                 return ModelType.LoRA
-            # "lora_A.weight" and "lora_B.weight" are associated with models in PEFT format. We don't support all PEFT
-            # LoRA models, but as of the time of writing, we support Diffusers FLUX PEFT LoRA models.
-            elif key.endswith(("to_k_lora.up.weight", "to_q_lora.down.weight", "lora_A.weight", "lora_B.weight")):
+            elif key.endswith(("to_k_lora.up.weight", "to_q_lora.down.weight")):
                 return ModelType.LoRA
             elif key.startswith(("controlnet", "control_model", "input_blocks")):
                 return ModelType.ControlNet
@@ -302,16 +283,9 @@ 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:
@@ -354,10 +328,7 @@ class ModelProbe(object):
                 # 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
-                ):
+                if "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
@@ -365,7 +336,7 @@ class ModelProbe(object):
                     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
+                    #   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-schnell"
@@ -472,10 +443,7 @@ class CheckpointProbeBase(ProbeBase):
 
     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
-        ):
+        if "double_blocks.0.img_attn.proj.weight.quant_state.bitsandbytes__nf4" in state_dict:
             return ModelFormat.BnbQuantizednf4b
         return ModelFormat("checkpoint")
 
@@ -502,10 +470,7 @@ 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
-        ):
+        if "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:
@@ -560,21 +525,12 @@ class LoRACheckpointProbe(CheckpointProbeBase):
     """Class for LoRA checkpoints."""
 
     def get_format(self) -> ModelFormat:
-        if is_state_dict_likely_in_flux_diffusers_format(self.checkpoint):
-            # TODO(ryand): This is an unusual case. In other places throughout the codebase, we treat
-            # ModelFormat.Diffusers as meaning that the model is in a directory. In this case, the model is a single
-            # file, but the weight keys are in the diffusers format.
-            return ModelFormat.Diffusers
-        return ModelFormat.LyCORIS
+        return ModelFormat("lycoris")
 
     def get_base_type(self) -> BaseModelType:
-        if is_state_dict_likely_in_flux_kohya_format(self.checkpoint) or is_state_dict_likely_in_flux_diffusers_format(
-            self.checkpoint
-        ):
-            return BaseModelType.Flux
+        checkpoint = self.checkpoint
+        token_vector_length = lora_token_vector_length(checkpoint)
 
-        # If we've gotten here, we assume that the model is a Stable Diffusion model.
-        token_vector_length = lora_token_vector_length(self.checkpoint)
         if token_vector_length == 768:
             return BaseModelType.StableDiffusion1
         elif token_vector_length == 1024:
@@ -791,27 +747,8 @@ class TextualInversionFolderProbe(FolderProbeBase):
 
 
 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")
+        return ModelFormat.T5Encoder
 
 
 class ONNXFolderProbe(PipelineFolderProbe):

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_patcher.py

@@ -5,18 +5,32 @@ from __future__ import annotations
 
 import pickle
 from contextlib import contextmanager
-from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
+from typing import Any, Dict, Generator, Iterator, List, Optional, Tuple, Type, Union
 
 import numpy as np
 import torch
-from diffusers import UNet2DConditionModel
+from diffusers import OnnxRuntimeModel, UNet2DConditionModel
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora import LoRAModelRaw
+from invokeai.backend.model_manager import AnyModel
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
+from invokeai.backend.stable_diffusion.extensions.lora import LoRAExt
 from invokeai.backend.textual_inversion import TextualInversionManager, TextualInversionModelRaw
+from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
+
+"""
+loras = [
+    (lora_model1, 0.7),
+    (lora_model2, 0.4),
+]
+with LoRAHelper.apply_lora_unet(unet, loras):
+    # unet with applied loras
+# unmodified unet
+
+"""
 
 
 class ModelPatcher:
@@ -40,6 +54,95 @@ class ModelPatcher:
         finally:
             unet.set_attn_processor(unet_orig_processors)
 
+    @staticmethod
+    def _resolve_lora_key(model: torch.nn.Module, lora_key: str, prefix: str) -> Tuple[str, torch.nn.Module]:
+        assert "." not in lora_key
+
+        if not lora_key.startswith(prefix):
+            raise Exception(f"lora_key with invalid prefix: {lora_key}, {prefix}")
+
+        module = model
+        module_key = ""
+        key_parts = lora_key[len(prefix) :].split("_")
+
+        submodule_name = key_parts.pop(0)
+
+        while len(key_parts) > 0:
+            try:
+                module = module.get_submodule(submodule_name)
+                module_key += "." + submodule_name
+                submodule_name = key_parts.pop(0)
+            except Exception:
+                submodule_name += "_" + key_parts.pop(0)
+
+        module = module.get_submodule(submodule_name)
+        module_key = (module_key + "." + submodule_name).lstrip(".")
+
+        return (module_key, module)
+
+    @classmethod
+    @contextmanager
+    def apply_lora_unet(
+        cls,
+        unet: UNet2DConditionModel,
+        loras: Iterator[Tuple[LoRAModelRaw, float]],
+        cached_weights: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> Generator[None, None, None]:
+        with cls.apply_lora(
+            unet,
+            loras=loras,
+            prefix="lora_unet_",
+            cached_weights=cached_weights,
+        ):
+            yield
+
+    @classmethod
+    @contextmanager
+    def apply_lora_text_encoder(
+        cls,
+        text_encoder: CLIPTextModel,
+        loras: Iterator[Tuple[LoRAModelRaw, float]],
+        cached_weights: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> Generator[None, None, None]:
+        with cls.apply_lora(text_encoder, loras=loras, prefix="lora_te_", cached_weights=cached_weights):
+            yield
+
+    @classmethod
+    @contextmanager
+    def apply_lora(
+        cls,
+        model: AnyModel,
+        loras: Iterator[Tuple[LoRAModelRaw, float]],
+        prefix: str,
+        cached_weights: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> Generator[None, None, None]:
+        """
+        Apply one or more LoRAs to a model.
+
+        :param model: The model to patch.
+        :param loras: An iterator that returns the LoRA to patch in and its patch weight.
+        :param prefix: A string prefix that precedes keys used in the LoRAs weight layers.
+        :cached_weights: Read-only copy of the model's state dict in CPU, for unpatching purposes.
+        """
+        original_weights = OriginalWeightsStorage(cached_weights)
+        try:
+            for lora_model, lora_weight in loras:
+                LoRAExt.patch_model(
+                    model=model,
+                    prefix=prefix,
+                    lora=lora_model,
+                    lora_weight=lora_weight,
+                    original_weights=original_weights,
+                )
+                del lora_model
+
+            yield
+
+        finally:
+            with torch.no_grad():
+                for param_key, weight in original_weights.get_changed_weights():
+                    model.get_parameter(param_key).copy_(weight)
+
     @classmethod
     @contextmanager
     def apply_ti(
@@ -179,6 +282,26 @@ class ModelPatcher:
 
 
 class ONNXModelPatcher:
+    @classmethod
+    @contextmanager
+    def apply_lora_unet(
+        cls,
+        unet: OnnxRuntimeModel,
+        loras: Iterator[Tuple[LoRAModelRaw, float]],
+    ) -> None:
+        with cls.apply_lora(unet, loras, "lora_unet_"):
+            yield
+
+    @classmethod
+    @contextmanager
+    def apply_lora_text_encoder(
+        cls,
+        text_encoder: OnnxRuntimeModel,
+        loras: List[Tuple[LoRAModelRaw, float]],
+    ) -> None:
+        with cls.apply_lora(text_encoder, loras, "lora_te_"):
+            yield
+
     # based on
     # https://github.com/ssube/onnx-web/blob/ca2e436f0623e18b4cfe8a0363fcfcf10508acf7/api/onnx_web/convert/diffusion/lora.py#L323
     @classmethod

invokeai/backend/stable_diffusion/extensions/lora.py

@@ -1,17 +1,18 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Tuple
 
+import torch
 from diffusers import UNet2DConditionModel
 
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoraPatcher
 from invokeai.backend.stable_diffusion.extensions.base import ExtensionBase
+from invokeai.backend.util.devices import TorchDevice
 
 if TYPE_CHECKING:
     from invokeai.app.invocations.model import ModelIdentifierField
     from invokeai.app.services.shared.invocation_context import InvocationContext
+    from invokeai.backend.lora import LoRAModelRaw
     from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
 
@@ -30,14 +31,107 @@ class LoRAExt(ExtensionBase):
     @contextmanager
     def patch_unet(self, unet: UNet2DConditionModel, original_weights: OriginalWeightsStorage):
         lora_model = self._node_context.models.load(self._model_id).model
-        assert isinstance(lora_model, LoRAModelRaw)
-        LoraPatcher.apply_lora_patch(
+        self.patch_model(
             model=unet,
             prefix="lora_unet_",
-            patch=lora_model,
-            patch_weight=self._weight,
+            lora=lora_model,
+            lora_weight=self._weight,
             original_weights=original_weights,
         )
         del lora_model
 
