resolve merge conflicts

docs/contributing/DOWNLOAD_QUEUE.md

@@ -128,7 +128,8 @@ The queue operates on a series of download job objects. These objects
 specify the source and destination of the download, and keep track of
 the progress of the download.
 
-The only job type currently implemented is `DownloadJob`, a pydantic object with the
+Two job types are defined. `DownloadJob` and
+`MultiFileDownloadJob`. The former is a pydantic object with the
 following fields:
 
 | **Field**      | **Type**        |  **Default**  | **Description** |
@@ -190,6 +191,33 @@ A cancelled job will have status `DownloadJobStatus.ERROR` and an
 `error_type` field of "DownloadJobCancelledException". In addition,
 the job's `cancelled` property will be set to True.
 
+The `MultiFileDownloadJob` is used for diffusers model downloads,
+which contain multiple files and directories under a common root:
+
+| **Field**      | **Type**        |  **Default**  | **Description** |
+|----------------|-----------------|---------------|-----------------|
+| _Fields passed in at job creation time_                               |
+| `download_parts` | Set[DownloadJob]|               | Component download jobs |
+| `dest`           | Path            |               | Where to download to              |
+| `on_start`       | Callable        |               | [optional] callback when the download starts |
+| `on_progress` | Callable | | [optional] callback called at intervals during download progress |
+| `on_complete`    | Callable        |               | [optional] callback called after successful download completion |
+| `on_error`       | Callable        |               | [optional] callback called after an error occurs  |
+| `id`             | int             | auto assigned | Job ID, an integer >= 0           |
+| _Fields updated over the course of the download task_
+| `status`         | DownloadJobStatus|              | Status code                                |
+| `download_path`  | Path |              | Path to the root of the downloaded files |
+| `bytes`          | int              | 0            | Bytes downloaded so far   |
+| `total_bytes`    | int              | 0            | Total size of the file at the remote site  |
+| `error_type`     | str              |              | String version of the exception that caused an error during download |
+| `error`          | str              |              | String version of the traceback associated with an error |
+| `cancelled`      | bool             | False        | Set to true if the job was cancelled by the caller|
+
+Note that the MultiFileDownloadJob does not support the `priority`,
+`job_started`, `job_ended` or `content_type` attributes. You can get
+these from the individual download jobs in `download_parts`.
+
+
 ### Callbacks
 
 Download jobs can be associated with a series of callbacks, each with
@@ -251,11 +279,40 @@ jobs using `list_jobs()`, fetch a single job by its with
 running jobs with `cancel_all_jobs()`, and wait for all jobs to finish
 with `join()`.
 
-#### job = queue.download(source, dest, priority, access_token)
+#### job = queue.download(source, dest, priority, access_token, on_start, on_progress, on_complete, on_cancelled, on_error)
 
 Create a new download job and put it on the queue, returning the
 DownloadJob object.
 
+#### multifile_job = queue.multifile_download(parts, dest, access_token, on_start, on_progress, on_complete, on_cancelled, on_error)
+
+This is similar to download(), but instead of taking a single source,
+it accepts a `parts` argument consisting of a list of
+`RemoteModelFile` objects. Each part corresponds to a URL/Path pair,
+where the URL is the location of the remote file, and the Path is the
+destination.
+
+`RemoteModelFile` can be imported from `invokeai.backend.model_manager.metadata`, and
+consists of a url/path pair. Note that the path *must* be relative.
+
+The method returns a `MultiFileDownloadJob`.
+
+
+```
+from invokeai.backend.model_manager.metadata import RemoteModelFile
+remote_file_1 = RemoteModelFile(url='http://www.foo.bar/my/pytorch_model.safetensors'',
+                                path='my_model/textencoder/pytorch_model.safetensors'
+			 			  )
+remote_file_2 = RemoteModelFile(url='http://www.bar.baz/vae.ckpt',
+                                path='my_model/vae/diffusers_model.safetensors'
+			 			  )
+job = queue.multifile_download(parts=[remote_file_1, remote_file_2],
+                               dest='/tmp/downloads',
+                               on_progress=TqdmProgress().update)
+queue.wait_for_job(job)
+print(f"The files were downloaded to {job.download_path}")
+```
+
 #### jobs = queue.list_jobs()
 
 Return a list of all active and inactive `DownloadJob`s.

docs/contributing/MODEL_MANAGER.md

@@ -397,18 +397,17 @@ In the event you wish to create a new installer, you may use the
 following initialization pattern:
 
 ```
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config import get_config
 from invokeai.app.services.model_records import ModelRecordServiceSQL
 from invokeai.app.services.model_install import ModelInstallService
 from invokeai.app.services.download import DownloadQueueService
-from invokeai.app.services.shared.sqlite import SqliteDatabase
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 from invokeai.backend.util.logging import InvokeAILogger
 
-config = InvokeAIAppConfig.get_config()
+config = get_config()
-config.parse_args()
 
 logger = InvokeAILogger.get_logger(config=config)
-db = SqliteDatabase(config, logger)
+db = SqliteDatabase(config.db_path, logger)
 record_store = ModelRecordServiceSQL(db)
 queue = DownloadQueueService()
 queue.start()
@@ -1367,12 +1366,20 @@ the in-memory loaded model:
 | `model`        | AnyModel               | The instantiated model (details below) |
 | `locker`       | ModelLockerBase        | A context manager that mediates the movement of the model into VRAM |
 
-Because the loader can return multiple model types, it is typed to
+### get_model_by_key(key, [submodel]) -> LoadedModel
-return `AnyModel`, a Union `ModelMixin`, `torch.nn.Module`,
+
-`IAIOnnxRuntimeModel`, `IPAdapter`, `IPAdapterPlus`, and
+The `get_model_by_key()` method will retrieve the model using its
-`EmbeddingModelRaw`. `ModelMixin` is the base class of all diffusers
+unique database key. For example:
-models, `EmbeddingModelRaw` is used for LoRA and TextualInversion
+
-models. The others are obvious.
+loaded_model = loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
+
+`get_model_by_key()` may raise any of the following exceptions:
+
+* `UnknownModelException`   -- key not in database
+* `ModelNotFoundException`  -- key in database but model not found at path
+* `NotImplementedException` -- the loader doesn't know how to load this type of model
+
+### Using the Loaded Model in Inference
 
 `LoadedModel` acts as a context manager. The context loads the model
 into the execution device (e.g. VRAM on CUDA systems), locks the model
@@ -1380,16 +1387,32 @@ in the execution device for the duration of the context, and returns
 the model. Use it like this:
 
 ```
-model_info = loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
+loaded_model_= loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
-with model_info as vae:
+with loaded_model as vae:
  image = vae.decode(latents)[0]
 ```
 
-`get_model_by_key()` may raise any of the following exceptions:
+The object returned by the LoadedModel context manager is an
+`AnyModel`, which is a Union of `ModelMixin`, `torch.nn.Module`,
+`IAIOnnxRuntimeModel`, `IPAdapter`, `IPAdapterPlus`, and
+`EmbeddingModelRaw`. `ModelMixin` is the base class of all diffusers
+models, `EmbeddingModelRaw` is used for LoRA and TextualInversion
+models. The others are obvious.
+
+In addition, you may call `LoadedModel.model_on_device()`, a context
+manager that returns a tuple of the model's state dict in CPU and the
+model itself in VRAM. It is used to optimize the LoRA patching and
+unpatching process:
+
+```
+loaded_model_= loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
+with loaded_model.model_on_device() as (state_dict, vae):
+ image = vae.decode(latents)[0]
+```
+
+Since not all models have state dicts, the `state_dict` return value
+can be None.
 
-* `UnknownModelException`   -- key not in database
-* `ModelNotFoundException`  -- key in database but model not found at path
-* `NotImplementedException` -- the loader doesn't know how to load this type of model
 
 ### Emitting model loading events
 
@@ -1578,3 +1601,59 @@ This method takes a model key, looks it up using the
 `ModelRecordServiceBase` object in `mm.store`, and passes the returned
 model configuration to `load_model_by_config()`.  It may raise a
 `NotImplementedException`.
+
+## Invocation Context Model Manager API
+
+Within invocations, the following methods are available from the
+`InvocationContext` object:
+
+### context.download_and_cache_model(source) -> Path
+
+This method accepts a `source` of a remote model, downloads and caches
+it locally, and then returns a Path to the local model. The source can
+be a direct download URL or a HuggingFace repo_id.
+
+In the case of HuggingFace repo_id, the following variants are
+recognized:
+
+* stabilityai/stable-diffusion-v4           -- default model
+* stabilityai/stable-diffusion-v4:fp16      -- fp16 variant
+* stabilityai/stable-diffusion-v4:fp16:vae  -- the fp16 vae subfolder
+* stabilityai/stable-diffusion-v4:onnx:vae  -- the onnx variant vae subfolder
+
+You can also point at an arbitrary individual file within a repo_id
+directory using this syntax:
+
+* stabilityai/stable-diffusion-v4::/checkpoints/sd4.safetensors
+
+### context.load_local_model(model_path, [loader]) -> LoadedModel
+
+This method loads a local model from the indicated path, returning a
+`LoadedModel`. The optional loader is a Callable that accepts a Path
+to the object, and returns a `AnyModel` object. If no loader is
+provided, then the method will use `torch.load()` for a .ckpt or .bin
+checkpoint file, `safetensors.torch.load_file()` for a safetensors
+checkpoint file, or `cls.from_pretrained()` for a directory that looks
+like a diffusers directory.
+
+### context.load_remote_model(source, [loader]) -> LoadedModel
+
+This method accepts a `source` of a remote model, downloads and caches
+it locally, loads it, and returns a `LoadedModel`. The source can be a
+direct download URL or a HuggingFace repo_id.
+
+In the case of HuggingFace repo_id, the following variants are
+recognized:
+
+* stabilityai/stable-diffusion-v4           -- default model
+* stabilityai/stable-diffusion-v4:fp16      -- fp16 variant
+* stabilityai/stable-diffusion-v4:fp16:vae  -- the fp16 vae subfolder
+* stabilityai/stable-diffusion-v4:onnx:vae  -- the onnx variant vae subfolder
+
+You can also point at an arbitrary individual file within a repo_id
+directory using this syntax:
+
+* stabilityai/stable-diffusion-v4::/checkpoints/sd4.safetensors
+
+
+

invokeai/app/api/dependencies.py

@@ -93,7 +93,7 @@ class ApiDependencies:
         conditioning = ObjectSerializerForwardCache(
             ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
         )
-        download_queue_service = DownloadQueueService(event_bus=events)
+        download_queue_service = DownloadQueueService(app_config=configuration, event_bus=events)
         model_images_service = ModelImageFileStorageDisk(model_images_folder / "model_images")
         model_manager = ModelManagerService.build_model_manager(
             app_config=configuration,

invokeai/app/api/routers/model_manager.py

@@ -9,7 +9,7 @@ from copy import deepcopy
 from typing import Any, Dict, List, Optional, Type
 
 from fastapi import Body, Path, Query, Response, UploadFile
-from fastapi.responses import FileResponse
+from fastapi.responses import FileResponse, HTMLResponse
 from fastapi.routing import APIRouter
 from PIL import Image
 from pydantic import AnyHttpUrl, BaseModel, ConfigDict, Field
@@ -502,6 +502,133 @@ async def install_model(
     return result
 
 
+@model_manager_router.get(
+    "/install/huggingface",
+    operation_id="install_hugging_face_model",
+    responses={
+        201: {"description": "The model is being installed"},
+        400: {"description": "Bad request"},
+        409: {"description": "There is already a model corresponding to this path or repo_id"},
+    },
+    status_code=201,
+    response_class=HTMLResponse,
+)
+async def install_hugging_face_model(
+    source: str = Query(description="HuggingFace repo_id to install"),
+) -> HTMLResponse:
+    """Install a Hugging Face model using a string identifier."""
+
+    def generate_html(title: str, heading: str, repo_id: str, is_error: bool, message: str | None = "") -> str:
+        if message:
+            message = f"<p>{message}</p>"
+        title_class = "error" if is_error else "success"
+        return f"""
+            <html>
+
+            <head>
+                <title>{title}</title>
+                <style>
+                    body {{
+                        text-align: center;
+                        background-color: hsl(220 12% 10% / 1);
+                        font-family: Helvetica, sans-serif;
+                        color: hsl(220 12% 86% / 1);
+                    }}
+
+                    .repo-id {{
+                        color: hsl(220 12% 68% / 1);
+                    }}
+
+                    .error {{
+                        color: hsl(0 42% 68% / 1)
+                    }}
+
+                    .message-box {{
+                        display: inline-block;
+                        border-radius: 5px;
+                        background-color: hsl(220 12% 20% / 1);
+                        padding-inline-end: 30px;
+                        padding: 20px;
+                        padding-inline-start: 30px;
+                        padding-inline-end: 30px;
+                    }}
+
+                    .container {{
+                        display: flex;
+                        width: 100%;
+                        height: 100%;
+                        align-items: center;
+                        justify-content: center;
+                    }}
+
+                    a {{
+                        color: inherit
+                    }}
+
+                    a:visited {{
+                        color: inherit
+                    }}
+
+                    a:active {{
+                        color: inherit
+                    }}
+                </style>
+            </head>
+
+            <body style="background-color: hsl(220 12% 10% / 1);">
+                <div class="container">
+                    <div class="message-box">
+                        <h2 class="{title_class}">{heading}</h2>
+                        {message}
+                        <p class="repo-id">Repo ID: {repo_id}</p>
+                    </div>
+                </div>
+            </body>
+
+            </html>
+        """
+
+    try:
+        metadata = HuggingFaceMetadataFetch().from_id(source)
+        assert isinstance(metadata, ModelMetadataWithFiles)
+    except UnknownMetadataException:
+        title = "Unable to Install Model"
+        heading = "No HuggingFace repository found with that repo ID."
+        message = "Ensure the repo ID is correct and try again."
+        return HTMLResponse(content=generate_html(title, heading, source, True, message), status_code=400)
+
+    logger = ApiDependencies.invoker.services.logger
+
+    try:
+        installer = ApiDependencies.invoker.services.model_manager.install
+        if metadata.is_diffusers:
+            installer.heuristic_import(
+                source=source,
+                inplace=False,
+            )
+        elif metadata.ckpt_urls is not None and len(metadata.ckpt_urls) == 1:
+            installer.heuristic_import(
+                source=str(metadata.ckpt_urls[0]),
+                inplace=False,
+            )
+        else:
+            title = "Unable to Install Model"
+            heading = "This HuggingFace repo has multiple models."
+            message = "Please use the Model Manager to install this model."
+            return HTMLResponse(content=generate_html(title, heading, source, True, message), status_code=200)
+
+        title = "Model Install Started"
+        heading = "Your HuggingFace model is installing now."
+        message = "You can close this tab and check the Model Manager for installation progress."
+        return HTMLResponse(content=generate_html(title, heading, source, False, message), status_code=201)
+    except Exception as e:
+        logger.error(str(e))
+        title = "Unable to Install Model"
+        heading = "There was an problem installing this model."
+        message = 'Please use the Model Manager directly to install this model. If the issue persists, ask for help on <a href="https://discord.gg/ZmtBAhwWhy">discord</a>.'
+        return HTMLResponse(content=generate_html(title, heading, source, True, message), status_code=500)
+
+
 @model_manager_router.get(
     "/install",
     operation_id="list_model_installs",

invokeai/app/invocations/blend_latents.py

@@ -0,0 +1,98 @@
+from typing import Any, Union
+
+import numpy as np
+import numpy.typing as npt
+import torch
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, LatentsField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "lblend",
+    title="Blend Latents",
+    tags=["latents", "blend"],
+    category="latents",
+    version="1.0.3",
+)
+class BlendLatentsInvocation(BaseInvocation):
+    """Blend two latents using a given alpha. Latents must have same size."""
+
+    latents_a: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    latents_b: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents_a = context.tensors.load(self.latents_a.latents_name)
+        latents_b = context.tensors.load(self.latents_b.latents_name)
+
+        if latents_a.shape != latents_b.shape:
+            raise Exception("Latents to blend must be the same size.")
+
+        device = TorchDevice.choose_torch_device()
+
+        def slerp(
+            t: Union[float, npt.NDArray[Any]],  # FIXME: maybe use np.float32 here?
+            v0: Union[torch.Tensor, npt.NDArray[Any]],
+            v1: Union[torch.Tensor, npt.NDArray[Any]],
+            DOT_THRESHOLD: float = 0.9995,
+        ) -> Union[torch.Tensor, npt.NDArray[Any]]:
+            """
+            Spherical linear interpolation
+            Args:
+                t (float/np.ndarray): Float value between 0.0 and 1.0
+                v0 (np.ndarray): Starting vector
+                v1 (np.ndarray): Final vector
+                DOT_THRESHOLD (float): Threshold for considering the two vectors as
+                                    colineal. Not recommended to alter this.
+            Returns:
+                v2 (np.ndarray): Interpolation vector between v0 and v1
+            """
+            inputs_are_torch = False
+            if not isinstance(v0, np.ndarray):
+                inputs_are_torch = True
+                v0 = v0.detach().cpu().numpy()
+            if not isinstance(v1, np.ndarray):
+                inputs_are_torch = True
+                v1 = v1.detach().cpu().numpy()
+
+            dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+            if np.abs(dot) > DOT_THRESHOLD:
+                v2 = (1 - t) * v0 + t * v1
+            else:
+                theta_0 = np.arccos(dot)
+                sin_theta_0 = np.sin(theta_0)
+                theta_t = theta_0 * t
+                sin_theta_t = np.sin(theta_t)
+                s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+                s1 = sin_theta_t / sin_theta_0
+                v2 = s0 * v0 + s1 * v1
+
+            if inputs_are_torch:
+                v2_torch: torch.Tensor = torch.from_numpy(v2).to(device)
+                return v2_torch
+            else:
+                assert isinstance(v2, np.ndarray)
+                return v2
+
+        # blend
+        bl = slerp(self.alpha, latents_a, latents_b)
+        assert isinstance(bl, torch.Tensor)
+        blended_latents: torch.Tensor = bl  # for type checking convenience
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        blended_latents = blended_latents.to("cpu")
+
+        TorchDevice.empty_cache()
+
+        name = context.tensors.save(tensor=blended_latents)
+        return LatentsOutput.build(latents_name=name, latents=blended_latents, seed=self.latents_a.seed)

invokeai/app/invocations/compel.py

@@ -81,9 +81,13 @@ class CompelInvocation(BaseInvocation):
 
         with (
             # apply all patches while the model is on the target device
-            text_encoder_info as text_encoder,
+            text_encoder_info.model_on_device() as (model_state_dict, text_encoder),
             tokenizer_info as tokenizer,
-            ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
+            ModelPatcher.apply_lora_text_encoder(
+                text_encoder,
+                loras=_lora_loader(),
+                model_state_dict=model_state_dict,
+            ),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
             ModelPatcher.apply_clip_skip(text_encoder, self.clip.skipped_layers),
             ModelPatcher.apply_ti(tokenizer, text_encoder, ti_list) as (
@@ -174,9 +178,14 @@ class SDXLPromptInvocationBase:
 
         with (
             # apply all patches while the model is on the target device
-            text_encoder_info as text_encoder,
+            text_encoder_info.model_on_device() as (state_dict, text_encoder),
             tokenizer_info as tokenizer,
-            ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
+            ModelPatcher.apply_lora(
+                text_encoder,
+                loras=_lora_loader(),
+                prefix=lora_prefix,
+                model_state_dict=state_dict,
+            ),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
             ModelPatcher.apply_clip_skip(text_encoder, clip_field.skipped_layers),
             ModelPatcher.apply_ti(tokenizer, text_encoder, ti_list) as (

invokeai/app/invocations/constants.py

@@ -1,6 +1,7 @@
 from typing import Literal
 
 from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
+from invokeai.backend.util.devices import TorchDevice
 
 LATENT_SCALE_FACTOR = 8
 """
@@ -15,3 +16,5 @@ SCHEDULER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
 
 IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
 """A literal type for PIL image modes supported by Invoke"""
+
+DEFAULT_PRECISION = TorchDevice.choose_torch_dtype()

invokeai/app/invocations/controlnet_image_processors.py

@@ -2,6 +2,7 @@
 # initial implementation by Gregg Helt, 2023
 # heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
 from builtins import bool, float
+from pathlib import Path
 from typing import Dict, List, Literal, Union
 
 import cv2
@@ -36,12 +37,13 @@ from invokeai.app.invocations.util import validate_begin_end_step, validate_weig
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, heuristic_resize
 from invokeai.backend.image_util.canny import get_canny_edges
-from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
+from invokeai.backend.image_util.depth_anything import DEPTH_ANYTHING_MODELS, DepthAnythingDetector
-from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
+from invokeai.backend.image_util.dw_openpose import DWPOSE_MODELS, DWOpenposeDetector
 from invokeai.backend.image_util.hed import HEDProcessor
 from invokeai.backend.image_util.lineart import LineartProcessor
 from invokeai.backend.image_util.lineart_anime import LineartAnimeProcessor
 from invokeai.backend.image_util.util import np_to_pil, pil_to_np
+from invokeai.backend.util.devices import TorchDevice
 
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, Classification, invocation, invocation_output
 
@@ -139,6 +141,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
         return context.images.get_pil(self.image.image_name, "RGB")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
+        self._context = context
         raw_image = self.load_image(context)
         # image type should be PIL.PngImagePlugin.PngImageFile ?
         processed_image = self.run_processor(raw_image)
@@ -284,7 +287,8 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     # depth_and_normal not supported in controlnet_aux v0.0.3
     # depth_and_normal: bool = InputField(default=False, description="whether to use depth and normal mode")
 
-    def run_processor(self, image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        # TODO: replace from_pretrained() calls with context.models.download_and_cache() (or similar)
         midas_processor = MidasDetector.from_pretrained("lllyasviel/Annotators")
         processed_image = midas_processor(
             image,
@@ -311,7 +315,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
     image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
-    def run_processor(self, image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
         normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
         processed_image = normalbae_processor(
             image, detect_resolution=self.detect_resolution, image_resolution=self.image_resolution
@@ -330,7 +334,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     thr_v: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_v`")
     thr_d: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_d`")
 
-    def run_processor(self, image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
         mlsd_processor = MLSDdetector.from_pretrained("lllyasviel/Annotators")
         processed_image = mlsd_processor(
             image,
@@ -353,7 +357,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
     scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
 
-    def run_processor(self, image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
         pidi_processor = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
         processed_image = pidi_processor(
             image,
@@ -381,7 +385,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     w: int = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
     f: int = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
 
-    def run_processor(self, image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
         content_shuffle_processor = ContentShuffleDetector()
         processed_image = content_shuffle_processor(
             image,
@@ -405,7 +409,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
 
-    def run_processor(self, image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
         zoe_depth_processor = ZoeDetector.from_pretrained("lllyasviel/Annotators")
         processed_image = zoe_depth_processor(image)
         return processed_image
@@ -426,7 +430,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
     image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
-    def run_processor(self, image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
         mediapipe_face_processor = MediapipeFaceDetector()
         processed_image = mediapipe_face_processor(
             image,
@@ -454,7 +458,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
     image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
-    def run_processor(self, image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
         leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
         processed_image = leres_processor(
             image,
@@ -496,8 +500,8 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
         np_img = cv2.resize(np_img, (W, H), interpolation=cv2.INTER_AREA)
         return np_img
 
-    def run_processor(self, img):
+    def run_processor(self, image: Image.Image) -> Image.Image:
-        np_img = np.array(img, dtype=np.uint8)
+        np_img = np.array(image, dtype=np.uint8)
         processed_np_image = self.tile_resample(
             np_img,
             # res=self.tile_size,
@@ -520,7 +524,7 @@ class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
     image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
-    def run_processor(self, image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
         # segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
         segment_anything_processor = SamDetectorReproducibleColors.from_pretrained(
             "ybelkada/segment-anything", subfolder="checkpoints"
@@ -566,7 +570,7 @@ class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
 
     color_map_tile_size: int = InputField(default=64, ge=1, description=FieldDescriptions.tile_size)
 
-    def run_processor(self, image: Image.Image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
         np_image = np.array(image, dtype=np.uint8)
         height, width = np_image.shape[:2]
 
@@ -601,10 +605,16 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     )
     resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
-    def run_processor(self, image: Image.Image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
-        depth_anything_detector = DepthAnythingDetector()
+        def loader(model_path: Path):
-        depth_anything_detector.load_model(model_size=self.model_size)
+            return DepthAnythingDetector.load_model(
+                model_path, model_size=self.model_size, device=TorchDevice.choose_torch_device()
+            )
 
+        with self._context.models.load_remote_model(
+            source=DEPTH_ANYTHING_MODELS[self.model_size], loader=loader
+        ) as model:
+            depth_anything_detector = DepthAnythingDetector(model, TorchDevice.choose_torch_device())
             processed_image = depth_anything_detector(image=image, resolution=self.resolution)
             return processed_image
 
@@ -624,8 +634,11 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
     draw_hands: bool = InputField(default=False)
     image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
-    def run_processor(self, image: Image.Image):
+    def run_processor(self, image: Image.Image) -> Image.Image:
-        dw_openpose = DWOpenposeDetector()
+        onnx_det = self._context.models.download_and_cache_model(DWPOSE_MODELS["yolox_l.onnx"])
+        onnx_pose = self._context.models.download_and_cache_model(DWPOSE_MODELS["dw-ll_ucoco_384.onnx"])
+
+        dw_openpose = DWOpenposeDetector(onnx_det=onnx_det, onnx_pose=onnx_pose)
         processed_image = dw_openpose(
             image,
             draw_face=self.draw_face,

invokeai/app/invocations/create_denoise_mask.py

@@ -0,0 +1,80 @@
+from typing import Optional
+
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import resize as tv_resize
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import DEFAULT_PRECISION
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField
+from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import DenoiseMaskOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+
+
+@invocation(
+    "create_denoise_mask",
+    title="Create Denoise Mask",
+    tags=["mask", "denoise"],
+    category="latents",
+    version="1.0.2",
+)
+class CreateDenoiseMaskInvocation(BaseInvocation):
+    """Creates mask for denoising model run."""
+
+    vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0)
+    image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
+    mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
+    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
+    fp32: bool = InputField(
+        default=DEFAULT_PRECISION == torch.float32,
+        description=FieldDescriptions.fp32,
+        ui_order=4,
+    )
+
+    def prep_mask_tensor(self, mask_image: Image.Image) -> torch.Tensor:
+        if mask_image.mode != "L":
+            mask_image = mask_image.convert("L")
+        mask_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+        if mask_tensor.dim() == 3:
+            mask_tensor = mask_tensor.unsqueeze(0)
+        # if shape is not None:
+        #    mask_tensor = tv_resize(mask_tensor, shape, T.InterpolationMode.BILINEAR)
+        return mask_tensor
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
+        if self.image is not None:
+            image = context.images.get_pil(self.image.image_name)
+            image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+            if image_tensor.dim() == 3:
+                image_tensor = image_tensor.unsqueeze(0)
+        else:
+            image_tensor = None
+
+        mask = self.prep_mask_tensor(
+            context.images.get_pil(self.mask.image_name),
+        )
+
+        if image_tensor is not None:
+            vae_info = context.models.load(self.vae.vae)
+
+            img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
+            masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
+            # TODO:
+            masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
+
+            masked_latents_name = context.tensors.save(tensor=masked_latents)
+        else:
+            masked_latents_name = None
+
+        mask_name = context.tensors.save(tensor=mask)
+
+        return DenoiseMaskOutput.build(
+            mask_name=mask_name,
+            masked_latents_name=masked_latents_name,
+            gradient=False,
+        )

invokeai/app/invocations/create_gradient_mask.py

@@ -0,0 +1,138 @@
+from typing import Literal, Optional
+
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image, ImageFilter
+from torchvision.transforms.functional import resize as tv_resize
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.constants import DEFAULT_PRECISION
+from invokeai.app.invocations.fields import (
+    DenoiseMaskField,
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    OutputField,
+)
+from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
+from invokeai.app.invocations.model import UNetField, VAEField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager import LoadedModel
+from invokeai.backend.model_manager.config import MainConfigBase, ModelVariantType
+from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+
+
+@invocation_output("gradient_mask_output")
+class GradientMaskOutput(BaseInvocationOutput):
+    """Outputs a denoise mask and an image representing the total gradient of the mask."""
+
+    denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
+    expanded_mask_area: ImageField = OutputField(
+        description="Image representing the total gradient area of the mask. For paste-back purposes."
+    )
+
+
+@invocation(
+    "create_gradient_mask",
+    title="Create Gradient Mask",
+    tags=["mask", "denoise"],
+    category="latents",
+    version="1.1.0",
+)
+class CreateGradientMaskInvocation(BaseInvocation):
+    """Creates mask for denoising model run."""
+
+    mask: ImageField = InputField(default=None, description="Image which will be masked", ui_order=1)
+    edge_radius: int = InputField(
+        default=16, ge=0, description="How far to blur/expand the edges of the mask", ui_order=2
+    )
+    coherence_mode: Literal["Gaussian Blur", "Box Blur", "Staged"] = InputField(default="Gaussian Blur", ui_order=3)
+    minimum_denoise: float = InputField(
+        default=0.0, ge=0, le=1, description="Minimum denoise level for the coherence region", ui_order=4
+    )
+    image: Optional[ImageField] = InputField(
+        default=None,
+        description="OPTIONAL: Only connect for specialized Inpainting models, masked_latents will be generated from the image with the VAE",
+        title="[OPTIONAL] Image",
+        ui_order=6,
+    )
+    unet: Optional[UNetField] = InputField(
+        description="OPTIONAL: If the Unet is a specialized Inpainting model, masked_latents will be generated from the image with the VAE",
+        default=None,
+        input=Input.Connection,
+        title="[OPTIONAL] UNet",
+        ui_order=5,
+    )
+    vae: Optional[VAEField] = InputField(
+        default=None,
+        description="OPTIONAL: Only connect for specialized Inpainting models, masked_latents will be generated from the image with the VAE",
+        title="[OPTIONAL] VAE",
+        input=Input.Connection,
+        ui_order=7,
+    )
+    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=8)
+    fp32: bool = InputField(
+        default=DEFAULT_PRECISION == torch.float32,
+        description=FieldDescriptions.fp32,
+        ui_order=9,
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> GradientMaskOutput:
+        mask_image = context.images.get_pil(self.mask.image_name, mode="L")
+        if self.edge_radius > 0:
+            if self.coherence_mode == "Box Blur":
+                blur_mask = mask_image.filter(ImageFilter.BoxBlur(self.edge_radius))
+            else:  # Gaussian Blur OR Staged
+                # Gaussian Blur uses standard deviation. 1/2 radius is a good approximation
+                blur_mask = mask_image.filter(ImageFilter.GaussianBlur(self.edge_radius / 2))
+
+            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(blur_mask, normalize=False)
+
+            # redistribute blur so that the original edges are 0 and blur outwards to 1
+            blur_tensor = (blur_tensor - 0.5) * 2
+
+            threshold = 1 - self.minimum_denoise
+
+            if self.coherence_mode == "Staged":
+                # wherever the blur_tensor is less than fully masked, convert it to threshold
+                blur_tensor = torch.where((blur_tensor < 1) & (blur_tensor > 0), threshold, blur_tensor)
+            else:
+                # wherever the blur_tensor is above threshold but less than 1, drop it to threshold
+                blur_tensor = torch.where((blur_tensor > threshold) & (blur_tensor < 1), threshold, blur_tensor)
+
+        else:
+            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+
+        mask_name = context.tensors.save(tensor=blur_tensor.unsqueeze(1))
+
+        # compute a [0, 1] mask from the blur_tensor
+        expanded_mask = torch.where((blur_tensor < 1), 0, 1)
+        expanded_mask_image = Image.fromarray((expanded_mask.squeeze(0).numpy() * 255).astype(np.uint8), mode="L")
+        expanded_image_dto = context.images.save(expanded_mask_image)
+
+        masked_latents_name = None
+        if self.unet is not None and self.vae is not None and self.image is not None:
+            # all three fields must be present at the same time
+            main_model_config = context.models.get_config(self.unet.unet.key)
+            assert isinstance(main_model_config, MainConfigBase)
+            if main_model_config.variant is ModelVariantType.Inpaint:
+                mask = blur_tensor
+                vae_info: LoadedModel = context.models.load(self.vae.vae)
+                image = context.images.get_pil(self.image.image_name)
+                image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+                if image_tensor.dim() == 3:
+                    image_tensor = image_tensor.unsqueeze(0)
+                img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
+                masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
+                masked_latents = ImageToLatentsInvocation.vae_encode(
+                    vae_info, self.fp32, self.tiled, masked_image.clone()
+                )
+                masked_latents_name = context.tensors.save(tensor=masked_latents)
+
+        return GradientMaskOutput(
+            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=masked_latents_name, gradient=True),
+            expanded_mask_area=ImageField(image_name=expanded_image_dto.image_name),
+        )

invokeai/app/invocations/crop_latents.py

@@ -0,0 +1,61 @@
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, LatentsField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
+
+# The Crop Latents node was copied from @skunkworxdark's implementation here:
+# https://github.com/skunkworxdark/XYGrid_nodes/blob/74647fa9c1fa57d317a94bd43ca689af7f0aae5e/images_to_grids.py#L1117C1-L1167C80
+@invocation(
+    "crop_latents",
+    title="Crop Latents",
+    tags=["latents", "crop"],
+    category="latents",
+    version="1.0.2",
+)
+# TODO(ryand): Named `CropLatentsCoreInvocation` to prevent a conflict with custom node `CropLatentsInvocation`.
+# Currently, if the class names conflict then 'GET /openapi.json' fails.
+class CropLatentsCoreInvocation(BaseInvocation):
+    """Crops a latent-space tensor to a box specified in image-space. The box dimensions and coordinates must be
+    divisible by the latent scale factor of 8.
+    """
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    x: int = InputField(
+        ge=0,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The left x coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+    y: int = InputField(
+        ge=0,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The top y coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+    width: int = InputField(
+        ge=1,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The width (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+    height: int = InputField(
+        ge=1,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The height (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+
+        x1 = self.x // LATENT_SCALE_FACTOR
+        y1 = self.y // LATENT_SCALE_FACTOR
+        x2 = x1 + (self.width // LATENT_SCALE_FACTOR)
+        y2 = y1 + (self.height // LATENT_SCALE_FACTOR)
+
+        cropped_latents = latents[..., y1:y2, x1:x2]
+
+        name = context.tensors.save(tensor=cropped_latents)
+
+        return LatentsOutput.build(latents_name=name, latents=cropped_latents)

invokeai/app/invocations/denoise_latents.py

@@ -0,0 +1,811 @@
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
+import inspect
+from contextlib import ExitStack
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
+
+import torch
+import torchvision
+import torchvision.transforms as T
+from diffusers.configuration_utils import ConfigMixin
+from diffusers.models.adapter import T2IAdapter
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+from diffusers.schedulers.scheduling_dpmsolver_sde import DPMSolverSDEScheduler
+from diffusers.schedulers.scheduling_tcd import TCDScheduler
+from diffusers.schedulers.scheduling_utils import SchedulerMixin as Scheduler
+from pydantic import field_validator
+from torchvision.transforms.functional import resize as tv_resize
+from transformers import CLIPVisionModelWithProjection
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
+from invokeai.app.invocations.controlnet_image_processors import ControlField
+from invokeai.app.invocations.fields import (
+    ConditioningField,
+    DenoiseMaskField,
+    FieldDescriptions,
+    Input,
+    InputField,
+    LatentsField,
+    UIType,
+)
+from invokeai.app.invocations.ip_adapter import IPAdapterField
+from invokeai.app.invocations.model import ModelIdentifierField, UNetField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.invocations.t2i_adapter import T2IAdapterField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.util.controlnet_utils import prepare_control_image
+from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
+from invokeai.backend.lora import LoRAModelRaw
+from invokeai.backend.model_manager import BaseModelType
+from invokeai.backend.model_patcher import ModelPatcher
+from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
+from invokeai.backend.stable_diffusion.diffusers_pipeline import (
+    ControlNetData,
+    StableDiffusionGeneratorPipeline,
+    T2IAdapterData,
+)
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
+    BasicConditioningInfo,
+    IPAdapterConditioningInfo,
+    IPAdapterData,
+    Range,
+    SDXLConditioningInfo,
+    TextConditioningData,
+    TextConditioningRegions,
+)
+from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
+from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.mask import to_standard_float_mask
+from invokeai.backend.util.silence_warnings import SilenceWarnings
+
+
+def get_scheduler(
+    context: InvocationContext,
+    scheduler_info: ModelIdentifierField,
+    scheduler_name: str,
+    seed: int,
+) -> Scheduler:
+    scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
+    orig_scheduler_info = context.models.load(scheduler_info)
+    with orig_scheduler_info as orig_scheduler:
+        scheduler_config = orig_scheduler.config
+
+    if "_backup" in scheduler_config:
+        scheduler_config = scheduler_config["_backup"]
+    scheduler_config = {
+        **scheduler_config,
+        **scheduler_extra_config,  # FIXME
+        "_backup": scheduler_config,
+    }
+
+    # make dpmpp_sde reproducable(seed can be passed only in initializer)
+    if scheduler_class is DPMSolverSDEScheduler:
+        scheduler_config["noise_sampler_seed"] = seed
+
+    scheduler = scheduler_class.from_config(scheduler_config)
+
+    # hack copied over from generate.py
+    if not hasattr(scheduler, "uses_inpainting_model"):
+        scheduler.uses_inpainting_model = lambda: False
+    assert isinstance(scheduler, Scheduler)
+    return scheduler
+
+
+@invocation(
+    "denoise_latents",
+    title="Denoise Latents",
+    tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
+    category="latents",
+    version="1.5.3",
+)
+class DenoiseLatentsInvocation(BaseInvocation):
+    """Denoises noisy latents to decodable images"""
+
+    positive_conditioning: Union[ConditioningField, list[ConditioningField]] = InputField(
+        description=FieldDescriptions.positive_cond, input=Input.Connection, ui_order=0
+    )
+    negative_conditioning: Union[ConditioningField, list[ConditioningField]] = InputField(
+        description=FieldDescriptions.negative_cond, input=Input.Connection, ui_order=1
+    )
+    noise: Optional[LatentsField] = InputField(
+        default=None,
+        description=FieldDescriptions.noise,
+        input=Input.Connection,
+        ui_order=3,
+    )
+    steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
+    cfg_scale: Union[float, List[float]] = InputField(
+        default=7.5, description=FieldDescriptions.cfg_scale, title="CFG Scale"
+    )
+    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)
+    scheduler: SCHEDULER_NAME_VALUES = InputField(
+        default="euler",
+        description=FieldDescriptions.scheduler,
+        ui_type=UIType.Scheduler,
+    )
+    unet: UNetField = InputField(
+        description=FieldDescriptions.unet,
+        input=Input.Connection,
+        title="UNet",
+        ui_order=2,
+    )
+    control: Optional[Union[ControlField, list[ControlField]]] = InputField(
+        default=None,
+        input=Input.Connection,
+        ui_order=5,
+    )
+    ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]] = InputField(
+        description=FieldDescriptions.ip_adapter,
+        title="IP-Adapter",
+        default=None,
+        input=Input.Connection,
+        ui_order=6,
+    )
+    t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]] = InputField(
+        description=FieldDescriptions.t2i_adapter,
+        title="T2I-Adapter",
+        default=None,
+        input=Input.Connection,
+        ui_order=7,
+    )
+    cfg_rescale_multiplier: float = InputField(
+        title="CFG Rescale Multiplier", default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
+    )
+    latents: Optional[LatentsField] = InputField(
+        default=None,
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+        ui_order=4,
+    )
+    denoise_mask: Optional[DenoiseMaskField] = InputField(
+        default=None,
+        description=FieldDescriptions.mask,
+        input=Input.Connection,
+        ui_order=8,
+    )
+
+    @field_validator("cfg_scale")
+    def ge_one(cls, v: Union[List[float], float]) -> Union[List[float], float]:
+        """validate that all cfg_scale values are >= 1"""
+        if isinstance(v, list):
+            for i in v:
+                if i < 1:
+                    raise ValueError("cfg_scale must be greater than 1")
+        else:
+            if v < 1:
+                raise ValueError("cfg_scale must be greater than 1")
+        return v
+
+    def _get_text_embeddings_and_masks(
+        self,
+        cond_list: list[ConditioningField],
+        context: InvocationContext,
+        device: torch.device,
+        dtype: torch.dtype,
+    ) -> tuple[Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]], list[Optional[torch.Tensor]]]:
+        """Get the text embeddings and masks from the input conditioning fields."""
+        text_embeddings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]] = []
+        text_embeddings_masks: list[Optional[torch.Tensor]] = []
+        for cond in cond_list:
+            cond_data = context.conditioning.load(cond.conditioning_name)
+            text_embeddings.append(cond_data.conditionings[0].to(device=device, dtype=dtype))
+
+            mask = cond.mask
+            if mask is not None:
+                mask = context.tensors.load(mask.tensor_name)
+            text_embeddings_masks.append(mask)
+
+        return text_embeddings, text_embeddings_masks
+
+    def _preprocess_regional_prompt_mask(
+        self, mask: Optional[torch.Tensor], target_height: int, target_width: int, dtype: torch.dtype
+    ) -> torch.Tensor:
+        """Preprocess a regional prompt mask to match the target height and width.
+        If mask is None, returns a mask of all ones with the target height and width.
+        If mask is not None, resizes the mask to the target height and width using 'nearest' interpolation.
+
+        Returns:
+            torch.Tensor: The processed mask. shape: (1, 1, target_height, target_width).
+        """
+
+        if mask is None:
+            return torch.ones((1, 1, target_height, target_width), dtype=dtype)
+
+        mask = to_standard_float_mask(mask, out_dtype=dtype)
+
+        tf = torchvision.transforms.Resize(
+            (target_height, target_width), interpolation=torchvision.transforms.InterpolationMode.NEAREST
+        )
+
+        # Add a batch dimension to the mask, because torchvision expects shape (batch, channels, h, w).
+        mask = mask.unsqueeze(0)  # Shape: (1, h, w) -> (1, 1, h, w)
+        resized_mask = tf(mask)
+        return resized_mask
+
+    def _concat_regional_text_embeddings(
+        self,
+        text_conditionings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]],
+        masks: Optional[list[Optional[torch.Tensor]]],
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+    ) -> tuple[Union[BasicConditioningInfo, SDXLConditioningInfo], Optional[TextConditioningRegions]]:
+        """Concatenate regional text embeddings into a single embedding and track the region masks accordingly."""
+        if masks is None:
+            masks = [None] * len(text_conditionings)
+        assert len(text_conditionings) == len(masks)
+
+        is_sdxl = type(text_conditionings[0]) is SDXLConditioningInfo
+
+        all_masks_are_none = all(mask is None for mask in masks)
+
+        text_embedding = []
+        pooled_embedding = None
+        add_time_ids = None
+        cur_text_embedding_len = 0
+        processed_masks = []
+        embedding_ranges = []
+
+        for prompt_idx, text_embedding_info in enumerate(text_conditionings):
+            mask = masks[prompt_idx]
+
+            if is_sdxl:
+                # We choose a random SDXLConditioningInfo's pooled_embeds and add_time_ids here, with a preference for
+                # prompts without a mask. We prefer prompts without a mask, because they are more likely to contain
+                # global prompt information.  In an ideal case, there should be exactly one global prompt without a
+                # mask, but we don't enforce this.