         yield
+
+    @classmethod
+    @torch.no_grad()
+    def patch_model(
+        cls,
+        model: torch.nn.Module,
+        prefix: str,
+        lora: LoRAModelRaw,
+        lora_weight: float,
+        original_weights: OriginalWeightsStorage,
+    ):
+        """
+        Apply one or more LoRAs to a model.
+        :param model: The model to patch.
+        :param lora: LoRA model to patch in.
+        :param lora_weight: LoRA patch weight.
+        :param prefix: A string prefix that precedes keys used in the LoRAs weight layers.
+        :param original_weights: Storage with original weights, filled by weights which lora patches, used for unpatching.
+        """
+
+        if lora_weight == 0:
+            return
+
+        # assert lora.device.type == "cpu"
+        for layer_key, layer in lora.layers.items():
+            if not layer_key.startswith(prefix):
+                continue
+
+            # TODO(ryand): A non-negligible amount of time is currently spent resolving LoRA keys. This
+            # should be improved in the following ways:
+            # 1. The key mapping could be more-efficiently pre-computed. This would save time every time a
+            #    LoRA model is applied.
+            # 2. From an API perspective, there's no reason that the `ModelPatcher` should be aware of the
+            #    intricacies of Stable Diffusion key resolution. It should just expect the input LoRA
+            #    weights to have valid keys.
+            assert isinstance(model, torch.nn.Module)
+            module_key, module = cls._resolve_lora_key(model, layer_key, prefix)
+
+            # All of the LoRA weight calculations will be done on the same device as the module weight.
+            # (Performance will be best if this is a CUDA device.)
+            device = module.weight.device
+            dtype = module.weight.dtype
+
+            layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
+
+            # We intentionally move to the target device first, then cast. Experimentally, this was found to
+            # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
+            # same thing in a single call to '.to(...)'.
+            layer.to(device=device)
+            layer.to(dtype=torch.float32)
+
+            # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
+            # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
+            for param_name, lora_param_weight in layer.get_parameters(module).items():
+                param_key = module_key + "." + param_name
+                module_param = module.get_parameter(param_name)
+
+                # save original weight
+                original_weights.save(param_key, module_param)
+
+                if module_param.shape != lora_param_weight.shape:
+                    # TODO: debug on lycoris
+                    lora_param_weight = lora_param_weight.reshape(module_param.shape)
+
+                lora_param_weight *= lora_weight * layer_scale
+                module_param += lora_param_weight.to(dtype=dtype)
+
+            layer.to(device=TorchDevice.CPU_DEVICE)
+
+    @staticmethod
+    def _resolve_lora_key(model: torch.nn.Module, lora_key: str, prefix: str) -> Tuple[str, torch.nn.Module]:
+        assert "." not in lora_key
+
+        if not lora_key.startswith(prefix):
+            raise Exception(f"lora_key with invalid prefix: {lora_key}, {prefix}")
+
+        module = model
+        module_key = ""
+        key_parts = lora_key[len(prefix) :].split("_")
+
+        submodule_name = key_parts.pop(0)
+
+        while len(key_parts) > 0:
+            try:
+                module = module.get_submodule(submodule_name)
+                module_key += "." + submodule_name
+                submodule_name = key_parts.pop(0)
+            except Exception:
+                submodule_name += "_" + key_parts.pop(0)
+
+        module = module.get_submodule(submodule_name)
+        module_key = (module_key + "." + submodule_name).lstrip(".")
+
+        return (module_key, module)

invokeai/frontend/web/.eslintrc.js

@@ -16,14 +16,6 @@ module.exports = {
     'no-promise-executor-return': 'error',
     // https://eslint.org/docs/latest/rules/require-await
     'require-await': 'error',
-    'no-restricted-properties': [
-      'error',
-      {
-        object: 'crypto',
-        property: 'randomUUID',
-        message: 'Use of crypto.randomUUID is not allowed as it is not available in all browsers.',
-      },
-    ],
   },
   overrides: [
     /**

invokeai/frontend/web/knip.ts

@@ -11,8 +11,6 @@ const config: KnipConfig = {
     'src/features/nodes/types/v2/**',
     // TODO(psyche): maybe we can clean up these utils after canvas v2 release
     'src/features/controlLayers/konva/util.ts',
-    // TODO(psyche): restore HRF functionality?
-    'src/features/hrf/**',
   ],
   ignoreBinaries: ['only-allow'],
   paths: {

invokeai/frontend/web/package.json

@@ -58,7 +58,7 @@
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
     "@fontsource-variable/inter": "^5.0.20",
-    "@invoke-ai/ui-library": "^0.0.33",
+    "@invoke-ai/ui-library": "^0.0.32",
     "@nanostores/react": "^0.7.3",
     "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",
@@ -92,7 +92,7 @@
     "react-i18next": "^14.1.3",
     "react-icons": "^5.2.1",
     "react-redux": "9.1.2",
-    "react-resizable-panels": "^2.1.2",
+    "react-resizable-panels": "^2.0.23",
     "react-use": "^17.5.1",
     "react-virtuoso": "^4.9.0",
     "reactflow": "^11.11.4",
@@ -136,7 +136,6 @@
     "@vitest/coverage-v8": "^1.5.0",
     "@vitest/ui": "^1.5.0",
     "concurrently": "^8.2.2",
-    "csstype": "^3.1.3",
     "dpdm": "^3.14.0",
     "eslint": "^8.57.0",
     "eslint-plugin-i18next": "^6.0.9",

invokeai/frontend/web/pnpm-lock.yaml

@@ -24,8 +24,8 @@ dependencies:
     specifier: ^5.0.20
     version: 5.0.20
   '@invoke-ai/ui-library':
-    specifier: ^0.0.33
-    version: 0.0.33(@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)
@@ -126,8 +126,8 @@ dependencies:
     specifier: 9.1.2
     version: 9.1.2(@types/react@18.3.3)(react@18.3.1)(redux@5.0.1)
   react-resizable-panels:
-    specifier: ^2.1.2
-    version: 2.1.2(react-dom@18.3.1)(react@18.3.1)
+    specifier: ^2.0.23
+    version: 2.0.23(react-dom@18.3.1)(react@18.3.1)
   react-use:
     specifier: ^17.5.1
     version: 17.5.1(react-dom@18.3.1)(react@18.3.1)
@@ -238,9 +238,6 @@ devDependencies:
   concurrently:
     specifier: ^8.2.2
     version: 8.2.2
-  csstype:
-    specifier: ^3.1.3
-    version: 3.1.3
   dpdm:
     specifier: ^3.14.0
     version: 3.14.0
@@ -2056,7 +2053,7 @@ packages:
     dependencies:
       '@chakra-ui/dom-utils': 2.1.0
       react: 18.3.1
-      react-focus-lock: 2.13.2(@types/react@18.3.3)(react@18.3.1)
+      react-focus-lock: 2.13.0(@types/react@18.3.3)(react@18.3.1)
     transitivePeerDependencies:
       - '@types/react'
     dev: false
@@ -2253,7 +2250,7 @@ packages:
       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)
-      react-remove-scroll: 2.6.0(@types/react@18.3.3)(react@18.3.1)
+      react-remove-scroll: 2.5.10(@types/react@18.3.3)(react@18.3.1)
     transitivePeerDependencies:
       - '@types/react'
     dev: false
@@ -3574,8 +3571,8 @@ packages:
       prettier: 3.3.3
     dev: true
 
-  /@invoke-ai/ui-library@0.0.33(@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-YLydTCOTUEgju4Ex6yXt/bvNBcO97y6zc1cGYjt7vtJMS8e6deA89cC5JejjbmVgntdnn49cDyeUxB8Z24gZew==}
+  /@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
@@ -10011,8 +10008,8 @@ packages:
     resolution: {integrity: sha512-nsO+KSNgo1SbJqJEYRE9ERzo7YtYbou/OqjSQKxV7jcKox7+usiUVZOAC+XnDOABXggQTno0Y1CpVnuWEc1boQ==}
     dev: false
 
-  /react-focus-lock@2.13.2(@types/react@18.3.3)(react@18.3.1):
-    resolution: {integrity: sha512-T/7bsofxYqnod2xadvuwjGKHOoL5GH7/EIPI5UyEvaU/c2CcphvGI371opFtuY/SYdbMsNiuF4HsHQ50nA/TKQ==}
+  /react-focus-lock@2.13.0(@types/react@18.3.3)(react@18.3.1):
+    resolution: {integrity: sha512-w7aIcTwZwNzUp2fYQDMICy+khFwVmKmOrLF8kNsPS+dz4Oi/oxoVJ2wCMVvX6rWGriM/+mYaTyp1MRmkcs2amw==}
     peerDependencies:
       '@types/react': ^16.8.0 || ^17.0.0 || ^18.0.0
       react: ^16.8.0 || ^17.0.0 || ^18.0.0
@@ -10147,6 +10144,25 @@ packages:
       tslib: 2.7.0
     dev: false
 
+  /react-remove-scroll@2.5.10(@types/react@18.3.3)(react@18.3.1):
+    resolution: {integrity: sha512-m3zvBRANPBw3qxVVjEIPEQinkcwlFZ4qyomuWVpNJdv4c6MvHfXV0C3L9Jx5rr3HeBHKNRX+1jreB5QloDIJjA==}
+    engines: {node: '>=10'}
+    peerDependencies:
+      '@types/react': ^16.8.0 || ^17.0.0 || ^18.0.0
+      react: ^16.8.0 || ^17.0.0 || ^18.0.0
+    peerDependenciesMeta:
+      '@types/react':
+        optional: true
+    dependencies:
+      '@types/react': 18.3.3
+      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: 2.7.0
+      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
+
   /react-remove-scroll@2.5.5(@types/react@18.3.3)(react@18.3.1):
     resolution: {integrity: sha512-ImKhrzJJsyXJfBZ4bzu8Bwpka14c/fQt0k+cyFp/PBhTfyDnU5hjOtM4AG/0AMyy8oKzOTR0lDgJIM7pYXI0kw==}
     engines: {node: '>=10'}
@@ -10166,27 +10182,8 @@ packages:
       use-sidecar: 1.1.2(@types/react@18.3.3)(react@18.3.1)
     dev: false
 
-  /react-remove-scroll@2.6.0(@types/react@18.3.3)(react@18.3.1):
-    resolution: {integrity: sha512-I2U4JVEsQenxDAKaVa3VZ/JeJZe0/2DxPWL8Tj8yLKctQJQiZM52pn/GWFpSp8dftjM3pSAHVJZscAnC/y+ySQ==}
-    engines: {node: '>=10'}
-    peerDependencies:
-      '@types/react': ^16.8.0 || ^17.0.0 || ^18.0.0
-      react: ^16.8.0 || ^17.0.0 || ^18.0.0
-    peerDependenciesMeta:
-      '@types/react':
-        optional: true
-    dependencies:
-      '@types/react': 18.3.3
-      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: 2.7.0
-      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
-
-  /react-resizable-panels@2.1.2(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-Ku2Bo7JvE8RpHhl4X1uhkdeT9auPBoxAOlGTqomDUUrBAX2mVGuHYZTcWvlnJSgx0QyHIxHECgGB5XVPUbUOkQ==}
+  /react-resizable-panels@2.0.23(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-8ZKTwTU11t/FYwiwhMdtZYYyFxic5U5ysRu2YwfkAgDbUJXFvnWSJqhnzkSlW+mnDoNAzDCrJhdOSXBPA76wug==}
     peerDependencies:
       react: ^16.14.0 || ^17.0.0 || ^18.0.0
       react-dom: ^16.14.0 || ^17.0.0 || ^18.0.0

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

@@ -93,7 +93,6 @@
         "copy": "Copy",
         "copyError": "$t(gallery.copy) Error",
         "on": "On",
-        "off": "Off",
         "or": "or",
         "checkpoint": "Checkpoint",
         "communityLabel": "Community",
@@ -135,7 +134,6 @@
         "nodes": "Workflows",
         "notInstalled": "Not $t(common.installed)",
         "openInNewTab": "Open in New Tab",
-        "openInViewer": "Open in Viewer",
         "orderBy": "Order By",
         "outpaint": "outpaint",
         "outputs": "Outputs",
@@ -375,7 +373,6 @@
         "useCache": "Use Cache"
     },
     "gallery": {
-        "gallery": "Gallery",
         "alwaysShowImageSizeBadge": "Always Show Image Size Badge",
         "assets": "Assets",
         "autoAssignBoardOnClick": "Auto-Assign Board on Click",
@@ -388,11 +385,11 @@
         "deleteImage_one": "Delete Image",
         "deleteImage_other": "Delete {{count}} Images",
         "deleteImagePermanent": "Deleted images cannot be restored.",
-        "displayBoardSearch": "Board Search",
-        "displaySearch": "Image Search",
+        "displayBoardSearch": "Display Board Search",
+        "displaySearch": "Display Search",
         "download": "Download",
         "exitBoardSearch": "Exit Board Search",
-        "exitSearch": "Exit Image Search",
+        "exitSearch": "Exit Search",
         "featuresWillReset": "If you delete this image, those features will immediately be reset.",
         "galleryImageSize": "Image Size",
         "gallerySettings": "Gallery Settings",
@@ -438,8 +435,7 @@
         "compareHelp1": "Hold <Kbd>Alt</Kbd> while clicking a gallery image or using the arrow keys to change the compare image.",
         "compareHelp2": "Press <Kbd>M</Kbd> to cycle through comparison modes.",
         "compareHelp3": "Press <Kbd>C</Kbd> to swap the compared images.",
-        "compareHelp4": "Press <Kbd>Z</Kbd> or <Kbd>Esc</Kbd> to exit.",
-        "toggleMiniViewer": "Toggle Mini Viewer"
+        "compareHelp4": "Press <Kbd>Z</Kbd> or <Kbd>Esc</Kbd> to exit."
     },
     "hotkeys": {
         "searchHotkeys": "Search Hotkeys",
@@ -1018,8 +1014,6 @@
             "noModelForControlAdapter": "Control Adapter #{{number}} has no model selected.",
             "incompatibleBaseModelForControlAdapter": "Control Adapter #{{number}} model is incompatible with main model.",
             "noModelSelected": "No model selected",
-            "canvasManagerNotLoaded": "Canvas Manager not loaded",
-            "canvasBusy": "Canvas is busy",
             "noPrompts": "No prompts generated",
             "noNodesInGraph": "No nodes in graph",
             "systemDisconnected": "System disconnected",
@@ -1051,11 +1045,12 @@
         "scaledHeight": "Scaled H",
         "scaledWidth": "Scaled W",
         "scheduler": "Scheduler",
-        "seamlessXAxis": "Seamless X Axis",
-        "seamlessYAxis": "Seamless Y Axis",
+        "seamlessXAxis": "Seamless Tiling X Axis",
+        "seamlessYAxis": "Seamless Tiling Y Axis",
         "seed": "Seed",
         "imageActions": "Image Actions",
-        "sendToCanvas": "Send To Canvas",
+        "sendToImg2Img": "Send to Image to Image",
+        "sendToUnifiedCanvas": "Send To Unified Canvas",
         "sendToUpscale": "Send To Upscale",
         "showOptionsPanel": "Show Side Panel (O or T)",
         "shuffle": "Shuffle Seed",
@@ -1196,8 +1191,8 @@
         "problemSavingMaskDesc": "Unable to export mask",
         "prunedQueue": "Pruned Queue",
         "resetInitialImage": "Reset Initial Image",
-        "sentToCanvas": "Sent to Canvas",
-        "sentToUpscale": "Sent to Upscale",
+        "sentToImageToImage": "Sent To Image To Image",
+        "sentToUnifiedCanvas": "Sent to Unified Canvas",
         "serverError": "Server Error",
         "sessionRef": "Session: {{sessionId}}",
         "setAsCanvasInitialImage": "Set as canvas initial image",
@@ -1659,9 +1654,6 @@
         "storeNotInitialized": "Store is not initialized"
     },
     "controlLayers": {
-        "bookmark": "Bookmark for Quick Switch",
-        "fitBboxToLayers": "Fit Bbox To Layers",
-        "removeBookmark": "Remove Bookmark",
         "saveCanvasToGallery": "Save Canvas To Gallery",
         "saveBboxToGallery": "Save Bbox To Gallery",
         "savedToGalleryOk": "Saved to Gallery",
@@ -1680,7 +1672,6 @@
         "clearCaches": "Clear Caches",
         "recalculateRects": "Recalculate Rects",
         "clipToBbox": "Clip Strokes to Bbox",
-        "compositeMaskedRegions": "Composite Masked Regions",
         "addLayer": "Add Layer",
         "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
@@ -1716,8 +1707,6 @@
         "inpaintMask": "Inpaint Mask",
         "regionalGuidance": "Regional Guidance",
         "ipAdapter": "IP Adapter",
-        "sendingToCanvas": "Sending to Canvas",
-        "sendingToGallery": "Sending to Gallery",
         "sendToGallery": "Send To Gallery",
         "sendToGalleryDesc": "Generations will be sent to the gallery.",
         "sendToCanvas": "Send To Canvas",
@@ -1736,12 +1725,12 @@
         "regionalGuidance_withCount_hidden": "Regional Guidance ({{count}} hidden)",
         "controlLayers_withCount_hidden": "Control Layers ({{count}} hidden)",
         "rasterLayers_withCount_hidden": "Raster Layers ({{count}} hidden)",
-        "globalIPAdapters_withCount_hidden": "Global IP Adapters ({{count}} hidden)",
+        "ipAdapters_withCount_hidden": "IP Adapters ({{count}} hidden)",
         "inpaintMasks_withCount_hidden": "Inpaint Masks ({{count}} hidden)",
         "regionalGuidance_withCount_visible": "Regional Guidance ({{count}})",
         "controlLayers_withCount_visible": "Control Layers ({{count}})",
         "rasterLayers_withCount_visible": "Raster Layers ({{count}})",
-        "globalIPAdapters_withCount_visible": "Global IP Adapters ({{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)",
@@ -1754,8 +1743,8 @@
         "clearProcessor": "Clear Processor",
         "resetProcessor": "Reset Processor to Defaults",
         "noLayersAdded": "No Layers Added",
-        "layer_one": "Layer",
-        "layer_other": "Layers",
+        "layers_one": "Layer",
+        "layers_other": "Layers",
         "objects_zero": "empty",
         "objects_one": "{{count}} object",
         "objects_other": "{{count}} objects",
@@ -1791,54 +1780,16 @@
             "bbox": "Bbox",
             "move": "Move",
             "view": "View",
+            "transform": "Transform",
             "colorPicker": "Color Picker"
         },
         "filter": {
             "filter": "Filter",
             "filters": "Filters",
             "filterType": "Filter Type",
-            "autoProcess": "Auto Process",
-            "reset": "Reset",
-            "process": "Process",
-            "apply": "Apply",
-            "cancel": "Cancel",
-            "spandrel": {
-                "label": "Image-to-Image Model",
-                "description": "Run an image-to-image model on the selected layer.",
-                "paramModel": "Model",
-                "paramAutoScale": "Auto Scale",
-                "paramAutoScaleDesc": "The selected model will be run until the target scale is reached.",
-                "paramScale": "Target Scale"
-            }
-        },
-        "transform": {
-            "transform": "Transform",
-            "fitToBbox": "Fit to Bbox",
-            "reset": "Reset",
+            "preview": "Preview",
             "apply": "Apply",
             "cancel": "Cancel"
-        },
-        "settings": {
-            "snapToGrid": {
-                "label": "Snap to Grid",
-                "on": "On",
-                "off": "Off"
-            }
-        },
-        "HUD": {
-            "bbox": "Bbox",
-            "scaledBbox": "Scaled Bbox",
-            "autoSave": "Auto Save",
-            "entityStatus": {
-                "selectedEntity": "Selected Entity",
-                "selectedEntityIs": "Selected Entity is",
-                "isFiltering": "is filtering",
-                "isTransforming": "is transforming",
-                "isLocked": "is locked",
-                "isHidden": "is hidden",
-                "isDisabled": "is disabled",
-                "enabled": "Enabled"
-            }
         }
     },
     "upscaling": {

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/src/app/components/ThemeLocaleProvider.tsx

@@ -21,16 +21,10 @@ function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
       direction,
       shadows: {
         ..._theme.shadows,
-        selected:
-          'inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-500), inset 0px 0px 0px 4px var(--invoke-colors-invokeBlue-800)',
-        hoverSelected:
-          'inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-400), inset 0px 0px 0px 4px var(--invoke-colors-invokeBlue-800)',
-        hoverUnselected:
-          'inset 0px 0px 0px 2px var(--invoke-colors-invokeBlue-300), inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-800)',
         selectedForCompare:
-          'inset 0px 0px 0px 3px var(--invoke-colors-invokeGreen-300), inset 0px 0px 0px 4px var(--invoke-colors-invokeGreen-800)',
+          '0px 0px 0px 1px var(--invoke-colors-base-900), 0px 0px 0px 4px var(--invoke-colors-green-400)',
         hoverSelectedForCompare:
-          'inset 0px 0px 0px 3px var(--invoke-colors-invokeGreen-200), inset 0px 0px 0px 4px var(--invoke-colors-invokeGreen-800)',
+          '0px 0px 0px 1px var(--invoke-colors-base-900), 0px 0px 0px 4px var(--invoke-colors-green-300)',
       },
     });
   }, [direction]);