+
+                # HACK(ryand): The fact that we have to choose a single pooled_embedding and add_time_ids here is a
+                # fundamental interface issue. The SDXL Compel nodes are not designed to be used in the way that we use
+                # them for regional prompting. Ideally, the DenoiseLatents invocation should accept a single
+                # pooled_embeds tensor and a list of standard text embeds with region masks. This change would be a
+                # pretty major breaking change to a popular node, so for now we use this hack.
+                if pooled_embedding is None or mask is None:
+                    pooled_embedding = text_embedding_info.pooled_embeds
+                if add_time_ids is None or mask is None:
+                    add_time_ids = text_embedding_info.add_time_ids
+
+            text_embedding.append(text_embedding_info.embeds)
+            if not all_masks_are_none:
+                embedding_ranges.append(
+                    Range(
+                        start=cur_text_embedding_len, end=cur_text_embedding_len + text_embedding_info.embeds.shape[1]
+                    )
+                )
+                processed_masks.append(
+                    self._preprocess_regional_prompt_mask(mask, latent_height, latent_width, dtype=dtype)
+                )
+
+            cur_text_embedding_len += text_embedding_info.embeds.shape[1]
+
+        text_embedding = torch.cat(text_embedding, dim=1)
+        assert len(text_embedding.shape) == 3  # batch_size, seq_len, token_len
+
+        regions = None
+        if not all_masks_are_none:
+            regions = TextConditioningRegions(
+                masks=torch.cat(processed_masks, dim=1),
+                ranges=embedding_ranges,
+            )
+
+        if is_sdxl:
+            return (
+                SDXLConditioningInfo(embeds=text_embedding, pooled_embeds=pooled_embedding, add_time_ids=add_time_ids),
+                regions,
+            )
+        return BasicConditioningInfo(embeds=text_embedding), regions
+
+    def get_conditioning_data(
+        self,
+        context: InvocationContext,
+        unet: UNet2DConditionModel,
+        latent_height: int,
+        latent_width: int,
+    ) -> TextConditioningData:
+        # Normalize self.positive_conditioning and self.negative_conditioning to lists.
+        cond_list = self.positive_conditioning
+        if not isinstance(cond_list, list):
+            cond_list = [cond_list]
+        uncond_list = self.negative_conditioning
+        if not isinstance(uncond_list, list):
+            uncond_list = [uncond_list]
+
+        cond_text_embeddings, cond_text_embedding_masks = self._get_text_embeddings_and_masks(
+            cond_list, context, unet.device, unet.dtype
+        )
+        uncond_text_embeddings, uncond_text_embedding_masks = self._get_text_embeddings_and_masks(
+            uncond_list, context, unet.device, unet.dtype
+        )
+
+        cond_text_embedding, cond_regions = self._concat_regional_text_embeddings(
+            text_conditionings=cond_text_embeddings,
+            masks=cond_text_embedding_masks,
+            latent_height=latent_height,
+            latent_width=latent_width,
+            dtype=unet.dtype,
+        )
+        uncond_text_embedding, uncond_regions = self._concat_regional_text_embeddings(
+            text_conditionings=uncond_text_embeddings,
+            masks=uncond_text_embedding_masks,
+            latent_height=latent_height,
+            latent_width=latent_width,
+            dtype=unet.dtype,
+        )
+
+        if isinstance(self.cfg_scale, list):
+            assert (
+                len(self.cfg_scale) == self.steps
+            ), "cfg_scale (list) must have the same length as the number of steps"
+
+        conditioning_data = TextConditioningData(
+            uncond_text=uncond_text_embedding,
+            cond_text=cond_text_embedding,
+            uncond_regions=uncond_regions,
+            cond_regions=cond_regions,
+            guidance_scale=self.cfg_scale,
+            guidance_rescale_multiplier=self.cfg_rescale_multiplier,
+        )
+        return conditioning_data
+
+    def create_pipeline(
+        self,
+        unet: UNet2DConditionModel,
+        scheduler: Scheduler,
+    ) -> StableDiffusionGeneratorPipeline:
+        class FakeVae:
+            class FakeVaeConfig:
+                def __init__(self) -> None:
+                    self.block_out_channels = [0]
+
+            def __init__(self) -> None:
+                self.config = FakeVae.FakeVaeConfig()
+
+        return StableDiffusionGeneratorPipeline(
+            vae=FakeVae(),  # TODO: oh...
+            text_encoder=None,
+            tokenizer=None,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=None,
+            feature_extractor=None,
+            requires_safety_checker=False,
+        )
+
+    def prep_control_data(
+        self,
+        context: InvocationContext,
+        control_input: Optional[Union[ControlField, List[ControlField]]],
+        latents_shape: List[int],
+        exit_stack: ExitStack,
+        do_classifier_free_guidance: bool = True,
+    ) -> Optional[List[ControlNetData]]:
+        # Assuming fixed dimensional scaling of LATENT_SCALE_FACTOR.
+        control_height_resize = latents_shape[2] * LATENT_SCALE_FACTOR
+        control_width_resize = latents_shape[3] * LATENT_SCALE_FACTOR
+        if control_input is None:
+            control_list = None
+        elif isinstance(control_input, list) and len(control_input) == 0:
+            control_list = None
+        elif isinstance(control_input, ControlField):
+            control_list = [control_input]
+        elif isinstance(control_input, list) and len(control_input) > 0 and isinstance(control_input[0], ControlField):
+            control_list = control_input
+        else:
+            control_list = None
+        if control_list is None:
+            return None
+        # After above handling, any control that is not None should now be of type list[ControlField].
+
+        # FIXME: add checks to skip entry if model or image is None
+        #        and if weight is None, populate with default 1.0?
+        controlnet_data = []
+        for control_info in control_list:
+            control_model = exit_stack.enter_context(context.models.load(control_info.control_model))
+
+            # control_models.append(control_model)
+            control_image_field = control_info.image
+            input_image = context.images.get_pil(control_image_field.image_name)
+            # self.image.image_type, self.image.image_name
+            # FIXME: still need to test with different widths, heights, devices, dtypes
+            #        and add in batch_size, num_images_per_prompt?
+            #        and do real check for classifier_free_guidance?
+            # prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
+            control_image = prepare_control_image(
+                image=input_image,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                width=control_width_resize,
+                height=control_height_resize,
+                # batch_size=batch_size * num_images_per_prompt,
+                # num_images_per_prompt=num_images_per_prompt,
+                device=control_model.device,
+                dtype=control_model.dtype,
+                control_mode=control_info.control_mode,
+                resize_mode=control_info.resize_mode,
+            )
+            control_item = ControlNetData(
+                model=control_model,  # model object
+                image_tensor=control_image,
+                weight=control_info.control_weight,
+                begin_step_percent=control_info.begin_step_percent,
+                end_step_percent=control_info.end_step_percent,
+                control_mode=control_info.control_mode,
+                # any resizing needed should currently be happening in prepare_control_image(),
+                #    but adding resize_mode to ControlNetData in case needed in the future
+                resize_mode=control_info.resize_mode,
+            )
+            controlnet_data.append(control_item)
+            # MultiControlNetModel has been refactored out, just need list[ControlNetData]
+
+        return controlnet_data
+
+    def prep_ip_adapter_image_prompts(
+        self,
+        context: InvocationContext,
+        ip_adapters: List[IPAdapterField],
+    ) -> List[Tuple[torch.Tensor, torch.Tensor]]:
+        """Run the IPAdapter CLIPVisionModel, returning image prompt embeddings."""
+        image_prompts = []
+        for single_ip_adapter in ip_adapters:
+            with context.models.load(single_ip_adapter.ip_adapter_model) as ip_adapter_model:
+                assert isinstance(ip_adapter_model, IPAdapter)
+                image_encoder_model_info = context.models.load(single_ip_adapter.image_encoder_model)
+                # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
+                single_ipa_image_fields = single_ip_adapter.image
+                if not isinstance(single_ipa_image_fields, list):
+                    single_ipa_image_fields = [single_ipa_image_fields]
+
+                single_ipa_images = [context.images.get_pil(image.image_name) for image in single_ipa_image_fields]
+                with image_encoder_model_info as image_encoder_model:
+                    assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
+                    # Get image embeddings from CLIP and ImageProjModel.
+                    image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
+                        single_ipa_images, image_encoder_model
+                    )
+                    image_prompts.append((image_prompt_embeds, uncond_image_prompt_embeds))
+
+        return image_prompts
+
+    def prep_ip_adapter_data(
+        self,
+        context: InvocationContext,
+        ip_adapters: List[IPAdapterField],
+        image_prompts: List[Tuple[torch.Tensor, torch.Tensor]],
+        exit_stack: ExitStack,
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+    ) -> Optional[List[IPAdapterData]]:
+        """If IP-Adapter is enabled, then this function loads the requisite models and adds the image prompt conditioning data."""
+        ip_adapter_data_list = []
+        for single_ip_adapter, (image_prompt_embeds, uncond_image_prompt_embeds) in zip(
+            ip_adapters, image_prompts, strict=True
+        ):
+            ip_adapter_model = exit_stack.enter_context(context.models.load(single_ip_adapter.ip_adapter_model))
+
+            mask_field = single_ip_adapter.mask
+            mask = context.tensors.load(mask_field.tensor_name) if mask_field is not None else None
+            mask = self._preprocess_regional_prompt_mask(mask, latent_height, latent_width, dtype=dtype)
+
+            ip_adapter_data_list.append(
+                IPAdapterData(
+                    ip_adapter_model=ip_adapter_model,
+                    weight=single_ip_adapter.weight,
+                    target_blocks=single_ip_adapter.target_blocks,
+                    begin_step_percent=single_ip_adapter.begin_step_percent,
+                    end_step_percent=single_ip_adapter.end_step_percent,
+                    ip_adapter_conditioning=IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds),
+                    mask=mask,
+                )
+            )
+
+        return ip_adapter_data_list if len(ip_adapter_data_list) > 0 else None
+
+    def run_t2i_adapters(
+        self,
+        context: InvocationContext,
+        t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
+        latents_shape: list[int],
+        do_classifier_free_guidance: bool,
+    ) -> Optional[list[T2IAdapterData]]:
+        if t2i_adapter is None:
+            return None
+
+        # Handle the possibility that t2i_adapter could be a list or a single T2IAdapterField.
+        if isinstance(t2i_adapter, T2IAdapterField):
+            t2i_adapter = [t2i_adapter]
+
+        if len(t2i_adapter) == 0:
+            return None
+
+        t2i_adapter_data = []
+        for t2i_adapter_field in t2i_adapter:
+            t2i_adapter_model_config = context.models.get_config(t2i_adapter_field.t2i_adapter_model.key)
+            t2i_adapter_loaded_model = context.models.load(t2i_adapter_field.t2i_adapter_model)
+            image = context.images.get_pil(t2i_adapter_field.image.image_name)
+
+            # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
+            if t2i_adapter_model_config.base == BaseModelType.StableDiffusion1:
+                max_unet_downscale = 8
+            elif t2i_adapter_model_config.base == BaseModelType.StableDiffusionXL:
+                max_unet_downscale = 4
+            else:
+                raise ValueError(f"Unexpected T2I-Adapter base model type: '{t2i_adapter_model_config.base}'.")
+
+            t2i_adapter_model: T2IAdapter
+            with t2i_adapter_loaded_model as t2i_adapter_model:
+                total_downscale_factor = t2i_adapter_model.total_downscale_factor
+
+                # Resize the T2I-Adapter input image.
+                # We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
+                # result will match the latent image's dimensions after max_unet_downscale is applied.
+                t2i_input_height = latents_shape[2] // max_unet_downscale * total_downscale_factor
+                t2i_input_width = latents_shape[3] // max_unet_downscale * total_downscale_factor
+
+                # Note: We have hard-coded `do_classifier_free_guidance=False`. This is because we only want to prepare
+                # a single image. If CFG is enabled, we will duplicate the resultant tensor after applying the
+                # T2I-Adapter model.
+                #
+                # Note: We re-use the `prepare_control_image(...)` from ControlNet for T2I-Adapter, because it has many
+                # of the same requirements (e.g. preserving binary masks during resize).
+                t2i_image = prepare_control_image(
+                    image=image,
+                    do_classifier_free_guidance=False,
+                    width=t2i_input_width,
+                    height=t2i_input_height,
+                    num_channels=t2i_adapter_model.config["in_channels"],  # mypy treats this as a FrozenDict
+                    device=t2i_adapter_model.device,
+                    dtype=t2i_adapter_model.dtype,
+                    resize_mode=t2i_adapter_field.resize_mode,
+                )
+
+                adapter_state = t2i_adapter_model(t2i_image)
+
+            if do_classifier_free_guidance:
+                for idx, value in enumerate(adapter_state):
+                    adapter_state[idx] = torch.cat([value] * 2, dim=0)
+
+            t2i_adapter_data.append(
+                T2IAdapterData(
+                    adapter_state=adapter_state,
+                    weight=t2i_adapter_field.weight,
+                    begin_step_percent=t2i_adapter_field.begin_step_percent,
+                    end_step_percent=t2i_adapter_field.end_step_percent,
+                )
+            )
+
+        return t2i_adapter_data
+
+    # original idea by https://github.com/AmericanPresidentJimmyCarter
+    # TODO: research more for second order schedulers timesteps
+    def init_scheduler(
+        self,
+        scheduler: Union[Scheduler, ConfigMixin],
+        device: torch.device,
+        steps: int,
+        denoising_start: float,
+        denoising_end: float,
+        seed: int,
+    ) -> Tuple[int, List[int], int, Dict[str, Any]]:
+        assert isinstance(scheduler, ConfigMixin)
+        if scheduler.config.get("cpu_only", False):
+            scheduler.set_timesteps(steps, device="cpu")
+            timesteps = scheduler.timesteps.to(device=device)
+        else:
+            scheduler.set_timesteps(steps, device=device)
+            timesteps = scheduler.timesteps
+
+        # skip greater order timesteps
+        _timesteps = timesteps[:: scheduler.order]
+
+        # get start timestep index
+        t_start_val = int(round(scheduler.config["num_train_timesteps"] * (1 - denoising_start)))
+        t_start_idx = len(list(filter(lambda ts: ts >= t_start_val, _timesteps)))
+
+        # get end timestep index
+        t_end_val = int(round(scheduler.config["num_train_timesteps"] * (1 - denoising_end)))
+        t_end_idx = len(list(filter(lambda ts: ts >= t_end_val, _timesteps[t_start_idx:])))
+
+        # apply order to indexes
+        t_start_idx *= scheduler.order
+        t_end_idx *= scheduler.order
+
+        init_timestep = timesteps[t_start_idx : t_start_idx + 1]
+        timesteps = timesteps[t_start_idx : t_start_idx + t_end_idx]
+        num_inference_steps = len(timesteps) // scheduler.order
+
+        scheduler_step_kwargs: Dict[str, Any] = {}
+        scheduler_step_signature = inspect.signature(scheduler.step)
+        if "generator" in scheduler_step_signature.parameters:
+            # At some point, someone decided that schedulers that accept a generator should use the original seed with
+            # all bits flipped. I don't know the original rationale for this, but now we must keep it like this for
+            # reproducibility.
+            #
+            # These Invoke-supported schedulers accept a generator as of 2024-06-04:
+            #   - DDIMScheduler
+            #   - DDPMScheduler
+            #   - DPMSolverMultistepScheduler
+            #   - EulerAncestralDiscreteScheduler
+            #   - EulerDiscreteScheduler
+            #   - KDPM2AncestralDiscreteScheduler
+            #   - LCMScheduler
+            #   - TCDScheduler
+            scheduler_step_kwargs.update({"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)})
+        if isinstance(scheduler, TCDScheduler):
+            scheduler_step_kwargs.update({"eta": 1.0})
+
+        return num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs
+
+    def prep_inpaint_mask(
+        self, context: InvocationContext, latents: torch.Tensor
+    ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], bool]:
+        if self.denoise_mask is None:
+            return None, None, False
+
+        mask = context.tensors.load(self.denoise_mask.mask_name)
+        mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
+        if self.denoise_mask.masked_latents_name is not None:
+            masked_latents = context.tensors.load(self.denoise_mask.masked_latents_name)
+        else:
+            masked_latents = torch.where(mask < 0.5, 0.0, latents)
+
+        return 1 - mask, masked_latents, self.denoise_mask.gradient
+
+    @torch.no_grad()
+    @SilenceWarnings()  # This quenches the NSFW nag from diffusers.
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        seed = None
+        noise = None
+        if self.noise is not None:
+            noise = context.tensors.load(self.noise.latents_name)
+            seed = self.noise.seed
+
+        if self.latents is not None:
+            latents = context.tensors.load(self.latents.latents_name)
+            if seed is None:
+                seed = self.latents.seed
+
+            if noise is not None and noise.shape[1:] != latents.shape[1:]:
+                raise Exception(f"Incompatable 'noise' and 'latents' shapes: {latents.shape=} {noise.shape=}")
+
+        elif noise is not None:
+            latents = torch.zeros_like(noise)
+        else:
+            raise Exception("'latents' or 'noise' must be provided!")
+
+        if seed is None:
+            seed = 0
+
+        mask, masked_latents, gradient_mask = self.prep_inpaint_mask(context, latents)
+
+        # TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets,
+        # below. Investigate whether this is appropriate.
+        t2i_adapter_data = self.run_t2i_adapters(
+            context,
+            self.t2i_adapter,
+            latents.shape,
+            do_classifier_free_guidance=True,
+        )
+
+        ip_adapters: List[IPAdapterField] = []
+        if self.ip_adapter is not None:
+            # ip_adapter could be a list or a single IPAdapterField. Normalize to a list here.
+            if isinstance(self.ip_adapter, list):
+                ip_adapters = self.ip_adapter
+            else:
+                ip_adapters = [self.ip_adapter]
+
+        # If there are IP adapters, the following line runs the adapters' CLIPVision image encoders to return
+        # a series of image conditioning embeddings. This is being done here rather than in the
+        # big model context below in order to use less VRAM on low-VRAM systems.
+        # The image prompts are then passed to prep_ip_adapter_data().
+        image_prompts = self.prep_ip_adapter_image_prompts(context=context, ip_adapters=ip_adapters)
+
+        # get the unet's config so that we can pass the base to dispatch_progress()
+        unet_config = context.models.get_config(self.unet.unet.key)
+
+        def step_callback(state: PipelineIntermediateState) -> None:
+            context.util.sd_step_callback(state, unet_config.base)
+
+        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
+            for lora in self.unet.loras:
+                lora_info = context.models.load(lora.lora)
+                assert isinstance(lora_info.model, LoRAModelRaw)
+                yield (lora_info.model, lora.weight)
+                del lora_info
+            return
+
+        unet_info = context.models.load(self.unet.unet)
+        assert isinstance(unet_info.model, UNet2DConditionModel)
+        with (
+            ExitStack() as exit_stack,
+            unet_info.model_on_device() as (model_state_dict, unet),
+            ModelPatcher.apply_freeu(unet, self.unet.freeu_config),
+            set_seamless(unet, self.unet.seamless_axes),  # FIXME
+            # Apply the LoRA after unet has been moved to its target device for faster patching.
+            ModelPatcher.apply_lora_unet(
+                unet,
+                loras=_lora_loader(),
+                model_state_dict=model_state_dict,
+            ),
+        ):
+            assert isinstance(unet, UNet2DConditionModel)
+            latents = latents.to(device=unet.device, dtype=unet.dtype)
+            if noise is not None:
+                noise = noise.to(device=unet.device, dtype=unet.dtype)
+            if mask is not None:
+                mask = mask.to(device=unet.device, dtype=unet.dtype)
+            if masked_latents is not None:
+                masked_latents = masked_latents.to(device=unet.device, dtype=unet.dtype)
+
+            scheduler = get_scheduler(
+                context=context,
+                scheduler_info=self.unet.scheduler,
+                scheduler_name=self.scheduler,
+                seed=seed,
+            )
+
+            pipeline = self.create_pipeline(unet, scheduler)
+
+            _, _, latent_height, latent_width = latents.shape
+            conditioning_data = self.get_conditioning_data(
+                context=context, unet=unet, latent_height=latent_height, latent_width=latent_width
+            )
+
+            controlnet_data = self.prep_control_data(
+                context=context,
+                control_input=self.control,
+                latents_shape=latents.shape,
+                # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
+                do_classifier_free_guidance=True,
+                exit_stack=exit_stack,
+            )
+
+            ip_adapter_data = self.prep_ip_adapter_data(
+                context=context,
+                ip_adapters=ip_adapters,
+                image_prompts=image_prompts,
+                exit_stack=exit_stack,
+                latent_height=latent_height,
+                latent_width=latent_width,
+                dtype=unet.dtype,
+            )
+
+            num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(
+                scheduler,
+                device=unet.device,
+                steps=self.steps,
+                denoising_start=self.denoising_start,
+                denoising_end=self.denoising_end,
+                seed=seed,
+            )
+
+            result_latents = pipeline.latents_from_embeddings(
+                latents=latents,
+                timesteps=timesteps,
+                init_timestep=init_timestep,
+                noise=noise,
+                seed=seed,
+                mask=mask,
+                masked_latents=masked_latents,
+                gradient_mask=gradient_mask,
+                num_inference_steps=num_inference_steps,
+                scheduler_step_kwargs=scheduler_step_kwargs,
+                conditioning_data=conditioning_data,
+                control_data=controlnet_data,
+                ip_adapter_data=ip_adapter_data,
+                t2i_adapter_data=t2i_adapter_data,
+                callback=step_callback,
+            )
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        result_latents = result_latents.to("cpu")
+        TorchDevice.empty_cache()
+
+        name = context.tensors.save(tensor=result_latents)
+        return LatentsOutput.build(latents_name=name, latents=result_latents, seed=None)

invokeai/app/invocations/ideal_size.py

@@ -0,0 +1,65 @@
+import math
+from typing import Tuple
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField
+from invokeai.app.invocations.model import UNetField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.config import BaseModelType
+
+
+@invocation_output("ideal_size_output")
+class IdealSizeOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+
+    width: int = OutputField(description="The ideal width of the image (in pixels)")
+    height: int = OutputField(description="The ideal height of the image (in pixels)")
+
+
+@invocation(
+    "ideal_size",
+    title="Ideal Size",
+    tags=["latents", "math", "ideal_size"],
+    version="1.0.3",
+)
+class IdealSizeInvocation(BaseInvocation):
+    """Calculates the ideal size for generation to avoid duplication"""
+
+    width: int = InputField(default=1024, description="Final image width")
+    height: int = InputField(default=576, description="Final image height")
+    unet: UNetField = InputField(default=None, description=FieldDescriptions.unet)
+    multiplier: float = InputField(
+        default=1.0,
+        description="Amount to multiply the model's dimensions by when calculating the ideal size (may result in "
+        "initial generation artifacts if too large)",
+    )
+
+    def trim_to_multiple_of(self, *args: int, multiple_of: int = LATENT_SCALE_FACTOR) -> Tuple[int, ...]:
+        return tuple((x - x % multiple_of) for x in args)
+
+    def invoke(self, context: InvocationContext) -> IdealSizeOutput:
+        unet_config = context.models.get_config(self.unet.unet.key)
+        aspect = self.width / self.height
+        dimension: float = 512
+        if unet_config.base == BaseModelType.StableDiffusion2:
+            dimension = 768
+        elif unet_config.base == BaseModelType.StableDiffusionXL:
+            dimension = 1024
+        dimension = dimension * self.multiplier
+        min_dimension = math.floor(dimension * 0.5)
+        model_area = dimension * dimension  # hardcoded for now since all models are trained on square images
+
+        if aspect > 1.0:
+            init_height = max(min_dimension, math.sqrt(model_area / aspect))
+            init_width = init_height * aspect
+        else:
+            init_width = max(min_dimension, math.sqrt(model_area * aspect))
+            init_height = init_width / aspect
+
+        scaled_width, scaled_height = self.trim_to_multiple_of(
+            math.floor(init_width),
+            math.floor(init_height),
+        )
+
+        return IdealSizeOutput(width=scaled_width, height=scaled_height)

invokeai/app/invocations/image_to_latents.py

@@ -0,0 +1,125 @@
+from functools import singledispatchmethod
+
+import einops
+import torch
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import DEFAULT_PRECISION
+from invokeai.app.invocations.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.model_manager import LoadedModel
+from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+
+
+@invocation(
+    "i2l",
+    title="Image to Latents",
+    tags=["latents", "image", "vae", "i2l"],
+    category="latents",
+    version="1.0.2",
+)
+class ImageToLatentsInvocation(BaseInvocation):
+    """Encodes an image into latents."""
+
+    image: ImageField = InputField(
+        description="The image to encode",
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
+    fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
+
+    @staticmethod
+    def vae_encode(vae_info: LoadedModel, upcast: bool, tiled: bool, image_tensor: torch.Tensor) -> torch.Tensor:
+        with vae_info as vae:
+            assert isinstance(vae, torch.nn.Module)
+            orig_dtype = vae.dtype
+            if upcast:
+                vae.to(dtype=torch.float32)
+
+                use_torch_2_0_or_xformers = hasattr(vae.decoder, "mid_block") and isinstance(
+                    vae.decoder.mid_block.attentions[0].processor,
+                    (
+                        AttnProcessor2_0,
+                        XFormersAttnProcessor,
+                        LoRAXFormersAttnProcessor,
+                        LoRAAttnProcessor2_0,
+                    ),
+                )
+                # if xformers or torch_2_0 is used attention block does not need
+                # to be in float32 which can save lots of memory
+                if use_torch_2_0_or_xformers:
+                    vae.post_quant_conv.to(orig_dtype)
+                    vae.decoder.conv_in.to(orig_dtype)
+                    vae.decoder.mid_block.to(orig_dtype)
+                # else:
+                #    latents = latents.float()
+
+            else:
+                vae.to(dtype=torch.float16)
+                # latents = latents.half()
+
+            if tiled:
+                vae.enable_tiling()
+            else:
+                vae.disable_tiling()
+
+            # non_noised_latents_from_image
+            image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
+            with torch.inference_mode():
+                latents = ImageToLatentsInvocation._encode_to_tensor(vae, image_tensor)
+
+            latents = vae.config.scaling_factor * latents
+            latents = latents.to(dtype=orig_dtype)
+
+        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, self.fp32, self.tiled, image_tensor)
+
+        latents = latents.to("cpu")
+        name = context.tensors.save(tensor=latents)
+        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
+
+    @singledispatchmethod
+    @staticmethod
+    def _encode_to_tensor(vae: AutoencoderKL, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
+        assert isinstance(vae, torch.nn.Module)
+        image_tensor_dist = vae.encode(image_tensor).latent_dist
+        latents: torch.Tensor = image_tensor_dist.sample().to(
+            dtype=vae.dtype
+        )  # FIXME: uses torch.randn. make reproducible!
+        return latents
+
+    @_encode_to_tensor.register
+    @staticmethod
+    def _(vae: AutoencoderTiny, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
+        assert isinstance(vae, torch.nn.Module)
+        latents: torch.FloatTensor = vae.encode(image_tensor).latents
+        return latents

invokeai/app/invocations/infill.py

@@ -42,15 +42,16 @@ class InfillImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
         """Infill the image with the specified method"""
         pass
 
-    def load_image(self, context: InvocationContext) -> tuple[Image.Image, bool]:
+    def load_image(self) -> tuple[Image.Image, bool]:
         """Process the image to have an alpha channel before being infilled"""
-        image = context.images.get_pil(self.image.image_name)
+        image = self._context.images.get_pil(self.image.image_name)
         has_alpha = True if image.mode == "RGBA" else False
         return image, has_alpha
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
+        self._context = context
         # Retrieve and process image to be infilled
-        input_image, has_alpha = self.load_image(context)
+        input_image, has_alpha = self.load_image()
 
         # If the input image has no alpha channel, return it
         if has_alpha is False:
@@ -133,7 +134,11 @@ class LaMaInfillInvocation(InfillImageProcessorInvocation):
     """Infills transparent areas of an image using the LaMa model"""
 
     def infill(self, image: Image.Image):
-        lama = LaMA()
+        with self._context.models.load_remote_model(
+            source="https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
+            loader=LaMA.load_jit_model,
+        ) as model:
+            lama = LaMA(model)
             return lama(image)
 
 

invokeai/app/invocations/latents_to_image.py

@@ -0,0 +1,107 @@
+import torch
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import DEFAULT_PRECISION
+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.stable_diffusion import set_seamless
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "l2i",
+    title="Latents to Image",
+    tags=["latents", "image", "vae", "l2i"],
+    category="latents",
+    version="1.2.2",
+)
+class LatentsToImageInvocation(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,
+    )
+    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
+    fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+
+        vae_info = context.models.load(self.vae.vae)
+        assert isinstance(vae_info.model, (UNet2DConditionModel, AutoencoderKL, AutoencoderTiny))
+        with set_seamless(vae_info.model, self.vae.seamless_axes), vae_info as vae:
+            assert isinstance(vae, torch.nn.Module)
+            latents = latents.to(vae.device)
+            if self.fp32:
+                vae.to(dtype=torch.float32)
+
+                use_torch_2_0_or_xformers = hasattr(vae.decoder, "mid_block") and isinstance(
+                    vae.decoder.mid_block.attentions[0].processor,
+                    (
+                        AttnProcessor2_0,
+                        XFormersAttnProcessor,
+                        LoRAXFormersAttnProcessor,
+                        LoRAAttnProcessor2_0,
+                    ),
+                )
+                # if xformers or torch_2_0 is used attention block does not need
+                # to be in float32 which can save lots of memory
+                if use_torch_2_0_or_xformers:
+                    vae.post_quant_conv.to(latents.dtype)
+                    vae.decoder.conv_in.to(latents.dtype)
+                    vae.decoder.mid_block.to(latents.dtype)
+                else:
+                    latents = latents.float()
+
+            else:
+                vae.to(dtype=torch.float16)
+                latents = latents.half()
+
+            if self.tiled or context.config.get().force_tiled_decode:
+                vae.enable_tiling()
+            else:
+                vae.disable_tiling()
+
+            # clear memory as vae decode can request a lot
+            TorchDevice.empty_cache()
+
+            with torch.inference_mode():
+                # copied from diffusers pipeline
+                latents = latents / vae.config.scaling_factor
+                image = vae.decode(latents, return_dict=False)[0]
+                image = (image / 2 + 0.5).clamp(0, 1)  # denormalize
+                # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+                np_image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+                image = VaeImageProcessor.numpy_to_pil(np_image)[0]
+
+        TorchDevice.empty_cache()
+
+        image_dto = context.images.save(image=image)
+
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/resize_latents.py

@@ -0,0 +1,103 @@
+from typing import Literal
+
+import torch
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    LatentsField,
+)
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.util.devices import TorchDevice
+
+LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
+
+
+@invocation(
+    "lresize",
+    title="Resize Latents",
+    tags=["latents", "resize"],
+    category="latents",
+    version="1.0.2",
+)
+class ResizeLatentsInvocation(BaseInvocation):
+    """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    width: int = InputField(
+        ge=64,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description=FieldDescriptions.width,
+    )
+    height: int = InputField(
+        ge=64,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description=FieldDescriptions.width,
+    )
+    mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
+    antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+        device = TorchDevice.choose_torch_device()
+
+        resized_latents = torch.nn.functional.interpolate(
+            latents.to(device),
+            size=(self.height // LATENT_SCALE_FACTOR, self.width // LATENT_SCALE_FACTOR),
+            mode=self.mode,
+            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
+        )
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        resized_latents = resized_latents.to("cpu")
+
+        TorchDevice.empty_cache()
+
+        name = context.tensors.save(tensor=resized_latents)
+        return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
+
+
+@invocation(
+    "lscale",
+    title="Scale Latents",
+    tags=["latents", "resize"],
+    category="latents",
+    version="1.0.2",
+)
+class ScaleLatentsInvocation(BaseInvocation):
+    """Scales latents by a given factor."""
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    scale_factor: float = InputField(gt=0, description=FieldDescriptions.scale_factor)
+    mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
+    antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+
+        device = TorchDevice.choose_torch_device()
+
+        # resizing
+        resized_latents = torch.nn.functional.interpolate(
+            latents.to(device),
+            scale_factor=self.scale_factor,
+            mode=self.mode,
+            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
+        )
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        resized_latents = resized_latents.to("cpu")
+        TorchDevice.empty_cache()
+
+        name = context.tensors.save(tensor=resized_latents)
+        return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)

invokeai/app/invocations/scheduler.py

@@ -0,0 +1,34 @@
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.constants import SCHEDULER_NAME_VALUES
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    InputField,
+    OutputField,
+    UIType,
+)
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
+
+@invocation_output("scheduler_output")
+class SchedulerOutput(BaseInvocationOutput):
+    scheduler: SCHEDULER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
+
+
+@invocation(
+    "scheduler",
+    title="Scheduler",
+    tags=["scheduler"],
+    category="latents",
+    version="1.0.0",
+)
+class SchedulerInvocation(BaseInvocation):
+    """Selects a scheduler."""
+
+    scheduler: SCHEDULER_NAME_VALUES = InputField(
+        default="euler",
+        description=FieldDescriptions.scheduler,
+        ui_type=UIType.Scheduler,
+    )
+
+    def invoke(self, context: InvocationContext) -> SchedulerOutput:
+        return SchedulerOutput(scheduler=self.scheduler)

invokeai/app/invocations/upscale.py

@@ -1,5 +1,4 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
-from pathlib import Path
 from typing import Literal
 
 import cv2
@@ -10,10 +9,8 @@ from pydantic import ConfigDict
 from invokeai.app.invocations.fields import ImageField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.download_with_progress import download_with_progress_bar
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
 from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
-from invokeai.backend.util.devices import TorchDevice
 
 from .baseinvocation import BaseInvocation, invocation
 from .fields import InputField, WithBoard, WithMetadata
@@ -52,7 +49,6 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         rrdbnet_model = None
         netscale = None
-        esrgan_model_path = None
 
         if self.model_name in [
             "RealESRGAN_x4plus.pth",
@@ -95,16 +91,14 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
             context.logger.error(msg)
             raise ValueError(msg)
 
-        esrgan_model_path = Path(context.config.get().models_path, f"core/upscaling/realesrgan/{self.model_name}")
+        loadnet = context.models.load_remote_model(
-
+            source=ESRGAN_MODEL_URLS[self.model_name],
-        # Downloads the ESRGAN model if it doesn't already exist
-        download_with_progress_bar(
-            name=self.model_name, url=ESRGAN_MODEL_URLS[self.model_name], dest_path=esrgan_model_path
         )
 
+        with loadnet as loadnet_model:
             upscaler = RealESRGAN(
                 scale=netscale,
-            model_path=esrgan_model_path,
+                loadnet=loadnet_model,
                 model=rrdbnet_model,
                 half=False,
                 tile=self.tile_size,
@@ -114,9 +108,8 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
             # TODO: This strips the alpha... is that okay?
             cv2_image = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
             upscaled_image = upscaler.upscale(cv2_image)
-        pil_image = Image.fromarray(cv2.cvtColor(upscaled_image, cv2.COLOR_BGR2RGB)).convert("RGBA")
 
-        TorchDevice.empty_cache()
+            pil_image = Image.fromarray(cv2.cvtColor(upscaled_image, cv2.COLOR_BGR2RGB)).convert("RGBA")
 
         image_dto = context.images.save(image=pil_image)
 

invokeai/app/services/config/config_default.py

@@ -86,6 +86,7 @@ class InvokeAIAppConfig(BaseSettings):
         patchmatch: Enable patchmatch inpaint code.
         models_dir: Path to the models directory.
         convert_cache_dir: Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.
+        download_cache_dir: Path to the directory that contains dynamically downloaded models.
         legacy_conf_dir: Path to directory of legacy checkpoint config files.
         db_dir: Path to InvokeAI databases directory.
         outputs_dir: Path to directory for outputs.
@@ -114,6 +115,7 @@ class InvokeAIAppConfig(BaseSettings):
         pil_compress_level: The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.
         max_queue_size: Maximum number of items in the session queue.
         max_threads: Maximum number of session queue execution threads. Autocalculated from number of GPUs if not set.
+        clear_queue_on_startup: Empties session queue on startup.
         allow_nodes: List of nodes to allow. Omit to allow all.
         deny_nodes: List of nodes to deny. Omit to deny none.
         node_cache_size: How many cached nodes to keep in memory.
@@ -148,7 +150,8 @@ class InvokeAIAppConfig(BaseSettings):
 
     # PATHS
     models_dir:                    Path = Field(default=Path("models"),     description="Path to the models directory.")
-    convert_cache_dir:             Path = Field(default=Path("models/.cache"), description="Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.")
+    convert_cache_dir:             Path = Field(default=Path("models/.convert_cache"), description="Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.")
+    download_cache_dir:            Path = Field(default=Path("models/.download_cache"), description="Path to the directory that contains dynamically downloaded models.")
     legacy_conf_dir:               Path = Field(default=Path("configs"), description="Path to directory of legacy checkpoint config files.")
     db_dir:                        Path = Field(default=Path("databases"),  description="Path to InvokeAI databases directory.")
     outputs_dir:                   Path = Field(default=Path("outputs"),    description="Path to directory for outputs.")
@@ -188,6 +191,7 @@ class InvokeAIAppConfig(BaseSettings):
     pil_compress_level:             int = Field(default=1,                  description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.")
     max_queue_size:                 int = Field(default=10000, gt=0,        description="Maximum number of items in the session queue.")
     max_threads:          Optional[int] = Field(default=None,               description="Maximum number of session queue execution threads. Autocalculated from number of GPUs if not set.")
+    clear_queue_on_startup:        bool = Field(default=False,              description="Empties session queue on startup.")
 
     # NODES
     allow_nodes:    Optional[list[str]] = Field(default=None,               description="List of nodes to allow. Omit to allow all.")
@@ -307,6 +311,11 @@ class InvokeAIAppConfig(BaseSettings):
         """Path to the converted cache models directory, resolved to an absolute path.."""
         return self._resolve(self.convert_cache_dir)
 
+    @property
+    def download_cache_path(self) -> Path:
+        """Path to the downloaded models directory, resolved to an absolute path.."""
+        return self._resolve(self.download_cache_dir)
+
     @property
     def custom_nodes_path(self) -> Path:
         """Path to the custom nodes directory, resolved to an absolute path.."""

invokeai/app/services/download/__init__.py

@@ -1,10 +1,17 @@
 """Init file for download queue."""
 
-from .download_base import DownloadJob, DownloadJobStatus, DownloadQueueServiceBase, UnknownJobIDException
+from .download_base import (
+    DownloadJob,
+    DownloadJobStatus,
+    DownloadQueueServiceBase,
+    MultiFileDownloadJob,
+    UnknownJobIDException,
+)
 from .download_default import DownloadQueueService, TqdmProgress
 
 __all__ = [
     "DownloadJob",
+    "MultiFileDownloadJob",
     "DownloadQueueServiceBase",
     "DownloadQueueService",
     "TqdmProgress",

invokeai/app/services/download/download_base.py

@@ -5,11 +5,13 @@ from abc import ABC, abstractmethod
 from enum import Enum
 from functools import total_ordering
 from pathlib import Path
-from typing import Any, Callable, List, Optional
+from typing import Any, Callable, List, Optional, Set, Union
 
 from pydantic import BaseModel, Field, PrivateAttr
 from pydantic.networks import AnyHttpUrl
 
+from invokeai.backend.model_manager.metadata import RemoteModelFile
+
 
 class DownloadJobStatus(str, Enum):
     """State of a download job."""
@@ -33,30 +35,23 @@ class ServiceInactiveException(Exception):
     """This exception is raised when user attempts to initiate a download before the service is started."""
 
 
-DownloadEventHandler = Callable[["DownloadJob"], None]
+SingleFileDownloadEventHandler = Callable[["DownloadJob"], None]
-DownloadExceptionHandler = Callable[["DownloadJob", Optional[Exception]], None]
+SingleFileDownloadExceptionHandler = Callable[["DownloadJob", Optional[Exception]], None]
+MultiFileDownloadEventHandler = Callable[["MultiFileDownloadJob"], None]
+MultiFileDownloadExceptionHandler = Callable[["MultiFileDownloadJob", Optional[Exception]], None]
+DownloadEventHandler = Union[SingleFileDownloadEventHandler, MultiFileDownloadEventHandler]
+DownloadExceptionHandler = Union[SingleFileDownloadExceptionHandler, MultiFileDownloadExceptionHandler]
 
 
-@total_ordering
+class DownloadJobBase(BaseModel):
-class DownloadJob(BaseModel):
+    """Base of classes to monitor and control downloads."""
-    """Class to monitor and control a model download request."""
 
-    # required variables to be passed in on creation
-    source: AnyHttpUrl = Field(description="Where to download from. Specific types specified in child classes.")
-    dest: Path = Field(description="Destination of downloaded model on local disk; a directory or file path")
-    access_token: Optional[str] = Field(default=None, description="authorization token for protected resources")
     # automatically assigned on creation
     id: int = Field(description="Numeric ID of this job", default=-1)  # default id is a sentinel
-    priority: int = Field(default=10, description="Queue priority; lower values are higher priority")
 
-    # set internally during download process
+    dest: Path = Field(description="Initial destination of downloaded model on local disk; a directory or file path")
+    download_path: Optional[Path] = Field(default=None, description="Final location of downloaded file or directory")
     status: DownloadJobStatus = Field(default=DownloadJobStatus.WAITING, description="Status of the download")
-    download_path: Optional[Path] = Field(default=None, description="Final location of downloaded file")
-    job_started: Optional[str] = Field(default=None, description="Timestamp for when the download job started")
-    job_ended: Optional[str] = Field(
-        default=None, description="Timestamp for when the download job ende1d (completed or errored)"
-    )
-    content_type: Optional[str] = Field(default=None, description="Content type of downloaded file")
     bytes: int = Field(default=0, description="Bytes downloaded so far")
     total_bytes: int = Field(default=0, description="Total file size (bytes)")
 
@@ -74,14 +69,6 @@ class DownloadJob(BaseModel):
     _on_cancelled: Optional[DownloadEventHandler] = PrivateAttr(default=None)
     _on_error: Optional[DownloadExceptionHandler] = PrivateAttr(default=None)
 
-    def __hash__(self) -> int:
-        """Return hash of the string representation of this object, for indexing."""
-        return hash(str(self))
-
-    def __le__(self, other: "DownloadJob") -> bool:
-        """Return True if this job's priority is less than another's."""
-        return self.priority <= other.priority
-
     def cancel(self) -> None:
         """Call to cancel the job."""
         self._cancelled = True
@@ -98,6 +85,11 @@ class DownloadJob(BaseModel):
         """Return true if job completed without errors."""
         return self.status == DownloadJobStatus.COMPLETED
 
+    @property
+    def waiting(self) -> bool:
+        """Return true if the job is waiting to run."""
+        return self.status == DownloadJobStatus.WAITING
+
     @property
     def running(self) -> bool:
         """Return true if the job is running."""
@@ -154,6 +146,37 @@ class DownloadJob(BaseModel):
         self._on_cancelled = on_cancelled
 
 
+@total_ordering
+class DownloadJob(DownloadJobBase):
+    """Class to monitor and control a model download request."""
+
+    # required variables to be passed in on creation
+    source: AnyHttpUrl = Field(description="Where to download from. Specific types specified in child classes.")
+    access_token: Optional[str] = Field(default=None, description="authorization token for protected resources")
+    priority: int = Field(default=10, description="Queue priority; lower values are higher priority")
+
+    # set internally during download process
+    job_started: Optional[str] = Field(default=None, description="Timestamp for when the download job started")
+    job_ended: Optional[str] = Field(
+        default=None, description="Timestamp for when the download job ende1d (completed or errored)"
+    )
+    content_type: Optional[str] = Field(default=None, description="Content type of downloaded file")
+
+    def __hash__(self) -> int:
+        """Return hash of the string representation of this object, for indexing."""
+        return hash(str(self))
+
+    def __le__(self, other: "DownloadJob") -> bool:
+        """Return True if this job's priority is less than another's."""
+        return self.priority <= other.priority
+
+
+class MultiFileDownloadJob(DownloadJobBase):
+    """Class to monitor and control multifile downloads."""
+
+    download_parts: Set[DownloadJob] = Field(default_factory=set, description="List of download parts.")
+
+
 class DownloadQueueServiceBase(ABC):
     """Multithreaded queue for downloading models via URL."""
 
@@ -201,6 +224,48 @@ class DownloadQueueServiceBase(ABC):
         """
         pass
 
+    @abstractmethod
+    def multifile_download(
+        self,
+        parts: List[RemoteModelFile],
+        dest: Path,
+        access_token: Optional[str] = None,
+        submit_job: bool = True,
+        on_start: Optional[DownloadEventHandler] = None,
+        on_progress: Optional[DownloadEventHandler] = None,
+        on_complete: Optional[DownloadEventHandler] = None,
+        on_cancelled: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadExceptionHandler] = None,
+    ) -> MultiFileDownloadJob:
+        """
+        Create and enqueue a multifile download job.
+
+        :param parts: Set of URL / filename pairs
+        :param dest: Path to download to. See below.
+        :param access_token: Access token to download the indicated files. If not provided,
+         each file's URL may be matched to an access token using the config file matching
+         system.
+        :param submit_job: If true [default] then submit the job for execution. Otherwise,
+         you will need to pass the job to submit_multifile_download().
+        :param on_start, on_progress, on_complete, on_error: Callbacks for the indicated
+         events.
+        :returns: A MultiFileDownloadJob object for monitoring the state of the download.
+
+        The `dest` argument is a Path object pointing to a directory. All downloads
+        with be placed inside this directory. The callbacks will receive the
+        MultiFileDownloadJob.
+        """
+        pass
+
+    @abstractmethod
+    def submit_multifile_download(self, job: MultiFileDownloadJob) -> None:
+        """
+        Enqueue a previously-created multi-file download job.
+
+        :param job: A MultiFileDownloadJob created with multifile_download()
+        """
+        pass
+
     @abstractmethod
     def submit_download_job(
         self,
@@ -252,7 +317,7 @@ class DownloadQueueServiceBase(ABC):
         pass
 
     @abstractmethod
-    def cancel_job(self, job: DownloadJob) -> None:
+    def cancel_job(self, job: DownloadJobBase) -> None:
         """Cancel the job, clearing partial downloads and putting it into ERROR state."""
         pass
 
@@ -262,7 +327,7 @@ class DownloadQueueServiceBase(ABC):
         pass
 
     @abstractmethod
-    def wait_for_job(self, job: DownloadJob, timeout: int = 0) -> DownloadJob:
+    def wait_for_job(self, job: DownloadJobBase, timeout: int = 0) -> DownloadJobBase:
         """Wait until the indicated download job has reached a terminal state.
 
         This will block until the indicated install job has completed,

invokeai/app/services/download/download_default.py

@@ -8,30 +8,32 @@ import time
 import traceback
 from pathlib import Path
 from queue import Empty, PriorityQueue
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set
+from typing import Any, Dict, List, Literal, Optional, Set
 
 import requests
 from pydantic.networks import AnyHttpUrl
 from requests import HTTPError
 from tqdm import tqdm
 
+from invokeai.app.services.config import InvokeAIAppConfig, get_config
+from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.util.misc import get_iso_timestamp
+from invokeai.backend.model_manager.metadata import RemoteModelFile
 from invokeai.backend.util.logging import InvokeAILogger
 
 from .download_base import (
     DownloadEventHandler,
     DownloadExceptionHandler,
     DownloadJob,
+    DownloadJobBase,
     DownloadJobCancelledException,
     DownloadJobStatus,
     DownloadQueueServiceBase,
+    MultiFileDownloadJob,
     ServiceInactiveException,
     UnknownJobIDException,
 )
 
-if TYPE_CHECKING:
-    from invokeai.app.services.events.events_base import EventServiceBase
-
 # Maximum number of bytes to download during each call to requests.iter_content()
 DOWNLOAD_CHUNK_SIZE = 100000
 
@@ -42,20 +44,24 @@ class DownloadQueueService(DownloadQueueServiceBase):
     def __init__(
         self,
         max_parallel_dl: int = 5,
+        app_config: Optional[InvokeAIAppConfig] = None,
         event_bus: Optional["EventServiceBase"] = None,
         requests_session: Optional[requests.sessions.Session] = None,
     ):
         """
         Initialize DownloadQueue.
 
+        :param app_config: InvokeAIAppConfig object
         :param max_parallel_dl: Number of simultaneous downloads allowed [5].
         :param requests_session: Optional requests.sessions.Session object, for unit tests.
         """
+        self._app_config = app_config or get_config()
         self._jobs: Dict[int, DownloadJob] = {}
+        self._download_part2parent: Dict[AnyHttpUrl, MultiFileDownloadJob] = {}
         self._next_job_id = 0
         self._queue: PriorityQueue[DownloadJob] = PriorityQueue()
         self._stop_event = threading.Event()
-        self._job_completed_event = threading.Event()
+        self._job_terminated_event = threading.Event()
         self._worker_pool: Set[threading.Thread] = set()
         self._lock = threading.Lock()
         self._logger = InvokeAILogger.get_logger("DownloadQueueService")
@@ -107,9 +113,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
             raise ServiceInactiveException(
                 "The download service is not currently accepting requests. Please call start() to initialize the service."
             )
-        with self._lock:
+        job.id = self._next_id()
-            job.id = self._next_job_id
-            self._next_job_id += 1
         job.set_callbacks(
             on_start=on_start,
             on_progress=on_progress,
@@ -141,7 +145,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
             source=source,
             dest=dest,
             priority=priority,
-            access_token=access_token,
+            access_token=access_token or self._lookup_access_token(source),
         )
         self.submit_download_job(
             job,
@@ -153,10 +157,63 @@ class DownloadQueueService(DownloadQueueServiceBase):
         )
         return job
 
+    def multifile_download(
+        self,
+        parts: List[RemoteModelFile],
+        dest: Path,
+        access_token: Optional[str] = None,
+        submit_job: bool = True,
+        on_start: Optional[DownloadEventHandler] = None,
+        on_progress: Optional[DownloadEventHandler] = None,
+        on_complete: Optional[DownloadEventHandler] = None,
+        on_cancelled: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadExceptionHandler] = None,
+    ) -> MultiFileDownloadJob:
+        mfdj = MultiFileDownloadJob(dest=dest, id=self._next_id())
+        mfdj.set_callbacks(
+            on_start=on_start,
+            on_progress=on_progress,
+            on_complete=on_complete,
+            on_cancelled=on_cancelled,
+            on_error=on_error,
+        )
+
+        for part in parts:
+            url = part.url
+            path = dest / part.path
+            assert path.is_relative_to(dest), "only relative download paths accepted"
+            job = DownloadJob(
+                source=url,
+                dest=path,
+                access_token=access_token,
+            )
+            mfdj.download_parts.add(job)
+            self._download_part2parent[job.source] = mfdj
+        if submit_job:
+            self.submit_multifile_download(mfdj)
+        return mfdj
+
+    def submit_multifile_download(self, job: MultiFileDownloadJob) -> None:
+        for download_job in job.download_parts:
+            self.submit_download_job(
+                download_job,
+                on_start=self._mfd_started,
+                on_progress=self._mfd_progress,
+                on_complete=self._mfd_complete,
+                on_cancelled=self._mfd_cancelled,
+                on_error=self._mfd_error,
+            )
+
     def join(self) -> None:
         """Wait for all jobs to complete."""
         self._queue.join()
 
+    def _next_id(self) -> int:
+        with self._lock:
+            id = self._next_job_id
+            self._next_job_id += 1
+        return id
+
     def list_jobs(self) -> List[DownloadJob]:
         """List all the jobs."""
         return list(self._jobs.values())
@@ -178,14 +235,14 @@ class DownloadQueueService(DownloadQueueServiceBase):
         except KeyError as excp:
             raise UnknownJobIDException("Unrecognized job") from excp
 
-    def cancel_job(self, job: DownloadJob) -> None:
+    def cancel_job(self, job: DownloadJobBase) -> None:
         """
         Cancel the indicated job.
 
         If it is running it will be stopped.
         job.status will be set to DownloadJobStatus.CANCELLED
         """
-        with self._lock:
+        if job.status in [DownloadJobStatus.WAITING, DownloadJobStatus.RUNNING]:
             job.cancel()
 
     def cancel_all_jobs(self) -> None:
@@ -194,12 +251,12 @@ class DownloadQueueService(DownloadQueueServiceBase):
             if not job.in_terminal_state:
                 self.cancel_job(job)
 
-    def wait_for_job(self, job: DownloadJob, timeout: int = 0) -> DownloadJob:
+    def wait_for_job(self, job: DownloadJobBase, timeout: int = 0) -> DownloadJobBase:
         """Block until the indicated job has reached terminal state, or when timeout limit reached."""
         start = time.time()
         while not job.in_terminal_state:
-            if self._job_completed_event.wait(timeout=0.25):  # in case we miss an event
+            if self._job_terminated_event.wait(timeout=0.25):  # in case we miss an event
-                self._job_completed_event.clear()
+                self._job_terminated_event.clear()
             if timeout > 0 and time.time() - start > timeout:
                 raise TimeoutError("Timeout exceeded")
         return job
@@ -228,22 +285,25 @@ class DownloadQueueService(DownloadQueueServiceBase):
                 job.job_started = get_iso_timestamp()
                 self._do_download(job)
                 self._signal_job_complete(job)
-            except (OSError, HTTPError) as excp:
-                job.error_type = excp.__class__.__name__ + f"({str(excp)})"
-                job.error = traceback.format_exc()
-                self._signal_job_error(job, excp)
             except DownloadJobCancelledException:
                 self._signal_job_cancelled(job)
                 self._cleanup_cancelled_job(job)
-
+            except Exception as excp:
+                job.error_type = excp.__class__.__name__ + f"({str(excp)})"
+                job.error = traceback.format_exc()
+                self._signal_job_error(job, excp)
             finally:
                 job.job_ended = get_iso_timestamp()
-                self._job_completed_event.set()  # signal a change to terminal state
+                self._job_terminated_event.set()  # signal a change to terminal state
+                self._download_part2parent.pop(job.source, None)  # if this is a subpart of a multipart job, remove it
+                self._job_terminated_event.set()
                 self._queue.task_done()
+
         self._logger.debug(f"Download queue worker thread {threading.current_thread().name} exiting.")
 
     def _do_download(self, job: DownloadJob) -> None:
         """Do the actual download."""
+
         url = job.source
         header = {"Authorization": f"Bearer {job.access_token}"} if job.access_token else {}
         open_mode = "wb"
@@ -335,38 +395,29 @@ class DownloadQueueService(DownloadQueueServiceBase):
     def _in_progress_path(self, path: Path) -> Path:
         return path.with_name(path.name + ".downloading")
 
+    def _lookup_access_token(self, source: AnyHttpUrl) -> Optional[str]:
+        # Pull the token from config if it exists and matches the URL
+        token = None
+        for pair in self._app_config.remote_api_tokens or []:
+            if re.search(pair.url_regex, str(source)):
+                token = pair.token
+                break
+        return token
+
     def _signal_job_started(self, job: DownloadJob) -> None:
         job.status = DownloadJobStatus.RUNNING
-        if job.on_start:
+        self._execute_cb(job, "on_start")
-            try:
-                job.on_start(job)
-            except Exception as e:
-                self._logger.error(
-                    f"An error occurred while processing the on_start callback: {traceback.format_exception(e)}"
-                )
         if self._event_bus:
             self._event_bus.emit_download_started(job)
 
     def _signal_job_progress(self, job: DownloadJob) -> None:
-        if job.on_progress:
+        self._execute_cb(job, "on_progress")
-            try:
-                job.on_progress(job)
-            except Exception as e:
-                self._logger.error(
-                    f"An error occurred while processing the on_progress callback: {traceback.format_exception(e)}"
-                )
         if self._event_bus:
             self._event_bus.emit_download_progress(job)
 
     def _signal_job_complete(self, job: DownloadJob) -> None:
         job.status = DownloadJobStatus.COMPLETED
-        if job.on_complete:
+        self._execute_cb(job, "on_complete")
-            try:
-                job.on_complete(job)
-            except Exception as e:
-                self._logger.error(
-                    f"An error occurred while processing the on_complete callback: {traceback.format_exception(e)}"
-                )
         if self._event_bus:
             self._event_bus.emit_download_complete(job)
 
@@ -374,26 +425,21 @@ class DownloadQueueService(DownloadQueueServiceBase):
         if job.status not in [DownloadJobStatus.RUNNING, DownloadJobStatus.WAITING]:
             return
         job.status = DownloadJobStatus.CANCELLED
-        if job.on_cancelled:
+        self._execute_cb(job, "on_cancelled")
-            try:
-                job.on_cancelled(job)
-            except Exception as e:
-                self._logger.error(
-                    f"An error occurred while processing the on_cancelled callback: {traceback.format_exception(e)}"
-                )
         if self._event_bus:
             self._event_bus.emit_download_cancelled(job)
 
+        # if multifile download, then signal the parent
+        if parent_job := self._download_part2parent.get(job.source, None):
+            if not parent_job.in_terminal_state:
+                parent_job.status = DownloadJobStatus.CANCELLED
+                self._execute_cb(parent_job, "on_cancelled")
+
     def _signal_job_error(self, job: DownloadJob, excp: Optional[Exception] = None) -> None:
         job.status = DownloadJobStatus.ERROR
         self._logger.error(f"{str(job.source)}: {traceback.format_exception(excp)}")
-        if job.on_error:
+        self._execute_cb(job, "on_error", excp)
-            try:
+
-                job.on_error(job, excp)
-            except Exception as e:
-                self._logger.error(
-                    f"An error occurred while processing the on_error callback: {traceback.format_exception(e)}"
-                )
         if self._event_bus:
             self._event_bus.emit_download_error(job)
 
@@ -406,6 +452,97 @@ class DownloadQueueService(DownloadQueueServiceBase):
         except OSError as excp:
             self._logger.warning(excp)
 
+    ########################################
+    # callbacks used for multifile downloads
+    ########################################
+    def _mfd_started(self, download_job: DownloadJob) -> None:
+        self._logger.info(f"File download started: {download_job.source}")
+        with self._lock:
+            mf_job = self._download_part2parent[download_job.source]
+            if mf_job.waiting:
+                mf_job.total_bytes = sum(x.total_bytes for x in mf_job.download_parts)
+                mf_job.status = DownloadJobStatus.RUNNING
+                assert download_job.download_path is not None
+                path_relative_to_destdir = download_job.download_path.relative_to(mf_job.dest)
+                mf_job.download_path = (
+                    mf_job.dest / path_relative_to_destdir.parts[0]
+                )  # keep just the first component of the path
+                self._execute_cb(mf_job, "on_start")
+
+    def _mfd_progress(self, download_job: DownloadJob) -> None:
+        with self._lock:
+            mf_job = self._download_part2parent[download_job.source]
+            if mf_job.cancelled:
+                for part in mf_job.download_parts:
+                    self.cancel_job(part)
+            elif mf_job.running:
+                mf_job.total_bytes = sum(x.total_bytes for x in mf_job.download_parts)
+                mf_job.bytes = sum(x.total_bytes for x in mf_job.download_parts)
+                self._execute_cb(mf_job, "on_progress")
+
+    def _mfd_complete(self, download_job: DownloadJob) -> None:
+        self._logger.info(f"Download complete: {download_job.source}")
+        with self._lock:
+            mf_job = self._download_part2parent[download_job.source]
+
+            # are there any more active jobs left in this task?
+            if mf_job.running and all(x.complete for x in mf_job.download_parts):
+                mf_job.status = DownloadJobStatus.COMPLETED
+                self._execute_cb(mf_job, "on_complete")
+
+            # we're done with this sub-job
+            self._job_terminated_event.set()
+
+    def _mfd_cancelled(self, download_job: DownloadJob) -> None:
+        with self._lock:
+            mf_job = self._download_part2parent[download_job.source]
+            assert mf_job is not None
+
+            if not mf_job.in_terminal_state:
+                self._logger.warning(f"Download cancelled: {download_job.source}")
+                mf_job.cancel()
+
+            for s in mf_job.download_parts:
+                self.cancel_job(s)
+
+    def _mfd_error(self, download_job: DownloadJob, excp: Optional[Exception] = None) -> None:
+        with self._lock:
+            mf_job = self._download_part2parent[download_job.source]
+            assert mf_job is not None
+            if not mf_job.in_terminal_state:
+                mf_job.status = download_job.status
+                mf_job.error = download_job.error
+                mf_job.error_type = download_job.error_type
+                self._execute_cb(mf_job, "on_error", excp)
+                self._logger.error(
+                    f"Cancelling {mf_job.dest} due to an error while downloading {download_job.source}: {str(excp)}"
+                )
+                for s in [x for x in mf_job.download_parts if x.running]:
+                    self.cancel_job(s)
+                self._download_part2parent.pop(download_job.source)
+                self._job_terminated_event.set()
+
+    def _execute_cb(
+        self,
+        job: DownloadJob | MultiFileDownloadJob,
+        callback_name: Literal[
+            "on_start",
+            "on_progress",
+            "on_complete",
+            "on_cancelled",
+            "on_error",
+        ],
+        excp: Optional[Exception] = None,
+    ) -> None:
+        if callback := getattr(job, callback_name, None):
+            args = [job, excp] if excp else [job]
+            try:
+                callback(*args)
+            except Exception as e:
+                self._logger.error(
+                    f"An error occurred while processing the {callback_name} callback: {traceback.format_exception(e)}"
+                )
+
 
 def get_pc_name_max(directory: str) -> int:
     if hasattr(os, "pathconf"):

invokeai/app/services/events/events_base.py

@@ -22,6 +22,7 @@ from invokeai.app.services.events.events_common import (
     ModelInstallCompleteEvent,
     ModelInstallDownloadProgressEvent,
     ModelInstallDownloadsCompleteEvent,
+    ModelInstallDownloadStartedEvent,
     ModelInstallErrorEvent,
     ModelInstallStartedEvent,
     ModelLoadCompleteEvent,
@@ -34,7 +35,6 @@ from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineInterme
 if TYPE_CHECKING:
     from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
     from invokeai.app.services.download.download_base import DownloadJob
-    from invokeai.app.services.events.events_common import EventBase
     from invokeai.app.services.model_install.model_install_common import ModelInstallJob
     from invokeai.app.services.session_processor.session_processor_common import ProgressImage
     from invokeai.app.services.session_queue.session_queue_common import (
@@ -145,6 +145,10 @@ class EventServiceBase:
 
     # region Model install
 
+    def emit_model_install_download_started(self, job: "ModelInstallJob") -> None:
+        """Emitted at intervals while the install job is started (remote models only)."""
+        self.dispatch(ModelInstallDownloadStartedEvent.build(job))
+
     def emit_model_install_download_progress(self, job: "ModelInstallJob") -> None:
         """Emitted at intervals while the install job is in progress (remote models only)."""
         self.dispatch(ModelInstallDownloadProgressEvent.build(job))

invokeai/app/services/events/events_common.py

@@ -417,6 +417,42 @@ class ModelLoadCompleteEvent(ModelEventBase):
         return cls(config=config, submodel_type=submodel_type)
 
 
+@payload_schema.register
+class ModelInstallDownloadStartedEvent(ModelEventBase):
+    """Event model for model_install_download_started"""
+
+    __event_name__ = "model_install_download_started"
+
+    id: int = Field(description="The ID of the install job")
+    source: str = Field(description="Source of the model; local path, repo_id or url")
+    local_path: str = Field(description="Where model is downloading to")
+    bytes: int = Field(description="Number of bytes downloaded so far")
+    total_bytes: int = Field(description="Total size of download, including all files")
+    parts: list[dict[str, int | str]] = Field(
+        description="Progress of downloading URLs that comprise the model, if any"
+    )
+
+    @classmethod
+    def build(cls, job: "ModelInstallJob") -> "ModelInstallDownloadStartedEvent":
+        parts: list[dict[str, str | int]] = [
+            {
+                "url": str(x.source),
+                "local_path": str(x.download_path),
+                "bytes": x.bytes,
+                "total_bytes": x.total_bytes,
+            }
+            for x in job.download_parts
+        ]
+        return cls(
+            id=job.id,
+            source=str(job.source),
+            local_path=job.local_path.as_posix(),
+            parts=parts,
+            bytes=job.bytes,
+            total_bytes=job.total_bytes,
+        )
+
+
 @payload_schema.register
 class ModelInstallDownloadProgressEvent(ModelEventBase):
     """Event model for model_install_download_progress"""

invokeai/app/services/model_install/model_install_base.py

@@ -13,7 +13,7 @@ from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_install.model_install_common import ModelInstallJob, ModelSource
 from invokeai.app.services.model_records import ModelRecordServiceBase
-from invokeai.backend.model_manager.config import AnyModelConfig
+from invokeai.backend.model_manager import AnyModelConfig
 
 
 class ModelInstallServiceBase(ABC):
@@ -243,12 +243,11 @@ class ModelInstallServiceBase(ABC):
         """
 
     @abstractmethod
-    def download_and_cache(self, source: Union[str, AnyHttpUrl], access_token: Optional[str] = None) -> Path:
+    def download_and_cache_model(self, source: str | AnyHttpUrl) -> Path:
         """
         Download the model file located at source to the models cache and return its Path.
 
-        :param source: A Url or a string that can be converted into one.
+        :param source: A string representing a URL or repo_id.
-        :param access_token: Optional access token to access restricted resources.
 
         The model file will be downloaded into the system-wide model cache
         (`models/.cache`) if it isn't already there. Note that the model cache

invokeai/app/services/model_install/model_install_common.py

@@ -8,7 +8,7 @@ from pydantic import BaseModel, Field, PrivateAttr, field_validator
 from pydantic.networks import AnyHttpUrl
 from typing_extensions import Annotated
 
-from invokeai.app.services.download import DownloadJob
+from invokeai.app.services.download import DownloadJob, MultiFileDownloadJob
 from invokeai.backend.model_manager import AnyModelConfig, ModelRepoVariant
 from invokeai.backend.model_manager.config import ModelSourceType
 from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
@@ -26,13 +26,6 @@ class InstallStatus(str, Enum):
     CANCELLED = "cancelled"  # terminated with an error message
 
 
-class ModelInstallPart(BaseModel):
-    url: AnyHttpUrl
-    path: Path
-    bytes: int = 0
-    total_bytes: int = 0
-
-
 class UnknownInstallJobException(Exception):
     """Raised when the status of an unknown job is requested."""
 
@@ -169,6 +162,7 @@ class ModelInstallJob(BaseModel):
     )
     # internal flags and transitory settings
     _install_tmpdir: Optional[Path] = PrivateAttr(default=None)
+    _multifile_job: Optional[MultiFileDownloadJob] = PrivateAttr(default=None)
     _exception: Optional[Exception] = PrivateAttr(default=None)
 
     def set_error(self, e: Exception) -> None:

invokeai/app/services/model_install/model_install_default.py

@@ -5,21 +5,22 @@ import os
 import re
 import threading
 import time
-from hashlib import sha256
 from pathlib import Path
 from queue import Empty, Queue
 from shutil import copyfile, copytree, move, rmtree
 from tempfile import mkdtemp
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
 
 import torch
 import yaml
 from huggingface_hub import HfFolder
 from pydantic.networks import AnyHttpUrl
+from pydantic_core import Url
 from requests import Session
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.download import DownloadJob, DownloadQueueServiceBase, TqdmProgress
+from invokeai.app.services.download import DownloadQueueServiceBase, MultiFileDownloadJob
+from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_install.model_install_base import ModelInstallServiceBase
 from invokeai.app.services.model_records import DuplicateModelException, ModelRecordServiceBase
@@ -44,6 +45,7 @@ from invokeai.backend.model_manager.search import ModelSearch
 from invokeai.backend.util import InvokeAILogger
 from invokeai.backend.util.catch_sigint import catch_sigint
 from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.util import slugify
 
 from .model_install_common import (
     MODEL_SOURCE_TO_TYPE_MAP,
@@ -58,9 +60,6 @@ from .model_install_common import (
 
 TMPDIR_PREFIX = "tmpinstall_"
 
-if TYPE_CHECKING:
-    from invokeai.app.services.events.events_base import EventServiceBase
-
 
 class ModelInstallService(ModelInstallServiceBase):
     """class for InvokeAI model installation."""
@@ -91,7 +90,7 @@ class ModelInstallService(ModelInstallServiceBase):
         self._downloads_changed_event = threading.Event()
         self._install_completed_event = threading.Event()
         self._download_queue = download_queue
-        self._download_cache: Dict[AnyHttpUrl, ModelInstallJob] = {}
+        self._download_cache: Dict[int, ModelInstallJob] = {}
         self._running = False
         self._session = session
         self._install_thread: Optional[threading.Thread] = None
@@ -210,33 +209,12 @@ class ModelInstallService(ModelInstallServiceBase):
         access_token: Optional[str] = None,
         inplace: Optional[bool] = False,
     ) -> ModelInstallJob:
-        variants = "|".join(ModelRepoVariant.__members__.values())
+        """Install a model using pattern matching to infer the type of source."""
-        hf_repoid_re = f"^([^/:]+/[^/:]+)(?::({variants})?(?::/?([^:]+))?)?$"
+        source_obj = self._guess_source(source)
-        source_obj: Optional[StringLikeSource] = None
+        if isinstance(source_obj, LocalModelSource):
-
+            source_obj.inplace = inplace
-        if Path(source).exists():  # A local file or directory
+        elif isinstance(source_obj, HFModelSource) or isinstance(source_obj, URLModelSource):
-            source_obj = LocalModelSource(path=Path(source), inplace=inplace)
+            source_obj.access_token = access_token
-        elif match := re.match(hf_repoid_re, source):
-            source_obj = HFModelSource(
-                repo_id=match.group(1),
-                variant=match.group(2) if match.group(2) else None,  # pass None rather than ''
-                subfolder=Path(match.group(3)) if match.group(3) else None,
-                access_token=access_token,
-            )
-        elif re.match(r"^https?://[^/]+", source):
-            # Pull the token from config if it exists and matches the URL
-            _token = access_token
-            if _token is None:
-                for pair in self.app_config.remote_api_tokens or []:
-                    if re.search(pair.url_regex, source):
-                        _token = pair.token
-                        break
-            source_obj = URLModelSource(
-                url=AnyHttpUrl(source),
-                access_token=_token,
-            )
-        else:
-            raise ValueError(f"Unsupported model source: '{source}'")
         return self.import_model(source_obj, config)
 
     def import_model(self, source: ModelSource, config: Optional[Dict[str, Any]] = None) -> ModelInstallJob:  # noqa D102
@@ -297,8 +275,9 @@ class ModelInstallService(ModelInstallServiceBase):
     def cancel_job(self, job: ModelInstallJob) -> None:
         """Cancel the indicated job."""
         job.cancel()
-        with self._lock:
+        self._logger.warning(f"Cancelling {job.source}")
-            self._cancel_download_parts(job)
+        if dj := job._multifile_job:
+            self._download_queue.cancel_job(dj)
 
     def prune_jobs(self) -> None:
         """Prune all completed and errored jobs."""
@@ -351,7 +330,7 @@ class ModelInstallService(ModelInstallServiceBase):
                     legacy_config_path = stanza.get("config")
                     if legacy_config_path:
                         # In v3, these paths were relative to the root. Migrate them to be relative to the legacy_conf_dir.
-                        legacy_config_path: Path = self._app_config.root_path / legacy_config_path
+                        legacy_config_path = self._app_config.root_path / legacy_config_path
                         if legacy_config_path.is_relative_to(self._app_config.legacy_conf_path):
                             legacy_config_path = legacy_config_path.relative_to(self._app_config.legacy_conf_path)
                         config["config_path"] = str(legacy_config_path)
@@ -392,38 +371,95 @@ class ModelInstallService(ModelInstallServiceBase):
             rmtree(model_path)
         self.unregister(key)
 
-    def download_and_cache(
+    @classmethod
+    def _download_cache_path(cls, source: Union[str, AnyHttpUrl], app_config: InvokeAIAppConfig) -> Path:
+        escaped_source = slugify(str(source))
+        return app_config.download_cache_path / escaped_source
+
+    def download_and_cache_model(
         self,
-        source: Union[str, AnyHttpUrl],
+        source: str | AnyHttpUrl,
-        access_token: Optional[str] = None,
-        timeout: int = 0,
     ) -> Path:
         """Download the model file located at source to the models cache and return its Path."""
-        model_hash = sha256(str(source).encode("utf-8")).hexdigest()[0:32]
+        model_path = self._download_cache_path(str(source), self._app_config)
-        model_path = self._app_config.convert_cache_path / model_hash
 
-        # We expect the cache directory to contain one and only one downloaded file.
+        # We expect the cache directory to contain one and only one downloaded file or directory.
         # We don't know the file's name in advance, as it is set by the download
         # content-disposition header.
         if model_path.exists():
-            contents = [x for x in model_path.iterdir() if x.is_file()]
+            contents: List[Path] = list(model_path.iterdir())
             if len(contents) > 0:
                 return contents[0]
 
         model_path.mkdir(parents=True, exist_ok=True)
-        job = self._download_queue.download(
+        model_source = self._guess_source(str(source))
-            source=AnyHttpUrl(str(source)),
+        remote_files, _ = self._remote_files_from_source(model_source)
+        job = self._multifile_download(
             dest=model_path,
-            access_token=access_token,
+            remote_files=remote_files,
-            on_progress=TqdmProgress().update,
+            subfolder=model_source.subfolder if isinstance(model_source, HFModelSource) else None,
         )
-        self._download_queue.wait_for_job(job, timeout)
+        files_string = "file" if len(remote_files) == 1 else "files"
+        self._logger.info(f"Queuing model download: {source} ({len(remote_files)} {files_string})")
+        self._download_queue.wait_for_job(job)
         if job.complete:
             assert job.download_path is not None
             return job.download_path
         else:
             raise Exception(job.error)
 
+    def _remote_files_from_source(
+        self, source: ModelSource
+    ) -> Tuple[List[RemoteModelFile], Optional[AnyModelRepoMetadata]]:
+        metadata = None
+        if isinstance(source, HFModelSource):
+            metadata = HuggingFaceMetadataFetch(self._session).from_id(source.repo_id, source.variant)
+            assert isinstance(metadata, ModelMetadataWithFiles)
+            return (
+                metadata.download_urls(
+                    variant=source.variant or self._guess_variant(),
+                    subfolder=source.subfolder,
+                    session=self._session,
+                ),
+                metadata,
+            )
+
+        if isinstance(source, URLModelSource):
+            try:
+                fetcher = self.get_fetcher_from_url(str(source.url))
+                kwargs: dict[str, Any] = {"session": self._session}
+                metadata = fetcher(**kwargs).from_url(source.url)
+                assert isinstance(metadata, ModelMetadataWithFiles)
+                return metadata.download_urls(session=self._session), metadata
+            except ValueError:
+                pass
+
+            return [RemoteModelFile(url=source.url, path=Path("."), size=0)], None
+
+        raise Exception(f"No files associated with {source}")
+
+    def _guess_source(self, source: str) -> ModelSource:
+        """Turn a source string into a ModelSource object."""
+        variants = "|".join(ModelRepoVariant.__members__.values())
+        hf_repoid_re = f"^([^/:]+/[^/:]+)(?::({variants})?(?::/?([^:]+))?)?$"
+        source_obj: Optional[StringLikeSource] = None
+
+        if Path(source).exists():  # A local file or directory
+            source_obj = LocalModelSource(path=Path(source))
+        elif match := re.match(hf_repoid_re, source):
+            source_obj = HFModelSource(
+                repo_id=match.group(1),
+                variant=ModelRepoVariant(match.group(2)) if match.group(2) else None,  # pass None rather than ''
+                subfolder=Path(match.group(3)) if match.group(3) else None,
+            )
+        elif re.match(r"^https?://[^/]+", source):
+            source_obj = URLModelSource(
+                url=Url(source),
+            )
+        else:
+            raise ValueError(f"Unsupported model source: '{source}'")
+        return source_obj
+
     # --------------------------------------------------------------------------------------------
     # Internal functions that manage the installer threads
     # --------------------------------------------------------------------------------------------
@@ -484,16 +520,19 @@ class ModelInstallService(ModelInstallServiceBase):
         job.config_out = self.record_store.get_model(key)
         self._signal_job_completed(job)
 
-    def _set_error(self, job: ModelInstallJob, excp: Exception) -> None:
+    def _set_error(self, install_job: ModelInstallJob, excp: Exception) -> None:
-        if any(x.content_type is not None and "text/html" in x.content_type for x in job.download_parts):
+        multifile_download_job = install_job._multifile_job
-            job.set_error(
+        if multifile_download_job and any(
+            x.content_type is not None and "text/html" in x.content_type for x in multifile_download_job.download_parts
+        ):
+            install_job.set_error(
                 InvalidModelConfigException(
-                    f"At least one file in {job.local_path} is an HTML page, not a model. This can happen when an access token is required to download."
+                    f"At least one file in {install_job.local_path} is an HTML page, not a model. This can happen when an access token is required to download."
                 )
             )
         else:
-            job.set_error(excp)
+            install_job.set_error(excp)
-        self._signal_job_errored(job)
+        self._signal_job_errored(install_job)
 
     # --------------------------------------------------------------------------------------------
     # Internal functions that manage the models directory
@@ -519,7 +558,6 @@ class ModelInstallService(ModelInstallServiceBase):
         This is typically only used during testing with a new DB or when using the memory DB, because those are the
         only situations in which we may have orphaned models in the models directory.
         """
-
         installed_model_paths = {
             (self._app_config.models_path / x.path).resolve() for x in self.record_store.all_models()
         }
@@ -531,7 +569,12 @@ class ModelInstallService(ModelInstallServiceBase):
             if resolved_path in installed_model_paths:
                 return True
             # Skip core models entirely - these aren't registered with the model manager.
-            if str(resolved_path).startswith(str(self.app_config.models_path / "core")):
+            for special_directory in [
+                self.app_config.models_path / "core",
+                self.app_config.convert_cache_dir,
+                self.app_config.download_cache_dir,
+            ]:
+                if resolved_path.is_relative_to(special_directory):
                     return False
             try:
                 model_id = self.register_path(model_path)
@@ -647,20 +690,15 @@ class ModelInstallService(ModelInstallServiceBase):
             inplace=source.inplace or False,
         )
 
-    def _import_from_hf(self, source: HFModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
+    def _import_from_hf(
+        self,
+        source: HFModelSource,
+        config: Optional[Dict[str, Any]] = None,
+    ) -> ModelInstallJob:
         # Add user's cached access token to HuggingFace requests
-        source.access_token = source.access_token or HfFolder.get_token()
+        if source.access_token is None:
-        if not source.access_token:
+            source.access_token = HfFolder.get_token()
-            self._logger.info("No HuggingFace access token present; some models may not be downloadable.")
+        remote_files, metadata = self._remote_files_from_source(source)
-
-        metadata = HuggingFaceMetadataFetch(self._session).from_id(source.repo_id, source.variant)
-        assert isinstance(metadata, ModelMetadataWithFiles)
-        remote_files = metadata.download_urls(
-            variant=source.variant or self._guess_variant(),
-            subfolder=source.subfolder,
-            session=self._session,
-        )
-
         return self._import_remote_model(
             source=source,
             config=config,
@@ -668,22 +706,12 @@ class ModelInstallService(ModelInstallServiceBase):
             metadata=metadata,
         )
 
-    def _import_from_url(self, source: URLModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
+    def _import_from_url(
-        # URLs from HuggingFace will be handled specially
+        self,
-        metadata = None
+        source: URLModelSource,
-        fetcher = None
+        config: Optional[Dict[str, Any]],
-        try:
+    ) -> ModelInstallJob:
-            fetcher = self.get_fetcher_from_url(str(source.url))
+        remote_files, metadata = self._remote_files_from_source(source)
-        except ValueError:
-            pass
-        kwargs: dict[str, Any] = {"session": self._session}
-        if fetcher is not None:
-            metadata = fetcher(**kwargs).from_url(source.url)
-        self._logger.debug(f"metadata={metadata}")
-        if metadata and isinstance(metadata, ModelMetadataWithFiles):
-            remote_files = metadata.download_urls(session=self._session)
-        else:
-            remote_files = [RemoteModelFile(url=source.url, path=Path("."), size=0)]
         return self._import_remote_model(
             source=source,
             config=config,
@@ -698,12 +726,9 @@ class ModelInstallService(ModelInstallServiceBase):
         metadata: Optional[AnyModelRepoMetadata],
         config: Optional[Dict[str, Any]],
     ) -> ModelInstallJob:
-        # TODO: Replace with tempfile.tmpdir() when multithreading is cleaned up.
-        # Currently the tmpdir isn't automatically removed at exit because it is
-        # being held in a daemon thread.
         if len(remote_files) == 0:
             raise ValueError(f"{source}: No downloadable files found")
-        tmpdir = Path(
+        destdir = Path(
             mkdtemp(
                 dir=self._app_config.models_path,
                 prefix=TMPDIR_PREFIX,
@@ -714,55 +739,28 @@ class ModelInstallService(ModelInstallServiceBase):
             source=source,
             config_in=config or {},
             source_metadata=metadata,
-            local_path=tmpdir,  # local path may change once the download has started due to content-disposition handling
+            local_path=destdir,  # local path may change once the download has started due to content-disposition handling
             bytes=0,
             total_bytes=0,
         )
-        # In the event that there is a subfolder specified in the source,
+        # remember the temporary directory for later removal
-        # we need to remove it from the destination path in order to avoid
+        install_job._install_tmpdir = destdir
-        # creating unwanted subfolders
+        install_job.total_bytes = sum((x.size or 0) for x in remote_files)
-        if isinstance(source, HFModelSource) and source.subfolder:
-            root = Path(remote_files[0].path.parts[0])
-            subfolder = root / source.subfolder
-        else:
-            root = Path(".")
-            subfolder = Path(".")
 
-        # we remember the path up to the top of the tmpdir so that it may be
+        multifile_job = self._multifile_download(
-        # removed safely at the end of the install process.
+            remote_files=remote_files,
-        install_job._install_tmpdir = tmpdir
+            dest=destdir,
-        assert install_job.total_bytes is not None  # to avoid type checking complaints in the loop below
+            subfolder=source.subfolder if isinstance(source, HFModelSource) else None,
-
-        files_string = "file" if len(remote_files) == 1 else "file"
-        self._logger.info(f"Queuing model install: {source} ({len(remote_files)} {files_string})")
-        self._logger.debug(f"remote_files={remote_files}")
-        for model_file in remote_files:
-            url = model_file.url
-            path = root / model_file.path.relative_to(subfolder)
-            self._logger.debug(f"Downloading {url} => {path}")
-            install_job.total_bytes += model_file.size
-            assert hasattr(source, "access_token")
-            dest = tmpdir / path.parent
-            dest.mkdir(parents=True, exist_ok=True)
-            download_job = DownloadJob(
-                source=url,
-                dest=dest,
             access_token=source.access_token,
+            submit_job=False,  # Important! Don't submit the job until we have set our _download_cache dict
         )
-            self._download_cache[download_job.source] = install_job  # matches a download job to an install job
+        self._download_cache[multifile_job.id] = install_job
-            install_job.download_parts.add(download_job)
+        install_job._multifile_job = multifile_job
-
-        # only start the jobs once install_job.download_parts is fully populated
-        for download_job in install_job.download_parts:
-            self._download_queue.submit_download_job(
-                download_job,
-                on_start=self._download_started_callback,
-                on_progress=self._download_progress_callback,
-                on_complete=self._download_complete_callback,
-                on_error=self._download_error_callback,
-                on_cancelled=self._download_cancelled_callback,
-            )
 
+        files_string = "file" if len(remote_files) == 1 else "files"
+        self._logger.info(f"Queueing model install: {source} ({len(remote_files)} {files_string})")
+        self._logger.debug(f"remote_files={remote_files}")
+        self._download_queue.submit_multifile_download(multifile_job)
         return install_job
 
     def _stat_size(self, path: Path) -> int:
@@ -774,88 +772,105 @@ class ModelInstallService(ModelInstallServiceBase):
                 size += sum(self._stat_size(Path(root, x)) for x in files)
         return size
 
+    def _multifile_download(
+        self,
+        remote_files: List[RemoteModelFile],
+        dest: Path,
+        subfolder: Optional[Path] = None,
+        access_token: Optional[str] = None,
+        submit_job: bool = True,
+    ) -> MultiFileDownloadJob:
+        # HuggingFace repo subfolders are a little tricky. If the name of the model is "sdxl-turbo", and
+        # we are installing the "vae" subfolder, we do not want to create an additional folder level, such
+        # as "sdxl-turbo/vae", nor do we want to put the contents of the vae folder directly into "sdxl-turbo".
+        # So what we do is to synthesize a folder named "sdxl-turbo_vae" here.
+        if subfolder:
+            top = Path(remote_files[0].path.parts[0])  # e.g. "sdxl-turbo/"
+            path_to_remove = top / subfolder.parts[-1]  # sdxl-turbo/vae/
+            path_to_add = Path(f"{top}_{subfolder}")
+        else:
+            path_to_remove = Path(".")
+            path_to_add = Path(".")
+
+        parts: List[RemoteModelFile] = []
+        for model_file in remote_files:
+            assert model_file.size is not None
+            parts.append(
+                RemoteModelFile(
+                    url=model_file.url,  # if a subfolder, then sdxl-turbo_vae/config.json
+                    path=path_to_add / model_file.path.relative_to(path_to_remove),
+                )
+            )
+
+        return self._download_queue.multifile_download(
+            parts=parts,
+            dest=dest,
+            access_token=access_token,
+            submit_job=submit_job,
+            on_start=self._download_started_callback,
+            on_progress=self._download_progress_callback,
+            on_complete=self._download_complete_callback,
+            on_error=self._download_error_callback,
+            on_cancelled=self._download_cancelled_callback,
+        )
+
     # ------------------------------------------------------------------
     # Callbacks are executed by the download queue in a separate thread
     # ------------------------------------------------------------------
-    def _download_started_callback(self, download_job: DownloadJob) -> None:
+    def _download_started_callback(self, download_job: MultiFileDownloadJob) -> None:
-        self._logger.info(f"Model download started: {download_job.source}")
         with self._lock:
-            install_job = self._download_cache[download_job.source]
+            if install_job := self._download_cache.get(download_job.id, None):
                 install_job.status = InstallStatus.DOWNLOADING
 
+                if install_job.local_path == install_job._install_tmpdir:  # first time
                     assert download_job.download_path
-            if install_job.local_path == install_job._install_tmpdir:
+                    install_job.local_path = download_job.download_path
-                partial_path = download_job.download_path.relative_to(install_job._install_tmpdir)
+                install_job.download_parts = download_job.download_parts
-                dest_name = partial_path.parts[0]
+                install_job.bytes = sum(x.bytes for x in download_job.download_parts)
-                install_job.local_path = install_job._install_tmpdir / dest_name
+                install_job.total_bytes = download_job.total_bytes
+                self._signal_job_download_started(install_job)
 
-            # Update the total bytes count for remote sources.