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

@@ -1,5 +1,5 @@
 import type { TypedStartListening } from '@reduxjs/toolkit';
-import { addListener, createListenerMiddleware } from '@reduxjs/toolkit';
+import { createListenerMiddleware } from '@reduxjs/toolkit';
 import { addAdHocPostProcessingRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/addAdHocPostProcessingRequestedListener';
 import { addStagingListeners } from 'app/store/middleware/listenerMiddleware/listeners/addCommitStagingAreaImageListener';
 import { addAnyEnqueuedListener } from 'app/store/middleware/listenerMiddleware/listeners/anyEnqueued';
@@ -9,7 +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 { addCancellationsListeners } from 'app/store/middleware/listenerMiddleware/listeners/cancellationsListeners';
 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';
@@ -41,8 +40,6 @@ export type AppStartListening = TypedStartListening<RootState, AppDispatch>;
 
 const startAppListening = listenerMiddleware.startListening as AppStartListening;
 
-export const addAppListener = addListener.withTypes<RootState, AppDispatch>();
-
 /**
  * The RTK listener middleware is a lightweight alternative sagas/observables.
  *
@@ -122,5 +119,3 @@ addDynamicPromptsListener(startAppListening);
 
 addSetDefaultSettingsListener(startAppListening);
 // addControlAdapterPreprocessor(startAppListening);
-
-addCancellationsListeners(startAppListening);

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

@@ -1,34 +1,37 @@
-import { isAnyOf } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { canvasReset, rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
-import { stagingAreaImageAccepted, stagingAreaReset } from 'features/controlLayers/store/canvasStagingAreaSlice';
+import {
+  sessionStagingAreaImageAccepted,
+  sessionStagingAreaReset,
+} from 'features/controlLayers/store/canvasSessionSlice';
+import { rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import type { CanvasRasterLayerState } from 'features/controlLayers/store/types';
 import { imageDTOToImageObject } from 'features/controlLayers/store/types';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
+import { $lastCanvasProgressEvent } from 'services/events/setEventListeners';
 import { assert } from 'tsafe';
 
 const log = logger('canvas');
 
-const matchCanvasOrStagingAreaRest = isAnyOf(stagingAreaReset, canvasReset);
-
 export const addStagingListeners = (startAppListening: AppStartListening) => {
   startAppListening({
-    matcher: matchCanvasOrStagingAreaRest,
+    actionCreator: sessionStagingAreaReset,
     effect: async (_, { dispatch }) => {
       try {
         const req = dispatch(
-          queueApi.endpoints.cancelByBatchDestination.initiate(
-            { destination: 'canvas' },
+          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({
@@ -49,11 +52,11 @@ export const addStagingListeners = (startAppListening: AppStartListening) => {
   });
 
   startAppListening({
-    actionCreator: stagingAreaImageAccepted,
+    actionCreator: sessionStagingAreaImageAccepted,
     effect: (action, api) => {
       const { index } = action.payload;
       const state = api.getState();
-      const stagingAreaImage = state.canvasStagingArea.stagedImages[index];
+      const stagingAreaImage = state.canvasSession.stagedImages[index];
 
       assert(stagingAreaImage, 'No staged image found to accept');
       const { x, y } = selectCanvasSlice(state).bbox.rect;
@@ -66,7 +69,7 @@ export const addStagingListeners = (startAppListening: AppStartListening) => {
       };
 
       api.dispatch(rasterLayerAdded({ overrides, isSelected: false }));
-      api.dispatch(stagingAreaReset());
+      api.dispatch(sessionStagingAreaReset());
     },
   });
 };

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

@@ -1,137 +0,0 @@
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { $lastCanvasProgressEvent } from 'features/controlLayers/store/canvasSlice';
-import { queueApi } from 'services/api/endpoints/queue';
-
-/**
- * To prevent a race condition where a progress event arrives after a successful cancellation, we need to keep track of
- * cancellations:
- * - In the route handlers above, we track and update the cancellations object
- * - When the user queues a, we should reset the cancellations, also handled int he route handlers above
- * - When we get a progress event, we should check if the event is cancelled before setting the event
- *
- * We have a few ways that cancellations are effected, so we need to track them all:
- * - by queue item id (in this case, we will compare the session_id and not the item_id)
- * - by batch id
- * - by destination
- * - by clearing the queue
- */
-type Cancellations = {
-  sessionIds: Set<string>;
-  batchIds: Set<string>;
-  destinations: Set<string>;
-  clearQueue: boolean;
-};
-
-const resetCancellations = (): void => {
-  cancellations.clearQueue = false;
-  cancellations.sessionIds.clear();
-  cancellations.batchIds.clear();
-  cancellations.destinations.clear();
-};
-
-const cancellations: Cancellations = {
-  sessionIds: new Set(),
-  batchIds: new Set(),
-  destinations: new Set(),
-  clearQueue: false,
-} as Readonly<Cancellations>;
-
-/**
- * Checks if an item is cancelled, used to prevent race conditions with event handling.
- *
- * To use this, provide the session_id, batch_id and destination from the event payload.
- */
-export const getIsCancelled = (item: {
-  session_id: string;
-  batch_id: string;
-  destination?: string | null;
-}): boolean => {
-  if (cancellations.clearQueue) {
-    return true;
-  }
-  if (cancellations.sessionIds.has(item.session_id)) {
-    return true;
-  }
-  if (cancellations.batchIds.has(item.batch_id)) {
-    return true;
-  }
-  if (item.destination && cancellations.destinations.has(item.destination)) {
-    return true;
-  }
-  return false;
-};
-
-export const addCancellationsListeners = (startAppListening: AppStartListening) => {
-  // When we get a cancellation, we may need to clear the last progress event - next few listeners handle those cases.
-  // Maybe we could use the `getIsCancelled` util here, but I think that could introduce _another_ race condition...
-  startAppListening({
-    matcher: queueApi.endpoints.enqueueBatch.matchFulfilled,
-    effect: () => {
-      resetCancellations();
-    },
-  });
-
-  startAppListening({
-    matcher: queueApi.endpoints.cancelByBatchDestination.matchFulfilled,
-    effect: (action) => {
-      cancellations.destinations.add(action.meta.arg.originalArgs.destination);
-
-      const event = $lastCanvasProgressEvent.get();
-      if (!event) {
-        return;
-      }
-      const { session_id, batch_id, destination } = event;
-      if (getIsCancelled({ session_id, batch_id, destination })) {
-        $lastCanvasProgressEvent.set(null);
-      }
-    },
-  });
-
-  startAppListening({
-    matcher: queueApi.endpoints.cancelQueueItem.matchFulfilled,
-    effect: (action) => {
-      cancellations.sessionIds.add(action.payload.session_id);
-
-      const event = $lastCanvasProgressEvent.get();
-      if (!event) {
-        return;
-      }
-      const { session_id, batch_id, destination } = event;
-      if (getIsCancelled({ session_id, batch_id, destination })) {
-        $lastCanvasProgressEvent.set(null);
-      }
-    },
-  });
-
-  startAppListening({
-    matcher: queueApi.endpoints.cancelByBatchIds.matchFulfilled,
-    effect: (action) => {
-      for (const batch_id of action.meta.arg.originalArgs.batch_ids) {
-        cancellations.batchIds.add(batch_id);
-      }
-      const event = $lastCanvasProgressEvent.get();
-      if (!event) {
-        return;
-      }
-      const { session_id, batch_id, destination } = event;
-      if (getIsCancelled({ session_id, batch_id, destination })) {
-        $lastCanvasProgressEvent.set(null);
-      }
-    },
-  });
-
-  startAppListening({
-    matcher: queueApi.endpoints.clearQueue.matchFulfilled,
-    effect: () => {
-      cancellations.clearQueue = true;
-      const event = $lastCanvasProgressEvent.get();
-      if (!event) {
-        return;
-      }
-      const { session_id, batch_id, destination } = event;
-      if (getIsCancelled({ session_id, batch_id, destination })) {
-        $lastCanvasProgressEvent.set(null);
-      }
-    },
-  });
-};

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

@@ -4,12 +4,8 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import type { SerializableObject } from 'common/types';
 import type { Result } from 'common/util/result';
 import { isErr, withResult, withResultAsync } from 'common/util/result';
-import { $canvasManager } from 'features/controlLayers/store/canvasSlice';
-import {
-  selectIsStaging,
-  stagingAreaReset,
-  stagingAreaStartedStaging,
-} from 'features/controlLayers/store/canvasStagingAreaSlice';
+import { $canvasManager } from 'features/controlLayers/konva/CanvasManager';
+import { sessionStagingAreaReset, sessionStartedStaging } from 'features/controlLayers/store/canvasSessionSlice';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
 import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph';
 import { buildSDXLGraph } from 'features/nodes/util/graph/generation/buildSDXLGraph';
@@ -35,14 +31,14 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
 
       let didStartStaging = false;
 
-      if (!selectIsStaging(state) && state.canvasSettings.sendToCanvas) {
-        dispatch(stagingAreaStartedStaging());
+      if (!state.canvasSession.isStaging && state.canvasSession.sendToCanvas) {
+        dispatch(sessionStartedStaging());
         didStartStaging = true;
       }
 
       const abortStaging = () => {
-        if (didStartStaging && selectIsStaging(getState())) {
-          dispatch(stagingAreaReset());
+        if (didStartStaging && getState().canvasSession.isStaging) {
+          dispatch(sessionStagingAreaReset());
         }
       };
 
@@ -74,7 +70,7 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
 
       const { g, noise, posCond } = buildGraphResult.value;
 
-      const destination = state.canvasSettings.sendToCanvas ? 'canvas' : 'gallery';
+      const destination = state.canvasSession.sendToCanvas ? 'canvas' : 'gallery';
 
       const prepareBatchResult = withResult(() =>
         prepareLinearUIBatch(state, g, prepend, noise, posCond, 'generation', destination)

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

@@ -1,5 +1,6 @@
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
 import { buildMultidiffusionUpscaleGraph } from 'features/nodes/util/graph/buildMultidiffusionUpscaleGraph';
 import { queueApi } from 'services/api/endpoints/queue';
@@ -10,6 +11,7 @@ export const addEnqueueRequestedUpscale = (startAppListening: AppStartListening)
       enqueueRequested.match(action) && action.payload.tabName === 'upscaling',
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
+      const { shouldShowProgressInViewer } = state.ui;
       const { prepend } = action.payload;
 
       const { g, noise, posCond } = await buildMultidiffusionUpscaleGraph(state);
@@ -23,6 +25,9 @@ export const addEnqueueRequestedUpscale = (startAppListening: AppStartListening)
       );
       try {
         await req.unwrap();
+        if (shouldShowProgressInViewer) {
+          dispatch(isImageViewerOpenChanged(true));
+        }
       } finally {
         req.reset();
       }

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

@@ -17,7 +17,7 @@ import type { ImageDTO } from 'services/api/types';
 
 const log = logger('gallery');
 
-//TODO(psyche): handle image deletion (canvas staging area?)
+//TODO(psyche): handle image deletion (canvas sessions?)
 
 // Some utils to delete images from different parts of the app
 const deleteNodesImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {

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

@@ -1,7 +1,6 @@
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { selectDefaultControlAdapter } from 'features/controlLayers/hooks/addLayerHooks';
 import {
   controlLayerAdded,
   ipaImageChanged,
@@ -13,7 +12,7 @@ import type { CanvasControlLayerState, CanvasRasterLayerState } from 'features/c
 import { imageDTOToImageObject } from 'features/controlLayers/store/types';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 import { isValidDrop } from 'features/dnd/util/isValidDrop';
-import { imageToCompareChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
+import { imageToCompareChanged, isImageViewerOpenChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { upscaleInitialImageChanged } from 'features/parameters/store/upscaleSlice';
 import { imagesApi } from 'services/api/endpoints/images';
@@ -104,14 +103,11 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         activeData.payloadType === 'IMAGE_DTO' &&
         activeData.payload.imageDTO
       ) {
-        const state = getState();
         const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
-        const { x, y } = selectCanvasSlice(state).bbox.rect;
-        const defaultControlAdapter = selectDefaultControlAdapter(state);
+        const { x, y } = selectCanvasSlice(getState()).bbox.rect;
         const overrides: Partial<CanvasControlLayerState> = {
           objects: [imageObject],
           position: { x, y },
-          controlAdapter: defaultControlAdapter,
         };
         dispatch(controlLayerAdded({ overrides, isSelected: true }));
         return;
@@ -146,6 +142,7 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
       ) {
         const { imageDTO } = activeData.payload;
         dispatch(imageToCompareChanged(imageDTO));
+        dispatch(isImageViewerOpenChanged(true));
         return;
       }
 

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

@@ -2,7 +2,6 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { ipaImageChanged, rgIPAdapterImageChanged } from 'features/controlLayers/store/canvasSlice';
 import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';
-import { boardIdSelected, galleryViewChanged } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { upscaleInitialImageChanged } from 'features/parameters/store/upscaleSlice';
 import { toast } from 'features/toast/toast';
@@ -45,8 +44,6 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
         if (!autoAddBoardId || autoAddBoardId === 'none') {
           const title = postUploadAction.title || DEFAULT_UPLOADED_TOAST.title;
           toast({ ...DEFAULT_UPLOADED_TOAST, title });
-          dispatch(boardIdSelected({ boardId: 'none' }));
-          dispatch(galleryViewChanged('assets'));
         } else {
           // Add this image to the board
           dispatch(
@@ -70,8 +67,6 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
             ...DEFAULT_UPLOADED_TOAST,
             description,
           });
-          dispatch(boardIdSelected({ boardId: autoAddBoardId }));
-          dispatch(galleryViewChanged('assets'));
         }
         return;
       }

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

@@ -13,7 +13,7 @@ import { loraDeleted } from 'features/controlLayers/store/lorasSlice';
 import { modelChanged, refinerModelChanged, vaeSelected } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import { getEntityIdentifier } from 'features/controlLayers/store/types';
-import { calculateNewSize } from 'features/parameters/components/Bbox/calculateNewSize';
+import { calculateNewSize } from 'features/parameters/components/DocumentSize/calculateNewSize';
 import { postProcessingModelChanged, upscaleModelChanged } from 'features/parameters/store/upscaleSlice';
 import { zParameterModel, zParameterVAEModel } from 'features/parameters/types/parameterSchemas';
 import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';

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

@@ -6,14 +6,12 @@ import { errorHandler } from 'app/store/enhancers/reduxRemember/errors';
 import type { SerializableObject } from 'common/types';
 import { deepClone } from 'common/util/deepClone';
 import { changeBoardModalSlice } from 'features/changeBoardModal/store/slice';
+import { canvasSessionPersistConfig, canvasSessionSlice } from 'features/controlLayers/store/canvasSessionSlice';
 import { canvasSettingsPersistConfig, canvasSettingsSlice } from 'features/controlLayers/store/canvasSettingsSlice';
 import { canvasPersistConfig, canvasSlice, canvasUndoableConfig } from 'features/controlLayers/store/canvasSlice';
-import {
-  canvasStagingAreaPersistConfig,
-  canvasStagingAreaSlice,
-} from 'features/controlLayers/store/canvasStagingAreaSlice';
 import { lorasPersistConfig, lorasSlice } from 'features/controlLayers/store/lorasSlice';
 import { paramsPersistConfig, paramsSlice } from 'features/controlLayers/store/paramsSlice';
+import { toolPersistConfig, toolSlice } from 'features/controlLayers/store/toolSlice';
 import { deleteImageModalSlice } from 'features/deleteImageModal/store/slice';
 import { dynamicPromptsPersistConfig, dynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { galleryPersistConfig, gallerySlice } from 'features/gallery/store/gallerySlice';
@@ -65,8 +63,9 @@ const allReducers = {
   [upscaleSlice.name]: upscaleSlice.reducer,
   [stylePresetSlice.name]: stylePresetSlice.reducer,
   [paramsSlice.name]: paramsSlice.reducer,
+  [toolSlice.name]: toolSlice.reducer,
   [canvasSettingsSlice.name]: canvasSettingsSlice.reducer,
-  [canvasStagingAreaSlice.name]: canvasStagingAreaSlice.reducer,
+  [canvasSessionSlice.name]: canvasSessionSlice.reducer,
   [lorasSlice.name]: lorasSlice.reducer,
 };
 
@@ -110,8 +109,9 @@ const persistConfigs: { [key in keyof typeof allReducers]?: PersistConfig } = {
   [upscalePersistConfig.name]: upscalePersistConfig,
   [stylePresetPersistConfig.name]: stylePresetPersistConfig,
   [paramsPersistConfig.name]: paramsPersistConfig,
+  [toolPersistConfig.name]: toolPersistConfig,
   [canvasSettingsPersistConfig.name]: canvasSettingsPersistConfig,
-  [canvasStagingAreaPersistConfig.name]: canvasStagingAreaPersistConfig,
+  [canvasSessionPersistConfig.name]: canvasSessionPersistConfig,
   [lorasPersistConfig.name]: lorasPersistConfig,
 };
 

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

@@ -6,51 +6,18 @@ import { useImageUploadButton } from 'common/hooks/useImageUploadButton';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 import ImageContextMenu from 'features/gallery/components/ImageContextMenu/ImageContextMenu';
 import type { MouseEvent, ReactElement, ReactNode, SyntheticEvent } from 'react';
-import { memo, useCallback, useMemo } from 'react';
+import { memo, useCallback, useMemo, useState } from 'react';
 import { PiImageBold, PiUploadSimpleBold } from 'react-icons/pi';
 import type { ImageDTO, PostUploadAction } from 'services/api/types';
 
 import IAIDraggable from './IAIDraggable';
 import IAIDroppable from './IAIDroppable';
+import SelectionOverlay from './SelectionOverlay';
 
 const defaultUploadElement = <Icon as={PiUploadSimpleBold} boxSize={16} />;
 
 const defaultNoContentFallback = <IAINoContentFallback icon={PiImageBold} />;
 
-const sx: SystemStyleObject = {
-  '.gallery-image-container::before': {
-    content: '""',
-    display: 'inline-block',
-    position: 'absolute',
-    top: 0,
-    left: 0,
-    right: 0,
-    bottom: 0,
-    pointerEvents: 'none',
-    borderRadius: 'base',
-  },
-  '&[data-selected="selected"]>.gallery-image-container::before': {
-    boxShadow:
-      'inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-500), inset 0px 0px 0px 4px var(--invoke-colors-invokeBlue-800)',
-  },
-  '&[data-selected="selectedForCompare"]>.gallery-image-container::before': {
-    boxShadow:
-      'inset 0px 0px 0px 3px var(--invoke-colors-invokeGreen-300), inset 0px 0px 0px 4px var(--invoke-colors-invokeGreen-800)',
-  },
-  '&:hover>.gallery-image-container::before': {
-    boxShadow:
-      'inset 0px 0px 0px 2px var(--invoke-colors-invokeBlue-300), inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-800)',
-  },
-  '&:hover[data-selected="selected"]>.gallery-image-container::before': {
-    boxShadow:
-      'inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-400), inset 0px 0px 0px 4px var(--invoke-colors-invokeBlue-800)',
-  },
-  '&:hover[data-selected="selectedForCompare"]>.gallery-image-container::before': {
-    boxShadow:
-      'inset 0px 0px 0px 3px var(--invoke-colors-invokeGreen-200), inset 0px 0px 0px 4px var(--invoke-colors-invokeGreen-800)',
-  },
-};
-
 type IAIDndImageProps = FlexProps & {
   imageDTO: ImageDTO | undefined;
   onError?: (event: SyntheticEvent<HTMLImageElement>) => void;
@@ -108,11 +75,13 @@ const IAIDndImage = (props: IAIDndImageProps) => {
     ...rest
   } = props;
 