+    def _download_progress_callback(self, download_job: MultiFileDownloadJob) -> None:
-            if not install_job.total_bytes:
-                install_job.total_bytes = sum(x.total_bytes for x in install_job.download_parts)
-
-    def _download_progress_callback(self, download_job: DownloadJob) -> None:
         with self._lock:
-            install_job = self._download_cache[download_job.source]
+            if install_job := self._download_cache.get(download_job.id, None):
                 if install_job.cancelled:  # This catches the case in which the caller directly calls job.cancel()
-                self._cancel_download_parts(install_job)
+                    self._download_queue.cancel_job(download_job)
                 else:
                     # update sizes
-                install_job.bytes = sum(x.bytes for x in install_job.download_parts)
+                    install_job.bytes = sum(x.bytes for x in download_job.download_parts)
+                    install_job.total_bytes = sum(x.total_bytes for x in download_job.download_parts)
                     self._signal_job_downloading(install_job)
 
-    def _download_complete_callback(self, download_job: DownloadJob) -> None:
+    def _download_complete_callback(self, download_job: MultiFileDownloadJob) -> None:
-        self._logger.info(f"Model download complete: {download_job.source}")
         with self._lock:
-            install_job = self._download_cache[download_job.source]
+            if install_job := self._download_cache.pop(download_job.id, None):
-
-            # are there any more active jobs left in this task?
-            if install_job.downloading and all(x.complete for x in install_job.download_parts):
                 self._signal_job_downloads_done(install_job)
-                self._put_in_queue(install_job)
+                self._put_in_queue(install_job)  # this starts the installation and registration
 
                 # Let other threads know that the number of downloads has changed
-            self._download_cache.pop(download_job.source, None)
                 self._downloads_changed_event.set()
 
-    def _download_error_callback(self, download_job: DownloadJob, excp: Optional[Exception] = None) -> None:
+    def _download_error_callback(self, download_job: MultiFileDownloadJob, excp: Optional[Exception] = None) -> None:
         with self._lock:
-            install_job = self._download_cache.pop(download_job.source, None)
+            if install_job := self._download_cache.pop(download_job.id, None):
-            assert install_job is not None
                 assert excp is not None
                 install_job.set_error(excp)
-            self._logger.error(
+                self._download_queue.cancel_job(download_job)
-                f"Cancelling {install_job.source} due to an error while downloading {download_job.source}: {str(excp)}"
-            )
-            self._cancel_download_parts(install_job)
 
                 # Let other threads know that the number of downloads has changed
                 self._downloads_changed_event.set()
 
-    def _download_cancelled_callback(self, download_job: DownloadJob) -> None:
+    def _download_cancelled_callback(self, download_job: MultiFileDownloadJob) -> None:
         with self._lock:
-            install_job = self._download_cache.pop(download_job.source, None)
+            if install_job := self._download_cache.pop(download_job.id, None):
-            if not install_job:
-                return
                 self._downloads_changed_event.set()
-            self._logger.warning(f"Model download canceled: {download_job.source}")
                 # if install job has already registered an error, then do not replace its status with cancelled
                 if not install_job.errored:
                     install_job.cancel()
-            self._cancel_download_parts(install_job)
 
                 # Let other threads know that the number of downloads has changed
                 self._downloads_changed_event.set()
 
-    def _cancel_download_parts(self, install_job: ModelInstallJob) -> None:
-        # on multipart downloads, _cancel_components() will get called repeatedly from the download callbacks
-        # do not lock here because it gets called within a locked context
-        for s in install_job.download_parts:
-            self._download_queue.cancel_job(s)
-
-        if all(x.in_terminal_state for x in install_job.download_parts):
-            # When all parts have reached their terminal state, we finalize the job to clean up the temporary directory and other resources
-            self._put_in_queue(install_job)
-
     # ------------------------------------------------------------------------------------------------
     # Internal methods that put events on the event bus
     # ------------------------------------------------------------------------------------------------
@@ -865,8 +880,18 @@ class ModelInstallService(ModelInstallServiceBase):
         if self._event_bus:
             self._event_bus.emit_model_install_started(job)
 
+    def _signal_job_download_started(self, job: ModelInstallJob) -> None:
+        if self._event_bus:
+            assert job._multifile_job is not None
+            assert job.bytes is not None
+            assert job.total_bytes is not None
+            self._event_bus.emit_model_install_download_started(job)
+
     def _signal_job_downloading(self, job: ModelInstallJob) -> None:
         if self._event_bus:
+            assert job._multifile_job is not None
+            assert job.bytes is not None
+            assert job.total_bytes is not None
             self._event_bus.emit_model_install_download_progress(job)
 
     def _signal_job_downloads_done(self, job: ModelInstallJob) -> None:
@@ -881,6 +906,8 @@ class ModelInstallService(ModelInstallServiceBase):
         self._logger.info(f"Model install complete: {job.source}")
         self._logger.debug(f"{job.local_path} registered key {job.config_out.key}")
         if self._event_bus:
+            assert job.local_path is not None
+            assert job.config_out is not None
             self._event_bus.emit_model_install_complete(job)
 
     def _signal_job_errored(self, job: ModelInstallJob) -> None:
@@ -896,7 +923,13 @@ class ModelInstallService(ModelInstallServiceBase):
             self._event_bus.emit_model_install_cancelled(job)
 
     @staticmethod
-    def get_fetcher_from_url(url: str) -> ModelMetadataFetchBase:
+    def get_fetcher_from_url(url: str) -> Type[ModelMetadataFetchBase]:
+        """
+        Return a metadata fetcher appropriate for provided url.
+
+        This used to be more useful, but the number of supported model
+        sources has been reduced to HuggingFace alone.
+        """
         if re.match(r"^https?://huggingface.co/[^/]+/[^/]+$", url.lower()):
             return HuggingFaceMetadataFetch
         raise ValueError(f"Unsupported model source: '{url}'")

invokeai/app/services/model_load/model_load_base.py

@@ -2,10 +2,11 @@
 """Base class for model loader."""
 
 from abc import ABC, abstractmethod
-from typing import Optional
+from pathlib import Path
+from typing import Callable, Optional
 
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
-from invokeai.backend.model_manager.load import LoadedModel
+from invokeai.backend.model_manager.load import LoadedModel, LoadedModelWithoutConfig
 from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
 
@@ -36,3 +37,27 @@ class ModelLoadServiceBase(ABC):
     @abstractmethod
     def gpu_count(self) -> int:
         """Return the number of GPUs we are configured to use."""
+
+    @abstractmethod
+    def load_model_from_path(
+        self, model_path: Path, loader: Optional[Callable[[Path], AnyModel]] = None
+    ) -> LoadedModelWithoutConfig:
+        """
+        Load the model file or directory located at the indicated Path.
+
+        This will load an arbitrary model file into the RAM cache. If the optional loader
+        argument is provided, the loader will be invoked to load the model into
+        memory. Otherwise the method will call safetensors.torch.load_file() or
+        torch.load() as appropriate to the file suffix.
+
+        Be aware that this returns a LoadedModelWithoutConfig object, which is the same as
+        LoadedModel, but without the config attribute.
+
+        Args:
+          model_path: A pathlib.Path to a checkpoint-style models file
+          loader: A Callable that expects a Path and returns a Dict[str, Tensor]
+
+        Returns:
+          A LoadedModel object.
+        """
+

invokeai/app/services/model_load/model_load_default.py

@@ -1,18 +1,26 @@
 # Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Team
 """Implementation of model loader service."""
 
-from typing import Optional, Type
+from pathlib import Path
+from typing import Callable, Optional, Type
+
+from picklescan.scanner import scan_file_path
+from safetensors.torch import load_file as safetensors_load_file
+from torch import load as torch_load
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.invoker import Invoker
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
 from invokeai.backend.model_manager.load import (
     LoadedModel,
+    LoadedModelWithoutConfig,
     ModelLoaderRegistry,
     ModelLoaderRegistryBase,
 )
 from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 from .model_load_base import ModelLoadServiceBase
@@ -81,3 +89,41 @@ class ModelLoadService(ModelLoadServiceBase):
             self._invoker.services.events.emit_model_load_complete(model_config, submodel_type)
 
         return loaded_model
+
+    def load_model_from_path(
+        self, model_path: Path, loader: Optional[Callable[[Path], AnyModel]] = None
+    ) -> LoadedModelWithoutConfig:
+        cache_key = str(model_path)
+        ram_cache = self.ram_cache
+        try:
+            return LoadedModelWithoutConfig(_locker=ram_cache.get(key=cache_key))
+        except IndexError:
+            pass
+
+        def torch_load_file(checkpoint: Path) -> AnyModel:
+            scan_result = scan_file_path(checkpoint)
+            if scan_result.infected_files != 0:
+                raise Exception("The model at {checkpoint} is potentially infected by malware. Aborting load.")
+            result = torch_load(checkpoint, map_location="cpu")
+            return result
+
+        def diffusers_load_directory(directory: Path) -> AnyModel:
+            load_class = GenericDiffusersLoader(
+                app_config=self._app_config,
+                logger=self._logger,
+                ram_cache=self._ram_cache,
+                convert_cache=self.convert_cache,
+            ).get_hf_load_class(directory)
+            return load_class.from_pretrained(model_path, torch_dtype=TorchDevice.choose_torch_dtype())
+
+        loader = loader or (
+            diffusers_load_directory
+            if model_path.is_dir()
+            else torch_load_file
+            if model_path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin"))
+            else lambda path: safetensors_load_file(path, device="cpu")
+        )
+        assert loader is not None
+        raw_model = loader(model_path)
+        ram_cache.put(key=cache_key, model=raw_model)
+        return LoadedModelWithoutConfig(_locker=ram_cache.get(key=cache_key))

invokeai/app/services/model_records/model_records_base.py

@@ -12,15 +12,13 @@ from pydantic import BaseModel, Field
 
 from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
-    ModelFormat,
-    ModelType,
-)
-from invokeai.backend.model_manager.config import (
     ControlAdapterDefaultSettings,
     MainModelDefaultSettings,
+    ModelFormat,
+    ModelType,
     ModelVariantType,
     SchedulerPredictionType,
 )

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -37,8 +37,12 @@ class SqliteSessionQueue(SessionQueueBase):
     def start(self, invoker: Invoker) -> None:
         self.__invoker = invoker
         self._set_in_progress_to_canceled()
+        if self.__invoker.services.configuration.clear_queue_on_startup:
+            clear_result = self.clear(DEFAULT_QUEUE_ID)
+            if clear_result.deleted > 0:
+                self.__invoker.services.logger.info(f"Cleared all {clear_result.deleted} queue items")
+        else:
             prune_result = self.prune(DEFAULT_QUEUE_ID)
-
             if prune_result.deleted > 0:
                 self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
 

invokeai/app/services/shared/invocation_context.py

@@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, Callable, Optional, Union
 
 import torch
 from PIL.Image import Image
+from pydantic.networks import AnyHttpUrl
 from torch import Tensor
 
 from invokeai.app.invocations.constants import IMAGE_MODES
@@ -23,7 +24,7 @@ from invokeai.backend.model_manager.config import (
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.load_base import LoadedModel
+from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
 from invokeai.backend.util.devices import TorchDevice
@@ -329,8 +330,10 @@ class ConditioningInterface(InvocationContextInterface):
 
 
 class ModelsInterface(InvocationContextInterface):
+    """Common API for loading, downloading and managing models."""
+
     def exists(self, identifier: Union[str, "ModelIdentifierField"]) -> bool:
-        """Checks if a model exists.
+        """Check if a model exists.
 
         Args:
             identifier: The key or ModelField representing the model.
@@ -340,13 +343,13 @@ class ModelsInterface(InvocationContextInterface):
         """
         if isinstance(identifier, str):
             return self._services.model_manager.store.exists(identifier)
-
+        else:
             return self._services.model_manager.store.exists(identifier.key)
 
     def load(
         self, identifier: Union[str, "ModelIdentifierField"], submodel_type: Optional[SubModelType] = None
     ) -> LoadedModel:
-        """Loads a model.
+        """Load a model.
 
         Args:
             identifier: The key or ModelField representing the model.
@@ -370,7 +373,7 @@ class ModelsInterface(InvocationContextInterface):
     def load_by_attrs(
         self, name: str, base: BaseModelType, type: ModelType, submodel_type: Optional[SubModelType] = None
     ) -> LoadedModel:
-        """Loads a model by its attributes.
+        """Load a model by its attributes.
 
         Args:
             name: Name of the model.
@@ -393,7 +396,7 @@ class ModelsInterface(InvocationContextInterface):
         return self._services.model_manager.load.load_model(configs[0], submodel_type)
 
     def get_config(self, identifier: Union[str, "ModelIdentifierField"]) -> AnyModelConfig:
-        """Gets a model's config.
+        """Get a model's config.
 
         Args:
             identifier: The key or ModelField representing the model.
@@ -403,11 +406,11 @@ class ModelsInterface(InvocationContextInterface):
         """
         if isinstance(identifier, str):
             return self._services.model_manager.store.get_model(identifier)
-
+        else:
             return self._services.model_manager.store.get_model(identifier.key)
 
     def search_by_path(self, path: Path) -> list[AnyModelConfig]:
-        """Searches for models by path.
+        """Search for models by path.
 
         Args:
             path: The path to search for.
@@ -424,7 +427,7 @@ class ModelsInterface(InvocationContextInterface):
         type: Optional[ModelType] = None,
         format: Optional[ModelFormat] = None,
     ) -> list[AnyModelConfig]:
-        """Searches for models by attributes.
+        """Search for models by attributes.
 
         Args:
             name: The name to search for (exact match).
@@ -443,6 +446,72 @@ class ModelsInterface(InvocationContextInterface):
             model_format=format,
         )
 
+    def download_and_cache_model(
+        self,
+        source: str | AnyHttpUrl,
+    ) -> Path:
+        """
+        Download the model file located at source to the models cache and return its Path.
+
+        This can be used to single-file install models and other resources of arbitrary types
+        which should not get registered with the database. If the model is already
+        installed, the cached path will be returned. Otherwise it will be downloaded.
+
+        Args:
+            source: A URL that points to the model, or a huggingface repo_id.
+
+        Returns:
+            Path to the downloaded model
+        """
+        return self._services.model_manager.install.download_and_cache_model(source=source)
+
+    def load_local_model(
+        self,
+        model_path: Path,
+        loader: Optional[Callable[[Path], AnyModel]] = None,
+    ) -> LoadedModelWithoutConfig:
+        """
+        Load the model file located at the indicated path
+
+        If a loader callable is provided, it will be invoked to load the model. Otherwise,
+        `safetensors.torch.load_file()` or `torch.load()` will be called to load the model.
+
+        Be aware that the LoadedModelWithoutConfig object has no `config` attribute
+
+        Args:
+            path: A model Path
+            loader: A Callable that expects a Path and returns a dict[str|int, Any]
+
+        Returns:
+            A LoadedModelWithoutConfig object.
+        """
+        return self._services.model_manager.load.load_model_from_path(model_path=model_path, loader=loader)
+
+    def load_remote_model(
+        self,
+        source: str | AnyHttpUrl,
+        loader: Optional[Callable[[Path], AnyModel]] = None,
+    ) -> LoadedModelWithoutConfig:
+        """
+        Download, cache, and load the model file located at the indicated URL or repo_id.
+
+        If the model is already downloaded, it will be loaded from the cache.
+
+        If the a loader callable is provided, it will be invoked to load the model. Otherwise,
+        `safetensors.torch.load_file()` or `torch.load()` will be called to load the model.
+
+        Be aware that the LoadedModelWithoutConfig object has no `config` attribute
+
+        Args:
+            source: A URL or huggingface repoid.
+            loader: A Callable that expects a Path and returns a dict[str|int, Any]
+
+        Returns:
+            A LoadedModelWithoutConfig object.
+        """
+        model_path = self._services.model_manager.install.download_and_cache_model(source=str(source))
+        return self._services.model_manager.load.load_model_from_path(model_path=model_path, loader=loader)
+
 
 class ConfigInterface(InvocationContextInterface):
     def get(self) -> InvokeAIAppConfig:

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

@@ -13,6 +13,7 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_7 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_8 import build_migration_8
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_9 import build_migration_9
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_10 import build_migration_10
+from invokeai.app.services.shared.sqlite_migrator.migrations.migration_11 import build_migration_11
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -43,6 +44,7 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_8(app_config=config))
     migrator.register_migration(build_migration_9())
     migrator.register_migration(build_migration_10())
+    migrator.register_migration(build_migration_11(app_config=config, logger=logger))
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_11.py

@@ -0,0 +1,75 @@
+import shutil
+import sqlite3
+from logging import Logger
+
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+LEGACY_CORE_MODELS = [
+    # OpenPose
+    "any/annotators/dwpose/yolox_l.onnx",
+    "any/annotators/dwpose/dw-ll_ucoco_384.onnx",
+    # DepthAnything
+    "any/annotators/depth_anything/depth_anything_vitl14.pth",
+    "any/annotators/depth_anything/depth_anything_vitb14.pth",
+    "any/annotators/depth_anything/depth_anything_vits14.pth",
+    # Lama inpaint
+    "core/misc/lama/lama.pt",
+    # RealESRGAN upscale
+    "core/upscaling/realesrgan/RealESRGAN_x4plus.pth",
+    "core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth",
+    "core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
+    "core/upscaling/realesrgan/RealESRGAN_x2plus.pth",
+]
+
+
+class Migration11Callback:
+    def __init__(self, app_config: InvokeAIAppConfig, logger: Logger) -> None:
+        self._app_config = app_config
+        self._logger = logger
+
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        self._remove_convert_cache()
+        self._remove_downloaded_models()
+        self._remove_unused_core_models()
+
+    def _remove_convert_cache(self) -> None:
+        """Rename models/.cache to models/.convert_cache."""
+        self._logger.info("Removing .cache directory. Converted models will now be cached in .convert_cache.")
+        legacy_convert_path = self._app_config.root_path / "models" / ".cache"
+        shutil.rmtree(legacy_convert_path, ignore_errors=True)
+
+    def _remove_downloaded_models(self) -> None:
+        """Remove models from their old locations; they will re-download when needed."""
+        self._logger.info(
+            "Removing legacy just-in-time models. Downloaded models will now be cached in .download_cache."
+        )
+        for model_path in LEGACY_CORE_MODELS:
+            legacy_dest_path = self._app_config.models_path / model_path
+            legacy_dest_path.unlink(missing_ok=True)
+
+    def _remove_unused_core_models(self) -> None:
+        """Remove unused core models and their directories."""
+        self._logger.info("Removing defunct core models.")
+        for dir in ["face_restoration", "misc", "upscaling"]:
+            path_to_remove = self._app_config.models_path / "core" / dir
+            shutil.rmtree(path_to_remove, ignore_errors=True)
+        shutil.rmtree(self._app_config.models_path / "any" / "annotators", ignore_errors=True)
+
+
+def build_migration_11(app_config: InvokeAIAppConfig, logger: Logger) -> Migration:
+    """
+    Build the migration from database version 10 to 11.
+
+    This migration does the following:
+    - Moves "core" models previously downloaded with download_with_progress_bar() into new
+      "models/.download_cache" directory.
+    - Renames "models/.cache" to "models/.convert_cache".
+    """
+    migration_11 = Migration(
+        from_version=10,
+        to_version=11,
+        callback=Migration11Callback(app_config=app_config, logger=logger),
+    )
+
+    return migration_11

invokeai/app/util/download_with_progress.py

@@ -1,51 +0,0 @@
-from pathlib import Path
-from urllib import request
-
-from tqdm import tqdm
-
-from invokeai.backend.util.logging import InvokeAILogger
-
-
-class ProgressBar:
-    """Simple progress bar for urllib.request.urlretrieve using tqdm."""
-
-    def __init__(self, model_name: str = "file"):
-        self.pbar = None
-        self.name = model_name
-
-    def __call__(self, block_num: int, block_size: int, total_size: int):
-        if not self.pbar:
-            self.pbar = tqdm(
-                desc=self.name,
-                initial=0,
-                unit="iB",
-                unit_scale=True,
-                unit_divisor=1000,
-                total=total_size,
-            )
-        self.pbar.update(block_size)
-
-
-def download_with_progress_bar(name: str, url: str, dest_path: Path) -> bool:
-    """Download a file from a URL to a destination path, with a progress bar.
-    If the file already exists, it will not be downloaded again.
-
-    Exceptions are not caught.
-
-    Args:
-        name (str): Name of the file being downloaded.
-        url (str): URL to download the file from.
-        dest_path (Path): Destination path to save the file to.
-
-    Returns:
-        bool: True if the file was downloaded, False if it already existed.
-    """
-    if dest_path.exists():
-        return False  # already downloaded
-
-    InvokeAILogger.get_logger().info(f"Downloading {name}...")
-
-    dest_path.parent.mkdir(parents=True, exist_ok=True)
-    request.urlretrieve(url, dest_path, ProgressBar(name))
-
-    return True

invokeai/backend/image_util/depth_anything/__init__.py

@@ -1,5 +1,5 @@
-import pathlib
+from pathlib import Path
-from typing import Literal, Union
+from typing import Literal
 
 import cv2
 import numpy as np
@@ -10,28 +10,17 @@ from PIL import Image
 from torchvision.transforms import Compose
 
 from invokeai.app.services.config.config_default import get_config
-from invokeai.app.util.download_with_progress import download_with_progress_bar
 from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
 from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
-from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 config = get_config()
 logger = InvokeAILogger.get_logger(config=config)
 
 DEPTH_ANYTHING_MODELS = {
-    "large": {
+    "large": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitl14.pth?download=true",
-        "url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitl14.pth?download=true",
+    "base": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitb14.pth?download=true",
-        "local": "any/annotators/depth_anything/depth_anything_vitl14.pth",
+    "small": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vits14.pth?download=true",
-    },
-    "base": {
-        "url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitb14.pth?download=true",
-        "local": "any/annotators/depth_anything/depth_anything_vitb14.pth",
-    },
-    "small": {
-        "url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vits14.pth?download=true",
-        "local": "any/annotators/depth_anything/depth_anything_vits14.pth",
-    },
 }
 
 
@@ -53,36 +42,27 @@ transform = Compose(
 
 
 class DepthAnythingDetector:
-    def __init__(self) -> None:
+    def __init__(self, model: DPT_DINOv2, device: torch.device) -> None:
-        self.model = None
+        self.model = model
-        self.model_size: Union[Literal["large", "base", "small"], None] = None
+        self.device = device
-        self.device = TorchDevice.choose_torch_device()
 
-    def load_model(self, model_size: Literal["large", "base", "small"] = "small"):
+    @staticmethod
-        DEPTH_ANYTHING_MODEL_PATH = config.models_path / DEPTH_ANYTHING_MODELS[model_size]["local"]
+    def load_model(
-        download_with_progress_bar(
+        model_path: Path, device: torch.device, model_size: Literal["large", "base", "small"] = "small"
-            pathlib.Path(DEPTH_ANYTHING_MODELS[model_size]["url"]).name,
+    ) -> DPT_DINOv2:
-            DEPTH_ANYTHING_MODELS[model_size]["url"],
+        match model_size:
-            DEPTH_ANYTHING_MODEL_PATH,
-        )
-
-        if not self.model or model_size != self.model_size:
-            del self.model
-            self.model_size = model_size
-
-            match self.model_size:
             case "small":
-                    self.model = DPT_DINOv2(encoder="vits", features=64, out_channels=[48, 96, 192, 384])
+                model = DPT_DINOv2(encoder="vits", features=64, out_channels=[48, 96, 192, 384])
             case "base":
-                    self.model = DPT_DINOv2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768])
+                model = DPT_DINOv2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768])
             case "large":
-                    self.model = DPT_DINOv2(encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024])
+                model = DPT_DINOv2(encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024])
 
-            self.model.load_state_dict(torch.load(DEPTH_ANYTHING_MODEL_PATH.as_posix(), map_location="cpu"))
+        model.load_state_dict(torch.load(model_path.as_posix(), map_location="cpu"))
-            self.model.eval()
+        model.eval()
 
-        self.model.to(self.device)
+        model.to(device)
-        return self.model
+        return model
 
     def __call__(self, image: Image.Image, resolution: int = 512) -> Image.Image:
         if not self.model:

invokeai/backend/image_util/dw_openpose/__init__.py

@@ -1,30 +1,53 @@
+from pathlib import Path
+from typing import Dict
+
 import numpy as np
 import torch
 from controlnet_aux.util import resize_image
 from PIL import Image
 
-from invokeai.backend.image_util.dw_openpose.utils import draw_bodypose, draw_facepose, draw_handpose
+from invokeai.backend.image_util.dw_openpose.utils import NDArrayInt, draw_bodypose, draw_facepose, draw_handpose
 from invokeai.backend.image_util.dw_openpose.wholebody import Wholebody
 
+DWPOSE_MODELS = {
+    "yolox_l.onnx": "https://huggingface.co/yzd-v/DWPose/resolve/main/yolox_l.onnx?download=true",
+    "dw-ll_ucoco_384.onnx": "https://huggingface.co/yzd-v/DWPose/resolve/main/dw-ll_ucoco_384.onnx?download=true",
+}
 
-def draw_pose(pose, H, W, draw_face=True, draw_body=True, draw_hands=True, resolution=512):
+
+def draw_pose(
+    pose: Dict[str, NDArrayInt | Dict[str, NDArrayInt]],
+    H: int,
+    W: int,
+    draw_face: bool = True,
+    draw_body: bool = True,
+    draw_hands: bool = True,
+    resolution: int = 512,
+) -> Image.Image:
     bodies = pose["bodies"]
     faces = pose["faces"]
     hands = pose["hands"]
+
+    assert isinstance(bodies, dict)
     candidate = bodies["candidate"]
+
+    assert isinstance(bodies, dict)
     subset = bodies["subset"]
+
     canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
 
     if draw_body:
         canvas = draw_bodypose(canvas, candidate, subset)
 
     if draw_hands:
+        assert isinstance(hands, np.ndarray)
         canvas = draw_handpose(canvas, hands)
 
     if draw_face:
-        canvas = draw_facepose(canvas, faces)
+        assert isinstance(hands, np.ndarray)
+        canvas = draw_facepose(canvas, faces)  # type: ignore
 
-    dwpose_image = resize_image(
+    dwpose_image: Image.Image = resize_image(
         canvas,
         resolution,
     )
@@ -39,11 +62,16 @@ class DWOpenposeDetector:
     Credits: https://github.com/IDEA-Research/DWPose
     """
 
-    def __init__(self) -> None:
+    def __init__(self, onnx_det: Path, onnx_pose: Path) -> None:
-        self.pose_estimation = Wholebody()
+        self.pose_estimation = Wholebody(onnx_det=onnx_det, onnx_pose=onnx_pose)
 
     def __call__(
-        self, image: Image.Image, draw_face=False, draw_body=True, draw_hands=False, resolution=512
+        self,
+        image: Image.Image,
+        draw_face: bool = False,
+        draw_body: bool = True,
+        draw_hands: bool = False,
+        resolution: int = 512,
     ) -> Image.Image:
         np_image = np.array(image)
         H, W, C = np_image.shape
@@ -79,3 +107,6 @@ class DWOpenposeDetector:
             return draw_pose(
                 pose, H, W, draw_face=draw_face, draw_hands=draw_hands, draw_body=draw_body, resolution=resolution
             )
+
+
+__all__ = ["DWPOSE_MODELS", "DWOpenposeDetector"]

invokeai/backend/image_util/dw_openpose/utils.py

@@ -5,11 +5,13 @@ import math
 import cv2
 import matplotlib
 import numpy as np
+import numpy.typing as npt
 
 eps = 0.01
+NDArrayInt = npt.NDArray[np.uint8]
 
 
-def draw_bodypose(canvas, candidate, subset):
+def draw_bodypose(canvas: NDArrayInt, candidate: NDArrayInt, subset: NDArrayInt) -> NDArrayInt:
     H, W, C = canvas.shape
     candidate = np.array(candidate)
     subset = np.array(subset)
@@ -88,7 +90,7 @@ def draw_bodypose(canvas, candidate, subset):
     return canvas
 
 
-def draw_handpose(canvas, all_hand_peaks):
+def draw_handpose(canvas: NDArrayInt, all_hand_peaks: NDArrayInt) -> NDArrayInt:
     H, W, C = canvas.shape
 
     edges = [
@@ -142,7 +144,7 @@ def draw_handpose(canvas, all_hand_peaks):
     return canvas
 
 
-def draw_facepose(canvas, all_lmks):
+def draw_facepose(canvas: NDArrayInt, all_lmks: NDArrayInt) -> NDArrayInt:
     H, W, C = canvas.shape
     for lmks in all_lmks:
         lmks = np.array(lmks)

invokeai/backend/image_util/dw_openpose/wholebody.py

@@ -2,47 +2,26 @@
 # Modified pathing to suit Invoke
 
 
+from pathlib import Path
+
 import numpy as np
 import onnxruntime as ort
 
 from invokeai.app.services.config.config_default import get_config
-from invokeai.app.util.download_with_progress import download_with_progress_bar
 from invokeai.backend.util.devices import TorchDevice
 
 from .onnxdet import inference_detector
 from .onnxpose import inference_pose
 
-DWPOSE_MODELS = {
-    "yolox_l.onnx": {
-        "local": "any/annotators/dwpose/yolox_l.onnx",
-        "url": "https://huggingface.co/yzd-v/DWPose/resolve/main/yolox_l.onnx?download=true",
-    },
-    "dw-ll_ucoco_384.onnx": {
-        "local": "any/annotators/dwpose/dw-ll_ucoco_384.onnx",
-        "url": "https://huggingface.co/yzd-v/DWPose/resolve/main/dw-ll_ucoco_384.onnx?download=true",
-    },
-}
-
 config = get_config()
 
 
 class Wholebody:
-    def __init__(self):
+    def __init__(self, onnx_det: Path, onnx_pose: Path):
         device = TorchDevice.choose_torch_device()
 
         providers = ["CUDAExecutionProvider"] if device.type == "cuda" else ["CPUExecutionProvider"]
 
-        DET_MODEL_PATH = config.models_path / DWPOSE_MODELS["yolox_l.onnx"]["local"]
-        download_with_progress_bar("yolox_l.onnx", DWPOSE_MODELS["yolox_l.onnx"]["url"], DET_MODEL_PATH)
-
-        POSE_MODEL_PATH = config.models_path / DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["local"]
-        download_with_progress_bar(
-            "dw-ll_ucoco_384.onnx", DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["url"], POSE_MODEL_PATH
-        )
-
-        onnx_det = DET_MODEL_PATH
-        onnx_pose = POSE_MODEL_PATH
-
         self.session_det = ort.InferenceSession(path_or_bytes=onnx_det, providers=providers)
         self.session_pose = ort.InferenceSession(path_or_bytes=onnx_pose, providers=providers)
 

invokeai/backend/image_util/infill_methods/lama.py

@@ -1,4 +1,4 @@
-import gc
+from pathlib import Path
 from typing import Any
 
 import numpy as np
@@ -6,9 +6,7 @@ import torch
 from PIL import Image
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config.config_default import get_config
+from invokeai.backend.model_manager.config import AnyModel
-from invokeai.app.util.download_with_progress import download_with_progress_bar
-from invokeai.backend.util.devices import TorchDevice
 
 
 def norm_img(np_img):
@@ -19,28 +17,11 @@ def norm_img(np_img):
     return np_img
 
 
-def load_jit_model(url_or_path, device):
-    model_path = url_or_path
-    logger.info(f"Loading model from: {model_path}")
-    model = torch.jit.load(model_path, map_location="cpu").to(device)
-    model.eval()
-    return model
-
-
 class LaMA:
+    def __init__(self, model: AnyModel):
+        self._model = model
+
     def __call__(self, input_image: Image.Image, *args: Any, **kwds: Any) -> Any:
-        device = TorchDevice.choose_torch_device()
-        model_location = get_config().models_path / "core/misc/lama/lama.pt"
-
-        if not model_location.exists():
-            download_with_progress_bar(
-                name="LaMa Inpainting Model",
-                url="https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
-                dest_path=model_location,
-            )
-
-        model = load_jit_model(model_location, device)
-
         image = np.asarray(input_image.convert("RGB"))
         image = norm_img(image)
 
@@ -48,20 +29,25 @@ class LaMA:
         mask = np.asarray(mask)
         mask = np.invert(mask)
         mask = norm_img(mask)
-
         mask = (mask > 0) * 1
+
+        device = next(self._model.buffers()).device
         image = torch.from_numpy(image).unsqueeze(0).to(device)
         mask = torch.from_numpy(mask).unsqueeze(0).to(device)
 
         with torch.inference_mode():
-            infilled_image = model(image, mask)
+            infilled_image = self._model(image, mask)
 
         infilled_image = infilled_image[0].permute(1, 2, 0).detach().cpu().numpy()
         infilled_image = np.clip(infilled_image * 255, 0, 255).astype("uint8")
         infilled_image = Image.fromarray(infilled_image)
 
-        del model
-        gc.collect()
-        torch.cuda.empty_cache()
-
         return infilled_image
+
+    @staticmethod
+    def load_jit_model(url_or_path: str | Path, device: torch.device | str = "cpu") -> torch.nn.Module:
+        model_path = url_or_path
+        logger.info(f"Loading model from: {model_path}")
+        model: torch.nn.Module = torch.jit.load(model_path, map_location="cpu").to(device)  # type: ignore
+        model.eval()
+        return model

invokeai/backend/image_util/realesrgan/realesrgan.py

@@ -1,6 +1,5 @@
 import math
 from enum import Enum
-from pathlib import Path
 from typing import Any, Optional
 
 import cv2
@@ -11,6 +10,7 @@ from cv2.typing import MatLike
 from tqdm import tqdm
 
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
+from invokeai.backend.model_manager.config import AnyModel
 from invokeai.backend.util.devices import TorchDevice
 
 """
@@ -52,7 +52,7 @@ class RealESRGAN:
     def __init__(
         self,
         scale: int,
-        model_path: Path,
+        loadnet: AnyModel,
         model: RRDBNet,
         tile: int = 0,
         tile_pad: int = 10,
@@ -67,8 +67,6 @@ class RealESRGAN:
         self.half = half
         self.device = TorchDevice.choose_torch_device()
 
-        loadnet = torch.load(model_path, map_location=torch.device("cpu"))
-
         # prefer to use params_ema
         if "params_ema" in loadnet:
             keyname = "params_ema"

invokeai/backend/ip_adapter/ip_adapter.py

@@ -125,13 +125,16 @@ class IPAdapter(RawModel):
             self.device, dtype=self.dtype
         )
 
-    def to(self, device: torch.device, dtype: Optional[torch.dtype] = None):
+    def to(
+        self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, non_blocking: bool = False
+    ):
+        if device is not None:
             self.device = device
         if dtype is not None:
             self.dtype = dtype
 
-        self._image_proj_model.to(device=self.device, dtype=self.dtype)
+        self._image_proj_model.to(device=self.device, dtype=self.dtype, non_blocking=non_blocking)
-        self.attn_weights.to(device=self.device, dtype=self.dtype)
+        self.attn_weights.to(device=self.device, dtype=self.dtype, non_blocking=non_blocking)
 
     def calc_size(self):
         # workaround for circular import

invokeai/backend/lora.py

@@ -61,9 +61,10 @@ class LoRALayerBase:
         self,
         device: Optional[torch.device] = None,
         dtype: Optional[torch.dtype] = None,
+        non_blocking: bool = False,
     ) -> None:
         if self.bias is not None:
-            self.bias = self.bias.to(device=device, dtype=dtype)
+            self.bias = self.bias.to(device=device, dtype=dtype, non_blocking=non_blocking)
 
 
 # TODO: find and debug lora/locon with bias
@@ -109,14 +110,15 @@ class LoRALayer(LoRALayerBase):
         self,
         device: Optional[torch.device] = None,
         dtype: Optional[torch.dtype] = None,
+        non_blocking: bool = False,
     ) -> None:
-        super().to(device=device, dtype=dtype)
+        super().to(device=device, dtype=dtype, non_blocking=non_blocking)
 
-        self.up = self.up.to(device=device, dtype=dtype)
+        self.up = self.up.to(device=device, dtype=dtype, non_blocking=non_blocking)
-        self.down = self.down.to(device=device, dtype=dtype)
+        self.down = self.down.to(device=device, dtype=dtype, non_blocking=non_blocking)
 
         if self.mid is not None:
-            self.mid = self.mid.to(device=device, dtype=dtype)
+            self.mid = self.mid.to(device=device, dtype=dtype, non_blocking=non_blocking)
 
 
 class LoHALayer(LoRALayerBase):
@@ -169,18 +171,19 @@ class LoHALayer(LoRALayerBase):
         self,
         device: Optional[torch.device] = None,
         dtype: Optional[torch.dtype] = None,
+        non_blocking: bool = False,
     ) -> None:
         super().to(device=device, dtype=dtype)
 
-        self.w1_a = self.w1_a.to(device=device, dtype=dtype)
+        self.w1_a = self.w1_a.to(device=device, dtype=dtype, non_blocking=non_blocking)
-        self.w1_b = self.w1_b.to(device=device, dtype=dtype)
+        self.w1_b = self.w1_b.to(device=device, dtype=dtype, non_blocking=non_blocking)
         if self.t1 is not None:
-            self.t1 = self.t1.to(device=device, dtype=dtype)
+            self.t1 = self.t1.to(device=device, dtype=dtype, non_blocking=non_blocking)
 
-        self.w2_a = self.w2_a.to(device=device, dtype=dtype)
+        self.w2_a = self.w2_a.to(device=device, dtype=dtype, non_blocking=non_blocking)
-        self.w2_b = self.w2_b.to(device=device, dtype=dtype)
+        self.w2_b = self.w2_b.to(device=device, dtype=dtype, non_blocking=non_blocking)
         if self.t2 is not None:
-            self.t2 = self.t2.to(device=device, dtype=dtype)
+            self.t2 = self.t2.to(device=device, dtype=dtype, non_blocking=non_blocking)
 
 
 class LoKRLayer(LoRALayerBase):
@@ -265,6 +268,7 @@ class LoKRLayer(LoRALayerBase):
         self,
         device: Optional[torch.device] = None,
         dtype: Optional[torch.dtype] = None,
+        non_blocking: bool = False,
     ) -> None:
         super().to(device=device, dtype=dtype)
 
@@ -273,19 +277,19 @@ class LoKRLayer(LoRALayerBase):
         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_a = self.w1_a.to(device=device, dtype=dtype, non_blocking=non_blocking)
-            self.w1_b = self.w1_b.to(device=device, dtype=dtype)
+            self.w1_b = self.w1_b.to(device=device, dtype=dtype, non_blocking=non_blocking)
 
         if self.w2 is not None:
-            self.w2 = self.w2.to(device=device, dtype=dtype)
+            self.w2 = self.w2.to(device=device, dtype=dtype, non_blocking=non_blocking)
         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_a = self.w2_a.to(device=device, dtype=dtype, non_blocking=non_blocking)
-            self.w2_b = self.w2_b.to(device=device, dtype=dtype)
+            self.w2_b = self.w2_b.to(device=device, dtype=dtype, non_blocking=non_blocking)
 
         if self.t2 is not None:
-            self.t2 = self.t2.to(device=device, dtype=dtype)
+            self.t2 = self.t2.to(device=device, dtype=dtype, non_blocking=non_blocking)
 
 
 class FullLayer(LoRALayerBase):
@@ -319,10 +323,11 @@ class FullLayer(LoRALayerBase):
         self,
         device: Optional[torch.device] = None,
         dtype: Optional[torch.dtype] = None,
+        non_blocking: bool = False,
     ) -> None:
         super().to(device=device, dtype=dtype)
 
-        self.weight = self.weight.to(device=device, dtype=dtype)
+        self.weight = self.weight.to(device=device, dtype=dtype, non_blocking=non_blocking)
 
 
 class IA3Layer(LoRALayerBase):
@@ -358,11 +363,12 @@ class IA3Layer(LoRALayerBase):
         self,
         device: Optional[torch.device] = None,
         dtype: Optional[torch.dtype] = None,
+        non_blocking: bool = False,
     ):
         super().to(device=device, dtype=dtype)
 
-        self.weight = self.weight.to(device=device, dtype=dtype)
+        self.weight = self.weight.to(device=device, dtype=dtype, non_blocking=non_blocking)
-        self.on_input = self.on_input.to(device=device, dtype=dtype)
+        self.on_input = self.on_input.to(device=device, dtype=dtype, non_blocking=non_blocking)
 
 
 AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer]
@@ -388,10 +394,11 @@ class LoRAModelRaw(RawModel):  # (torch.nn.Module):
         self,
         device: Optional[torch.device] = None,
         dtype: Optional[torch.dtype] = None,
+        non_blocking: bool = False,
     ) -> None:
         # TODO: try revert if exception?
         for _key, layer in self.layers.items():
-            layer.to(device=device, dtype=dtype)
+            layer.to(device=device, dtype=dtype, non_blocking=non_blocking)
 
     def calc_size(self) -> int:
         model_size = 0
@@ -514,7 +521,7 @@ class LoRAModelRaw(RawModel):  # (torch.nn.Module):
             # lower memory consumption by removing already parsed layer values
             state_dict[layer_key].clear()
 
-            layer.to(device=device, dtype=dtype)
+            layer.to(device=device, dtype=dtype, non_blocking=True)
             model.layers[layer_key] = layer
 
         return model

invokeai/backend/model_hash/hash_validator.py

@@ -0,0 +1,24 @@
+import json
+from base64 import b64decode
+
+
+def validate_hash(hash: str):
+    if ":" not in hash:
+        return
+    for enc_hash in hashes:
+        alg, hash_ = hash.split(":")
+        if alg == "blake3":
+            alg = "blake3_single"
+        map = json.loads(b64decode(enc_hash))
+        if alg in map:
+            if hash_ == map[alg]:
+                raise Exception("Unrecoverable Model Error")
+
+
+hashes: list[str] = [
+    "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",
+    "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",
+    "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",
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+    "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",
+]

invokeai/backend/model_manager/config.py

@@ -31,12 +31,13 @@ from typing_extensions import Annotated, Any, Dict
 
 from invokeai.app.invocations.constants import SCHEDULER_NAME_VALUES
 from invokeai.app.util.misc import uuid_string
+from invokeai.backend.model_hash.hash_validator import validate_hash
 
 from ..raw_model import RawModel
 
 # ModelMixin is the base class for all diffusers and transformers models
 # RawModel is the InvokeAI wrapper class for ip_adapters, loras, textual_inversion and onnx runtime
-AnyModel = Union[ModelMixin, RawModel, torch.nn.Module]
+AnyModel = Union[ModelMixin, RawModel, torch.nn.Module, Dict[str, torch.Tensor]]
 
 
 class InvalidModelConfigException(Exception):
@@ -115,7 +116,7 @@ class SchedulerPredictionType(str, Enum):
 class ModelRepoVariant(str, Enum):
     """Various hugging face variants on the diffusers format."""
 
-    Default = ""  # model files without "fp16" or other qualifier - empty str
+    Default = ""  # model files without "fp16" or other qualifier
     FP16 = "fp16"
     FP32 = "fp32"
     ONNX = "onnx"
@@ -448,4 +449,6 @@ class ModelConfigFactory(object):
             model.key = key
         if isinstance(model, CheckpointConfigBase) and timestamp is not None:
             model.converted_at = timestamp
+        if model:
+            validate_hash(model.hash)
         return model  # type: ignore

invokeai/backend/model_manager/load/__init__.py

@@ -7,7 +7,7 @@ from importlib import import_module
 from pathlib import Path
 
 from .convert_cache.convert_cache_default import ModelConvertCache
-from .load_base import LoadedModel, ModelLoaderBase
+from .load_base import LoadedModel, LoadedModelWithoutConfig, ModelLoaderBase
 from .load_default import ModelLoader
 from .model_cache.model_cache_default import ModelCache
 from .model_loader_registry import ModelLoaderRegistry, ModelLoaderRegistryBase
@@ -19,6 +19,7 @@ for module in loaders:
 
 __all__ = [
     "LoadedModel",
+    "LoadedModelWithoutConfig",
     "ModelCache",
     "ModelConvertCache",
     "ModelLoaderBase",

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

@@ -7,6 +7,7 @@ from pathlib import Path
 
 from invokeai.backend.util import GIG, directory_size
 from invokeai.backend.util.logging import InvokeAILogger
+from invokeai.backend.util.util import safe_filename
 
 from .convert_cache_base import ModelConvertCacheBase
 
@@ -35,6 +36,7 @@ class ModelConvertCache(ModelConvertCacheBase):
 
     def cache_path(self, key: str) -> Path:
         """Return the path for a model with the indicated key."""
+        key = safe_filename(self._cache_path, key)
         return self._cache_path / key
 
     def make_room(self, size: float) -> None:

invokeai/backend/model_manager/load/load_base.py

@@ -4,10 +4,13 @@ Base class for model loading in InvokeAI.
 """
 
 from abc import ABC, abstractmethod
+from contextlib import contextmanager
 from dataclasses import dataclass
 from logging import Logger
 from pathlib import Path
-from typing import Any, Optional
+from typing import Any, Dict, Generator, Optional, Tuple
+
+import torch
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_manager.config import (
@@ -20,10 +23,44 @@ from invokeai.backend.model_manager.load.model_cache.model_cache_base import Mod
 
 
 @dataclass
-class LoadedModel:
+class LoadedModelWithoutConfig:
-    """Context manager object that mediates transfer from RAM<->VRAM."""
+    """
+    Context manager object that mediates transfer from RAM<->VRAM.
+
+    This is a context manager object that has two distinct APIs:
+
+    1. Older API (deprecated):
+    Use the LoadedModel object directly as a context manager.
+    It will move the model into VRAM (on CUDA devices), and
+    return the model in a form suitable for passing to torch.
+    Example:
+    ```
+    loaded_model_= loader.get_model_by_key('f13dd932', SubModelType('vae'))
+    with loaded_model as vae:
+      image = vae.decode(latents)[0]
+    ```
+
+    2. Newer API (recommended):
+    Call the LoadedModel's `model_on_device()` method in a
+    context. It returns a tuple consisting of a copy of
+    the model's state dict in CPU RAM followed by a copy
+    of the model in VRAM. The state dict is provided to allow
+    LoRAs and other model patchers to return the model to
+    its unpatched state without expensive copy and restore
+    operations.
+
+    Example:
+    ```
+    loaded_model_= loader.get_model_by_key('f13dd932', SubModelType('vae'))
+    with loaded_model.model_on_device() as (state_dict, vae):
+        image = vae.decode(latents)[0]
+    ```
+
+    The state_dict should be treated as a read-only object and
+    never modified. Also be aware that some loadable models do
+    not have a state_dict, in which case this value will be None.
+    """
 
-    config: AnyModelConfig
     _locker: ModelLockerBase
 
     def __enter__(self) -> AnyModel:
@@ -34,12 +71,29 @@ class LoadedModel:
         """Context exit."""
         self._locker.unlock()
 
+    @contextmanager
+    def model_on_device(self) -> Generator[Tuple[Optional[Dict[str, torch.Tensor]], AnyModel], None, None]:
+        """Return a tuple consisting of the model's state dict (if it exists) and the locked model on execution device."""
+        locked_model = self._locker.lock()
+        try:
+            state_dict = self._locker.get_state_dict()
+            yield (state_dict, locked_model)
+        finally:
+            self._locker.unlock()
+
     @property
     def model(self) -> AnyModel:
         """Return the model without locking it."""
         return self._locker.model
 
 
+@dataclass
+class LoadedModel(LoadedModelWithoutConfig):
+    """Context manager object that mediates transfer from RAM<->VRAM."""
+
+    config: Optional[AnyModelConfig] = None
+
+
 # TODO(MM2):
 # Some "intermediary" subclasses in the ModelLoaderBase class hierarchy define methods that their subclasses don't
 # know about. I think the problem may be related to this class being an ABC.

invokeai/backend/model_manager/load/load_default.py

@@ -16,7 +16,7 @@ from invokeai.backend.model_manager.config import DiffusersConfigBase, ModelType
 from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
 from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
-from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data, calc_model_size_by_fs
+from invokeai.backend.model_manager.load.model_util import calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.util.devices import TorchDevice
 
@@ -84,7 +84,7 @@ class ModelLoader(ModelLoaderBase):
         except IndexError:
             pass
 
-        cache_path: Path = self._convert_cache.cache_path(config.key)
+        cache_path: Path = self._convert_cache.cache_path(str(model_path))
         if self._needs_conversion(config, model_path, cache_path):
             loaded_model = self._do_convert(config, model_path, cache_path, submodel_type)
         else:
@@ -95,7 +95,6 @@ class ModelLoader(ModelLoaderBase):
             config.key,
             submodel_type=submodel_type,
             model=loaded_model,
-            size=calc_model_size_by_data(loaded_model),
         )
 
         return self._ram_cache.get(
@@ -126,9 +125,7 @@ class ModelLoader(ModelLoaderBase):
                 if subtype == submodel_type:
                     continue
                 if submodel := getattr(pipeline, subtype.value, None):
-                    self._ram_cache.put(
+                    self._ram_cache.put(config.key, submodel_type=subtype, model=submodel)
-                        config.key, submodel_type=subtype, model=submodel, size=calc_model_size_by_data(submodel)
-                    )
         return getattr(pipeline, submodel_type.value) if submodel_type else pipeline
 
     def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:

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

@@ -31,6 +31,11 @@ class ModelLockerBase(ABC):
         """Unlock the contained model, and remove it from VRAM."""
         pass
 
+    @abstractmethod
+    def get_state_dict(self) -> Optional[Dict[str, torch.Tensor]]:
+        """Return the state dict (if any) for the cached model."""
+        pass
+
     @property
     @abstractmethod
     def model(self) -> AnyModel:
@@ -43,11 +48,33 @@ T = TypeVar("T")
 
 @dataclass
 class CacheRecord(Generic[T]):
-    """Elements of the cache."""
+    """
+    Elements of the cache:
+
+    key: Unique key for each model, same as used in the models database.
+    model: Model in memory.
+    state_dict: A read-only copy of the model's state dict in RAM. It will be
+                used as a template for creating a copy in the VRAM.
+    size: Size of the model
+    loaded: True if the model's state dict is currently in VRAM
+
+    Before a model is executed, the state_dict template is copied into VRAM,
+    and then injected into the model. When the model is finished, the VRAM
+    copy of the state dict is deleted, and the RAM version is reinjected
+    into the model.
+
+    The state_dict should be treated as a read-only attribute. Do not attempt
+    to patch or otherwise modify it. Instead, patch the copy of the state_dict
+    after it is loaded into the execution device (e.g. CUDA) using the `LoadedModel`
+    context manager call `model_on_device()`.
+    """
 
     key: str
     size: int
     model: T
+    device: torch.device
+    state_dict: Optional[Dict[str, torch.Tensor]]
+    size: int
     loaded: bool = False
     _locks: int = 0
 
@@ -147,7 +174,6 @@ class ModelCacheBase(ABC, Generic[T]):
         self,
         key: str,
         model: T,
-        size: int,
         submodel_type: Optional[SubModelType] = None,
     ) -> None:
         """Store model under key and optional submodel_type."""

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

@@ -29,7 +29,8 @@ from typing import Dict, Generator, List, Optional, Set
 import torch
 
 from invokeai.backend.model_manager import AnyModel, SubModelType
-from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot
+from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
+from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
@@ -206,16 +207,17 @@ class ModelCache(ModelCacheBase[AnyModel]):
         self,
         key: str,
         model: AnyModel,
-        size: int,
         submodel_type: Optional[SubModelType] = None,
     ) -> None:
         """Store model under key and optional submodel_type."""
-        with self._ram_lock:
         key = self._make_cache_key(key, submodel_type)
         if key in self._cached_models:
             return
+        size = calc_model_size_by_data(model)
         self.make_room(size)
-            cache_record = CacheRecord(key, model=model, size=size)
+
+        state_dict = model.state_dict() if isinstance(model, torch.nn.Module) else None
+        cache_record = CacheRecord(key=key, model=model, device=self.storage_device, state_dict=state_dict, size=size)
         self._cached_models[key] = cache_record
         self._cache_stack.append(key)
 
@@ -277,6 +279,106 @@ class ModelCache(ModelCacheBase[AnyModel]):
         else:
             return model_key
 
+    def offload_unlocked_models(self, size_required: int) -> None:
+        """Move any unused models from VRAM."""
+        reserved = self._max_vram_cache_size * GIG
+        vram_in_use = torch.cuda.memory_allocated() + size_required
+        self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM needed for models; max allowed={(reserved/GIG):.2f}GB")
+        for _, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
+            if vram_in_use <= reserved:
+                break
+            if not cache_entry.loaded:
+                continue
+            if not cache_entry.locked:
+                self.move_model_to_device(cache_entry, self.storage_device)
+                cache_entry.loaded = False
+                vram_in_use = torch.cuda.memory_allocated() + size_required
+                self.logger.debug(
+                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GIG):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GIG):.2f}GB"
+                )
+
+        TorchDevice.empty_cache()
+
+    def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
+        """Move model into the indicated device.
+
+        :param cache_entry: The CacheRecord for the model
+        :param target_device: The torch.device to move the model into
+
+        May raise a torch.cuda.OutOfMemoryError
+        """
+        self.logger.debug(f"Called to move {cache_entry.key} to {target_device}")
+        source_device = cache_entry.device
+
+        # Note: We compare device types only so that 'cuda' == 'cuda:0'.
+        # This would need to be revised to support multi-GPU.
+        if torch.device(source_device).type == torch.device(target_device).type:
+            return
+
+        # Some models don't have a `to` method, in which case they run in RAM/CPU.
+        if not hasattr(cache_entry.model, "to"):
+            return
+
+        # This roundabout method for moving the model around is done to avoid
+        # the cost of moving the model from RAM to VRAM and then back from VRAM to RAM.
+        # When moving to VRAM, we copy (not move) each element of the state dict from
+        # RAM to a new state dict in VRAM, and then inject it into the model.
+        # This operation is slightly faster than running `to()` on the whole model.
+        #
+        # When the model needs to be removed from VRAM we simply delete the copy
+        # of the state dict in VRAM, and reinject the state dict that is cached
+        # in RAM into the model. So this operation is very fast.
+        start_model_to_time = time.time()
+        snapshot_before = self._capture_memory_snapshot()
+
+        try:
+            if cache_entry.state_dict is not None:
+                assert hasattr(cache_entry.model, "load_state_dict")
+                if target_device == self.storage_device:
+                    cache_entry.model.load_state_dict(cache_entry.state_dict, assign=True)
+                else:
+                    new_dict: Dict[str, torch.Tensor] = {}
+                    for k, v in cache_entry.state_dict.items():
+                        new_dict[k] = v.to(torch.device(target_device), copy=True, non_blocking=True)
+                    cache_entry.model.load_state_dict(new_dict, assign=True)
+            cache_entry.model.to(target_device, non_blocking=True)
+            cache_entry.device = target_device
+        except Exception as e:  # blow away cache entry
+            self._delete_cache_entry(cache_entry)
+            raise e
+
+        snapshot_after = self._capture_memory_snapshot()
+        end_model_to_time = time.time()
+        self.logger.debug(
+            f"Moved model '{cache_entry.key}' from {source_device} to"
+            f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s."
+            f"Estimated model size: {(cache_entry.size/GIG):.3f} GB."
+            f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
+        )
+
+        if (
+            snapshot_before is not None
+            and snapshot_after is not None
+            and snapshot_before.vram is not None
+            and snapshot_after.vram is not None
+        ):
+            vram_change = abs(snapshot_before.vram - snapshot_after.vram)
+
+            # If the estimated model size does not match the change in VRAM, log a warning.
+            if not math.isclose(
+                vram_change,
+                cache_entry.size,
+                rel_tol=0.1,
+                abs_tol=10 * MB,
+            ):
+                self.logger.debug(
+                    f"Moving model '{cache_entry.key}' from {source_device} to"
+                    f" {target_device} caused an unexpected change in VRAM usage. The model's"
+                    " estimated size may be incorrect. Estimated model size:"
+                    f" {(cache_entry.size/GIG):.3f} GB.\n"
+                    f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
+                )
+
     def print_cuda_stats(self) -> None:
         """Log CUDA diagnostics."""
         vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG)

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

@@ -2,8 +2,7 @@
 Base class and implementation of a class that moves models in and out of VRAM.
 """
 
-import copy
+from typing import Dict, Optional
-from typing import Optional
 
 import torch
 
@@ -26,42 +25,25 @@ class ModelLocker(ModelLockerBase):
         """
         self._cache = cache
         self._cache_entry = cache_entry
-        self._execution_device: Optional[torch.device] = None
 
     @property
     def model(self) -> AnyModel:
         """Return the model without moving it around."""
         return self._cache_entry.model
 
-    # ---------------------------- NOTE -----------------
+    def get_state_dict(self) -> Optional[Dict[str, torch.Tensor]]:
-    # Ryan suggests keeping a copy of the model's state dict in CPU and copying it
+        """Return the state dict (if any) for the cached model."""
-    # into the GPU with code like this:
+        return self._cache_entry.state_dict
-    #
-    # def state_dict_to(state_dict: dict[str, torch.Tensor], device: torch.device) -> dict[str, torch.Tensor]:
-    #    new_state_dict: dict[str, torch.Tensor] = {}
-    #    for k, v in state_dict.items():
-    #       new_state_dict[k] = v.to(device=device, copy=True, non_blocking=True)
-    #    return new_state_dict
-    #
-    # I believe we'd then use load_state_dict() to inject the state dict into the model.
-    # See: https://pytorch.org/tutorials/beginner/saving_loading_models.html
-    # ---------------------------- NOTE -----------------
 
     def lock(self) -> AnyModel:
         """Move the model into the execution device (GPU) and lock it."""
-        if not hasattr(self.model, "to"):
-            return self.model
-
-        # NOTE that the model has to have the to() method in order for this code to move it into GPU!
         self._cache_entry.lock()
         try:
-            # We wait for a gpu to be free - may raise a ValueError
+            if self._cache.lazy_offloading:
-            self._execution_device = self._cache.get_execution_device()
+                self._cache.offload_unlocked_models(self._cache_entry.size)
-            self._cache.logger.debug(f"Locking {self._cache_entry.key} in {self._execution_device}")
+            self._cache.move_model_to_device(self._cache_entry, self._cache.get_execution_device())
-            model_in_gpu = copy.deepcopy(self._cache_entry.model)
-            if hasattr(model_in_gpu, "to"):
-                model_in_gpu.to(self._execution_device)
             self._cache_entry.loaded = True
+            self._cache.logger.debug(f"Locking {self._cache_entry.key} in {self._cache.execution_device}")
             self._cache.print_cuda_stats()
         except torch.cuda.OutOfMemoryError:
             self._cache.logger.warning("Insufficient GPU memory to load model. Aborting")
@@ -70,11 +52,10 @@ class ModelLocker(ModelLockerBase):
         except Exception:
             self._cache_entry.unlock()
             raise
-        return model_in_gpu
+
+        return self.model
 
     def unlock(self) -> None:
         """Call upon exit from context."""
-        if not hasattr(self.model, "to"):
-            return
         self._cache_entry.unlock()
         self._cache.print_cuda_stats()

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

@@ -65,14 +65,11 @@ class GenericDiffusersLoader(ModelLoader):
         else:
             try:
                 config = self._load_diffusers_config(model_path, config_name="config.json")
-                class_name = config.get("_class_name", None)
+                if class_name := config.get("_class_name"):
-                if class_name:
                     result = self._hf_definition_to_type(module="diffusers", class_name=class_name)
-                if config.get("model_type", None) == "clip_vision_model":
+                elif class_name := config.get("architectures"):
-                    class_name = config.get("architectures")
-                    assert class_name is not None
                     result = self._hf_definition_to_type(module="transformers", class_name=class_name[0])
-                if not class_name:
+                else:
                     raise InvalidModelConfigException("Unable to decipher Load Class based on given config.json")
             except KeyError as e:
                 raise InvalidModelConfigException("An expected config.json file is missing from this model.") from e

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

@@ -22,8 +22,7 @@ from .generic_diffusers import GenericDiffusersLoader
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.VAE, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.VAE, format=ModelFormat.Checkpoint)
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.VAE, format=ModelFormat.Checkpoint)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.VAE, format=ModelFormat.Checkpoint)
 class VAELoader(GenericDiffusersLoader):
     """Class to load VAE models."""
 
@@ -40,10 +39,6 @@ class VAELoader(GenericDiffusersLoader):
             return True
 
     def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
-        # TODO(MM2): check whether sdxl VAE models convert.
-        if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
-            raise Exception(f"VAE conversion not supported for model type: {config.base}")
-        else:
         assert isinstance(config, CheckpointConfigBase)
         config_file = self._app_config.legacy_conf_path / config.config_path
 

invokeai/backend/model_manager/metadata/fetch/huggingface.py

@@ -83,7 +83,7 @@ class HuggingFaceMetadataFetch(ModelMetadataFetchBase):
             assert s.size is not None
             files.append(
                 RemoteModelFile(
-                    url=hf_hub_url(id, s.rfilename, revision=variant),
+                    url=hf_hub_url(id, s.rfilename, revision=variant or "main"),
                     path=Path(name, s.rfilename),
                     size=s.size,
                     sha256=s.lfs.get("sha256") if s.lfs else None,

invokeai/backend/model_manager/metadata/metadata_base.py

@@ -37,9 +37,12 @@ class RemoteModelFile(BaseModel):
 
     url: AnyHttpUrl = Field(description="The url to download this model file")
     path: Path = Field(description="The path to the file, relative to the model root")
-    size: int = Field(description="The size of this file, in bytes")
+    size: Optional[int] = Field(description="The size of this file, in bytes", default=0)
     sha256: Optional[str] = Field(description="SHA256 hash of this model (not always available)", default=None)
 
+    def __hash__(self) -> int:
+        return hash(str(self))
+
 
 class ModelMetadataBase(BaseModel):
     """Base class for model metadata information."""

invokeai/backend/model_manager/probe.py

@@ -10,7 +10,7 @@ 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.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
-from invokeai.backend.util.util import SilenceWarnings
+from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 from .config import (
     AnyModelConfig,
@@ -451,8 +451,16 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
 
 class VaeCheckpointProbe(CheckpointProbeBase):
     def get_base_type(self) -> BaseModelType:
-        # I can't find any standalone 2.X VAEs to test with!
+        # VAEs of all base types have the same structure, so we wimp out and
-        return BaseModelType.StableDiffusion1
+        # guess using the name.
+        for regexp, basetype in [
+            (r"xl", BaseModelType.StableDiffusionXL),
+            (r"sd2", BaseModelType.StableDiffusion2),
+            (r"vae", BaseModelType.StableDiffusion1),
+        ]:
+            if re.search(regexp, self.model_path.name, re.IGNORECASE):
+                return basetype
+        raise InvalidModelConfigException("Cannot determine base type")
 
 
 class LoRACheckpointProbe(CheckpointProbeBase):

invokeai/backend/model_patcher.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 
 import pickle
 from contextlib import contextmanager
-from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
+from typing import Any, Dict, Generator, Iterator, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -66,8 +66,14 @@ class ModelPatcher:
         cls,
         unet: UNet2DConditionModel,
         loras: Iterator[Tuple[LoRAModelRaw, float]],
-    ) -> None:
+        model_state_dict: Optional[Dict[str, torch.Tensor]] = None,
-        with cls.apply_lora(unet, loras, "lora_unet_"):
+    ) -> Generator[None, None, None]:
+        with cls.apply_lora(
+            unet,
+            loras=loras,
+            prefix="lora_unet_",
+            model_state_dict=model_state_dict,
+        ):
             yield
 
     @classmethod
@@ -76,28 +82,9 @@ class ModelPatcher:
         cls,
         text_encoder: CLIPTextModel,
         loras: Iterator[Tuple[LoRAModelRaw, float]],
-    ) -> None:
+        model_state_dict: Optional[Dict[str, torch.Tensor]] = None,
-        with cls.apply_lora(text_encoder, loras, "lora_te_"):
+    ) -> Generator[None, None, None]:
-            yield
+        with cls.apply_lora(text_encoder, loras=loras, prefix="lora_te_", model_state_dict=model_state_dict):
-
-    @classmethod
-    @contextmanager
-    def apply_sdxl_lora_text_encoder(
-        cls,
-        text_encoder: CLIPTextModel,
-        loras: List[Tuple[LoRAModelRaw, float]],
-    ) -> None:
-        with cls.apply_lora(text_encoder, loras, "lora_te1_"):
-            yield
-
-    @classmethod
-    @contextmanager
-    def apply_sdxl_lora_text_encoder2(
-        cls,
-        text_encoder: CLIPTextModel,
-        loras: List[Tuple[LoRAModelRaw, float]],
-    ) -> None:
-        with cls.apply_lora(text_encoder, loras, "lora_te2_"):
             yield
 
     @classmethod
@@ -107,7 +94,16 @@ class ModelPatcher:
         model: AnyModel,
         loras: Iterator[Tuple[LoRAModelRaw, float]],
         prefix: str,
-    ) -> None:
+        model_state_dict: 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.
+        :model_state_dict: Read-only copy of the model's state dict in CPU, for unpatching purposes.
+        """
         original_weights = {}
         try:
             with torch.no_grad():
@@ -133,6 +129,9 @@ class ModelPatcher:
                         dtype = module.weight.dtype
 
                         if module_key not in original_weights:
+                            if model_state_dict is not None:  # we were provided with the CPU copy of the state dict
+                                original_weights[module_key] = model_state_dict[module_key + ".weight"]
+                            else:
                                 original_weights[module_key] = module.weight.detach().to(device="cpu", copy=True)
 
                         layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
@@ -140,12 +139,12 @@ class ModelPatcher:
                         # We intentionally move to the target device first, then cast. Experimentally, this was found to
                         # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
                         # same thing in a single call to '.to(...)'.
-                        layer.to(device=device)
+                        layer.to(device=device, non_blocking=True)
-                        layer.to(dtype=torch.float32)
+                        layer.to(dtype=torch.float32, non_blocking=True)
                         # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
                         # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
                         layer_weight = layer.get_weight(module.weight) * (lora_weight * layer_scale)
-                        layer.to(device=torch.device("cpu"))
+                        layer.to(device=torch.device("cpu"), non_blocking=True)
 
                         assert isinstance(layer_weight, torch.Tensor)  # mypy thinks layer_weight is a float|Any ??!
                         if module.weight.shape != layer_weight.shape:
@@ -154,7 +153,7 @@ class ModelPatcher:
                             layer_weight = layer_weight.reshape(module.weight.shape)
 
                         assert isinstance(layer_weight, torch.Tensor)  # mypy thinks layer_weight is a float|Any ??!
-                        module.weight += layer_weight.to(dtype=dtype)
+                        module.weight += layer_weight.to(dtype=dtype, non_blocking=True)
 
             yield  # wait for context manager exit
 
@@ -162,7 +161,7 @@ class ModelPatcher:
             assert hasattr(model, "get_submodule")  # mypy not picking up fact that torch.nn.Module has get_submodule()
             with torch.no_grad():
                 for module_key, weight in original_weights.items():
-                    model.get_submodule(module_key).weight.copy_(weight)
+                    model.get_submodule(module_key).weight.copy_(weight, non_blocking=True)
 
     @classmethod
     @contextmanager

invokeai/backend/onnx/onnx_runtime.py

@@ -6,6 +6,7 @@ from typing import Any, List, Optional, Tuple, Union
 
 import numpy as np
 import onnx
+import torch
 from onnx import numpy_helper
 from onnxruntime import InferenceSession, SessionOptions, get_available_providers
 
@@ -188,6 +189,15 @@ class IAIOnnxRuntimeModel(RawModel):
         # return self.io_binding.copy_outputs_to_cpu()
         return self.session.run(None, inputs)
 
+    # compatability with RawModel ABC
+    def to(
+        self,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        non_blocking: bool = False,
+    ) -> None:
+        pass
+
     # compatability with diffusers load code
     @classmethod
     def from_pretrained(

invokeai/backend/raw_model.py

@@ -10,6 +10,20 @@ The term 'raw' was introduced to describe a wrapper around a torch.nn.Module
 that adds additional methods and attributes.
 """
 
+from abc import ABC, abstractmethod
+from typing import Optional
 
-class RawModel:
+import torch
-    """Base class for 'Raw' model wrappers."""
+
+
+class RawModel(ABC):
+    """Abstract base class for 'Raw' model wrappers."""
+
+    @abstractmethod
+    def to(
+        self,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        non_blocking: bool = False,
+    ) -> None:
+        pass

invokeai/backend/textual_inversion.py

@@ -65,6 +65,18 @@ class TextualInversionModelRaw(RawModel):
 
         return result
 
+    def to(
+        self,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        non_blocking: bool = False,
+    ) -> None:
+        if not torch.cuda.is_available():
+            return
+        for emb in [self.embedding, self.embedding_2]:
+            if emb is not None:
+                emb.to(device=device, dtype=dtype, non_blocking=non_blocking)
+
 
 class TextualInversionManager(BaseTextualInversionManager):
     """TextualInversionManager implements the BaseTextualInversionManager ABC from the compel library."""

invokeai/backend/util/silence_warnings.py

@@ -1,29 +1,36 @@
-"""Context class to silence transformers and diffusers warnings."""
-
 import warnings
-from typing import Any
+from contextlib import ContextDecorator
 
-from diffusers import logging as diffusers_logging
+from diffusers.utils import logging as diffusers_logging
 from transformers import logging as transformers_logging
 
 
-class SilenceWarnings(object):
+# Inherit from ContextDecorator to allow using SilenceWarnings as both a context manager and a decorator.
-    """Use in context to temporarily turn off warnings from transformers & diffusers modules.
+class SilenceWarnings(ContextDecorator):
+    """A context manager that disables warnings from transformers & diffusers modules while active.
 
+    As context manager:
+    ```
     with SilenceWarnings():
         # do something
+    ```
+
+    As decorator:
+    ```
+    @SilenceWarnings()
+    def some_function():
+        # do something
+    ```
     """
 
-    def __init__(self) -> None:
-        self.transformers_verbosity = transformers_logging.get_verbosity()
-        self.diffusers_verbosity = diffusers_logging.get_verbosity()
-
     def __enter__(self) -> None:
+        self._transformers_verbosity = transformers_logging.get_verbosity()
+        self._diffusers_verbosity = diffusers_logging.get_verbosity()
         transformers_logging.set_verbosity_error()
         diffusers_logging.set_verbosity_error()
         warnings.simplefilter("ignore")
 
-    def __exit__(self, *args: Any) -> None:
+    def __exit__(self, *args) -> None:
-        transformers_logging.set_verbosity(self.transformers_verbosity)
+        transformers_logging.set_verbosity(self._transformers_verbosity)
-        diffusers_logging.set_verbosity(self.diffusers_verbosity)
+        diffusers_logging.set_verbosity(self._diffusers_verbosity)
         warnings.simplefilter("default")

invokeai/backend/util/util.py

@@ -1,17 +1,43 @@
 import base64
 import io
 import os
-import warnings
+import re
+import unicodedata
 from pathlib import Path
 
-from diffusers import logging as diffusers_logging
 from PIL import Image
-from transformers import logging as transformers_logging
 
 # actual size of a gig
 GIG = 1073741824
 
 
+def slugify(value: str, allow_unicode: bool = False) -> str:
+    """
+    Convert to ASCII if 'allow_unicode' is False. Convert spaces or repeated
+    dashes to single dashes. Remove characters that aren't alphanumerics,
+    underscores, or hyphens. Replace slashes with underscores.
+    Convert to lowercase. Also strip leading and
+    trailing whitespace, dashes, and underscores.
+
+    Adapted from Django: https://github.com/django/django/blob/main/django/utils/text.py
+    """
+    value = str(value)
+    if allow_unicode:
+        value = unicodedata.normalize("NFKC", value)
+    else:
+        value = unicodedata.normalize("NFKD", value).encode("ascii", "ignore").decode("ascii")
+    value = re.sub(r"[/]", "_", value.lower())
+    value = re.sub(r"[^.\w\s-]", "", value.lower())
+    return re.sub(r"[-\s]+", "-", value).strip("-_")
+
+
+def safe_filename(directory: Path, value: str) -> str:
+    """Make a string safe to use as a filename."""
+    escaped_string = slugify(value)
+    max_name_length = os.pathconf(directory, "PC_NAME_MAX") if hasattr(os, "pathconf") else 256
+    return escaped_string[len(escaped_string) - max_name_length :]
+
+
 def directory_size(directory: Path) -> int:
     """
     Return the aggregate size of all files in a directory (bytes).
@@ -51,21 +77,3 @@ class Chdir(object):
 
     def __exit__(self, *args):
         os.chdir(self.original)
-
-
-class SilenceWarnings(object):
-    """Context manager to temporarily lower verbosity of diffusers & transformers warning messages."""
-
-    def __enter__(self):
-        """Set verbosity to error."""
-        self.transformers_verbosity = transformers_logging.get_verbosity()
-        self.diffusers_verbosity = diffusers_logging.get_verbosity()
-        transformers_logging.set_verbosity_error()
-        diffusers_logging.set_verbosity_error()
-        warnings.simplefilter("ignore")
-
-    def __exit__(self, type, value, traceback):
-        """Restore logger verbosity to state before context was entered."""
-        transformers_logging.set_verbosity(self.transformers_verbosity)
-        diffusers_logging.set_verbosity(self.diffusers_verbosity)
-        warnings.simplefilter("default")

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

@@ -1021,7 +1021,8 @@
         "float": "Kommazahlen",
         "enum": "Aufzählung",
         "fullyContainNodes": "Vollständig ausgewählte Nodes auswählen",
-        "editMode": "Im Workflow-Editor bearbeiten"
+        "editMode": "Im Workflow-Editor bearbeiten",
+        "resetToDefaultValue": "Auf Standardwert zurücksetzen"
     },
     "hrf": {
         "enableHrf": "Korrektur für hohe Auflösungen",

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

@@ -6,7 +6,7 @@
         "settingsLabel": "Ajustes",
         "img2img": "Imagen a Imagen",
         "unifiedCanvas": "Lienzo Unificado",
-        "nodes": "Editor del flujo de trabajo",
+        "nodes": "Flujos de trabajo",
         "upload": "Subir imagen",
         "load": "Cargar",
         "statusDisconnected": "Desconectado",
@@ -14,7 +14,7 @@
         "discordLabel": "Discord",
         "back": "Atrás",
         "loading": "Cargando",
-        "postprocessing": "Tratamiento posterior",
+        "postprocessing": "Postprocesado",
         "txt2img": "De texto a imagen",
         "accept": "Aceptar",
         "cancel": "Cancelar",
@@ -42,7 +42,42 @@
         "copy": "Copiar",
         "beta": "Beta",
         "on": "En",
-        "aboutDesc": "¿Utilizas Invoke para trabajar? Mira aquí:"
+        "aboutDesc": "¿Utilizas Invoke para trabajar? Mira aquí:",
+        "installed": "Instalado",
+        "green": "Verde",
+        "editor": "Editor",
+        "orderBy": "Ordenar por",
+        "file": "Archivo",
+        "goTo": "Ir a",
+        "imageFailedToLoad": "No se puede cargar la imagen",
+        "saveAs": "Guardar Como",
+        "somethingWentWrong": "Algo salió mal",
+        "nextPage": "Página Siguiente",
+        "selected": "Seleccionado",
+        "tab": "Tabulador",
+        "positivePrompt": "Prompt Positivo",
+        "negativePrompt": "Prompt Negativo",
+        "error": "Error",
+        "format": "formato",
+        "unknown": "Desconocido",
+        "input": "Entrada",
+        "nodeEditor": "Editor de nodos",
+        "template": "Plantilla",
+        "prevPage": "Página Anterior",
+        "red": "Rojo",
+        "alpha": "Transparencia",
+        "outputs": "Salidas",
+        "editing": "Editando",
+        "learnMore": "Aprende más",
+        "enabled": "Activado",
+        "disabled": "Desactivado",
+        "folder": "Carpeta",
+        "updated": "Actualizado",
+        "created": "Creado",
+        "save": "Guardar",
+        "unknownError": "Error Desconocido",
+        "blue": "Azul",
+        "viewingDesc": "Revisar imágenes en una vista de galería grande"
     },
     "gallery": {
         "galleryImageSize": "Tamaño de la imagen",
@@ -467,7 +502,8 @@
         "about": "Acerca de",
         "createIssue": "Crear un problema",
         "resetUI": "Interfaz de usuario $t(accessibility.reset)",
-        "mode": "Modo"
+        "mode": "Modo",
+        "submitSupportTicket": "Enviar Ticket de Soporte"
     },
     "nodes": {
         "zoomInNodes": "Acercar",
@@ -543,5 +579,17 @@
         "layers_one": "Capa",
         "layers_many": "Capas",
         "layers_other": "Capas"
+    },
+    "controlnet": {
+        "crop": "Cortar",
+        "delete": "Eliminar",
+        "depthAnythingDescription": "Generación de mapa de profundidad usando la técnica de Depth Anything",
+        "duplicate": "Duplicar",
+        "colorMapDescription": "Genera un mapa de color desde la imagen",
+        "depthMidasDescription": "Crea un mapa de profundidad con Midas",
+        "balanced": "Equilibrado",
+        "beginEndStepPercent": "Inicio / Final Porcentaje de pasos",
+        "detectResolution": "Detectar resolución",
+        "beginEndStepPercentShort": "Inicio / Final %"
     }
 }

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

@@ -45,7 +45,7 @@
         "outputs": "Risultati",
         "data": "Dati",
         "somethingWentWrong": "Qualcosa è andato storto",
-        "copyError": "$t(gallery.copy) Errore",
+        "copyError": "Errore $t(gallery.copy)",
         "input": "Ingresso",
         "notInstalled": "Non $t(common.installed)",
         "unknownError": "Errore sconosciuto",
@@ -85,7 +85,11 @@
         "viewing": "Visualizza",
         "viewingDesc": "Rivedi le immagini in un'ampia vista della galleria",
         "editing": "Modifica",
-        "editingDesc": "Modifica nell'area Livelli di controllo"
+        "editingDesc": "Modifica nell'area Livelli di controllo",
+        "enabled": "Abilitato",
+        "disabled": "Disabilitato",
+        "comparingDesc": "Confronta due immagini",
+        "comparing": "Confronta"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -122,14 +126,30 @@
         "bulkDownloadRequestedDesc": "La tua richiesta di download è in preparazione. L'operazione potrebbe richiedere alcuni istanti.",
         "bulkDownloadRequestFailed": "Problema durante la preparazione del download",
         "bulkDownloadFailed": "Scaricamento fallito",
-        "alwaysShowImageSizeBadge": "Mostra sempre le dimensioni dell'immagine"
+        "alwaysShowImageSizeBadge": "Mostra sempre le dimensioni dell'immagine",
+        "openInViewer": "Apri nel visualizzatore",
+        "selectForCompare": "Seleziona per il confronto",
+        "selectAnImageToCompare": "Seleziona un'immagine da confrontare",
+        "slider": "Cursore",
+        "sideBySide": "Fianco a Fianco",
+        "compareImage": "Immagine di confronto",
+        "viewerImage": "Immagine visualizzata",
+        "hover": "Al passaggio del mouse",
+        "swapImages": "Scambia le immagini",
+        "compareOptions": "Opzioni di confronto",
+        "stretchToFit": "Scala per adattare",
+        "exitCompare": "Esci dal confronto",
+        "compareHelp1": "Tieni premuto <Kbd>Alt</Kbd> mentre fai clic su un'immagine della galleria o usi i tasti freccia per cambiare l'immagine di confronto.",
+        "compareHelp2": "Premi <Kbd>M</Kbd> per scorrere le modalità di confronto.",
+        "compareHelp3": "Premi <Kbd>C</Kbd> per scambiare le immagini confrontate.",
+        "compareHelp4": "Premi <Kbd>Z</Kbd> o <Kbd>Esc</Kbd> per uscire."
     },
     "hotkeys": {
         "keyboardShortcuts": "Tasti di scelta rapida",
         "appHotkeys": "Applicazione",
         "generalHotkeys": "Generale",
         "galleryHotkeys": "Galleria",
-        "unifiedCanvasHotkeys": "Tela Unificata",
+        "unifiedCanvasHotkeys": "Tela",
         "invoke": {
             "title": "Invoke",
             "desc": "Genera un'immagine"
@@ -147,8 +167,8 @@
             "desc": "Apre e chiude il pannello delle opzioni"
         },
         "pinOptions": {
-            "title": "Appunta le opzioni",
+            "title": "Fissa le opzioni",
-            "desc": "Blocca il pannello delle opzioni"
+            "desc": "Fissa il pannello delle opzioni"
         },
         "toggleGallery": {
             "title": "Attiva/disattiva galleria",
@@ -332,14 +352,14 @@
             "title": "Annulla e cancella"
         },
         "resetOptionsAndGallery": {
-            "title": "Ripristina Opzioni e Galleria",
+            "title": "Ripristina le opzioni e la galleria",
-            "desc": "Reimposta le opzioni e i pannelli della galleria"
+            "desc": "Reimposta i pannelli delle opzioni e della galleria"
         },
         "searchHotkeys": "Cerca tasti di scelta rapida",
         "noHotkeysFound": "Nessun tasto di scelta rapida trovato",
         "toggleOptionsAndGallery": {
             "desc": "Apre e chiude le opzioni e i pannelli della galleria",
-            "title": "Attiva/disattiva le Opzioni e la Galleria"
+            "title": "Attiva/disattiva le opzioni e la galleria"
         },
         "clearSearch": "Cancella ricerca",
         "remixImage": {
@@ -348,7 +368,7 @@
         },
         "toggleViewer": {
             "title": "Attiva/disattiva il visualizzatore di immagini",
-            "desc": "Passa dal Visualizzatore immagini all'area di lavoro per la scheda corrente."
+            "desc": "Passa dal visualizzatore immagini all'area di lavoro per la scheda corrente."
         }
     },
     "modelManager": {
@@ -378,7 +398,7 @@
         "convertToDiffusers": "Converti in Diffusori",
         "convertToDiffusersHelpText2": "Questo processo sostituirà la voce in Gestione Modelli con la versione Diffusori dello stesso modello.",
         "convertToDiffusersHelpText4": "Questo è un processo una tantum. Potrebbero essere necessari circa 30-60 secondi a seconda delle specifiche del tuo computer.",
-        "convertToDiffusersHelpText5": "Assicurati di avere spazio su disco sufficiente. I modelli generalmente variano tra 2 GB e 7 GB di dimensioni.",
+        "convertToDiffusersHelpText5": "Assicurati di avere spazio su disco sufficiente. I modelli generalmente variano tra 2 GB e 7 GB in dimensione.",
         "convertToDiffusersHelpText6": "Vuoi convertire questo modello?",
         "modelConverted": "Modello convertito",
         "alpha": "Alpha",
@@ -528,7 +548,7 @@
             "layer": {
                 "initialImageNoImageSelected": "Nessuna immagine iniziale selezionata",
                 "t2iAdapterIncompatibleDimensions": "L'adattatore T2I richiede che la dimensione dell'immagine sia un multiplo di {{multiple}}",
-                "controlAdapterNoModelSelected": "Nessun modello di Adattatore di Controllo selezionato",
+                "controlAdapterNoModelSelected": "Nessun modello di adattatore di controllo selezionato",
                 "controlAdapterIncompatibleBaseModel": "Il modello base dell'adattatore di controllo non è compatibile",
                 "controlAdapterNoImageSelected": "Nessuna immagine dell'adattatore di controllo selezionata",
                 "controlAdapterImageNotProcessed": "Immagine dell'adattatore di controllo non elaborata",
@@ -606,25 +626,25 @@
         "canvasMerged": "Tela unita",
         "sentToImageToImage": "Inviato a Generazione da immagine",
         "sentToUnifiedCanvas": "Inviato alla Tela",
-        "parametersNotSet": "Parametri non impostati",
+        "parametersNotSet": "Parametri non richiamati",
         "metadataLoadFailed": "Impossibile caricare i metadati",
         "serverError": "Errore del Server",
-        "connected": "Connesso al Server",
+        "connected": "Connesso al server",
         "canceled": "Elaborazione annullata",
         "uploadFailedInvalidUploadDesc": "Deve essere una singola immagine PNG o JPEG",
-        "parameterSet": "{{parameter}} impostato",
+        "parameterSet": "Parametro richiamato",
-        "parameterNotSet": "{{parameter}} non impostato",
+        "parameterNotSet": "Parametro non richiamato",
         "problemCopyingImage": "Impossibile copiare l'immagine",
-        "baseModelChangedCleared_one": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modello incompatibile",
+        "baseModelChangedCleared_one": "Cancellato o disabilitato {{count}} sottomodello incompatibile",
-        "baseModelChangedCleared_many": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modelli incompatibili",
+        "baseModelChangedCleared_many": "Cancellati o disabilitati {{count}} sottomodelli incompatibili",
-        "baseModelChangedCleared_other": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modelli incompatibili",
+        "baseModelChangedCleared_other": "Cancellati o disabilitati {{count}} sottomodelli incompatibili",
         "imageSavingFailed": "Salvataggio dell'immagine non riuscito",
         "canvasSentControlnetAssets": "Tela inviata a ControlNet & Risorse",
         "problemCopyingCanvasDesc": "Impossibile copiare la tela",
         "loadedWithWarnings": "Flusso di lavoro caricato con avvisi",
         "canvasCopiedClipboard": "Tela copiata negli appunti",
         "maskSavedAssets": "Maschera salvata nelle risorse",
-        "problemDownloadingCanvas": "Problema durante il download della tela",
+        "problemDownloadingCanvas": "Problema durante lo scarico della tela",
         "problemMergingCanvas": "Problema nell'unione delle tele",
         "imageUploaded": "Immagine caricata",
         "addedToBoard": "Aggiunto alla bacheca",
@@ -658,7 +678,17 @@
         "problemDownloadingImage": "Impossibile scaricare l'immagine",
         "prunedQueue": "Coda ripulita",
         "modelImportCanceled": "Importazione del modello annullata",
-        "parameters": "Parametri"
+        "parameters": "Parametri",
+        "parameterSetDesc": "{{parameter}} richiamato",
+        "parameterNotSetDesc": "Impossibile richiamare {{parameter}}",
+        "parameterNotSetDescWithMessage": "Impossibile richiamare {{parameter}}: {{message}}",
+        "parametersSet": "Parametri richiamati",
+        "errorCopied": "Errore copiato",
+        "outOfMemoryError": "Errore di memoria esaurita",
+        "baseModelChanged": "Modello base modificato",
+        "sessionRef": "Sessione: {{sessionId}}",
+        "somethingWentWrong": "Qualcosa è andato storto",
+        "outOfMemoryErrorDesc": "Le impostazioni della generazione attuale superano la capacità del sistema. Modifica le impostazioni e riprova."