+  const [isHovered, setIsHovered] = useState(false);
   const handleMouseOver = useCallback(
     (e: MouseEvent<HTMLDivElement>) => {
       if (onMouseOver) {
         onMouseOver(e);
       }
+      setIsHovered(true);
     },
     [onMouseOver]
   );
@@ -121,6 +90,7 @@ const IAIDndImage = (props: IAIDndImageProps) => {
       if (onMouseOut) {
         onMouseOut(e);
       }
+      setIsHovered(false);
     },
     [onMouseOut]
   );
@@ -171,13 +141,10 @@ const IAIDndImage = (props: IAIDndImageProps) => {
           minH={minSize ? minSize : undefined}
           userSelect="none"
           cursor={isDragDisabled || !imageDTO ? 'default' : 'pointer'}
-          sx={withHoverOverlay ? sx : undefined}
-          data-selected={isSelectedForCompare ? 'selectedForCompare' : isSelected ? 'selected' : undefined}
           {...rest}
         >
           {imageDTO && (
             <Flex
-              className="gallery-image-container"
               w="full"
               h="full"
               position={fitContainer ? 'absolute' : 'relative'}
@@ -200,6 +167,11 @@ const IAIDndImage = (props: IAIDndImageProps) => {
                 data-testid={dataTestId}
               />
               {withMetadataOverlay && <ImageMetadataOverlay imageDTO={imageDTO} />}
+              <SelectionOverlay
+                isSelected={isSelected}
+                isSelectedForCompare={isSelectedForCompare}
+                isHovered={withHoverOverlay ? isHovered : false}
+              />
             </Flex>
           )}
           {!imageDTO && !isUploadDisabled && (

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

@@ -0,0 +1,104 @@
+import type { SystemStyleObject } from '@invoke-ai/ui-library';
+import { Box, Flex, IconButton, Tooltip, useToken } from '@invoke-ai/ui-library';
+import type { ReactElement, ReactNode } from 'react';
+import { memo, useCallback, useMemo } from 'react';
+
+type IconSwitchProps = {
+  isChecked: boolean;
+  onChange: (checked: boolean) => void;
+  iconChecked: ReactElement;
+  tooltipChecked?: ReactNode;
+  iconUnchecked: ReactElement;
+  tooltipUnchecked?: ReactNode;
+  ariaLabel: string;
+};
+
+const getSx = (padding: string | number): SystemStyleObject => ({
+  transition: 'left 0.1s ease-in-out, transform 0.1s ease-in-out',
+  '&[data-checked="true"]': {
+    left: `calc(100% - ${padding})`,
+    transform: 'translateX(-100%)',
+  },
+  '&[data-checked="false"]': {
+    left: padding,
+    transform: 'translateX(0)',
+  },
+});
+
+export const IconSwitch = memo(
+  ({
+    isChecked,
+    onChange,
+    iconChecked,
+    tooltipChecked,
+    iconUnchecked,
+    tooltipUnchecked,
+    ariaLabel,
+  }: IconSwitchProps) => {
+    const onUncheck = useCallback(() => {
+      onChange(false);
+    }, [onChange]);
+    const onCheck = useCallback(() => {
+      onChange(true);
+    }, [onChange]);
+
+    const gap = useToken('space', 1.5);
+    const sx = useMemo(() => getSx(gap), [gap]);
+
+    return (
+      <Flex
+        position="relative"
+        bg="base.800"
+        borderRadius="base"
+        alignItems="center"
+        justifyContent="center"
+        h="full"
+        p={gap}
+        gap={gap}
+      >
+        <Box
+          position="absolute"
+          borderRadius="base"
+          bg="invokeBlue.400"
+          w={12}
+          top={gap}
+          bottom={gap}
+          data-checked={isChecked}
+          sx={sx}
+        />
+        <Tooltip hasArrow label={tooltipUnchecked}>
+          <IconButton
+            size="sm"
+            fontSize={16}
+            icon={iconUnchecked}
+            onClick={onUncheck}
+            variant={!isChecked ? 'solid' : 'ghost'}
+            colorScheme={!isChecked ? 'invokeBlue' : 'base'}
+            aria-label={ariaLabel}
+            data-checked={!isChecked}
+            w={12}
+            alignSelf="stretch"
+            h="auto"
+          />
+        </Tooltip>
+        <Tooltip hasArrow label={tooltipChecked}>
+          <IconButton
+            size="sm"
+            fontSize={16}
+            icon={iconChecked}
+            onClick={onCheck}
+            variant={isChecked ? 'solid' : 'ghost'}
+            colorScheme={isChecked ? 'invokeBlue' : 'base'}
+            aria-label={ariaLabel}
+            data-checked={isChecked}
+            w={12}
+            alignSelf="stretch"
+            h="auto"
+          />
+        </Tooltip>
+      </Flex>
+    );
+  }
+);
+
+IconSwitch.displayName = 'IconSwitch';

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

@@ -0,0 +1,46 @@
+import { Box } from '@invoke-ai/ui-library';
+import { memo, useMemo } from 'react';
+
+type Props = {
+  isSelected: boolean;
+  isSelectedForCompare: boolean;
+  isHovered: boolean;
+};
+const SelectionOverlay = ({ isSelected, isSelectedForCompare, isHovered }: Props) => {
+  const shadow = useMemo(() => {
+    if (isSelectedForCompare && isHovered) {
+      return 'hoverSelectedForCompare';
+    }
+    if (isSelectedForCompare && !isHovered) {
+      return 'selectedForCompare';
+    }
+    if (isSelected && isHovered) {
+      return 'hoverSelected';
+    }
+    if (isSelected && !isHovered) {
+      return 'selected';
+    }
+    if (!isSelected && isHovered) {
+      return 'hoverUnselected';
+    }
+    return undefined;
+  }, [isHovered, isSelected, isSelectedForCompare]);
+  return (
+    <Box
+      className="selection-box"
+      position="absolute"
+      top={0}
+      insetInlineEnd={0}
+      bottom={0}
+      insetInlineStart={0}
+      borderRadius="base"
+      opacity={isSelected || isSelectedForCompare ? 1 : 0.7}
+      transitionProperty="common"
+      transitionDuration="0.1s"
+      pointerEvents="none"
+      shadow={shadow}
+    />
+  );
+};
+
+export default memo(SelectionOverlay);

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

@@ -1,7 +1,6 @@
 import { useStore } from '@nanostores/react';
 import type { WritableAtom } from 'nanostores';
 import { atom } from 'nanostores';
-import { useCallback, useState } from 'react';
 
 type UseBoolean = {
   isTrue: boolean;
@@ -51,24 +50,4 @@ export const buildUseBoolean = (initialValue: boolean): [() => UseBoolean, Writa
  * Hook to manage a boolean state. Use this for a local boolean state.
  * @param initialValue Initial value of the boolean
  */
-export const useBoolean = (initialValue: boolean) => {
-  const [isTrue, set] = useState(initialValue);
-
-  const setTrue = useCallback(() => {
-    set(true);
-  }, [set]);
-  const setFalse = useCallback(() => {
-    set(false);
-  }, [set]);
-  const toggle = useCallback(() => {
-    set((val) => !val);
-  }, [set]);
-
-  return {
-    isTrue,
-    setTrue,
-    setFalse,
-    set,
-    toggle,
-  };
-};
+export const useBoolean = (initialValue: boolean) => buildUseBoolean(initialValue)[0]();

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

@@ -114,13 +114,4 @@ export const useGlobalHotkeys = () => {
     },
     [dispatch, isModelManagerEnabled]
   );
-
-  useHotkeys(
-    isModelManagerEnabled ? '6' : '5',
-    () => {
-      dispatch(setActiveTab('gallery'));
-      setScopes([]);
-    },
-    [dispatch, isModelManagerEnabled]
-  );
 };

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

@@ -2,7 +2,6 @@ import { useStore } from '@nanostores/react';
 import { $isConnected } from 'app/hooks/useSocketIO';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import { useCanvasManagerSafe } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import { selectDynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
@@ -18,7 +17,6 @@ import { selectSystemSlice } from 'features/system/store/systemSlice';
 import { selectActiveTab } from 'features/ui/store/uiSelectors';
 import i18n from 'i18next';
 import { forEach, upperFirst } from 'lodash-es';
-import { atom } from 'nanostores';
 import { useMemo } from 'react';
 import { getConnectedEdges } from 'reactflow';
 
@@ -30,7 +28,7 @@ const LAYER_TYPE_TO_TKEY = {
   control_layer: 'controlLayers.globalControlAdapter',
 } as const;
 
-const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy: boolean) =>
+const createSelector = (templates: Templates, isConnected: boolean) =>
   createMemoizedSelector(
     [
       selectSystemSlice,
@@ -119,10 +117,6 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
           reasons.push({ content: i18n.t('upscaling.missingTileControlNetModel') });
         }
       } else {
-        if (canvasIsBusy) {
-          reasons.push({ content: i18n.t('parameters.invoke.canvasBusy') });
-        }
-
         if (dynamicPrompts.prompts.length === 0 && getShouldProcessPrompt(positivePrompt)) {
           reasons.push({ content: i18n.t('parameters.invoke.noPrompts') });
         }
@@ -134,7 +128,7 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
         canvas.controlLayers.entities
           .filter((controlLayer) => controlLayer.isEnabled)
           .forEach((controlLayer, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
+            const layerLiteral = i18n.t('controlLayers.layers_one');
             const layerNumber = i + 1;
             const layerType = i18n.t(LAYER_TYPE_TO_TKEY['control_layer']);
             const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
@@ -164,7 +158,7 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
         canvas.ipAdapters.entities
           .filter((entity) => entity.isEnabled)
           .forEach((entity, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
+            const layerLiteral = i18n.t('controlLayers.layers_one');
             const layerNumber = i + 1;
             const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
             const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
@@ -192,7 +186,7 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
         canvas.regions.entities
           .filter((entity) => entity.isEnabled)
           .forEach((entity, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
+            const layerLiteral = i18n.t('controlLayers.layers_one');
             const layerNumber = i + 1;
             const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
             const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
@@ -229,7 +223,7 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
         canvas.rasterLayers.entities
           .filter((entity) => entity.isEnabled)
           .forEach((entity, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
+            const layerLiteral = i18n.t('controlLayers.layers_one');
             const layerNumber = i + 1;
             const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
             const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
@@ -246,17 +240,10 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
     }
   );
 