     },
     "tooltip": {
         "feature": {
@@ -674,7 +704,7 @@
         "layer": "Livello",
         "base": "Base",
         "mask": "Maschera",
-        "maskingOptions": "Opzioni di mascheramento",
+        "maskingOptions": "Opzioni maschera",
         "enableMask": "Abilita maschera",
         "preserveMaskedArea": "Mantieni area mascherata",
         "clearMask": "Cancella maschera (Shift+C)",
@@ -745,7 +775,8 @@
         "mode": "Modalità",
         "resetUI": "$t(accessibility.reset) l'Interfaccia Utente",
         "createIssue": "Segnala un problema",
-        "about": "Informazioni"
+        "about": "Informazioni",
+        "submitSupportTicket": "Invia ticket di supporto"
     },
     "nodes": {
         "zoomOutNodes": "Rimpicciolire",
@@ -790,7 +821,7 @@
         "workflowNotes": "Note",
         "versionUnknown": " Versione sconosciuta",
         "unableToValidateWorkflow": "Impossibile convalidare il flusso di lavoro",
-        "updateApp": "Aggiorna App",
+        "updateApp": "Aggiorna Applicazione",
         "unableToLoadWorkflow": "Impossibile caricare il flusso di lavoro",
         "updateNode": "Aggiorna nodo",
         "version": "Versione",
@@ -882,11 +913,14 @@
         "missingNode": "Nodo di invocazione mancante",
         "missingInvocationTemplate": "Modello di invocazione mancante",
         "missingFieldTemplate": "Modello di campo mancante",
-        "singleFieldType": "{{name}} (Singola)"
+        "singleFieldType": "{{name}} (Singola)",
+        "imageAccessError": "Impossibile trovare l'immagine {{image_name}}, ripristino delle impostazioni predefinite",
+        "boardAccessError": "Impossibile trovare la bacheca {{board_id}}, ripristino ai valori predefiniti",
+        "modelAccessError": "Impossibile trovare il modello {{key}}, ripristino ai valori predefiniti"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
-        "menuItemAutoAdd": "Aggiungi automaticamente a questa Bacheca",
+        "menuItemAutoAdd": "Aggiungi automaticamente a questa bacheca",
         "cancel": "Annulla",
         "addBoard": "Aggiungi Bacheca",
         "bottomMessage": "L'eliminazione di questa bacheca e delle sue immagini ripristinerà tutte le funzionalità che le stanno attualmente utilizzando.",
@@ -898,7 +932,7 @@
         "myBoard": "Bacheca",
         "searchBoard": "Cerca bacheche ...",
         "noMatching": "Nessuna bacheca corrispondente",
-        "selectBoard": "Seleziona una Bacheca",
+        "selectBoard": "Seleziona una bacheca",
         "uncategorized": "Non categorizzato",
         "downloadBoard": "Scarica la bacheca",
         "deleteBoardOnly": "solo la Bacheca",
@@ -919,7 +953,7 @@
         "control": "Controllo",
         "crop": "Ritaglia",
         "depthMidas": "Profondità (Midas)",
-        "detectResolution": "Rileva risoluzione",
+        "detectResolution": "Rileva la risoluzione",
         "controlMode": "Modalità di controllo",
         "cannyDescription": "Canny rilevamento bordi",
         "depthZoe": "Profondità (Zoe)",
@@ -930,7 +964,7 @@
         "showAdvanced": "Mostra opzioni Avanzate",
         "bgth": "Soglia rimozione sfondo",
         "importImageFromCanvas": "Importa immagine dalla Tela",
-        "lineartDescription": "Converte l'immagine in lineart",
+        "lineartDescription": "Converte l'immagine in linea",
         "importMaskFromCanvas": "Importa maschera dalla Tela",
         "hideAdvanced": "Nascondi opzioni avanzate",
         "resetControlImage": "Reimposta immagine di controllo",
@@ -946,7 +980,7 @@
         "pidiDescription": "Elaborazione immagini PIDI",
         "fill": "Riempie",
         "colorMapDescription": "Genera una mappa dei colori dall'immagine",
-        "lineartAnimeDescription": "Elaborazione lineart in stile anime",
+        "lineartAnimeDescription": "Elaborazione linea in stile anime",
         "imageResolution": "Risoluzione dell'immagine",
         "colorMap": "Colore",
         "lowThreshold": "Soglia inferiore",

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

@@ -87,7 +87,11 @@
         "viewing": "Просмотр",
         "editing": "Редактирование",
         "viewingDesc": "Просмотр изображений в режиме большой галереи",
-        "editingDesc": "Редактировать на холсте слоёв управления"
+        "editingDesc": "Редактировать на холсте слоёв управления",
+        "enabled": "Включено",
+        "disabled": "Отключено",
+        "comparingDesc": "Сравнение двух изображений",
+        "comparing": "Сравнение"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -124,7 +128,23 @@
         "bulkDownloadRequested": "Подготовка к скачиванию",
         "bulkDownloadRequestedDesc": "Ваш запрос на скачивание готовится. Это может занять несколько минут.",
         "bulkDownloadRequestFailed": "Возникла проблема при подготовке скачивания",
-        "alwaysShowImageSizeBadge": "Всегда показывать значок размера изображения"
+        "alwaysShowImageSizeBadge": "Всегда показывать значок размера изображения",
+        "openInViewer": "Открыть в просмотрщике",
+        "selectForCompare": "Выбрать для сравнения",
+        "hover": "Наведение",
+        "swapImages": "Поменять местами",
+        "stretchToFit": "Растягивание до нужного размера",
+        "exitCompare": "Выйти из сравнения",
+        "compareHelp4": "Нажмите <Kbd>Z</Kbd> или <Kbd>Esc</Kbd> для выхода.",
+        "compareImage": "Сравнить изображение",
+        "viewerImage": "Изображение просмотрщика",
+        "selectAnImageToCompare": "Выберите изображение для сравнения",
+        "slider": "Слайдер",
+        "sideBySide": "Бок о бок",
+        "compareOptions": "Варианты сравнения",
+        "compareHelp1": "Удерживайте <Kbd>Alt</Kbd> при нажатии на изображение в галерее или при помощи клавиш со стрелками, чтобы изменить сравниваемое изображение.",
+        "compareHelp2": "Нажмите <Kbd>M</Kbd>, чтобы переключиться между режимами сравнения.",
+        "compareHelp3": "Нажмите <Kbd>C</Kbd>, чтобы поменять местами сравниваемые изображения."
     },
     "hotkeys": {
         "keyboardShortcuts": "Горячие клавиши",
@@ -528,7 +548,20 @@
             "missingFieldTemplate": "Отсутствует шаблон поля",
             "addingImagesTo": "Добавление изображений в",
             "invoke": "Создать",
-            "imageNotProcessedForControlAdapter": "Изображение адаптера контроля №{{number}} не обрабатывается"
+            "imageNotProcessedForControlAdapter": "Изображение адаптера контроля №{{number}} не обрабатывается",
+            "layer": {
+                "controlAdapterImageNotProcessed": "Изображение адаптера контроля не обработано",
+                "ipAdapterNoModelSelected": "IP адаптер не выбран",
+                "controlAdapterNoModelSelected": "не выбрана модель адаптера контроля",
+                "controlAdapterIncompatibleBaseModel": "несовместимая базовая модель адаптера контроля",
+                "controlAdapterNoImageSelected": "не выбрано изображение контрольного адаптера",
+                "initialImageNoImageSelected": "начальное изображение не выбрано",
+                "rgNoRegion": "регион не выбран",
+                "rgNoPromptsOrIPAdapters": "нет текстовых запросов или IP-адаптеров",
+                "ipAdapterIncompatibleBaseModel": "несовместимая базовая модель IP-адаптера",
+                "t2iAdapterIncompatibleDimensions": "Адаптер T2I требует, чтобы размеры изображения были кратны {{multiple}}",
+                "ipAdapterNoImageSelected": "изображение IP-адаптера не выбрано"
+            }
         },
         "isAllowedToUpscale": {
             "useX2Model": "Изображение слишком велико для увеличения с помощью модели x4. Используйте модель x2",
@@ -606,12 +639,12 @@
         "connected": "Подключено к серверу",
         "canceled": "Обработка отменена",
         "uploadFailedInvalidUploadDesc": "Должно быть одно изображение в формате PNG или JPEG",
-        "parameterNotSet": "Параметр {{parameter}} не задан",
+        "parameterNotSet": "Параметр не задан",
-        "parameterSet": "Параметр {{parameter}} задан",
+        "parameterSet": "Параметр задан",
         "problemCopyingImage": "Не удается скопировать изображение",
-        "baseModelChangedCleared_one": "Базовая модель изменила, очистила или отключила {{count}} несовместимую подмодель",
+        "baseModelChangedCleared_one": "Очищена или отключена {{count}} несовместимая подмодель",
-        "baseModelChangedCleared_few": "Базовая модель изменила, очистила или отключила {{count}} несовместимые подмодели",
+        "baseModelChangedCleared_few": "Очищены или отключены {{count}} несовместимые подмодели",
-        "baseModelChangedCleared_many": "Базовая модель изменила, очистила или отключила {{count}} несовместимых подмоделей",
+        "baseModelChangedCleared_many": "Очищены или отключены {{count}} несовместимых подмоделей",
         "imageSavingFailed": "Не удалось сохранить изображение",
         "canvasSentControlnetAssets": "Холст отправлен в ControlNet и ресурсы",
         "problemCopyingCanvasDesc": "Невозможно экспортировать базовый слой",
@@ -652,7 +685,17 @@
         "resetInitialImage": "Сбросить начальное изображение",
         "prunedQueue": "Урезанная очередь",
         "modelImportCanceled": "Импорт модели отменен",
-        "parameters": "Параметры"
+        "parameters": "Параметры",
+        "parameterSetDesc": "Задан {{parameter}}",
+        "parameterNotSetDesc": "Невозможно задать {{parameter}}",
+        "baseModelChanged": "Базовая модель сменена",
+        "parameterNotSetDescWithMessage": "Не удалось задать {{parameter}}: {{message}}",
+        "parametersSet": "Параметры заданы",
+        "errorCopied": "Ошибка скопирована",
+        "sessionRef": "Сессия: {{sessionId}}",
+        "outOfMemoryError": "Ошибка нехватки памяти",
+        "outOfMemoryErrorDesc": "Ваши текущие настройки генерации превышают возможности системы. Пожалуйста, измените настройки и повторите попытку.",
+        "somethingWentWrong": "Что-то пошло не так"
     },
     "tooltip": {
         "feature": {
@@ -739,7 +782,8 @@
         "loadMore": "Загрузить больше",
         "resetUI": "$t(accessibility.reset) интерфейс",
         "createIssue": "Сообщить о проблеме",
-        "about": "Об этом"
+        "about": "Об этом",
+        "submitSupportTicket": "Отправить тикет в службу поддержки"
     },
     "nodes": {
         "zoomInNodes": "Увеличьте масштаб",
@@ -832,7 +876,7 @@
         "workflowName": "Название",
         "collection": "Коллекция",
         "unknownErrorValidatingWorkflow": "Неизвестная ошибка при проверке рабочего процесса",
-        "collectionFieldType": "Коллекция {{name}}",
+        "collectionFieldType": "{{name}} (Коллекция)",
         "workflowNotes": "Примечания",
         "string": "Строка",
         "unknownNodeType": "Неизвестный тип узла",
@@ -848,7 +892,7 @@
         "targetNodeDoesNotExist": "Недопустимое ребро: целевой/входной узел {{node}} не существует",
         "mismatchedVersion": "Недопустимый узел: узел {{node}} типа {{type}} имеет несоответствующую версию (попробовать обновить?)",
         "unknownFieldType": "$t(nodes.unknownField) тип: {{type}}",
-        "collectionOrScalarFieldType": "Коллекция | Скаляр {{name}}",
+        "collectionOrScalarFieldType": "{{name}} (Один или коллекция)",
         "betaDesc": "Этот вызов находится в бета-версии. Пока он не станет стабильным, в нем могут происходить изменения при обновлении приложений. Мы планируем поддерживать этот вызов в течение длительного времени.",
         "nodeVersion": "Версия узла",
         "loadingNodes": "Загрузка узлов...",
@@ -870,7 +914,16 @@
         "noFieldsViewMode": "В этом рабочем процессе нет выбранных полей для отображения. Просмотрите полный рабочий процесс для настройки значений.",
         "graph": "График",
         "showEdgeLabels": "Показать метки на ребрах",
-        "showEdgeLabelsHelp": "Показать метки на ребрах, указывающие на соединенные узлы"
+        "showEdgeLabelsHelp": "Показать метки на ребрах, указывающие на соединенные узлы",
+        "cannotMixAndMatchCollectionItemTypes": "Невозможно смешивать и сопоставлять типы элементов коллекции",
+        "missingNode": "Отсутствует узел вызова",
+        "missingInvocationTemplate": "Отсутствует шаблон вызова",
+        "missingFieldTemplate": "Отсутствующий шаблон поля",
+        "singleFieldType": "{{name}} (Один)",
+        "noGraph": "Нет графика",
+        "imageAccessError": "Невозможно найти изображение {{image_name}}, сбрасываем на значение по умолчанию",
+        "boardAccessError": "Невозможно найти доску {{board_id}}, сбрасываем на значение по умолчанию",
+        "modelAccessError": "Невозможно найти модель {{key}}, сброс на модель по умолчанию"
     },
     "controlnet": {
         "amult": "a_mult",
@@ -1441,7 +1494,16 @@
         "clearQueueAlertDialog2": "Вы уверены, что хотите очистить очередь?",
         "item": "Элемент",
         "graphFailedToQueue": "Не удалось поставить график в очередь",
-        "openQueue": "Открыть очередь"
+        "openQueue": "Открыть очередь",
+        "prompts_one": "Запрос",
+        "prompts_few": "Запроса",
+        "prompts_many": "Запросов",
+        "iterations_one": "Итерация",
+        "iterations_few": "Итерации",
+        "iterations_many": "Итераций",
+        "generations_one": "Генерация",
+        "generations_few": "Генерации",
+        "generations_many": "Генераций"
     },
     "sdxl": {
         "refinerStart": "Запуск доработчика",

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

@@ -1,6 +1,6 @@
 {
     "common": {
-        "nodes": "節點",
+        "nodes": "工作流程",
         "img2img": "圖片轉圖片",
         "statusDisconnected": "已中斷連線",
         "back": "返回",
@@ -11,17 +11,239 @@
         "reportBugLabel": "回報錯誤",
         "githubLabel": "GitHub",
         "hotkeysLabel": "快捷鍵",
-        "languagePickerLabel": "切換語言",
+        "languagePickerLabel": "語言",
         "unifiedCanvas": "統一畫布",
         "cancel": "取消",
-        "txt2img": "文字轉圖片"
+        "txt2img": "文字轉圖片",
+        "controlNet": "ControlNet",
+        "advanced": "進階",
+        "folder": "資料夾",
+        "installed": "已安裝",
+        "accept": "接受",
+        "goTo": "前往",
+        "input": "輸入",
+        "random": "隨機",
+        "selected": "已選擇",
+        "communityLabel": "社群",
+        "loading": "載入中",
+        "delete": "刪除",
+        "copy": "複製",
+        "error": "錯誤",
+        "file": "檔案",
+        "format": "格式",
+        "imageFailedToLoad": "無法載入圖片"
     },
     "accessibility": {
         "invokeProgressBar": "Invoke 進度條",
         "uploadImage": "上傳圖片",
-        "reset": "重設",
+        "reset": "重置",
         "nextImage": "下一張圖片",
         "previousImage": "上一張圖片",
-        "menu": "選單"
+        "menu": "選單",
+        "loadMore": "載入更多",
+        "about": "關於",
+        "createIssue": "建立問題",
+        "resetUI": "$t(accessibility.reset) 介面",
+        "submitSupportTicket": "提交支援工單",
+        "mode": "模式"
+    },
+    "boards": {
+        "loading": "載入中…",
+        "movingImagesToBoard_other": "正在移動 {{count}} 張圖片至板上：",
+        "move": "移動",
+        "uncategorized": "未分類",
+        "cancel": "取消"
+    },
+    "metadata": {
+        "workflow": "工作流程",
+        "steps": "步數",
+        "model": "模型",
+        "seed": "種子",
+        "vae": "VAE",
+        "seamless": "無縫",
+        "metadata": "元數據",
+        "width": "寬度",
+        "height": "高度"
+    },
+    "accordions": {
+        "control": {
+            "title": "控制"
+        },
+        "compositing": {
+            "title": "合成"
+        },
+        "advanced": {
+            "title": "進階",
+            "options": "$t(accordions.advanced.title) 選項"
+        }
+    },
+    "hotkeys": {
+        "nodesHotkeys": "節點",
+        "cancel": {
+            "title": "取消"
+        },
+        "generalHotkeys": "一般",
+        "keyboardShortcuts": "快捷鍵",
+        "appHotkeys": "應用程式"
+    },
+    "modelManager": {
+        "advanced": "進階",
+        "allModels": "全部模型",
+        "variant": "變體",
+        "config": "配置",
+        "model": "模型",
+        "selected": "已選擇",
+        "huggingFace": "HuggingFace",
+        "install": "安裝",
+        "metadata": "元數據",
+        "delete": "刪除",
+        "description": "描述",
+        "cancel": "取消",
+        "convert": "轉換",
+        "manual": "手動",
+        "none": "無",
+        "name": "名稱",
+        "load": "載入",
+        "height": "高度",
+        "width": "寬度",
+        "search": "搜尋",
+        "vae": "VAE",
+        "settings": "設定"
+    },
+    "controlnet": {
+        "mlsd": "M-LSD",
+        "canny": "Canny",
+        "duplicate": "重複",
+        "none": "無",
+        "pidi": "PIDI",
+        "h": "H",
+        "balanced": "平衡",
+        "crop": "裁切",
+        "processor": "處理器",
+        "control": "控制",
+        "f": "F",
+        "lineart": "線條藝術",
+        "w": "W",
+        "hed": "HED",
+        "delete": "刪除"
+    },
+    "queue": {
+        "queue": "佇列",
+        "canceled": "已取消",
+        "failed": "已失敗",
+        "completed": "已完成",
+        "cancel": "取消",
+        "session": "工作階段",
+        "batch": "批量",
+        "item": "項目",
+        "completedIn": "完成於",
+        "notReady": "無法排隊"
+    },
+    "parameters": {
+        "cancel": {
+            "cancel": "取消"
+        },
+        "height": "高度",
+        "type": "類型",
+        "symmetry": "對稱性",
+        "images": "圖片",
+        "width": "寬度",
+        "coherenceMode": "模式",
+        "seed": "種子",
+        "general": "一般",
+        "strength": "強度",
+        "steps": "步數",
+        "info": "資訊"
+    },
+    "settings": {
+        "beta": "Beta",
+        "developer": "開發者",
+        "general": "一般",
+        "models": "模型"
+    },
+    "popovers": {
+        "paramModel": {
+            "heading": "模型"
+        },
+        "compositingCoherenceMode": {
+            "heading": "模式"
+        },
+        "paramSteps": {
+            "heading": "步數"
+        },
+        "controlNetProcessor": {
+            "heading": "處理器"
+        },
+        "paramVAE": {
+            "heading": "VAE"
+        },
+        "paramHeight": {
+            "heading": "高度"
+        },
+        "paramSeed": {
+            "heading": "種子"
+        },
+        "paramWidth": {
+            "heading": "寬度"
+        },
+        "refinerSteps": {
+            "heading": "步數"
+        }
+    },
+    "unifiedCanvas": {
+        "undo": "復原",
+        "mask": "遮罩",
+        "eraser": "橡皮擦",
+        "antialiasing": "抗鋸齒",
+        "redo": "重做",
+        "layer": "圖層",
+        "accept": "接受",
+        "brush": "刷子",
+        "move": "移動",
+        "brushSize": "大小"
+    },
+    "nodes": {
+        "workflowName": "名稱",
+        "notes": "註釋",
+        "workflowVersion": "版本",
+        "workflowNotes": "註釋",
+        "executionStateError": "錯誤",
+        "unableToUpdateNodes_other": "無法更新 {{count}} 個節點",
+        "integer": "整數",
+        "workflow": "工作流程",
+        "enum": "枚舉",
+        "edit": "編輯",
+        "string": "字串",
+        "workflowTags": "標籤",
+        "node": "節點",
+        "boolean": "布林值",
+        "workflowAuthor": "作者",
+        "version": "版本",
+        "executionStateCompleted": "已完成",
+        "edge": "邊緣",
+        "versionUnknown": " 版本未知"
+    },
+    "sdxl": {
+        "steps": "步數",
+        "loading": "載入中…",
+        "refiner": "精煉器"
+    },
+    "gallery": {
+        "copy": "複製",
+        "download": "下載",
+        "loading": "載入中"
+    },
+    "ui": {
+        "tabs": {
+            "models": "模型",
+            "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
+            "queue": "佇列"
+        }
+    },
+    "models": {
+        "loading": "載入中"
+    },
+    "workflows": {
+        "name": "名稱"
     }
 }

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

@@ -22,7 +22,13 @@ import type { BatchConfig } from 'services/api/types';
 import { socketInvocationComplete } from 'services/events/actions';
 import { assert } from 'tsafe';
 
-const matcher = isAnyOf(caLayerImageChanged, caLayerProcessorConfigChanged, caLayerModelChanged, caLayerRecalled);
+const matcher = isAnyOf(
+  caLayerImageChanged,
+  caLayerProcessedImageChanged,
+  caLayerProcessorConfigChanged,
+  caLayerModelChanged,
+  caLayerRecalled
+);
 
 const DEBOUNCE_MS = 300;
 const log = logger('session');
@@ -73,9 +79,10 @@ export const addControlAdapterPreprocessor = (startAppListening: AppStartListeni
       const originalConfig = originalLayer?.controlAdapter.processorConfig;
 
       const image = layer.controlAdapter.image;
+      const processedImage = layer.controlAdapter.processedImage;
       const config = layer.controlAdapter.processorConfig;
 
-      if (isEqual(config, originalConfig) && isEqual(image, originalImage)) {
+      if (isEqual(config, originalConfig) && isEqual(image, originalImage) && processedImage) {
         // Neither config nor image have changed, we can bail
         return;
       }

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

@@ -5,15 +5,86 @@ import {
   socketModelInstallCancelled,
   socketModelInstallComplete,
   socketModelInstallDownloadProgress,
+  socketModelInstallDownloadsComplete,
+  socketModelInstallDownloadStarted,
   socketModelInstallError,
+  socketModelInstallStarted,
 } from 'services/events/actions';
 
+/**
+ * A model install has two main stages - downloading and installing. All these events are namespaced under `model_install_`
+ * which is a bit misleading. For example, a `model_install_started` event is actually fired _after_ the model has fully
+ * downloaded and is being "physically" installed.
+ *
+ * Note: the download events are only fired for remote model installs, not local.
+ *
+ * Here's the expected flow:
+ * - API receives install request, model manager preps the install
+ * - `model_install_download_started` fired when the download starts
+ * - `model_install_download_progress` fired continually until the download is complete
+ * - `model_install_download_complete` fired when the download is complete
+ * - `model_install_started` fired when the "physical" installation starts
+ * - `model_install_complete` fired when the installation is complete
+ * - `model_install_cancelled` fired if the installation is cancelled
+ * - `model_install_error` fired if the installation has an error
+ */
+
+const selectModelInstalls = modelsApi.endpoints.listModelInstalls.select();
+
 export const addModelInstallEventListener = (startAppListening: AppStartListening) => {
   startAppListening({
-    actionCreator: socketModelInstallDownloadProgress,
+    actionCreator: socketModelInstallDownloadStarted,
-    effect: async (action, { dispatch }) => {
+    effect: async (action, { dispatch, getState }) => {
-      const { bytes, total_bytes, id } = action.payload.data;
+      const { id } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
 
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
+        dispatch(
+          modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
+            const modelImport = draft.find((m) => m.id === id);
+            if (modelImport) {
+              modelImport.status = 'downloading';
+            }
+            return draft;
+          })
+        );
+      }
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketModelInstallStarted,
+    effect: async (action, { dispatch, getState }) => {
+      const { id } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
+
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
+        dispatch(
+          modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
+            const modelImport = draft.find((m) => m.id === id);
+            if (modelImport) {
+              modelImport.status = 'running';
+            }
+            return draft;
+          })
+        );
+      }
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketModelInstallDownloadProgress,
+    effect: async (action, { dispatch, getState }) => {
+      const { bytes, total_bytes, id } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
+
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
         dispatch(
           modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
             const modelImport = draft.find((m) => m.id === id);
@@ -25,14 +96,20 @@ export const addModelInstallEventListener = (startAppListening: AppStartListenin
             return draft;
           })
         );
+      }
     },
   });
 
   startAppListening({
     actionCreator: socketModelInstallComplete,
-    effect: (action, { dispatch }) => {
+    effect: (action, { dispatch, getState }) => {
       const { id } = action.payload.data;
 
+      const { data } = selectModelInstalls(getState());
+
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
         dispatch(
           modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
             const modelImport = draft.find((m) => m.id === id);
@@ -42,6 +119,8 @@ export const addModelInstallEventListener = (startAppListening: AppStartListenin
             return draft;
           })
         );
+      }
+
       dispatch(api.util.invalidateTags([{ type: 'ModelConfig', id: LIST_TAG }]));
       dispatch(api.util.invalidateTags([{ type: 'ModelScanFolderResults', id: LIST_TAG }]));
     },
@@ -49,9 +128,13 @@ export const addModelInstallEventListener = (startAppListening: AppStartListenin
 
   startAppListening({
     actionCreator: socketModelInstallError,
-    effect: (action, { dispatch }) => {
+    effect: (action, { dispatch, getState }) => {
       const { id, error, error_type } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
 
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
         dispatch(
           modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
             const modelImport = draft.find((m) => m.id === id);
@@ -63,14 +146,19 @@ export const addModelInstallEventListener = (startAppListening: AppStartListenin
             return draft;
           })
         );
+      }
     },
   });
 
   startAppListening({
     actionCreator: socketModelInstallCancelled,
-    effect: (action, { dispatch }) => {
+    effect: (action, { dispatch, getState }) => {
       const { id } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
 
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
         dispatch(
           modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
             const modelImport = draft.find((m) => m.id === id);
@@ -80,6 +168,29 @@ export const addModelInstallEventListener = (startAppListening: AppStartListenin
             return draft;
           })
         );
+      }
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketModelInstallDownloadsComplete,
+    effect: (action, { dispatch, getState }) => {
+      const { id } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
+
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
+        dispatch(
+          modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
+            const modelImport = draft.find((m) => m.id === id);
+            if (modelImport) {
+              modelImport.status = 'downloads_done';
+            }
+            return draft;
+          })
+        );
+      }
     },
   });
 };

invokeai/frontend/web/src/features/controlLayers/components/CALayer/CALayerControlAdapterWrapper.tsx

@@ -4,6 +4,7 @@ import {
   caLayerControlModeChanged,
   caLayerImageChanged,
   caLayerModelChanged,
+  caLayerProcessedImageChanged,
   caLayerProcessorConfigChanged,
   caOrIPALayerBeginEndStepPctChanged,
   caOrIPALayerWeightChanged,
@@ -84,6 +85,14 @@ export const CALayerControlAdapterWrapper = memo(({ layerId }: Props) => {
     [dispatch, layerId]
   );
 
+  const onErrorLoadingImage = useCallback(() => {
+    dispatch(caLayerImageChanged({ layerId, imageDTO: null }));
+  }, [dispatch, layerId]);
+
+  const onErrorLoadingProcessedImage = useCallback(() => {
+    dispatch(caLayerProcessedImageChanged({ layerId, imageDTO: null }));
+  }, [dispatch, layerId]);
+
   const droppableData = useMemo<CALayerImageDropData>(
     () => ({
       actionType: 'SET_CA_LAYER_IMAGE',
@@ -114,6 +123,8 @@ export const CALayerControlAdapterWrapper = memo(({ layerId }: Props) => {
       onChangeImage={onChangeImage}
       droppableData={droppableData}
       postUploadAction={postUploadAction}
+      onErrorLoadingImage={onErrorLoadingImage}
+      onErrorLoadingProcessedImage={onErrorLoadingProcessedImage}
     />
   );
 });

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

@@ -28,6 +28,8 @@ type Props = {
   onChangeProcessorConfig: (processorConfig: ProcessorConfig | null) => void;
   onChangeModel: (modelConfig: ControlNetModelConfig | T2IAdapterModelConfig) => void;
   onChangeImage: (imageDTO: ImageDTO | null) => void;
+  onErrorLoadingImage: () => void;
+  onErrorLoadingProcessedImage: () => void;
   droppableData: TypesafeDroppableData;
   postUploadAction: PostUploadAction;
 };
@@ -41,6 +43,8 @@ export const ControlAdapter = memo(
     onChangeProcessorConfig,
     onChangeModel,
     onChangeImage,
+    onErrorLoadingImage,
+    onErrorLoadingProcessedImage,
     droppableData,
     postUploadAction,
   }: Props) => {
@@ -91,6 +95,8 @@ export const ControlAdapter = memo(
               onChangeImage={onChangeImage}
               droppableData={droppableData}
               postUploadAction={postUploadAction}
+              onErrorLoadingImage={onErrorLoadingImage}
+              onErrorLoadingProcessedImage={onErrorLoadingProcessedImage}
             />
           </Flex>
         </Flex>

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

@@ -27,10 +27,19 @@ type Props = {
   onChangeImage: (imageDTO: ImageDTO | null) => void;
   droppableData: TypesafeDroppableData;
   postUploadAction: PostUploadAction;
+  onErrorLoadingImage: () => void;
+  onErrorLoadingProcessedImage: () => void;
 };
 
 export const ControlAdapterImagePreview = memo(
-  ({ controlAdapter, onChangeImage, droppableData, postUploadAction }: Props) => {
+  ({
+    controlAdapter,
+    onChangeImage,
+    droppableData,
+    postUploadAction,
+    onErrorLoadingImage,
+    onErrorLoadingProcessedImage,
+  }: Props) => {
     const { t } = useTranslation();
     const dispatch = useAppDispatch();
     const autoAddBoardId = useAppSelector((s) => s.gallery.autoAddBoardId);
@@ -128,10 +137,23 @@ export const ControlAdapterImagePreview = memo(
       controlAdapter.processorConfig !== null;
 
     useEffect(() => {
-      if (isConnected && (isErrorControlImage || isErrorProcessedControlImage)) {
+      if (!isConnected) {
-        handleResetControlImage();
+        return;
       }
-    }, [handleResetControlImage, isConnected, isErrorControlImage, isErrorProcessedControlImage]);
+      if (isErrorControlImage) {
+        onErrorLoadingImage();
+      }
+      if (isErrorProcessedControlImage) {
+        onErrorLoadingProcessedImage();
+      }
+    }, [
+      handleResetControlImage,
+      isConnected,
+      isErrorControlImage,
+      isErrorProcessedControlImage,
+      onErrorLoadingImage,
+      onErrorLoadingProcessedImage,
+    ]);
 
     return (
       <Flex
@@ -167,6 +189,7 @@ export const ControlAdapterImagePreview = memo(
             droppableData={droppableData}
             imageDTO={processedControlImage}
             isUploadDisabled={true}
+            onError={handleResetControlImage}
           />
         </Box>
 

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

@@ -4,20 +4,35 @@ import { createSelector } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { useMouseEvents } from 'features/controlLayers/hooks/mouseEventHooks';
+import { BRUSH_SPACING_PCT, MAX_BRUSH_SPACING_PX, MIN_BRUSH_SPACING_PX } from 'features/controlLayers/konva/constants';
+import { setStageEventHandlers } from 'features/controlLayers/konva/events';
+import { debouncedRenderers, renderers as normalRenderers } from 'features/controlLayers/konva/renderers';
 import {
+  $brushSize,
+  $brushSpacingPx,
+  $isDrawing,
+  $lastAddedPoint,
   $lastCursorPos,
   $lastMouseDownPos,
+  $selectedLayerId,
+  $selectedLayerType,
+  $shouldInvertBrushSizeScrollDirection,
   $tool,
+  brushSizeChanged,
   isRegionalGuidanceLayer,
   layerBboxChanged,
   layerTranslated,
+  rgLayerLineAdded,
+  rgLayerPointsAdded,
+  rgLayerRectAdded,
   selectControlLayersSlice,
 } from 'features/controlLayers/store/controlLayersSlice';
-import { debouncedRenderers, renderers as normalRenderers } from 'features/controlLayers/util/renderers';
+import type { AddLineArg, AddPointToLineArg, AddRectArg } from 'features/controlLayers/store/types';
 import Konva from 'konva';
 import type { IRect } from 'konva/lib/types';
+import { clamp } from 'lodash-es';
 import { memo, useCallback, useLayoutEffect, useMemo, useState } from 'react';
+import { getImageDTO } from 'services/api/endpoints/images';
 import { useDevicePixelRatio } from 'use-device-pixel-ratio';
 import { v4 as uuidv4 } from 'uuid';
 
@@ -47,7 +62,6 @@ const useStageRenderer = (
   const dispatch = useAppDispatch();
   const state = useAppSelector((s) => s.controlLayers.present);
   const tool = useStore($tool);
-  const mouseEventHandlers = useMouseEvents();
   const lastCursorPos = useStore($lastCursorPos);
   const lastMouseDownPos = useStore($lastMouseDownPos);
   const selectedLayerIdColor = useAppSelector(selectSelectedLayerColor);
@@ -56,6 +70,26 @@ const useStageRenderer = (
   const layerCount = useMemo(() => state.layers.length, [state.layers]);
   const renderers = useMemo(() => (asPreview ? debouncedRenderers : normalRenderers), [asPreview]);
   const dpr = useDevicePixelRatio({ round: false });
+  const shouldInvertBrushSizeScrollDirection = useAppSelector((s) => s.canvas.shouldInvertBrushSizeScrollDirection);
+  const brushSpacingPx = useMemo(
+    () => clamp(state.brushSize / BRUSH_SPACING_PCT, MIN_BRUSH_SPACING_PX, MAX_BRUSH_SPACING_PX),
+    [state.brushSize]
+  );
+
+  useLayoutEffect(() => {
+    $brushSize.set(state.brushSize);
+    $brushSpacingPx.set(brushSpacingPx);
+    $selectedLayerId.set(state.selectedLayerId);
+    $selectedLayerType.set(selectedLayerType);
+    $shouldInvertBrushSizeScrollDirection.set(shouldInvertBrushSizeScrollDirection);
+  }, [
+    brushSpacingPx,
+    selectedLayerIdColor,
+    selectedLayerType,
+    shouldInvertBrushSizeScrollDirection,
+    state.brushSize,
+    state.selectedLayerId,
+  ]);
 
   const onLayerPosChanged = useCallback(
     (layerId: string, x: number, y: number) => {
@@ -71,6 +105,31 @@ const useStageRenderer = (
     [dispatch]
   );
 
+  const onRGLayerLineAdded = useCallback(
+    (arg: AddLineArg) => {
+      dispatch(rgLayerLineAdded(arg));
+    },
+    [dispatch]
+  );
+  const onRGLayerPointAddedToLine = useCallback(
+    (arg: AddPointToLineArg) => {
+      dispatch(rgLayerPointsAdded(arg));
+    },
+    [dispatch]
+  );
+  const onRGLayerRectAdded = useCallback(
+    (arg: AddRectArg) => {
+      dispatch(rgLayerRectAdded(arg));
+    },
+    [dispatch]
+  );
+  const onBrushSizeChanged = useCallback(
+    (size: number) => {
+      dispatch(brushSizeChanged(size));
+    },
+    [dispatch]
+  );
+
   useLayoutEffect(() => {
     log.trace('Initializing stage');
     if (!container) {
@@ -88,21 +147,29 @@ const useStageRenderer = (
     if (asPreview) {
       return;
     }
-    stage.on('mousedown', mouseEventHandlers.onMouseDown);
+    const cleanup = setStageEventHandlers({
-    stage.on('mouseup', mouseEventHandlers.onMouseUp);
+      stage,
-    stage.on('mousemove', mouseEventHandlers.onMouseMove);
+      $tool,
-    stage.on('mouseleave', mouseEventHandlers.onMouseLeave);
+      $isDrawing,
-    stage.on('wheel', mouseEventHandlers.onMouseWheel);
+      $lastMouseDownPos,
+      $lastCursorPos,
+      $lastAddedPoint,
+      $brushSize,
+      $brushSpacingPx,
+      $selectedLayerId,
+      $selectedLayerType,
+      $shouldInvertBrushSizeScrollDirection,
+      onRGLayerLineAdded,
+      onRGLayerPointAddedToLine,
+      onRGLayerRectAdded,
+      onBrushSizeChanged,
+    });
 
     return () => {
-      log.trace('Cleaning up stage listeners');
+      log.trace('Removing stage listeners');
-      stage.off('mousedown', mouseEventHandlers.onMouseDown);
+      cleanup();
-      stage.off('mouseup', mouseEventHandlers.onMouseUp);
-      stage.off('mousemove', mouseEventHandlers.onMouseMove);
-      stage.off('mouseleave', mouseEventHandlers.onMouseLeave);
-      stage.off('wheel', mouseEventHandlers.onMouseWheel);
     };
-  }, [stage, asPreview, mouseEventHandlers]);
+  }, [asPreview, onBrushSizeChanged, onRGLayerLineAdded, onRGLayerPointAddedToLine, onRGLayerRectAdded, stage]);
 
   useLayoutEffect(() => {
     log.trace('Updating stage dimensions');
@@ -160,7 +227,7 @@ const useStageRenderer = (
 
   useLayoutEffect(() => {
     log.trace('Rendering layers');
-    renderers.renderLayers(stage, state.layers, state.globalMaskLayerOpacity, tool, onLayerPosChanged);
+    renderers.renderLayers(stage, state.layers, state.globalMaskLayerOpacity, tool, getImageDTO, onLayerPosChanged);
   }, [
     stage,
     state.layers,

invokeai/frontend/web/src/features/controlLayers/hooks/mouseEventHooks.ts

@@ -1,233 +0,0 @@
-import { $ctrl, $meta } from '@invoke-ai/ui-library';
-import { useStore } from '@nanostores/react';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { calculateNewBrushSize } from 'features/canvas/hooks/useCanvasZoom';
-import {
-  $isDrawing,
-  $lastCursorPos,
-  $lastMouseDownPos,
-  $tool,
-  brushSizeChanged,
-  rgLayerLineAdded,
-  rgLayerPointsAdded,
-  rgLayerRectAdded,
-} from 'features/controlLayers/store/controlLayersSlice';
-import type Konva from 'konva';
-import type { KonvaEventObject } from 'konva/lib/Node';
-import type { Vector2d } from 'konva/lib/types';
-import { clamp } from 'lodash-es';
-import { useCallback, useMemo, useRef } from 'react';
-
-const getIsFocused = (stage: Konva.Stage) => {
-  return stage.container().contains(document.activeElement);
-};
-const getIsMouseDown = (e: KonvaEventObject<MouseEvent>) => e.evt.buttons === 1;
-
-const SNAP_PX = 10;
-
-export const snapPosToStage = (pos: Vector2d, stage: Konva.Stage) => {
-  const snappedPos = { ...pos };
-  // Get the normalized threshold for snapping to the edge of the stage
-  const thresholdX = SNAP_PX / stage.scaleX();
-  const thresholdY = SNAP_PX / stage.scaleY();
-  const stageWidth = stage.width() / stage.scaleX();
-  const stageHeight = stage.height() / stage.scaleY();
-  // Snap to the edge of the stage if within threshold
-  if (pos.x - thresholdX < 0) {
-    snappedPos.x = 0;
-  } else if (pos.x + thresholdX > stageWidth) {
-    snappedPos.x = Math.floor(stageWidth);
-  }
-  if (pos.y - thresholdY < 0) {
-    snappedPos.y = 0;
-  } else if (pos.y + thresholdY > stageHeight) {
-    snappedPos.y = Math.floor(stageHeight);
-  }
-  return snappedPos;
-};
-
-export const getScaledFlooredCursorPosition = (stage: Konva.Stage) => {
-  const pointerPosition = stage.getPointerPosition();
-  const stageTransform = stage.getAbsoluteTransform().copy();
-  if (!pointerPosition) {
-    return;
-  }
-  const scaledCursorPosition = stageTransform.invert().point(pointerPosition);
-  return {
-    x: Math.floor(scaledCursorPosition.x),
-    y: Math.floor(scaledCursorPosition.y),
-  };
-};
-
-const syncCursorPos = (stage: Konva.Stage): Vector2d | null => {
-  const pos = getScaledFlooredCursorPosition(stage);
-  if (!pos) {
-    return null;
-  }
-  $lastCursorPos.set(pos);
-  return pos;
-};
-
-const BRUSH_SPACING_PCT = 10;
-const MIN_BRUSH_SPACING_PX = 5;
-const MAX_BRUSH_SPACING_PX = 15;
-
-export const useMouseEvents = () => {
-  const dispatch = useAppDispatch();
-  const selectedLayerId = useAppSelector((s) => s.controlLayers.present.selectedLayerId);
-  const selectedLayerType = useAppSelector((s) => {
-    const selectedLayer = s.controlLayers.present.layers.find((l) => l.id === s.controlLayers.present.selectedLayerId);
-    if (!selectedLayer) {
-      return null;
-    }
-    return selectedLayer.type;
-  });
-  const tool = useStore($tool);
-  const lastCursorPosRef = useRef<[number, number] | null>(null);
-  const shouldInvertBrushSizeScrollDirection = useAppSelector((s) => s.canvas.shouldInvertBrushSizeScrollDirection);
-  const brushSize = useAppSelector((s) => s.controlLayers.present.brushSize);
-  const brushSpacingPx = useMemo(
-    () => clamp(brushSize / BRUSH_SPACING_PCT, MIN_BRUSH_SPACING_PX, MAX_BRUSH_SPACING_PX),
-    [brushSize]
-  );
-
-  const onMouseDown = useCallback(
-    (e: KonvaEventObject<MouseEvent>) => {
-      const stage = e.target.getStage();
-      if (!stage) {
-        return;
-      }
-      const pos = syncCursorPos(stage);
-      if (!pos || !selectedLayerId || selectedLayerType !== 'regional_guidance_layer') {
-        return;
-      }
-      if (tool === 'brush' || tool === 'eraser') {
-        dispatch(
-          rgLayerLineAdded({
-            layerId: selectedLayerId,
-            points: [pos.x, pos.y, pos.x, pos.y],
-            tool,
-          })
-        );
-        $isDrawing.set(true);
-        $lastMouseDownPos.set(pos);
-      } else if (tool === 'rect') {
-        $lastMouseDownPos.set(snapPosToStage(pos, stage));
-      }
-    },
-    [dispatch, selectedLayerId, selectedLayerType, tool]
-  );
-
-  const onMouseUp = useCallback(
-    (e: KonvaEventObject<MouseEvent>) => {
-      const stage = e.target.getStage();
-      if (!stage) {
-        return;
-      }
-      const pos = $lastCursorPos.get();
-      if (!pos || !selectedLayerId || selectedLayerType !== 'regional_guidance_layer') {
-        return;
-      }
-      const lastPos = $lastMouseDownPos.get();
-      const tool = $tool.get();
-      if (lastPos && selectedLayerId && tool === 'rect') {
-        const snappedPos = snapPosToStage(pos, stage);
-        dispatch(
-          rgLayerRectAdded({
-            layerId: selectedLayerId,
-            rect: {
-              x: Math.min(snappedPos.x, lastPos.x),
-              y: Math.min(snappedPos.y, lastPos.y),
-              width: Math.abs(snappedPos.x - lastPos.x),
-              height: Math.abs(snappedPos.y - lastPos.y),
-            },
-          })
-        );
-      }
-      $isDrawing.set(false);
-      $lastMouseDownPos.set(null);
-    },
-    [dispatch, selectedLayerId, selectedLayerType]
-  );
-
-  const onMouseMove = useCallback(
-    (e: KonvaEventObject<MouseEvent>) => {
-      const stage = e.target.getStage();
-      if (!stage) {
-        return;
-      }
-      const pos = syncCursorPos(stage);
-      if (!pos || !selectedLayerId || selectedLayerType !== 'regional_guidance_layer') {
-        return;
-      }
-      if (getIsFocused(stage) && getIsMouseDown(e) && (tool === 'brush' || tool === 'eraser')) {
-        if ($isDrawing.get()) {
-          // Continue the last line
-          if (lastCursorPosRef.current) {
-            // Dispatching redux events impacts perf substantially - using brush spacing keeps dispatches to a reasonable number
-            if (Math.hypot(lastCursorPosRef.current[0] - pos.x, lastCursorPosRef.current[1] - pos.y) < brushSpacingPx) {
-              return;
-            }
-          }
-          lastCursorPosRef.current = [pos.x, pos.y];
-          dispatch(rgLayerPointsAdded({ layerId: selectedLayerId, point: lastCursorPosRef.current }));
-        } else {
-          // Start a new line
-          dispatch(rgLayerLineAdded({ layerId: selectedLayerId, points: [pos.x, pos.y, pos.x, pos.y], tool }));
-        }
-        $isDrawing.set(true);
-      }
-    },
-    [brushSpacingPx, dispatch, selectedLayerId, selectedLayerType, tool]
-  );
-
-  const onMouseLeave = useCallback(
-    (e: KonvaEventObject<MouseEvent>) => {
-      const stage = e.target.getStage();
-      if (!stage) {
-        return;
-      }
-      const pos = syncCursorPos(stage);
-      $isDrawing.set(false);
-      $lastCursorPos.set(null);
-      $lastMouseDownPos.set(null);
-      if (!pos || !selectedLayerId || selectedLayerType !== 'regional_guidance_layer') {
-        return;
-      }
-      if (getIsFocused(stage) && getIsMouseDown(e) && (tool === 'brush' || tool === 'eraser')) {
-        dispatch(rgLayerPointsAdded({ layerId: selectedLayerId, point: [pos.x, pos.y] }));
-      }
-    },
-    [selectedLayerId, selectedLayerType, tool, dispatch]
-  );
-
-  const onMouseWheel = useCallback(
-    (e: KonvaEventObject<WheelEvent>) => {
-      e.evt.preventDefault();
-
-      if (selectedLayerType !== 'regional_guidance_layer' || (tool !== 'brush' && tool !== 'eraser')) {
-        return;
-      }
-      // checking for ctrl key is pressed or not,
-      // so that brush size can be controlled using ctrl + scroll up/down
-
-      // Invert the delta if the property is set to true
-      let delta = e.evt.deltaY;
-      if (shouldInvertBrushSizeScrollDirection) {
-        delta = -delta;
-      }
-
-      if ($ctrl.get() || $meta.get()) {
-        dispatch(brushSizeChanged(calculateNewBrushSize(brushSize, delta)));
-      }
-    },
-    [selectedLayerType, tool, shouldInvertBrushSizeScrollDirection, dispatch, brushSize]
-  );
-
-  const handlers = useMemo(
-    () => ({ onMouseDown, onMouseUp, onMouseMove, onMouseLeave, onMouseWheel }),
-    [onMouseDown, onMouseUp, onMouseMove, onMouseLeave, onMouseWheel]
-  );
-
-  return handlers;
-};

invokeai/frontend/web/src/features/controlLayers/konva/bbox.ts

@@ -1,11 +1,10 @@
 import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
 import { imageDataToDataURL } from 'features/canvas/util/blobToDataURL';
-import { RG_LAYER_OBJECT_GROUP_NAME } from 'features/controlLayers/store/controlLayersSlice';
 import Konva from 'konva';
 import type { IRect } from 'konva/lib/types';
 import { assert } from 'tsafe';
 
-const GET_CLIENT_RECT_CONFIG = { skipTransform: true };
+import { RG_LAYER_OBJECT_GROUP_NAME } from './naming';
 
 type Extents = {
   minX: number;
@@ -14,10 +13,13 @@ type Extents = {
   maxY: number;
 };
 
+const GET_CLIENT_RECT_CONFIG = { skipTransform: true };
+
+//#region getImageDataBbox
 /**
  * Get the bounding box of an image.