-const dummyAtom = atom(true);
-
 export const useIsReadyToEnqueue = () => {
   const templates = useStore($templates);
   const isConnected = useStore($isConnected);
-  const canvasManager = useCanvasManagerSafe();
-  const canvasIsBusy = useStore(canvasManager?.$isBusy ?? dummyAtom);
-  const selector = useMemo(
-    () => createSelector(templates, isConnected, canvasIsBusy),
-    [templates, isConnected, canvasIsBusy]
-  );
+  const selector = useMemo(() => createSelector(templates, isConnected), [templates, isConnected]);
   const value = useAppSelector(selector);
   return value;
 };

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

@@ -1,9 +1,6 @@
 export const roundDownToMultiple = (num: number, multiple: number): number => {
   return Math.floor(num / multiple) * multiple;
 };
-export const roundUpToMultiple = (num: number, multiple: number): number => {
-  return Math.ceil(num / multiple) * multiple;
-};
 
 export const roundToMultiple = (num: number, multiple: number): number => {
   return Math.round(num / multiple) * multiple;

invokeai/frontend/web/src/features/controlLayers/components/CanvasAddEntityButtons.tsx

@@ -1,22 +1,34 @@
 import { Button, ButtonGroup, Flex } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
 import {
-  useAddControlLayer,
-  useAddInpaintMask,
-  useAddIPAdapter,
-  useAddRasterLayer,
-  useAddRegionalGuidance,
-} from 'features/controlLayers/hooks/addLayerHooks';
-import { memo } from 'react';
+  controlLayerAdded,
+  inpaintMaskAdded,
+  ipaAdded,
+  rasterLayerAdded,
+  rgAdded,
+} from 'features/controlLayers/store/canvasSlice';
+import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiPlusBold } from 'react-icons/pi';
 
 export const CanvasAddEntityButtons = memo(() => {
   const { t } = useTranslation();
-  const addInpaintMask = useAddInpaintMask();
-  const addRegionalGuidance = useAddRegionalGuidance();
-  const addRasterLayer = useAddRasterLayer();
-  const addControlLayer = useAddControlLayer();
-  const addIPAdapter = useAddIPAdapter();
+  const dispatch = useAppDispatch();
+  const addInpaintMask = useCallback(() => {
+    dispatch(inpaintMaskAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addRegionalGuidance = useCallback(() => {
+    dispatch(rgAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addRasterLayer = useCallback(() => {
+    dispatch(rasterLayerAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addControlLayer = useCallback(() => {
+    dispatch(controlLayerAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addIPAdapter = useCallback(() => {
+    dispatch(ipaAdded({ isSelected: true }));
+  }, [dispatch]);
 
   return (
     <Flex flexDir="column" w="full" h="full" alignItems="center">
@@ -34,7 +46,7 @@ export const CanvasAddEntityButtons = memo(() => {
           {t('controlLayers.controlLayer')}
         </Button>
         <Button variant="ghost" justifyContent="flex-start" leftIcon={<PiPlusBold />} onClick={addIPAdapter}>
-          {t('controlLayers.globalIPAdapter')}
+          {t('controlLayers.ipAdapter')}
         </Button>
       </ButtonGroup>
     </Flex>

invokeai/frontend/web/src/features/controlLayers/components/CanvasDropArea.tsx

@@ -1,6 +1,7 @@
 import { Flex } from '@invoke-ai/ui-library';
 import IAIDroppable from 'common/components/IAIDroppable';
 import type { AddControlLayerFromImageDropData, AddRasterLayerFromImageDropData } from 'features/dnd/types';
+import { useIsImageViewerOpen } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { memo } from 'react';
 
 const addRasterLayerFromImageDropData: AddRasterLayerFromImageDropData = {
@@ -14,6 +15,12 @@ const addControlLayerFromImageDropData: AddControlLayerFromImageDropData = {
 };
 
 export const CanvasDropArea = memo(() => {
+  const isImageViewerOpen = useIsImageViewerOpen();
+
+  if (isImageViewerOpen) {
+    return null;
+  }
+
   return (
     <>
       <Flex position="absolute" top={0} right={0} bottom="50%" left={0} gap={2} pointerEvents="none">

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListActionBar.tsx

@@ -0,0 +1,20 @@
+import { Flex, Spacer } from '@invoke-ai/ui-library';
+import { EntityListActionBarAddLayerButton } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarAddLayerMenuButton';
+import { EntityListActionBarDeleteButton } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarDeleteButton';
+import { EntityListActionBarSelectedEntityFill } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarSelectedEntityFill';
+import { SelectedEntityOpacity } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarSelectedEntityOpacity';
+import { memo } from 'react';
+
+export const EntityListActionBar = memo(() => {
+  return (
+    <Flex w="full" py={1} px={1} gap={2} alignItems="center">
+      <SelectedEntityOpacity />
+      <Spacer />
+      <EntityListActionBarSelectedEntityFill />
+      <EntityListActionBarAddLayerButton />
+      <EntityListActionBarDeleteButton />
+    </Flex>
+  );
+});
+
+EntityListActionBar.displayName = 'EntityListActionBar';

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListActionBarAddLayerMenuButton.tsx

@@ -0,0 +1,28 @@
+import { IconButton, Menu, MenuButton, MenuList } from '@invoke-ai/ui-library';
+import { CanvasEntityListMenuItems } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarAddLayerMenuItems';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiPlusBold } from 'react-icons/pi';
+
+export const EntityListActionBarAddLayerButton = memo(() => {
+  const { t } = useTranslation();
+
+  return (
+    <Menu>
+      <MenuButton
+        as={IconButton}
+        size="sm"
+        tooltip={t('controlLayers.addLayer')}
+        aria-label={t('controlLayers.addLayer')}
+        icon={<PiPlusBold />}
+        variant="ghost"
+        data-testid="control-layers-add-layer-menu-button"
+      />
+      <MenuList>
+        <CanvasEntityListMenuItems />
+      </MenuList>
+    </Menu>
+  );
+});
+
+EntityListActionBarAddLayerButton.displayName = 'EntityListActionBarAddLayerButton';

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListActionBarAddLayerMenuItems.tsx

@@ -0,0 +1,54 @@
+import { MenuItem } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import {
+  controlLayerAdded,
+  inpaintMaskAdded,
+  ipaAdded,
+  rasterLayerAdded,
+  rgAdded,
+} from 'features/controlLayers/store/canvasSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiPlusBold } from 'react-icons/pi';
+
+export const CanvasEntityListMenuItems = memo(() => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const addInpaintMask = useCallback(() => {
+    dispatch(inpaintMaskAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addRegionalGuidance = useCallback(() => {
+    dispatch(rgAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addRasterLayer = useCallback(() => {
+    dispatch(rasterLayerAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addControlLayer = useCallback(() => {
+    dispatch(controlLayerAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addIPAdapter = useCallback(() => {
+    dispatch(ipaAdded({ isSelected: true }));
+  }, [dispatch]);
+
+  return (
+    <>
+      <MenuItem icon={<PiPlusBold />} onClick={addInpaintMask}>
+        {t('controlLayers.inpaintMask')}
+      </MenuItem>
+      <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance}>
+        {t('controlLayers.regionalGuidance')}
+      </MenuItem>
+      <MenuItem icon={<PiPlusBold />} onClick={addRasterLayer}>
+        {t('controlLayers.rasterLayer')}
+      </MenuItem>
+      <MenuItem icon={<PiPlusBold />} onClick={addControlLayer}>
+        {t('controlLayers.controlLayer')}
+      </MenuItem>
+      <MenuItem icon={<PiPlusBold />} onClick={addIPAdapter}>
+        {t('controlLayers.ipAdapter')}
+      </MenuItem>
+    </>
+  );
+});
+
+CanvasEntityListMenuItems.displayName = 'CanvasEntityListMenu';

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListActionBarDeleteButton.tsx

@@ -0,0 +1,39 @@
+import { IconButton, useShiftModifier } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { allEntitiesDeleted, entityDeleted } from 'features/controlLayers/store/canvasSlice';
+import { selectEntityCount, selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiTrashSimpleFill } from 'react-icons/pi';
+
+export const EntityListActionBarDeleteButton = memo(() => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
+  const entityCount = useAppSelector(selectEntityCount);
+  const shift = useShiftModifier();
+  const onClick = useCallback(() => {
+    if (shift) {
+      dispatch(allEntitiesDeleted());
+      return;
+    }
+    if (!selectedEntityIdentifier) {
+      return;
+    }
+    dispatch(entityDeleted({ entityIdentifier: selectedEntityIdentifier }));
+  }, [dispatch, selectedEntityIdentifier, shift]);
+
+  return (
+    <IconButton
+      onClick={onClick}
+      isDisabled={shift ? entityCount === 0 : !selectedEntityIdentifier}
+      size="sm"
+      variant="ghost"
+      aria-label={shift ? t('controlLayers.deleteAll') : t('controlLayers.deleteSelected')}
+      tooltip={shift ? t('controlLayers.deleteAll') : t('controlLayers.deleteSelected')}
+      icon={<PiTrashSimpleFill />}
+    />
+  );
+});
+
+EntityListActionBarDeleteButton.displayName = 'EntityListActionBarDeleteButton';

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListActionBarSelectedEntityFill.tsx

@@ -9,7 +9,7 @@ import { type FillStyle, isMaskEntityIdentifier, type RgbColor } from 'features/
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-export const EntityListSelectedEntityActionBarFill = memo(() => {
+export const EntityListActionBarSelectedEntityFill = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
@@ -67,4 +67,4 @@ export const EntityListSelectedEntityActionBarFill = memo(() => {
   );
 });
 
-EntityListSelectedEntityActionBarFill.displayName = 'EntityListSelectedEntityActionBarFill';
+EntityListActionBarSelectedEntityFill.displayName = 'EntityListActionBarSelectedEntityFill';