  * @param imageData The ImageData object to get the bounding box of.
- * @returns The minimum and maximum x and y values of the image's bounding box.
+ * @returns The minimum and maximum x and y values of the image's bounding box, or null if the image has no pixels.
  */
 const getImageDataBbox = (imageData: ImageData): Extents | null => {
   const { data, width, height } = imageData;
@@ -51,7 +53,9 @@ const getImageDataBbox = (imageData: ImageData): Extents | null => {
 
   return isEmpty ? null : { minX, minY, maxX, maxY };
 };
+//#endregion
 
+//#region getIsolatedRGLayerClone
 /**
  * Clones a regional guidance konva layer onto an offscreen stage/canvas. This allows the pixel data for a given layer
  * to be captured, manipulated or analyzed without interference from other layers.
@@ -88,7 +92,9 @@ const getIsolatedRGLayerClone = (layer: Konva.Layer): { stageClone: Konva.Stage;
 
   return { stageClone, layerClone };
 };
+//#endregion
 
+//#region getLayerBboxPixels
 /**
  * Get the bounding box of a regional prompt konva layer. This function has special handling for regional prompt layers.
  * @param layer The konva layer to get the bounding box of.
@@ -137,7 +143,9 @@ export const getLayerBboxPixels = (layer: Konva.Layer, preview: boolean = false)
 
   return correctedLayerBbox;
 };
+//#endregion
 
+//#region getLayerBboxFast
 /**
  * Get the bounding box of a konva layer. This function is faster than `getLayerBboxPixels` but less accurate. It
  * should only be used when there are no eraser strokes or shapes in the layer.
@@ -153,3 +161,4 @@ export const getLayerBboxFast = (layer: Konva.Layer): IRect => {
     height: Math.floor(bbox.height),
   };
 };
+//#endregion

invokeai/frontend/web/src/features/controlLayers/konva/constants.ts

@@ -0,0 +1,36 @@
+/**
+ * A transparency checker pattern image.
+ * This is invokeai/frontend/web/public/assets/images/transparent_bg.png as a dataURL
+ */
+export const TRANSPARENCY_CHECKER_PATTERN =
+  'data:image/png;base64,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';
+
+/**
+ * The color of a bounding box stroke when its object is selected.
+ */
+export const BBOX_SELECTED_STROKE = 'rgba(78, 190, 255, 1)';
+
+/**
+ * The inner border color for the brush preview.
+ */
+export const BRUSH_BORDER_INNER_COLOR = 'rgba(0,0,0,1)';
+
+/**
+ * The outer border color for the brush preview.
+ */
+export const BRUSH_BORDER_OUTER_COLOR = 'rgba(255,255,255,0.8)';
+
+/**
+ * The target spacing of individual points of brush strokes, as a percentage of the brush size.
+ */
+export const BRUSH_SPACING_PCT = 10;
+
+/**
+ * The minimum brush spacing in pixels.
+ */
+export const MIN_BRUSH_SPACING_PX = 5;
+
+/**
+ * The maximum brush spacing in pixels.
+ */
+export const MAX_BRUSH_SPACING_PX = 15;

invokeai/frontend/web/src/features/controlLayers/konva/events.ts

@@ -0,0 +1,201 @@
+import { calculateNewBrushSize } from 'features/canvas/hooks/useCanvasZoom';
+import {
+  getIsFocused,
+  getIsMouseDown,
+  getScaledFlooredCursorPosition,
+  snapPosToStage,
+} from 'features/controlLayers/konva/util';
+import type { AddLineArg, AddPointToLineArg, AddRectArg, Layer, Tool } from 'features/controlLayers/store/types';
+import type Konva from 'konva';
+import type { Vector2d } from 'konva/lib/types';
+import type { WritableAtom } from 'nanostores';
+
+import { TOOL_PREVIEW_LAYER_ID } from './naming';
+
+type SetStageEventHandlersArg = {
+  stage: Konva.Stage;
+  $tool: WritableAtom<Tool>;
+  $isDrawing: WritableAtom<boolean>;
+  $lastMouseDownPos: WritableAtom<Vector2d | null>;
+  $lastCursorPos: WritableAtom<Vector2d | null>;
+  $lastAddedPoint: WritableAtom<Vector2d | null>;
+  $brushSize: WritableAtom<number>;
+  $brushSpacingPx: WritableAtom<number>;
+  $selectedLayerId: WritableAtom<string | null>;
+  $selectedLayerType: WritableAtom<Layer['type'] | null>;
+  $shouldInvertBrushSizeScrollDirection: WritableAtom<boolean>;
+  onRGLayerLineAdded: (arg: AddLineArg) => void;
+  onRGLayerPointAddedToLine: (arg: AddPointToLineArg) => void;
+  onRGLayerRectAdded: (arg: AddRectArg) => void;
+  onBrushSizeChanged: (size: number) => void;
+};
+
+const syncCursorPos = (stage: Konva.Stage, $lastCursorPos: WritableAtom<Vector2d | null>) => {
+  const pos = getScaledFlooredCursorPosition(stage);
+  if (!pos) {
+    return null;
+  }
+  $lastCursorPos.set(pos);
+  return pos;
+};
+
+export const setStageEventHandlers = ({
+  stage,
+  $tool,
+  $isDrawing,
+  $lastMouseDownPos,
+  $lastCursorPos,
+  $lastAddedPoint,
+  $brushSize,
+  $brushSpacingPx,
+  $selectedLayerId,
+  $selectedLayerType,
+  $shouldInvertBrushSizeScrollDirection,
+  onRGLayerLineAdded,
+  onRGLayerPointAddedToLine,
+  onRGLayerRectAdded,
+  onBrushSizeChanged,
+}: SetStageEventHandlersArg): (() => void) => {
+  stage.on('mouseenter', (e) => {
+    const stage = e.target.getStage();
+    if (!stage) {
+      return;
+    }
+    const tool = $tool.get();
+    stage.findOne<Konva.Layer>(`#${TOOL_PREVIEW_LAYER_ID}`)?.visible(tool === 'brush' || tool === 'eraser');
+  });
+
+  stage.on('mousedown', (e) => {
+    const stage = e.target.getStage();
+    if (!stage) {
+      return;
+    }
+    const tool = $tool.get();
+    const pos = syncCursorPos(stage, $lastCursorPos);
+    const selectedLayerId = $selectedLayerId.get();
+    const selectedLayerType = $selectedLayerType.get();
+    if (!pos || !selectedLayerId || selectedLayerType !== 'regional_guidance_layer') {
+      return;
+    }
+    if (tool === 'brush' || tool === 'eraser') {
+      onRGLayerLineAdded({
+        layerId: selectedLayerId,
+        points: [pos.x, pos.y, pos.x, pos.y],
+        tool,
+      });
+      $isDrawing.set(true);
+      $lastMouseDownPos.set(pos);
+    } else if (tool === 'rect') {
+      $lastMouseDownPos.set(snapPosToStage(pos, stage));
+    }
+  });
+
+  stage.on('mouseup', (e) => {
+    const stage = e.target.getStage();
+    if (!stage) {
+      return;
+    }
+    const pos = $lastCursorPos.get();
+    const selectedLayerId = $selectedLayerId.get();
+    const selectedLayerType = $selectedLayerType.get();
+
+    if (!pos || !selectedLayerId || selectedLayerType !== 'regional_guidance_layer') {
+      return;
+    }
+    const lastPos = $lastMouseDownPos.get();
+    const tool = $tool.get();
+    if (lastPos && selectedLayerId && tool === 'rect') {
+      const snappedPos = snapPosToStage(pos, stage);
+      onRGLayerRectAdded({
+        layerId: selectedLayerId,
+        rect: {
+          x: Math.min(snappedPos.x, lastPos.x),
+          y: Math.min(snappedPos.y, lastPos.y),
+          width: Math.abs(snappedPos.x - lastPos.x),
+          height: Math.abs(snappedPos.y - lastPos.y),
+        },
+      });
+    }
+    $isDrawing.set(false);
+    $lastMouseDownPos.set(null);
+  });
+
+  stage.on('mousemove', (e) => {
+    const stage = e.target.getStage();
+    if (!stage) {
+      return;
+    }
+    const tool = $tool.get();
+    const pos = syncCursorPos(stage, $lastCursorPos);
+    const selectedLayerId = $selectedLayerId.get();
+    const selectedLayerType = $selectedLayerType.get();
+
+    stage.findOne<Konva.Layer>(`#${TOOL_PREVIEW_LAYER_ID}`)?.visible(tool === 'brush' || tool === 'eraser');
+
+    if (!pos || !selectedLayerId || selectedLayerType !== 'regional_guidance_layer') {
+      return;
+    }
+    if (getIsFocused(stage) && getIsMouseDown(e) && (tool === 'brush' || tool === 'eraser')) {
+      if ($isDrawing.get()) {
+        // Continue the last line
+        const lastAddedPoint = $lastAddedPoint.get();
+        if (lastAddedPoint) {
+          // Dispatching redux events impacts perf substantially - using brush spacing keeps dispatches to a reasonable number
+          if (Math.hypot(lastAddedPoint.x - pos.x, lastAddedPoint.y - pos.y) < $brushSpacingPx.get()) {
+            return;
+          }
+        }
+        $lastAddedPoint.set({ x: pos.x, y: pos.y });
+        onRGLayerPointAddedToLine({ layerId: selectedLayerId, point: [pos.x, pos.y] });
+      } else {
+        // Start a new line
+        onRGLayerLineAdded({ layerId: selectedLayerId, points: [pos.x, pos.y, pos.x, pos.y], tool });
+      }
+      $isDrawing.set(true);
+    }
+  });
+
+  stage.on('mouseleave', (e) => {
+    const stage = e.target.getStage();
+    if (!stage) {
+      return;
+    }
+    const pos = syncCursorPos(stage, $lastCursorPos);
+    $isDrawing.set(false);
+    $lastCursorPos.set(null);
+    $lastMouseDownPos.set(null);
+    const selectedLayerId = $selectedLayerId.get();
+    const selectedLayerType = $selectedLayerType.get();
+    const tool = $tool.get();
+
+    stage.findOne<Konva.Layer>(`#${TOOL_PREVIEW_LAYER_ID}`)?.visible(false);
+
+    if (!pos || !selectedLayerId || selectedLayerType !== 'regional_guidance_layer') {
+      return;
+    }
+    if (getIsFocused(stage) && getIsMouseDown(e) && (tool === 'brush' || tool === 'eraser')) {
+      onRGLayerPointAddedToLine({ layerId: selectedLayerId, point: [pos.x, pos.y] });
+    }
+  });
+
+  stage.on('wheel', (e) => {
+    e.evt.preventDefault();
+    const selectedLayerType = $selectedLayerType.get();
+    const tool = $tool.get();
+    if (selectedLayerType !== 'regional_guidance_layer' || (tool !== 'brush' && tool !== 'eraser')) {
+      return;
+    }
+
+    // Invert the delta if the property is set to true
+    let delta = e.evt.deltaY;
+    if ($shouldInvertBrushSizeScrollDirection.get()) {
+      delta = -delta;
+    }
+
+    if (e.evt.ctrlKey || e.evt.metaKey) {
+      onBrushSizeChanged(calculateNewBrushSize($brushSize.get(), delta));
+    }
+  });
+
+  return () => stage.off('mousedown mouseup mousemove mouseenter mouseleave wheel');
+};

invokeai/frontend/web/src/features/controlLayers/konva/filters.ts

@@ -0,0 +1,21 @@
+/**
+ * Konva filters
+ * https://konvajs.org/docs/filters/Custom_Filter.html
+ */
+
+/**
+ * Calculates the lightness (HSL) of a given pixel and sets the alpha channel to that value.
+ * This is useful for edge maps and other masks, to make the black areas transparent.
+ * @param imageData The image data to apply the filter to
+ */
+export const LightnessToAlphaFilter = (imageData: ImageData): void => {
+  const len = imageData.data.length / 4;
+  for (let i = 0; i < len; i++) {
+    const r = imageData.data[i * 4 + 0] as number;
+    const g = imageData.data[i * 4 + 1] as number;
+    const b = imageData.data[i * 4 + 2] as number;
+    const cMin = Math.min(r, g, b);
+    const cMax = Math.max(r, g, b);
+    imageData.data[i * 4 + 3] = (cMin + cMax) / 2;
+  }
+};

invokeai/frontend/web/src/features/controlLayers/konva/naming.ts

@@ -0,0 +1,38 @@
+/**
+ * This file contains IDs, names, and ID getters for konva layers and objects.
+ */
+
+// IDs for singleton Konva layers and objects
+export const TOOL_PREVIEW_LAYER_ID = 'tool_preview_layer';
+export const TOOL_PREVIEW_BRUSH_GROUP_ID = 'tool_preview_layer.brush_group';
+export const TOOL_PREVIEW_BRUSH_FILL_ID = 'tool_preview_layer.brush_fill';
+export const TOOL_PREVIEW_BRUSH_BORDER_INNER_ID = 'tool_preview_layer.brush_border_inner';
+export const TOOL_PREVIEW_BRUSH_BORDER_OUTER_ID = 'tool_preview_layer.brush_border_outer';
+export const TOOL_PREVIEW_RECT_ID = 'tool_preview_layer.rect';
+export const BACKGROUND_LAYER_ID = 'background_layer';
+export const BACKGROUND_RECT_ID = 'background_layer.rect';
+export const NO_LAYERS_MESSAGE_LAYER_ID = 'no_layers_message';
+
+// Names for Konva layers and objects (comparable to CSS classes)
+export const CA_LAYER_NAME = 'control_adapter_layer';
+export const CA_LAYER_IMAGE_NAME = 'control_adapter_layer.image';
+export const RG_LAYER_NAME = 'regional_guidance_layer';
+export const RG_LAYER_LINE_NAME = 'regional_guidance_layer.line';
+export const RG_LAYER_OBJECT_GROUP_NAME = 'regional_guidance_layer.object_group';
+export const RG_LAYER_RECT_NAME = 'regional_guidance_layer.rect';
+export const INITIAL_IMAGE_LAYER_ID = 'singleton_initial_image_layer';
+export const INITIAL_IMAGE_LAYER_NAME = 'initial_image_layer';
+export const INITIAL_IMAGE_LAYER_IMAGE_NAME = 'initial_image_layer.image';
+export const LAYER_BBOX_NAME = 'layer.bbox';
+export const COMPOSITING_RECT_NAME = 'compositing-rect';
+
+// Getters for non-singleton layer and object IDs
+export const getRGLayerId = (layerId: string) => `${RG_LAYER_NAME}_${layerId}`;
+export const getRGLayerLineId = (layerId: string, lineId: string) => `${layerId}.line_${lineId}`;
+export const getRGLayerRectId = (layerId: string, lineId: string) => `${layerId}.rect_${lineId}`;
+export const getRGLayerObjectGroupId = (layerId: string, groupId: string) => `${layerId}.objectGroup_${groupId}`;
+export const getLayerBboxId = (layerId: string) => `${layerId}.bbox`;
+export const getCALayerId = (layerId: string) => `control_adapter_layer_${layerId}`;
+export const getCALayerImageId = (layerId: string, imageName: string) => `${layerId}.image_${imageName}`;
+export const getIILayerImageId = (layerId: string, imageName: string) => `${layerId}.image_${imageName}`;
+export const getIPALayerId = (layerId: string) => `ip_adapter_layer_${layerId}`;

invokeai/frontend/web/src/features/controlLayers/konva/renderers.ts

@@ -1,8 +1,7 @@
-import { getStore } from 'app/store/nanostores/store';
 import { rgbaColorToString, rgbColorToString } from 'features/canvas/util/colorToString';
-import { getScaledFlooredCursorPosition, snapPosToStage } from 'features/controlLayers/hooks/mouseEventHooks';
+import { getLayerBboxFast, getLayerBboxPixels } from 'features/controlLayers/konva/bbox';
+import { LightnessToAlphaFilter } from 'features/controlLayers/konva/filters';
 import {
-  $tool,
   BACKGROUND_LAYER_ID,
   BACKGROUND_RECT_ID,
   CA_LAYER_IMAGE_NAME,
@@ -14,10 +13,6 @@ import {
   getRGLayerObjectGroupId,
   INITIAL_IMAGE_LAYER_IMAGE_NAME,
   INITIAL_IMAGE_LAYER_NAME,
-  isControlAdapterLayer,
-  isInitialImageLayer,
-  isRegionalGuidanceLayer,
-  isRenderableLayer,
   LAYER_BBOX_NAME,
   NO_LAYERS_MESSAGE_LAYER_ID,
   RG_LAYER_LINE_NAME,
@@ -30,6 +25,13 @@ import {
   TOOL_PREVIEW_BRUSH_GROUP_ID,
   TOOL_PREVIEW_LAYER_ID,
   TOOL_PREVIEW_RECT_ID,
+} from 'features/controlLayers/konva/naming';
+import { getScaledFlooredCursorPosition, snapPosToStage } from 'features/controlLayers/konva/util';
+import {
+  isControlAdapterLayer,
+  isInitialImageLayer,
+  isRegionalGuidanceLayer,
+  isRenderableLayer,
 } from 'features/controlLayers/store/controlLayersSlice';
 import type {
   ControlAdapterLayer,
@@ -40,61 +42,46 @@ import type {
   VectorMaskLine,
   VectorMaskRect,
 } from 'features/controlLayers/store/types';
-import { getLayerBboxFast, getLayerBboxPixels } from 'features/controlLayers/util/bbox';
 import { t } from 'i18next';
 import Konva from 'konva';
 import type { IRect, Vector2d } from 'konva/lib/types';
 import { debounce } from 'lodash-es';
 import type { RgbColor } from 'react-colorful';
-import { imagesApi } from 'services/api/endpoints/images';
+import type { ImageDTO } from 'services/api/types';
 import { assert } from 'tsafe';
 import { v4 as uuidv4 } from 'uuid';
 
-const BBOX_SELECTED_STROKE = 'rgba(78, 190, 255, 1)';
+import {
-const BRUSH_BORDER_INNER_COLOR = 'rgba(0,0,0,1)';
+  BBOX_SELECTED_STROKE,
-const BRUSH_BORDER_OUTER_COLOR = 'rgba(255,255,255,0.8)';
+  BRUSH_BORDER_INNER_COLOR,
-// This is invokeai/frontend/web/public/assets/images/transparent_bg.png as a dataURL
+  BRUSH_BORDER_OUTER_COLOR,
-export const STAGE_BG_DATAURL =
+  TRANSPARENCY_CHECKER_PATTERN,
-  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAIAAAAC64paAAAEsmlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPD94cGFja2V0IGJlZ2luPSLvu78iIGlkPSJXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQiPz4KPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNS41LjAiPgogPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4KICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgeG1sbnM6ZXhpZj0iaHR0cDovL25zLmFkb2JlLmNvbS9leGlmLzEuMC8iCiAgICB4bWxuczp0aWZmPSJodHRwOi8vbnMuYWRvYmUuY29tL3RpZmYvMS4wLyIKICAgIHhtbG5zOnBob3Rvc2hvcD0iaHR0cDovL25zLmFkb2JlLmNvbS9waG90b3Nob3AvMS4wLyIKICAgIHhtbG5zOnhtcD0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wLyIKICAgIHhtbG5zOnhtcE1NPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvbW0vIgogICAgeG1sbnM6c3RFdnQ9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9zVHlwZS9SZXNvdXJjZUV2ZW50IyIKICAgZXhpZjpQaXhlbFhEaW1lbnNpb249IjIwIgogICBleGlmOlBpeGVsWURpbWVuc2lvbj0iMjAiCiAgIGV4aWY6Q29sb3JTcGFjZT0iMSIKICAgdGlmZjpJbWFnZVdpZHRoPSIyMCIKICAgdGlmZjpJbWFnZUxlbmd0aD0iMjAiCiAgIHRpZmY6UmVzb2x1dGlvblVuaXQ9IjIiCiAgIHRpZmY6WFJlc29sdXRpb249IjMwMC8xIgogICB0aWZmOllSZXNvbHV0aW9uPSIzMDAvMSIKICAgcGhvdG9zaG9wOkNvbG9yTW9kZT0iMyIKICAgcGhvdG9zaG9wOklDQ1Byb2ZpbGU9InNSR0IgSUVDNjE5NjYtMi4xIgogICB4bXA6TW9kaWZ5RGF0ZT0iMjAyNC0wNC0yM1QwODoyMDo0NysxMDowMCIKICAgeG1wOk1ldGFkYXRhRGF0ZT0iMjAyNC0wNC0yM1QwODoyMDo0NysxMDowMCI+CiAgIDx4bXBNTTpIaXN0b3J5PgogICAgPHJkZjpTZXE+CiAgICAgPHJkZjpsaQogICAgICBzdEV2dDphY3Rpb249InByb2R1Y2VkIgogICAgICBzdEV2dDpzb2Z0d2FyZUFnZW50PSJBZmZpbml0eSBQaG90byAxLjEwLjgiCiAgICAgIHN0RXZ0OndoZW49IjIwMjQtMDQtMjNUMDg6MjA6NDcrMTA6MDAiLz4KICAgIDwvcmRmOlNlcT4KICAgPC94bXBNTTpIaXN0b3J5PgogIDwvcmRmOkRlc2NyaXB0aW9uPgogPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KPD94cGFja2V0IGVuZD0iciI/Pn9pdVgAAAGBaUNDUHNSR0IgSUVDNjE5NjYtMi4xAAAokXWR3yuDURjHP5uJmKghFy6WxpVpqMWNMgm1tGbKr5vt3S+1d3t73y3JrXKrKHHj1wV/AbfKtVJESq53TdywXs9rakv2nJ7zfM73nOfpnOeAPZJRVMPhAzWb18NTAffC4pK7oYiDTjpw4YgqhjYeCgWpaR8P2Kx457Vq1T73rzXHE4YCtkbhMUXT88LTwsG1vGbxrnC7ko7Ghc+F+3W5oPC9pcfKXLQ4VeYvi/VIeALsbcLuVBXHqlhJ66qwvByPmikov/exXuJMZOfnJPaId2MQZooAbmaYZAI/g4zK7MfLEAOyoka+7yd/lpzkKjJrrKOzSoo0efpFLUj1hMSk6AkZGdat/v/tq5EcHipXdwag/sU033qhYQdK26b5eWyapROoe4arbCU/dwQj76JvVzTPIbRuwsV1RYvtweUWdD1pUT36I9WJ25NJeD2DlkVw3ULTcrlnv/ucPkJkQ77qBvYPoE/Ot658AxagZ8FoS/a7AAAACXBIWXMAAC4jAAAuIwF4pT92AAAAL0lEQVQ4jWM8ffo0A25gYmKCR5YJjxxBMKp5ZGhm/P//Px7pM2fO0MrmUc0jQzMAB2EIhZC3pUYAAAAASUVORK5CYII=';
+} from './constants';
 
-const mapId = (object: { id: string }) => object.id;
+const mapId = (object: { id: string }): string => object.id;
 
-const selectRenderableLayers = (n: Konva.Node) =>
+/**
+ * Konva selection callback to select all renderable layers. This includes RG, CA and II layers.
+ */
+const selectRenderableLayers = (n: Konva.Node): boolean =>
   n.name() === RG_LAYER_NAME || n.name() === CA_LAYER_NAME || n.name() === INITIAL_IMAGE_LAYER_NAME;
 
-const selectVectorMaskObjects = (node: Konva.Node) => {
+/**
+ * Konva selection callback to select RG mask objects. This includes lines and rects.
+ */
+const selectVectorMaskObjects = (node: Konva.Node): boolean => {
   return node.name() === RG_LAYER_LINE_NAME || node.name() === RG_LAYER_RECT_NAME;
 };
 
 /**
- * Creates the brush preview layer.
+ * Creates the singleton tool preview layer and all its objects.
- * @param stage The konva stage to render on.
+ * @param stage The konva stage
- * @returns The brush preview layer.
  */
-const createToolPreviewLayer = (stage: Konva.Stage) => {
+const createToolPreviewLayer = (stage: Konva.Stage): Konva.Layer => {
   // Initialize the brush preview layer & add to the stage
   const toolPreviewLayer = new Konva.Layer({ id: TOOL_PREVIEW_LAYER_ID, visible: false, listening: false });
   stage.add(toolPreviewLayer);
 
-  // Add handlers to show/hide the brush preview layer
-  stage.on('mousemove', (e) => {
-    const tool = $tool.get();
-    e.target
-      .getStage()
-      ?.findOne<Konva.Layer>(`#${TOOL_PREVIEW_LAYER_ID}`)
-      ?.visible(tool === 'brush' || tool === 'eraser');
-  });
-  stage.on('mouseleave', (e) => {
-    e.target.getStage()?.findOne<Konva.Layer>(`#${TOOL_PREVIEW_LAYER_ID}`)?.visible(false);
-  });
-  stage.on('mouseenter', (e) => {
-    const tool = $tool.get();
-    e.target
-      .getStage()
-      ?.findOne<Konva.Layer>(`#${TOOL_PREVIEW_LAYER_ID}`)
-      ?.visible(tool === 'brush' || tool === 'eraser');
-  });
-
   // Create the brush preview group & circles
   const brushPreviewGroup = new Konva.Group({ id: TOOL_PREVIEW_BRUSH_GROUP_ID });
   const brushPreviewFill = new Konva.Circle({
@@ -121,7 +108,7 @@ const createToolPreviewLayer = (stage: Konva.Stage) => {
   brushPreviewGroup.add(brushPreviewBorderOuter);
   toolPreviewLayer.add(brushPreviewGroup);
 
-  // Create the rect preview
+  // Create the rect preview - this is a rectangle drawn from the last mouse down position to the current cursor position
   const rectPreview = new Konva.Rect({ id: TOOL_PREVIEW_RECT_ID, listening: false, stroke: 'white', strokeWidth: 1 });
   toolPreviewLayer.add(rectPreview);
 
@@ -130,12 +117,14 @@ const createToolPreviewLayer = (stage: Konva.Stage) => {
 
 /**
  * Renders the brush preview for the selected tool.
- * @param stage The konva stage to render on.
+ * @param stage The konva stage
- * @param tool The selected tool.
+ * @param tool The selected tool
- * @param color The selected layer's color.
+ * @param color The selected layer's color
- * @param cursorPos The cursor position.
+ * @param selectedLayerType The selected layer's type
- * @param lastMouseDownPos The position of the last mouse down event - used for the rect tool.
+ * @param globalMaskLayerOpacity The global mask layer opacity
- * @param brushSize The brush size.
+ * @param cursorPos The cursor position
+ * @param lastMouseDownPos The position of the last mouse down event - used for the rect tool
+ * @param brushSize The brush size
  */
 const renderToolPreview = (
   stage: Konva.Stage,
@@ -146,7 +135,7 @@ const renderToolPreview = (
   cursorPos: Vector2d | null,
   lastMouseDownPos: Vector2d | null,
   brushSize: number
-) => {
+): void => {
   const layerCount = stage.find(selectRenderableLayers).length;
   // Update the stage's pointer style
   if (layerCount === 0) {
@@ -162,7 +151,7 @@ const renderToolPreview = (
     // Move rect gets a crosshair
     stage.container().style.cursor = 'crosshair';
   } else {
-    // Else we use the brush preview
+    // Else we hide the native cursor and use the konva-rendered brush preview
     stage.container().style.cursor = 'none';
   }
 
@@ -227,28 +216,29 @@ const renderToolPreview = (
 };
 
 /**
- * Creates a vector mask layer.
+ * Creates a regional guidance layer.
- * @param stage The konva stage to attach the layer to.
+ * @param stage The konva stage
- * @param reduxLayer The redux layer to create the konva layer from.
+ * @param layerState The regional guidance layer state
- * @param onLayerPosChanged Callback for when the layer's position changes.
+ * @param onLayerPosChanged Callback for when the layer's position changes
  */
-const createRegionalGuidanceLayer = (
+const createRGLayer = (
   stage: Konva.Stage,
-  reduxLayer: RegionalGuidanceLayer,
+  layerState: RegionalGuidanceLayer,
   onLayerPosChanged?: (layerId: string, x: number, y: number) => void
-) => {
+): Konva.Layer => {
   // This layer hasn't been added to the konva state yet
   const konvaLayer = new Konva.Layer({
-    id: reduxLayer.id,
+    id: layerState.id,
     name: RG_LAYER_NAME,
     draggable: true,
     dragDistance: 0,
   });
 
-  // Create a `dragmove` listener for this layer
+  // When a drag on the layer finishes, update the layer's position in state. During the drag, konva handles changing
+  // the position - we do not need to call this on the `dragmove` event.
   if (onLayerPosChanged) {
     konvaLayer.on('dragend', function (e) {
-      onLayerPosChanged(reduxLayer.id, Math.floor(e.target.x()), Math.floor(e.target.y()));
+      onLayerPosChanged(layerState.id, Math.floor(e.target.x()), Math.floor(e.target.y()));
     });
   }
 
@@ -258,7 +248,7 @@ const createRegionalGuidanceLayer = (
     if (!cursorPos) {
       return this.getAbsolutePosition();
     }
-    // Prevent the user from dragging the layer out of the stage bounds.
+    // Prevent the user from dragging the layer out of the stage bounds by constaining the cursor position to the stage bounds
     if (
       cursorPos.x < 0 ||
       cursorPos.x > stage.width() / stage.scaleX() ||
@@ -272,7 +262,7 @@ const createRegionalGuidanceLayer = (
 
   // The object group holds all of the layer's objects (e.g. lines and rects)
   const konvaObjectGroup = new Konva.Group({
-    id: getRGLayerObjectGroupId(reduxLayer.id, uuidv4()),
+    id: getRGLayerObjectGroupId(layerState.id, uuidv4()),
     name: RG_LAYER_OBJECT_GROUP_NAME,
     listening: false,
   });
@@ -284,47 +274,51 @@ const createRegionalGuidanceLayer = (
 };
 
 /**
- * Creates a konva line from a redux vector mask line.
+ * Creates a konva line from a vector mask line.
- * @param reduxObject The redux object to create the konva line from.
+ * @param vectorMaskLine The vector mask line state
- * @param konvaGroup The konva group to add the line to.
+ * @param layerObjectGroup The konva layer's object group to add the line to
  */
-const createVectorMaskLine = (reduxObject: VectorMaskLine, konvaGroup: Konva.Group): Konva.Line => {
+const createVectorMaskLine = (vectorMaskLine: VectorMaskLine, layerObjectGroup: Konva.Group): Konva.Line => {
-  const vectorMaskLine = new Konva.Line({
+  const konvaLine = new Konva.Line({
-    id: reduxObject.id,
+    id: vectorMaskLine.id,
-    key: reduxObject.id,
+    key: vectorMaskLine.id,
     name: RG_LAYER_LINE_NAME,
-    strokeWidth: reduxObject.strokeWidth,
+    strokeWidth: vectorMaskLine.strokeWidth,
     tension: 0,
     lineCap: 'round',
     lineJoin: 'round',
     shadowForStrokeEnabled: false,
-    globalCompositeOperation: reduxObject.tool === 'brush' ? 'source-over' : 'destination-out',
+    globalCompositeOperation: vectorMaskLine.tool === 'brush' ? 'source-over' : 'destination-out',
     listening: false,
   });
-  konvaGroup.add(vectorMaskLine);
+  layerObjectGroup.add(konvaLine);
-  return vectorMaskLine;
+  return konvaLine;
 };
 
 /**
- * Creates a konva rect from a redux vector mask rect.
+ * Creates a konva rect from a vector mask rect.
- * @param reduxObject The redux object to create the konva rect from.
+ * @param vectorMaskRect The vector mask rect state
- * @param konvaGroup The konva group to add the rect to.
+ * @param layerObjectGroup The konva layer's object group to add the line to
  */
-const createVectorMaskRect = (reduxObject: VectorMaskRect, konvaGroup: Konva.Group): Konva.Rect => {
+const createVectorMaskRect = (vectorMaskRect: VectorMaskRect, layerObjectGroup: Konva.Group): Konva.Rect => {
-  const vectorMaskRect = new Konva.Rect({
+  const konvaRect = new Konva.Rect({
-    id: reduxObject.id,
+    id: vectorMaskRect.id,
-    key: reduxObject.id,
+    key: vectorMaskRect.id,
     name: RG_LAYER_RECT_NAME,
-    x: reduxObject.x,
+    x: vectorMaskRect.x,
-    y: reduxObject.y,
+    y: vectorMaskRect.y,
-    width: reduxObject.width,
+    width: vectorMaskRect.width,
-    height: reduxObject.height,
+    height: vectorMaskRect.height,
     listening: false,
   });
-  konvaGroup.add(vectorMaskRect);
+  layerObjectGroup.add(konvaRect);
-  return vectorMaskRect;
+  return konvaRect;
 };
 
+/**
+ * Creates the "compositing rect" for a layer.
+ * @param konvaLayer The konva layer
+ */
 const createCompositingRect = (konvaLayer: Konva.Layer): Konva.Rect => {
   const compositingRect = new Konva.Rect({ name: COMPOSITING_RECT_NAME, listening: false });
   konvaLayer.add(compositingRect);
@@ -332,41 +326,41 @@ const createCompositingRect = (konvaLayer: Konva.Layer): Konva.Rect => {
 };
 
 /**
- * Renders a vector mask layer.
+ * Renders a regional guidance layer.
- * @param stage The konva stage to render on.
+ * @param stage The konva stage
- * @param reduxLayer The redux vector mask layer to render.
+ * @param layerState The regional guidance layer state
- * @param reduxLayerIndex The index of the layer in the redux store.
+ * @param globalMaskLayerOpacity The global mask layer opacity
- * @param globalMaskLayerOpacity The opacity of the global mask layer.
+ * @param tool The current tool
- * @param tool The current tool.
+ * @param onLayerPosChanged Callback for when the layer's position changes
  */
-const renderRegionalGuidanceLayer = (
+const renderRGLayer = (
   stage: Konva.Stage,
-  reduxLayer: RegionalGuidanceLayer,
+  layerState: RegionalGuidanceLayer,
   globalMaskLayerOpacity: number,
   tool: Tool,
   onLayerPosChanged?: (layerId: string, x: number, y: number) => void
 ): void => {
   const konvaLayer =
-    stage.findOne<Konva.Layer>(`#${reduxLayer.id}`) ??
+    stage.findOne<Konva.Layer>(`#${layerState.id}`) ?? createRGLayer(stage, layerState, onLayerPosChanged);
-    createRegionalGuidanceLayer(stage, reduxLayer, onLayerPosChanged);
 
   // Update the layer's position and listening state
   konvaLayer.setAttrs({
     listening: tool === 'move', // The layer only listens when using the move tool - otherwise the stage is handling mouse events
-    x: Math.floor(reduxLayer.x),
+    x: Math.floor(layerState.x),
-    y: Math.floor(reduxLayer.y),
+    y: Math.floor(layerState.y),
   });
 
   // Convert the color to a string, stripping the alpha - the object group will handle opacity.
-  const rgbColor = rgbColorToString(reduxLayer.previewColor);
+  const rgbColor = rgbColorToString(layerState.previewColor);
 
   const konvaObjectGroup = konvaLayer.findOne<Konva.Group>(`.${RG_LAYER_OBJECT_GROUP_NAME}`);
-  assert(konvaObjectGroup, `Object group not found for layer ${reduxLayer.id}`);
+  assert(konvaObjectGroup, `Object group not found for layer ${layerState.id}`);
 
   // We use caching to handle "global" layer opacity, but caching is expensive and we should only do it when required.
   let groupNeedsCache = false;
 
-  const objectIds = reduxLayer.maskObjects.map(mapId);
+  const objectIds = layerState.maskObjects.map(mapId);
+  // Destroy any objects that are no longer in the redux state
   for (const objectNode of konvaObjectGroup.find(selectVectorMaskObjects)) {
     if (!objectIds.includes(objectNode.id())) {
       objectNode.destroy();
@@ -374,15 +368,15 @@ const renderRegionalGuidanceLayer = (
     }
   }
 
-  for (const reduxObject of reduxLayer.maskObjects) {
+  for (const maskObject of layerState.maskObjects) {
-    if (reduxObject.type === 'vector_mask_line') {
+    if (maskObject.type === 'vector_mask_line') {
       const vectorMaskLine =
-        stage.findOne<Konva.Line>(`#${reduxObject.id}`) ?? createVectorMaskLine(reduxObject, konvaObjectGroup);
+        stage.findOne<Konva.Line>(`#${maskObject.id}`) ?? createVectorMaskLine(maskObject, konvaObjectGroup);
 
       // Only update the points if they have changed. The point values are never mutated, they are only added to the
       // array, so checking the length is sufficient to determine if we need to re-cache.
-      if (vectorMaskLine.points().length !== reduxObject.points.length) {
+      if (vectorMaskLine.points().length !== maskObject.points.length) {
-        vectorMaskLine.points(reduxObject.points);
+        vectorMaskLine.points(maskObject.points);
         groupNeedsCache = true;
       }
       // Only update the color if it has changed.
@@ -390,9 +384,9 @@ const renderRegionalGuidanceLayer = (
         vectorMaskLine.stroke(rgbColor);
         groupNeedsCache = true;
       }
-    } else if (reduxObject.type === 'vector_mask_rect') {
+    } else if (maskObject.type === 'vector_mask_rect') {
       const konvaObject =
-        stage.findOne<Konva.Rect>(`#${reduxObject.id}`) ?? createVectorMaskRect(reduxObject, konvaObjectGroup);
+        stage.findOne<Konva.Rect>(`#${maskObject.id}`) ?? createVectorMaskRect(maskObject, konvaObjectGroup);
 
       // Only update the color if it has changed.
       if (konvaObject.fill() !== rgbColor) {
@@ -403,8 +397,8 @@ const renderRegionalGuidanceLayer = (
   }
 
   // Only update layer visibility if it has changed.
-  if (konvaLayer.visible() !== reduxLayer.isEnabled) {
+  if (konvaLayer.visible() !== layerState.isEnabled) {
-    konvaLayer.visible(reduxLayer.isEnabled);
+    konvaLayer.visible(layerState.isEnabled);
     groupNeedsCache = true;
   }
 
@@ -428,7 +422,7 @@ const renderRegionalGuidanceLayer = (
    * Instead, with the special handling, the effect is as if you drew all the shapes at 100% opacity, flattened them to
    * a single raster image, and _then_ applied the 50% opacity.
    */
-  if (reduxLayer.isSelected && tool !== 'move') {
+  if (layerState.isSelected && tool !== 'move') {
     // We must clear the cache first so Konva will re-draw the group with the new compositing rect
     if (konvaObjectGroup.isCached()) {
       konvaObjectGroup.clearCache();
@@ -438,7 +432,7 @@ const renderRegionalGuidanceLayer = (
 
     compositingRect.setAttrs({
       // The rect should be the size of the layer - use the fast method if we don't have a pixel-perfect bbox already
-      ...(!reduxLayer.bboxNeedsUpdate && reduxLayer.bbox ? reduxLayer.bbox : getLayerBboxFast(konvaLayer)),
+      ...(!layerState.bboxNeedsUpdate && layerState.bbox ? layerState.bbox : getLayerBboxFast(konvaLayer)),
       fill: rgbColor,
       opacity: globalMaskLayerOpacity,
       // Draw this rect only where there are non-transparent pixels under it (e.g. the mask shapes)
@@ -459,9 +453,14 @@ const renderRegionalGuidanceLayer = (
   }
 };
 
-const createInitialImageLayer = (stage: Konva.Stage, reduxLayer: InitialImageLayer): Konva.Layer => {
+/**
+ * Creates an initial image konva layer.