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListActionBarSelectedEntityOpacity.tsx

@@ -22,7 +22,7 @@ import {
   selectEntity,
   selectSelectedEntityIdentifier,
 } from 'features/controlLayers/store/selectors';
-import { isRenderableEntity } from 'features/controlLayers/store/types';
+import { isDrawableEntity } from 'features/controlLayers/store/types';
 import { clamp, round } from 'lodash-es';
 import type { KeyboardEvent } from 'react';
 import { memo, useCallback, useEffect, useState } from 'react';
@@ -37,11 +37,11 @@ function formatPct(v: number | string) {
   return `${round(Number(v), 2).toLocaleString()}%`;
 }
 
-function mapSliderValueToRawValue(value: number) {
+function mapSliderValueToOpacity(value: number) {
   return value / 100;
 }
 
-function mapRawValueToSliderValue(opacity: number) {
+function mapOpacityToSliderValue(opacity: number) {
   return opacity * 100;
 }
 
@@ -50,14 +50,14 @@ function formatSliderValue(value: number) {
 }
 
 const marks = [
-  mapRawValueToSliderValue(0),
-  mapRawValueToSliderValue(0.25),
-  mapRawValueToSliderValue(0.5),
-  mapRawValueToSliderValue(0.75),
-  mapRawValueToSliderValue(1),
+  mapOpacityToSliderValue(0),
+  mapOpacityToSliderValue(0.25),
+  mapOpacityToSliderValue(0.5),
+  mapOpacityToSliderValue(0.75),
+  mapOpacityToSliderValue(1),
 ];
 
-const sliderDefaultValue = mapRawValueToSliderValue(1);
+const sliderDefaultValue = mapOpacityToSliderValue(1);
 
 const snapCandidates = marks.slice(1, marks.length - 1);
 
@@ -70,14 +70,14 @@ const selectOpacity = createSelector(selectCanvasSlice, (canvas) => {
   if (!selectedEntity) {
     return 1; // fallback to 100% opacity
   }
-  if (!isRenderableEntity(selectedEntity)) {
+  if (!isDrawableEntity(selectedEntity)) {
     return 1; // fallback to 100% opacity
   }
   // Opacity is a float from 0-1, but we want to display it as a percentage
   return selectedEntity.opacity;
 });
 
-export const EntityListSelectedEntityActionBarOpacity = memo(() => {
+export const SelectedEntityOpacity = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
@@ -95,7 +95,7 @@ export const EntityListSelectedEntityActionBarOpacity = memo(() => {
       if (!$shift.get()) {
         snappedOpacity = snapToNearest(opacity, snapCandidates, 2);
       }
-      const mappedOpacity = mapSliderValueToRawValue(snappedOpacity);
+      const mappedOpacity = mapSliderValueToOpacity(snappedOpacity);
 
       dispatch(entityOpacityChanged({ entityIdentifier: selectedEntityIdentifier, opacity: mappedOpacity }));
     },
@@ -193,4 +193,4 @@ export const EntityListSelectedEntityActionBarOpacity = memo(() => {
   );
 });
 
-EntityListSelectedEntityActionBarOpacity.displayName = 'EntityListSelectedEntityActionBarOpacity';
+SelectedEntityOpacity.displayName = 'SelectedEntityOpacity';

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListGlobalActionBarAddLayerMenu.tsx

@@ -1,54 +0,0 @@
-import { IconButton, Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import {
-  useAddControlLayer,
-  useAddInpaintMask,
-  useAddIPAdapter,
-  useAddRasterLayer,
-  useAddRegionalGuidance,
-} from 'features/controlLayers/hooks/addLayerHooks';
-import { memo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiPlusBold } from 'react-icons/pi';
-
-export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
-  const { t } = useTranslation();
-  const addInpaintMask = useAddInpaintMask();
-  const addRegionalGuidance = useAddRegionalGuidance();
-  const addRasterLayer = useAddRasterLayer();
-  const addControlLayer = useAddControlLayer();
-  const addIPAdapter = useAddIPAdapter();
-
-  return (
-    <Menu>
-      <MenuButton
-        as={IconButton}
-        size="sm"
-        variant="link"
-        alignSelf="stretch"
-        tooltip={t('controlLayers.addLayer')}
-        aria-label={t('controlLayers.addLayer')}
-        icon={<PiPlusBold />}
-        data-testid="control-layers-add-layer-menu-button"
-      />
-      <MenuList>
-        <MenuItem icon={<PiPlusBold />} onClick={addInpaintMask}>
-          {t('controlLayers.inpaintMask')}
-        </MenuItem>
-        <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance}>
-          {t('controlLayers.regionalGuidance')}
-        </MenuItem>
-        <MenuItem icon={<PiPlusBold />} onClick={addRasterLayer}>
-          {t('controlLayers.rasterLayer')}
-        </MenuItem>
-        <MenuItem icon={<PiPlusBold />} onClick={addControlLayer}>
-          {t('controlLayers.controlLayer')}
-        </MenuItem>
-        <MenuItem icon={<PiPlusBold />} onClick={addIPAdapter}>
-          {t('controlLayers.globalIPAdapter')}
-        </MenuItem>
-      </MenuList>
-    </Menu>
-  );
-});
-
-EntityListGlobalActionBarAddLayerMenu.displayName = 'EntityListGlobalActionBarAddLayerMenu';

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBar.tsx

@@ -1,26 +0,0 @@
-import { Flex, Spacer } from '@invoke-ai/ui-library';
-import { EntityListGlobalActionBarAddLayerMenu } from 'features/controlLayers/components/CanvasEntityList/EntityListGlobalActionBarAddLayerMenu';
-import { EntityListSelectedEntityActionBarDuplicateButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarDuplicateButton';
-import { EntityListSelectedEntityActionBarFill } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarFill';
-import { EntityListSelectedEntityActionBarFilterButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarFilterButton';
-import { EntityListSelectedEntityActionBarOpacity } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarOpacity';
-import { EntityListSelectedEntityActionBarTransformButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarTransformButton';
-import { memo } from 'react';
-
-export const EntityListSelectedEntityActionBar = memo(() => {
-  return (
-    <Flex w="full" gap={2} alignItems="center" ps={1}>
-      <EntityListSelectedEntityActionBarOpacity />
-      <Spacer />
-      <EntityListSelectedEntityActionBarFill />
-      <Flex h="full">
-        <EntityListSelectedEntityActionBarFilterButton />
-        <EntityListSelectedEntityActionBarTransformButton />
-        <EntityListSelectedEntityActionBarDuplicateButton />
-        <EntityListGlobalActionBarAddLayerMenu />
-      </Flex>
-    </Flex>
-  );
-});
-
-EntityListSelectedEntityActionBar.displayName = 'EntityListSelectedEntityActionBar';

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarDuplicateButton.tsx

@@ -1,34 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { entityDuplicated } from 'features/controlLayers/store/canvasSlice';
-import { selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiCopyFill } from 'react-icons/pi';
-
-export const EntityListSelectedEntityActionBarDuplicateButton = memo(() => {
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-  const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
-  const onClick = useCallback(() => {
-    if (!selectedEntityIdentifier) {
-      return;
-    }
-    dispatch(entityDuplicated({ entityIdentifier: selectedEntityIdentifier }));
-  }, [dispatch, selectedEntityIdentifier]);
-
-  return (
-    <IconButton
-      onClick={onClick}
-      isDisabled={!selectedEntityIdentifier}
-      size="sm"
-      variant="link"
-      alignSelf="stretch"
-      aria-label={t('controlLayers.duplicate')}
-      tooltip={t('controlLayers.duplicate')}
-      icon={<PiCopyFill />}
-    />
-  );
-});
-
-EntityListSelectedEntityActionBarDuplicateButton.displayName = 'EntityListSelectedEntityActionBarDuplicateButton';

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarFilterButton.tsx

@@ -1,37 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useEntityFilter } from 'features/controlLayers/hooks/useEntityFilter';
-import { selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
-import { isFilterableEntityIdentifier } from 'features/controlLayers/store/types';
-import { memo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiShootingStarBold } from 'react-icons/pi';
-
-export const EntityListSelectedEntityActionBarFilterButton = memo(() => {
-  const { t } = useTranslation();
-  const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
-  const filter = useEntityFilter(selectedEntityIdentifier);
-
-  if (!selectedEntityIdentifier) {
-    return null;
-  }
-
-  if (!isFilterableEntityIdentifier(selectedEntityIdentifier)) {
-    return null;
-  }
-
-  return (
-    <IconButton
-      onClick={filter.start}
-      isDisabled={filter.isDisabled}
-      size="sm"
-      variant="link"
-      alignSelf="stretch"
-      aria-label={t('controlLayers.filter.filter')}
-      tooltip={t('controlLayers.filter.filter')}
-      icon={<PiShootingStarBold />}
-    />
-  );
-});
-
-EntityListSelectedEntityActionBarFilterButton.displayName = 'EntityListSelectedEntityActionBarFilterButton';

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarTransformButton.tsx

@@ -1,37 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useEntityTransform } from 'features/controlLayers/hooks/useEntityTransform';
-import { selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
-import { isTransformableEntityIdentifier } from 'features/controlLayers/store/types';
-import { memo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiFrameCornersBold } from 'react-icons/pi';
-
-export const EntityListSelectedEntityActionBarTransformButton = memo(() => {
-  const { t } = useTranslation();
-  const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
-  const transform = useEntityTransform(selectedEntityIdentifier);
-
-  if (!selectedEntityIdentifier) {
-    return null;
-  }
-
-  if (!isTransformableEntityIdentifier(selectedEntityIdentifier)) {
-    return null;
-  }
-
-  return (
-    <IconButton
-      onClick={transform.start}
-      isDisabled={transform.isDisabled}
-      size="sm"
-      variant="link"
-      alignSelf="stretch"
-      aria-label={t('controlLayers.transform.transform')}
-      tooltip={t('controlLayers.transform.transform')}
-      icon={<PiFrameCornersBold />}
-    />
-  );
-});
-
-EntityListSelectedEntityActionBarTransformButton.displayName = 'EntityListSelectedEntityActionBarTransformButton';