+ * @param stage The konva stage
+ * @param layerState The initial image layer state
+ */
+const createIILayer = (stage: Konva.Stage, layerState: InitialImageLayer): Konva.Layer => {
   const konvaLayer = new Konva.Layer({
-    id: reduxLayer.id,
+    id: layerState.id,
     name: INITIAL_IMAGE_LAYER_NAME,
     imageSmoothingEnabled: true,
     listening: false,
@@ -470,20 +469,27 @@ const createInitialImageLayer = (stage: Konva.Stage, reduxLayer: InitialImageLay
   return konvaLayer;
 };
 
-const createInitialImageLayerImage = (konvaLayer: Konva.Layer, image: HTMLImageElement): Konva.Image => {
+/**
+ * Creates the konva image for an initial image layer.
+ * @param konvaLayer The konva layer
+ * @param imageEl The image element
+ */
+const createIILayerImage = (konvaLayer: Konva.Layer, imageEl: HTMLImageElement): Konva.Image => {
   const konvaImage = new Konva.Image({
     name: INITIAL_IMAGE_LAYER_IMAGE_NAME,
-    image,
+    image: imageEl,
   });
   konvaLayer.add(konvaImage);
   return konvaImage;
 };
 
-const updateInitialImageLayerImageAttrs = (
+/**
-  stage: Konva.Stage,
+ * Updates an initial image layer's attributes (width, height, opacity, visibility).
-  konvaImage: Konva.Image,
+ * @param stage The konva stage
-  reduxLayer: InitialImageLayer
+ * @param konvaImage The konva image
-) => {
+ * @param layerState The initial image layer state
+ */
+const updateIILayerImageAttrs = (stage: Konva.Stage, konvaImage: Konva.Image, layerState: InitialImageLayer): void => {
   // Konva erroneously reports NaN for width and height when the stage is hidden. This causes errors when caching,
   // but it doesn't seem to break anything.
   // TODO(psyche): Investigate and report upstream.
@@ -492,46 +498,55 @@ const updateInitialImageLayerImageAttrs = (
   if (
     konvaImage.width() !== newWidth ||
     konvaImage.height() !== newHeight ||
-    konvaImage.visible() !== reduxLayer.isEnabled
+    konvaImage.visible() !== layerState.isEnabled
   ) {
     konvaImage.setAttrs({
-      opacity: reduxLayer.opacity,
+      opacity: layerState.opacity,
       scaleX: 1,
       scaleY: 1,
       width: stage.width() / stage.scaleX(),
       height: stage.height() / stage.scaleY(),
-      visible: reduxLayer.isEnabled,
+      visible: layerState.isEnabled,
     });
   }
-  if (konvaImage.opacity() !== reduxLayer.opacity) {
+  if (konvaImage.opacity() !== layerState.opacity) {
-    konvaImage.opacity(reduxLayer.opacity);
+    konvaImage.opacity(layerState.opacity);
   }
 };
 
-const updateInitialImageLayerImageSource = async (
+/**
+ * Update an initial image layer's image source when the image changes.
+ * @param stage The konva stage
+ * @param konvaLayer The konva layer
+ * @param layerState The initial image layer state
+ * @param getImageDTO A function to retrieve an image DTO from the server, used to update the image source
+ */
+const updateIILayerImageSource = async (
   stage: Konva.Stage,
   konvaLayer: Konva.Layer,
-  reduxLayer: InitialImageLayer
+  layerState: InitialImageLayer,
-) => {
+  getImageDTO: (imageName: string) => Promise<ImageDTO | null>
-  if (reduxLayer.image) {
+): Promise<void> => {
-    const imageName = reduxLayer.image.name;
+  if (layerState.image) {
-    const req = getStore().dispatch(imagesApi.endpoints.getImageDTO.initiate(imageName));
+    const imageName = layerState.image.name;
-    const imageDTO = await req.unwrap();
+    const imageDTO = await getImageDTO(imageName);
-    req.unsubscribe();
+    if (!imageDTO) {
+      return;
+    }
     const imageEl = new Image();
-    const imageId = getIILayerImageId(reduxLayer.id, imageName);
+    const imageId = getIILayerImageId(layerState.id, imageName);
     imageEl.onload = () => {
       // Find the existing image or create a new one - must find using the name, bc the id may have just changed
       const konvaImage =
         konvaLayer.findOne<Konva.Image>(`.${INITIAL_IMAGE_LAYER_IMAGE_NAME}`) ??
-        createInitialImageLayerImage(konvaLayer, imageEl);
+        createIILayerImage(konvaLayer, imageEl);
 
       // Update the image's attributes
       konvaImage.setAttrs({
         id: imageId,
         image: imageEl,
       });
-      updateInitialImageLayerImageAttrs(stage, konvaImage, reduxLayer);
+      updateIILayerImageAttrs(stage, konvaImage, layerState);
       imageEl.id = imageId;
     };
     imageEl.src = imageDTO.image_url;
@@ -540,14 +555,24 @@ const updateInitialImageLayerImageSource = async (
   }
 };
 
-const renderInitialImageLayer = (stage: Konva.Stage, reduxLayer: InitialImageLayer) => {
+/**
-  const konvaLayer = stage.findOne<Konva.Layer>(`#${reduxLayer.id}`) ?? createInitialImageLayer(stage, reduxLayer);
+ * Renders an initial image layer.
+ * @param stage The konva stage
+ * @param layerState The initial image layer state
+ * @param getImageDTO A function to retrieve an image DTO from the server, used to update the image source
+ */
+const renderIILayer = (
+  stage: Konva.Stage,
+  layerState: InitialImageLayer,
+  getImageDTO: (imageName: string) => Promise<ImageDTO | null>
+): void => {
+  const konvaLayer = stage.findOne<Konva.Layer>(`#${layerState.id}`) ?? createIILayer(stage, layerState);
   const konvaImage = konvaLayer.findOne<Konva.Image>(`.${INITIAL_IMAGE_LAYER_IMAGE_NAME}`);
   const canvasImageSource = konvaImage?.image();
   let imageSourceNeedsUpdate = false;
   if (canvasImageSource instanceof HTMLImageElement) {
-    const image = reduxLayer.image;
+    const image = layerState.image;
-    if (image && canvasImageSource.id !== getCALayerImageId(reduxLayer.id, image.name)) {
+    if (image && canvasImageSource.id !== getCALayerImageId(layerState.id, image.name)) {
       imageSourceNeedsUpdate = true;
     } else if (!image) {
       imageSourceNeedsUpdate = true;
@@ -557,15 +582,20 @@ const renderInitialImageLayer = (stage: Konva.Stage, reduxLayer: InitialImageLay
   }
 
   if (imageSourceNeedsUpdate) {
-    updateInitialImageLayerImageSource(stage, konvaLayer, reduxLayer);
+    updateIILayerImageSource(stage, konvaLayer, layerState, getImageDTO);
   } else if (konvaImage) {
-    updateInitialImageLayerImageAttrs(stage, konvaImage, reduxLayer);
+    updateIILayerImageAttrs(stage, konvaImage, layerState);
   }
 };
 
-const createControlNetLayer = (stage: Konva.Stage, reduxLayer: ControlAdapterLayer): Konva.Layer => {
+/**
+ * Creates a control adapter layer.
+ * @param stage The konva stage
+ * @param layerState The control adapter layer state
+ */
+const createCALayer = (stage: Konva.Stage, layerState: ControlAdapterLayer): Konva.Layer => {
   const konvaLayer = new Konva.Layer({
-    id: reduxLayer.id,
+    id: layerState.id,
     name: CA_LAYER_NAME,
     imageSmoothingEnabled: true,
     listening: false,
@@ -574,39 +604,53 @@ const createControlNetLayer = (stage: Konva.Stage, reduxLayer: ControlAdapterLay
   return konvaLayer;
 };
 
-const createControlNetLayerImage = (konvaLayer: Konva.Layer, image: HTMLImageElement): Konva.Image => {
+/**
+ * Creates a control adapter layer image.
+ * @param konvaLayer The konva layer
+ * @param imageEl The image element
+ */
+const createCALayerImage = (konvaLayer: Konva.Layer, imageEl: HTMLImageElement): Konva.Image => {
   const konvaImage = new Konva.Image({
     name: CA_LAYER_IMAGE_NAME,
-    image,
+    image: imageEl,
   });
   konvaLayer.add(konvaImage);
   return konvaImage;
 };
 
-const updateControlNetLayerImageSource = async (
+/**
+ * Updates the image source for a control adapter layer. This includes loading the image from the server and updating the konva image.
+ * @param stage The konva stage
+ * @param konvaLayer The konva layer
+ * @param layerState The control adapter layer state
+ * @param getImageDTO A function to retrieve an image DTO from the server, used to update the image source
+ */
+const updateCALayerImageSource = async (
   stage: Konva.Stage,
   konvaLayer: Konva.Layer,
-  reduxLayer: ControlAdapterLayer
+  layerState: ControlAdapterLayer,
-) => {
+  getImageDTO: (imageName: string) => Promise<ImageDTO | null>
-  const image = reduxLayer.controlAdapter.processedImage ?? reduxLayer.controlAdapter.image;
+): Promise<void> => {
+  const image = layerState.controlAdapter.processedImage ?? layerState.controlAdapter.image;
   if (image) {
     const imageName = image.name;
-    const req = getStore().dispatch(imagesApi.endpoints.getImageDTO.initiate(imageName));
+    const imageDTO = await getImageDTO(imageName);
-    const imageDTO = await req.unwrap();
+    if (!imageDTO) {
-    req.unsubscribe();
+      return;
+    }
     const imageEl = new Image();
-    const imageId = getCALayerImageId(reduxLayer.id, imageName);
+    const imageId = getCALayerImageId(layerState.id, imageName);
     imageEl.onload = () => {
       // Find the existing image or create a new one - must find using the name, bc the id may have just changed
       const konvaImage =
-        konvaLayer.findOne<Konva.Image>(`.${CA_LAYER_IMAGE_NAME}`) ?? createControlNetLayerImage(konvaLayer, imageEl);
+        konvaLayer.findOne<Konva.Image>(`.${CA_LAYER_IMAGE_NAME}`) ?? createCALayerImage(konvaLayer, imageEl);
 
       // Update the image's attributes
       konvaImage.setAttrs({
         id: imageId,
         image: imageEl,
       });
-      updateControlNetLayerImageAttrs(stage, konvaImage, reduxLayer);
+      updateCALayerImageAttrs(stage, konvaImage, layerState);
       // Must cache after this to apply the filters
       konvaImage.cache();
       imageEl.id = imageId;
@@ -617,11 +661,17 @@ const updateControlNetLayerImageSource = async (
   }
 };
 
-const updateControlNetLayerImageAttrs = (
+/**
+ * Updates the image attributes for a control adapter layer's image (width, height, visibility, opacity, filters).
+ * @param stage The konva stage
+ * @param konvaImage The konva image
+ * @param layerState The control adapter layer state
+ */
+const updateCALayerImageAttrs = (
   stage: Konva.Stage,
   konvaImage: Konva.Image,
-  reduxLayer: ControlAdapterLayer
+  layerState: ControlAdapterLayer
-) => {
+): void => {
   let needsCache = false;
   // Konva erroneously reports NaN for width and height when the stage is hidden. This causes errors when caching,
   // but it doesn't seem to break anything.
@@ -632,36 +682,47 @@ const updateControlNetLayerImageAttrs = (
   if (
     konvaImage.width() !== newWidth ||
     konvaImage.height() !== newHeight ||
-    konvaImage.visible() !== reduxLayer.isEnabled ||
+    konvaImage.visible() !== layerState.isEnabled ||
-    hasFilter !== reduxLayer.isFilterEnabled
+    hasFilter !== layerState.isFilterEnabled
   ) {
     konvaImage.setAttrs({
-      opacity: reduxLayer.opacity,
+      opacity: layerState.opacity,
       scaleX: 1,
       scaleY: 1,
       width: stage.width() / stage.scaleX(),
       height: stage.height() / stage.scaleY(),
-      visible: reduxLayer.isEnabled,
+      visible: layerState.isEnabled,
-      filters: reduxLayer.isFilterEnabled ? [LightnessToAlphaFilter] : [],
+      filters: layerState.isFilterEnabled ? [LightnessToAlphaFilter] : [],
     });
     needsCache = true;
   }
-  if (konvaImage.opacity() !== reduxLayer.opacity) {
+  if (konvaImage.opacity() !== layerState.opacity) {
-    konvaImage.opacity(reduxLayer.opacity);
+    konvaImage.opacity(layerState.opacity);
   }
   if (needsCache) {
     konvaImage.cache();
   }
 };
 
-const renderControlNetLayer = (stage: Konva.Stage, reduxLayer: ControlAdapterLayer) => {
+/**
-  const konvaLayer = stage.findOne<Konva.Layer>(`#${reduxLayer.id}`) ?? createControlNetLayer(stage, reduxLayer);
+ * Renders a control adapter layer. If the layer doesn't already exist, it is created. Otherwise, the layer is updated
+ * with the current image source and attributes.
+ * @param stage The konva stage
+ * @param layerState The control adapter layer state
+ * @param getImageDTO A function to retrieve an image DTO from the server, used to update the image source
+ */
+const renderCALayer = (
+  stage: Konva.Stage,
+  layerState: ControlAdapterLayer,
+  getImageDTO: (imageName: string) => Promise<ImageDTO | null>
+): void => {
+  const konvaLayer = stage.findOne<Konva.Layer>(`#${layerState.id}`) ?? createCALayer(stage, layerState);
   const konvaImage = konvaLayer.findOne<Konva.Image>(`.${CA_LAYER_IMAGE_NAME}`);
   const canvasImageSource = konvaImage?.image();
   let imageSourceNeedsUpdate = false;
   if (canvasImageSource instanceof HTMLImageElement) {
-    const image = reduxLayer.controlAdapter.processedImage ?? reduxLayer.controlAdapter.image;
+    const image = layerState.controlAdapter.processedImage ?? layerState.controlAdapter.image;
-    if (image && canvasImageSource.id !== getCALayerImageId(reduxLayer.id, image.name)) {
+    if (image && canvasImageSource.id !== getCALayerImageId(layerState.id, image.name)) {
       imageSourceNeedsUpdate = true;
     } else if (!image) {
       imageSourceNeedsUpdate = true;
@@ -671,44 +732,46 @@ const renderControlNetLayer = (stage: Konva.Stage, reduxLayer: ControlAdapterLay
   }
 
   if (imageSourceNeedsUpdate) {
-    updateControlNetLayerImageSource(stage, konvaLayer, reduxLayer);
+    updateCALayerImageSource(stage, konvaLayer, layerState, getImageDTO);
   } else if (konvaImage) {
-    updateControlNetLayerImageAttrs(stage, konvaImage, reduxLayer);
+    updateCALayerImageAttrs(stage, konvaImage, layerState);
   }
 };
 
 /**
  * Renders the layers on the stage.
- * @param stage The konva stage to render on.
+ * @param stage The konva stage
- * @param reduxLayers Array of the layers from the redux store.
+ * @param layerStates Array of all layer states
- * @param layerOpacity The opacity of the layer.
+ * @param globalMaskLayerOpacity The global mask layer opacity
- * @param onLayerPosChanged Callback for when the layer's position changes. This is optional to allow for offscreen rendering.
+ * @param tool The current tool
- * @returns
+ * @param getImageDTO A function to retrieve an image DTO from the server, used to update the image source
+ * @param onLayerPosChanged Callback for when the layer's position changes
  */
 const renderLayers = (
   stage: Konva.Stage,
-  reduxLayers: Layer[],
+  layerStates: Layer[],
   globalMaskLayerOpacity: number,
   tool: Tool,
+  getImageDTO: (imageName: string) => Promise<ImageDTO | null>,
   onLayerPosChanged?: (layerId: string, x: number, y: number) => void
-) => {
+): void => {
-  const reduxLayerIds = reduxLayers.filter(isRenderableLayer).map(mapId);
+  const layerIds = layerStates.filter(isRenderableLayer).map(mapId);
   // Remove un-rendered layers
   for (const konvaLayer of stage.find<Konva.Layer>(selectRenderableLayers)) {
-    if (!reduxLayerIds.includes(konvaLayer.id())) {
+    if (!layerIds.includes(konvaLayer.id())) {
       konvaLayer.destroy();
     }
   }
 
-  for (const reduxLayer of reduxLayers) {
+  for (const layer of layerStates) {
-    if (isRegionalGuidanceLayer(reduxLayer)) {
+    if (isRegionalGuidanceLayer(layer)) {
-      renderRegionalGuidanceLayer(stage, reduxLayer, globalMaskLayerOpacity, tool, onLayerPosChanged);
+      renderRGLayer(stage, layer, globalMaskLayerOpacity, tool, onLayerPosChanged);
     }
-    if (isControlAdapterLayer(reduxLayer)) {
+    if (isControlAdapterLayer(layer)) {
-      renderControlNetLayer(stage, reduxLayer);
+      renderCALayer(stage, layer, getImageDTO);
     }
-    if (isInitialImageLayer(reduxLayer)) {
+    if (isInitialImageLayer(layer)) {
-      renderInitialImageLayer(stage, reduxLayer);
+      renderIILayer(stage, layer, getImageDTO);
     }
     // IP Adapter layers are not rendered
   }
@@ -716,13 +779,12 @@ const renderLayers = (
 
 /**
  * Creates a bounding box rect for a layer.
- * @param reduxLayer The redux layer to create the bounding box for.
+ * @param layerState The layer state for the layer to create the bounding box for
- * @param konvaLayer The konva layer to attach the bounding box to.
+ * @param konvaLayer The konva layer to attach the bounding box to
- * @param onBboxMouseDown Callback for when the bounding box is clicked.
  */
-const createBboxRect = (reduxLayer: Layer, konvaLayer: Konva.Layer) => {
+const createBboxRect = (layerState: Layer, konvaLayer: Konva.Layer): Konva.Rect => {
   const rect = new Konva.Rect({
-    id: getLayerBboxId(reduxLayer.id),
+    id: getLayerBboxId(layerState.id),
     name: LAYER_BBOX_NAME,
     strokeWidth: 1,
     visible: false,
@@ -733,12 +795,12 @@ const createBboxRect = (reduxLayer: Layer, konvaLayer: Konva.Layer) => {
 
 /**
  * Renders the bounding boxes for the layers.
- * @param stage The konva stage to render on
+ * @param stage The konva stage
- * @param reduxLayers An array of all redux layers to draw bboxes for
+ * @param layerStates An array of layers to draw bboxes for
  * @param tool The current tool
  * @returns
  */
-const renderBboxes = (stage: Konva.Stage, reduxLayers: Layer[], tool: Tool) => {
+const renderBboxes = (stage: Konva.Stage, layerStates: Layer[], tool: Tool): void => {
   // Hide all bboxes so they don't interfere with getClientRect
   for (const bboxRect of stage.find<Konva.Rect>(`.${LAYER_BBOX_NAME}`)) {
     bboxRect.visible(false);
@@ -749,39 +811,39 @@ const renderBboxes = (stage: Konva.Stage, reduxLayers: Layer[], tool: Tool) => {
     return;
   }
 
-  for (const reduxLayer of reduxLayers.filter(isRegionalGuidanceLayer)) {
+  for (const layer of layerStates.filter(isRegionalGuidanceLayer)) {
-    if (!reduxLayer.bbox) {
+    if (!layer.bbox) {
       continue;
     }
-    const konvaLayer = stage.findOne<Konva.Layer>(`#${reduxLayer.id}`);
+    const konvaLayer = stage.findOne<Konva.Layer>(`#${layer.id}`);
-    assert(konvaLayer, `Layer ${reduxLayer.id} not found in stage`);
+    assert(konvaLayer, `Layer ${layer.id} not found in stage`);
 
-    const bboxRect = konvaLayer.findOne<Konva.Rect>(`.${LAYER_BBOX_NAME}`) ?? createBboxRect(reduxLayer, konvaLayer);
+    const bboxRect = konvaLayer.findOne<Konva.Rect>(`.${LAYER_BBOX_NAME}`) ?? createBboxRect(layer, konvaLayer);
 
     bboxRect.setAttrs({
-      visible: !reduxLayer.bboxNeedsUpdate,
+      visible: !layer.bboxNeedsUpdate,
-      listening: reduxLayer.isSelected,
+      listening: layer.isSelected,
-      x: reduxLayer.bbox.x,
+      x: layer.bbox.x,
-      y: reduxLayer.bbox.y,
+      y: layer.bbox.y,
-      width: reduxLayer.bbox.width,
+      width: layer.bbox.width,
-      height: reduxLayer.bbox.height,
+      height: layer.bbox.height,
-      stroke: reduxLayer.isSelected ? BBOX_SELECTED_STROKE : '',
+      stroke: layer.isSelected ? BBOX_SELECTED_STROKE : '',
     });
   }
 };
 
 /**
  * Calculates the bbox of each regional guidance layer. Only calculates if the mask has changed.
- * @param stage The konva stage to render on.
+ * @param stage The konva stage
- * @param reduxLayers An array of redux layers to calculate bboxes for
+ * @param layerStates An array of layers to calculate bboxes for
  * @param onBboxChanged Callback for when the bounding box changes
  */
 const updateBboxes = (
   stage: Konva.Stage,
-  reduxLayers: Layer[],
+  layerStates: Layer[],
   onBboxChanged: (layerId: string, bbox: IRect | null) => void
-) => {
+): void => {
-  for (const rgLayer of reduxLayers.filter(isRegionalGuidanceLayer)) {
+  for (const rgLayer of layerStates.filter(isRegionalGuidanceLayer)) {
     const konvaLayer = stage.findOne<Konva.Layer>(`#${rgLayer.id}`);
     assert(konvaLayer, `Layer ${rgLayer.id} not found in stage`);
     // We only need to recalculate the bbox if the layer has changed
@@ -808,7 +870,7 @@ const updateBboxes = (
 
 /**
  * Creates the background layer for the stage.
- * @param stage The konva stage to render on
+ * @param stage The konva stage
  */
 const createBackgroundLayer = (stage: Konva.Stage): Konva.Layer => {
   const layer = new Konva.Layer({
@@ -829,17 +891,17 @@ const createBackgroundLayer = (stage: Konva.Stage): Konva.Layer => {
   image.onload = () => {
     background.fillPatternImage(image);
   };
-  image.src = STAGE_BG_DATAURL;
+  image.src = TRANSPARENCY_CHECKER_PATTERN;
   return layer;
 };
 
 /**
  * Renders the background layer for the stage.
- * @param stage The konva stage to render on
+ * @param stage The konva stage
  * @param width The unscaled width of the canvas
  * @param height The unscaled height of the canvas
  */
-const renderBackground = (stage: Konva.Stage, width: number, height: number) => {
+const renderBackground = (stage: Konva.Stage, width: number, height: number): void => {
   const layer = stage.findOne<Konva.Layer>(`#${BACKGROUND_LAYER_ID}`) ?? createBackgroundLayer(stage);
 
   const background = layer.findOne<Konva.Rect>(`#${BACKGROUND_RECT_ID}`);
@@ -880,6 +942,10 @@ const arrangeLayers = (stage: Konva.Stage, layerIds: string[]): void => {
   stage.findOne<Konva.Layer>(`#${TOOL_PREVIEW_LAYER_ID}`)?.zIndex(nextZIndex++);
 };
 
+/**
+ * Creates the "no layers" fallback layer
+ * @param stage The konva stage
+ */
 const createNoLayersMessageLayer = (stage: Konva.Stage): Konva.Layer => {
   const noLayersMessageLayer = new Konva.Layer({
     id: NO_LAYERS_MESSAGE_LAYER_ID,
@@ -891,7 +957,7 @@ const createNoLayersMessageLayer = (stage: Konva.Stage): Konva.Layer => {
     y: 0,
     align: 'center',
     verticalAlign: 'middle',
-    text: t('controlLayers.noLayersAdded'),
+    text: t('controlLayers.noLayersAdded', 'No Layers Added'),
     fontFamily: '"Inter Variable", sans-serif',
     fontStyle: '600',
     fill: 'white',
@@ -901,7 +967,14 @@ const createNoLayersMessageLayer = (stage: Konva.Stage): Konva.Layer => {
   return noLayersMessageLayer;
 };
 
-const renderNoLayersMessage = (stage: Konva.Stage, layerCount: number, width: number, height: number) => {
+/**
+ * Renders the "no layers" message when there are no layers to render
+ * @param stage The konva stage
+ * @param layerCount The current number of layers
+ * @param width The target width of the text
+ * @param height The target height of the text
+ */
+const renderNoLayersMessage = (stage: Konva.Stage, layerCount: number, width: number, height: number): void => {
   const noLayersMessageLayer =
     stage.findOne<Konva.Layer>(`#${NO_LAYERS_MESSAGE_LAYER_ID}`) ?? createNoLayersMessageLayer(stage);
   if (layerCount === 0) {
@@ -936,20 +1009,3 @@ export const debouncedRenderers = {
   arrangeLayers: debounce(arrangeLayers, DEBOUNCE_MS),
   updateBboxes: debounce(updateBboxes, DEBOUNCE_MS),
 };
-
-/**
- * Calculates the lightness (HSL) of a given pixel and sets the alpha channel to that value.
- * This is useful for edge maps and other masks, to make the black areas transparent.
- * @param imageData The image data to apply the filter to
- */
-const LightnessToAlphaFilter = (imageData: ImageData) => {
-  const len = imageData.data.length / 4;
-  for (let i = 0; i < len; i++) {
-    const r = imageData.data[i * 4 + 0] as number;
-    const g = imageData.data[i * 4 + 1] as number;
-    const b = imageData.data[i * 4 + 2] as number;
-    const cMin = Math.min(r, g, b);
-    const cMax = Math.max(r, g, b);
-    imageData.data[i * 4 + 3] = (cMin + cMax) / 2;
-  }
-};

invokeai/frontend/web/src/features/controlLayers/konva/util.ts

@@ -0,0 +1,67 @@
+import type Konva from 'konva';
+import type { KonvaEventObject } from 'konva/lib/Node';
+import type { Vector2d } from 'konva/lib/types';
+
+//#region getScaledFlooredCursorPosition
+/**
+ * Gets the scaled and floored cursor position on the stage. If the cursor is not currently over the stage, returns null.
+ * @param stage The konva stage
+ */
+export const getScaledFlooredCursorPosition = (stage: Konva.Stage): Vector2d | null => {
+  const pointerPosition = stage.getPointerPosition();
+  const stageTransform = stage.getAbsoluteTransform().copy();
+  if (!pointerPosition) {
+    return null;
+  }
+  const scaledCursorPosition = stageTransform.invert().point(pointerPosition);
+  return {
+    x: Math.floor(scaledCursorPosition.x),
+    y: Math.floor(scaledCursorPosition.y),
+  };
+};
+//#endregion
+
+//#region snapPosToStage
+/**
+ * Snaps a position to the edge of the stage if within a threshold of the edge
+ * @param pos The position to snap
+ * @param stage The konva stage
+ * @param snapPx The snap threshold in pixels
+ */
+export const snapPosToStage = (pos: Vector2d, stage: Konva.Stage, snapPx = 10): Vector2d => {
+  const snappedPos = { ...pos };
+  // Get the normalized threshold for snapping to the edge of the stage
+  const thresholdX = snapPx / stage.scaleX();
+  const thresholdY = snapPx / stage.scaleY();
+  const stageWidth = stage.width() / stage.scaleX();
+  const stageHeight = stage.height() / stage.scaleY();
+  // Snap to the edge of the stage if within threshold
+  if (pos.x - thresholdX < 0) {
+    snappedPos.x = 0;
+  } else if (pos.x + thresholdX > stageWidth) {
+    snappedPos.x = Math.floor(stageWidth);
+  }
+  if (pos.y - thresholdY < 0) {
+    snappedPos.y = 0;
+  } else if (pos.y + thresholdY > stageHeight) {
+    snappedPos.y = Math.floor(stageHeight);
+  }
+  return snappedPos;
+};
+//#endregion
+
+//#region getIsMouseDown
+/**
+ * Checks if the left mouse button is currently pressed
+ * @param e The konva event
+ */
+export const getIsMouseDown = (e: KonvaEventObject<MouseEvent>): boolean => e.evt.buttons === 1;
+//#endregion
+
+//#region getIsFocused
+/**
+ * Checks if the stage is currently focused
+ * @param stage The konva stage
+ */
+export const getIsFocused = (stage: Konva.Stage): boolean => stage.container().contains(document.activeElement);
+//#endregion

invokeai/frontend/web/src/features/controlLayers/store/controlLayersSlice.ts

@@ -4,6 +4,14 @@ import type { PersistConfig, RootState } from 'app/store/store';
 import { moveBackward, moveForward, moveToBack, moveToFront } from 'common/util/arrayUtils';
 import { deepClone } from 'common/util/deepClone';
 import { roundDownToMultiple } from 'common/util/roundDownToMultiple';
+import {
+  getCALayerId,
+  getIPALayerId,
+  getRGLayerId,
+  getRGLayerLineId,
+  getRGLayerRectId,
+  INITIAL_IMAGE_LAYER_ID,
+} from 'features/controlLayers/konva/naming';
 import type {
   CLIPVisionModelV2,
   ControlModeV2,
@@ -36,6 +44,9 @@ import { assert } from 'tsafe';
 import { v4 as uuidv4 } from 'uuid';
 
 import type {
+  AddLineArg,
+  AddPointToLineArg,
+  AddRectArg,
   ControlAdapterLayer,
   ControlLayersState,
   DrawingTool,
@@ -492,11 +503,11 @@ export const controlLayersSlice = createSlice({
         layer.bboxNeedsUpdate = true;
         layer.uploadedMaskImage = null;
       },
-      prepare: (payload: { layerId: string; points: [number, number, number, number]; tool: DrawingTool }) => ({
+      prepare: (payload: AddLineArg) => ({
         payload: { ...payload, lineUuid: uuidv4() },
       }),
     },
-    rgLayerPointsAdded: (state, action: PayloadAction<{ layerId: string; point: [number, number] }>) => {
+    rgLayerPointsAdded: (state, action: PayloadAction<AddPointToLineArg>) => {
       const { layerId, point } = action.payload;
       const layer = selectRGLayerOrThrow(state, layerId);
       const lastLine = layer.maskObjects.findLast(isLine);
@@ -529,7 +540,7 @@ export const controlLayersSlice = createSlice({
         layer.bboxNeedsUpdate = true;
         layer.uploadedMaskImage = null;
       },
-      prepare: (payload: { layerId: string; rect: IRect }) => ({ payload: { ...payload, rectUuid: uuidv4() } }),
+      prepare: (payload: AddRectArg) => ({ payload: { ...payload, rectUuid: uuidv4() } }),
     },
     rgLayerMaskImageUploaded: (state, action: PayloadAction<{ layerId: string; imageDTO: ImageDTO }>) => {
       const { layerId, imageDTO } = action.payload;
@@ -883,45 +894,21 @@ const migrateControlLayersState = (state: any): any => {
   return state;
 };
 
+// Ephemeral interaction state
 export const $isDrawing = atom(false);
 export const $lastMouseDownPos = atom<Vector2d | null>(null);
 export const $tool = atom<Tool>('brush');
 export const $lastCursorPos = atom<Vector2d | null>(null);
+export const $isPreviewVisible = atom(true);
+export const $lastAddedPoint = atom<Vector2d | null>(null);
 
-// IDs for singleton Konva layers and objects
+// Some nanostores that are manually synced to redux state to provide imperative access
-export const TOOL_PREVIEW_LAYER_ID = 'tool_preview_layer';
+// TODO(psyche): This is a hack, figure out another way to handle this...
-export const TOOL_PREVIEW_BRUSH_GROUP_ID = 'tool_preview_layer.brush_group';
+export const $brushSize = atom<number>(0);
-export const TOOL_PREVIEW_BRUSH_FILL_ID = 'tool_preview_layer.brush_fill';
+export const $brushSpacingPx = atom<number>(0);
-export const TOOL_PREVIEW_BRUSH_BORDER_INNER_ID = 'tool_preview_layer.brush_border_inner';
+export const $selectedLayerId = atom<string | null>(null);
-export const TOOL_PREVIEW_BRUSH_BORDER_OUTER_ID = 'tool_preview_layer.brush_border_outer';
+export const $selectedLayerType = atom<Layer['type'] | null>(null);
-export const TOOL_PREVIEW_RECT_ID = 'tool_preview_layer.rect';
+export const $shouldInvertBrushSizeScrollDirection = atom(false);
-export const BACKGROUND_LAYER_ID = 'background_layer';
-export const BACKGROUND_RECT_ID = 'background_layer.rect';
-export const NO_LAYERS_MESSAGE_LAYER_ID = 'no_layers_message';
-
-// Names (aka classes) for Konva layers and objects
-export const CA_LAYER_NAME = 'control_adapter_layer';
-export const CA_LAYER_IMAGE_NAME = 'control_adapter_layer.image';
-export const RG_LAYER_NAME = 'regional_guidance_layer';
-export const RG_LAYER_LINE_NAME = 'regional_guidance_layer.line';
-export const RG_LAYER_OBJECT_GROUP_NAME = 'regional_guidance_layer.object_group';
-export const RG_LAYER_RECT_NAME = 'regional_guidance_layer.rect';
-export const INITIAL_IMAGE_LAYER_ID = 'singleton_initial_image_layer';
-export const INITIAL_IMAGE_LAYER_NAME = 'initial_image_layer';
-export const INITIAL_IMAGE_LAYER_IMAGE_NAME = 'initial_image_layer.image';
-export const LAYER_BBOX_NAME = 'layer.bbox';
-export const COMPOSITING_RECT_NAME = 'compositing-rect';
-
-// Getters for non-singleton layer and object IDs
-export const getRGLayerId = (layerId: string) => `${RG_LAYER_NAME}_${layerId}`;
-const getRGLayerLineId = (layerId: string, lineId: string) => `${layerId}.line_${lineId}`;
-const getRGLayerRectId = (layerId: string, lineId: string) => `${layerId}.rect_${lineId}`;
-export const getRGLayerObjectGroupId = (layerId: string, groupId: string) => `${layerId}.objectGroup_${groupId}`;
-export const getLayerBboxId = (layerId: string) => `${layerId}.bbox`;
-export const getCALayerId = (layerId: string) => `control_adapter_layer_${layerId}`;
-export const getCALayerImageId = (layerId: string, imageName: string) => `${layerId}.image_${imageName}`;
-export const getIILayerImageId = (layerId: string, imageName: string) => `${layerId}.image_${imageName}`;
-export const getIPALayerId = (layerId: string) => `ip_adapter_layer_${layerId}`;
 
 export const controlLayersPersistConfig: PersistConfig<ControlLayersState> = {
   name: controlLayersSlice.name,

invokeai/frontend/web/src/features/controlLayers/store/types.ts

@@ -17,6 +17,7 @@ import {
   zParameterPositivePrompt,
   zParameterStrength,
 } from 'features/parameters/types/parameterSchemas';
+import type { IRect } from 'konva/lib/types';
 import { z } from 'zod';
 
 const zTool = z.enum(['brush', 'eraser', 'move', 'rect']);
@@ -129,3 +130,7 @@ export type ControlLayersState = {
     aspectRatio: AspectRatioState;
   };
 };
+
+export type AddLineArg = { layerId: string; points: [number, number, number, number]; tool: DrawingTool };
+export type AddPointToLineArg = { layerId: string; point: [number, number] };
+export type AddRectArg = { layerId: string; rect: IRect };

invokeai/frontend/web/src/features/controlLayers/util/getLayerBlobs.ts

@@ -1,66 +0,0 @@
-import { getStore } from 'app/store/nanostores/store';
-import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
-import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
-import { isRegionalGuidanceLayer, RG_LAYER_NAME } from 'features/controlLayers/store/controlLayersSlice';
-import { renderers } from 'features/controlLayers/util/renderers';
-import Konva from 'konva';
-import { assert } from 'tsafe';
-
-/**
- * Get the blobs of all regional prompt layers. Only visible layers are returned.
- * @param layerIds The IDs of the layers to get blobs for. If not provided, all regional prompt layers are used.
- * @param preview Whether to open a new tab displaying each layer.
- * @returns A map of layer IDs to blobs.
- */
-export const getRegionalPromptLayerBlobs = async (
-  layerIds?: string[],
-  preview: boolean = false
-): Promise<Record<string, Blob>> => {
-  const state = getStore().getState();
-  const { layers } = state.controlLayers.present;
-  const { width, height } = state.controlLayers.present.size;
-  const reduxLayers = layers.filter(isRegionalGuidanceLayer);
-  const container = document.createElement('div');
-  const stage = new Konva.Stage({ container, width, height });
-  renderers.renderLayers(stage, reduxLayers, 1, 'brush');
-
-  const konvaLayers = stage.find<Konva.Layer>(`.${RG_LAYER_NAME}`);
-  const blobs: Record<string, Blob> = {};
-
-  // First remove all layers
-  for (const layer of konvaLayers) {
-    layer.remove();
-  }
-
-  // Next render each layer to a blob
-  for (const layer of konvaLayers) {
-    if (layerIds && !layerIds.includes(layer.id())) {
-      continue;
-    }
-    const reduxLayer = reduxLayers.find((l) => l.id === layer.id());
-    assert(reduxLayer, `Redux layer ${layer.id()} not found`);
-    stage.add(layer);
-    const blob = await new Promise<Blob>((resolve) => {
-      stage.toBlob({
-        callback: (blob) => {
-          assert(blob, 'Blob is null');
-          resolve(blob);
-        },
-      });
-    });
-
-    if (preview) {
-      const base64 = await blobToDataURL(blob);
-      openBase64ImageInTab([
-        {
-          base64,
-          caption: `${reduxLayer.id}: ${reduxLayer.positivePrompt} / ${reduxLayer.negativePrompt}`,
-        },
-      ]);
-    }
-    layer.remove();
-    blobs[layer.id()] = blob;
-  }
-
-  return blobs;
-};

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataGraphTabContent.tsx

@@ -28,7 +28,9 @@ const ImageMetadataGraphTabContent = ({ image }: Props) => {
     return <IAINoContentFallback label={t('nodes.noGraph')} />;
   }
 
-  return <DataViewer data={graph} label={t('nodes.graph')} />;
+  return (
+    <DataViewer fileName={`${image.image_name.replace('.png', '')}_graph`} data={graph} label={t('nodes.graph')} />
+  );
 };
 
 export default memo(ImageMetadataGraphTabContent);

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

@@ -68,14 +68,22 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
           </TabPanel>
           <TabPanel>
             {metadata ? (
-              <DataViewer data={metadata} label={t('metadata.metadata')} />
+              <DataViewer
+                fileName={`${image.image_name.replace('.png', '')}_metadata`}
+                data={metadata}
+                label={t('metadata.metadata')}
+              />
             ) : (
               <IAINoContentFallback label={t('metadata.noMetaData')} />
             )}
           </TabPanel>
           <TabPanel>
             {image ? (
-              <DataViewer data={image} label={t('metadata.imageDetails')} />
+              <DataViewer
+                fileName={`${image.image_name.replace('.png', '')}_details`}
+                data={image}
+                label={t('metadata.imageDetails')}
+              />
             ) : (
               <IAINoContentFallback label={t('metadata.noImageDetails')} />
             )}

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataWorkflowTabContent.tsx

@@ -28,7 +28,13 @@ const ImageMetadataWorkflowTabContent = ({ image }: Props) => {
     return <IAINoContentFallback label={t('nodes.noWorkflow')} />;
   }
 
-  return <DataViewer data={workflow} label={t('metadata.workflow')} />;
+  return (
+    <DataViewer
+      fileName={`${image.image_name.replace('.png', '')}_workflow`}
+      data={workflow}
+      label={t('metadata.workflow')}
+    />
+  );
 };
 
 export default memo(ImageMetadataWorkflowTabContent);

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageComparisonHover.tsx

@@ -3,7 +3,7 @@ import { useAppSelector } from 'app/store/storeHooks';
 import { useBoolean } from 'common/hooks/useBoolean';
 import { preventDefault } from 'common/util/stopPropagation';
 import type { Dimensions } from 'features/canvas/store/canvasTypes';
-import { STAGE_BG_DATAURL } from 'features/controlLayers/util/renderers';
+import { TRANSPARENCY_CHECKER_PATTERN } from 'features/controlLayers/konva/constants';
 import { ImageComparisonLabel } from 'features/gallery/components/ImageViewer/ImageComparisonLabel';
 import { memo, useMemo, useRef } from 'react';
 
@@ -78,7 +78,7 @@ export const ImageComparisonHover = memo(({ firstImage, secondImage, containerDi
               left={0}
               right={0}
               bottom={0}
-              backgroundImage={STAGE_BG_DATAURL}
+              backgroundImage={TRANSPARENCY_CHECKER_PATTERN}
               backgroundRepeat="repeat"
               opacity={0.2}
             />

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageComparisonSlider.tsx

@@ -2,7 +2,7 @@ import { Box, Flex, Icon, Image } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
 import { preventDefault } from 'common/util/stopPropagation';
 import type { Dimensions } from 'features/canvas/store/canvasTypes';
-import { STAGE_BG_DATAURL } from 'features/controlLayers/util/renderers';
+import { TRANSPARENCY_CHECKER_PATTERN } from 'features/controlLayers/konva/constants';
 import { ImageComparisonLabel } from 'features/gallery/components/ImageViewer/ImageComparisonLabel';
 import { memo, useCallback, useEffect, useMemo, useRef, useState } from 'react';
 import { PiCaretLeftBold, PiCaretRightBold } from 'react-icons/pi';
@@ -120,7 +120,7 @@ export const ImageComparisonSlider = memo(({ firstImage, secondImage, containerD
             left={0}
             right={0}
             bottom={0}
-            backgroundImage={STAGE_BG_DATAURL}
+            backgroundImage={TRANSPARENCY_CHECKER_PATTERN}
             backgroundRepeat="repeat"
             opacity={0.2}
           />

invokeai/frontend/web/src/features/metadata/util/handlers.ts

@@ -1,4 +1,7 @@
+import { getStore } from 'app/store/nanostores/store';
+import { deepClone } from 'common/util/deepClone';
 import { objectKeys } from 'common/util/objectKeys';
+import { shouldConcatPromptsChanged } from 'features/controlLayers/store/controlLayersSlice';
 import type { Layer } from 'features/controlLayers/store/types';
 import type { LoRA } from 'features/lora/store/loraSlice';
 import type {
@@ -16,6 +19,7 @@ import { validators } from 'features/metadata/util/validators';
 import type { ModelIdentifierField } from 'features/nodes/types/common';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
+import { size } from 'lodash-es';
 import { assert } from 'tsafe';
 
 import { parsers } from './parsers';
@@ -376,54 +380,25 @@ export const handlers = {
   }),
 } as const;
 
+type ParsedValue = Awaited<ReturnType<(typeof handlers)[keyof typeof handlers]['parse']>>;
+type RecallResults = Partial<Record<keyof typeof handlers, ParsedValue>>;
+
 export const parseAndRecallPrompts = async (metadata: unknown) => {
-  const results = await Promise.allSettled([
+  const keysToRecall: (keyof typeof handlers)[] = [
-    handlers.positivePrompt.parse(metadata).then((positivePrompt) => {
+    'positivePrompt',
-      if (!handlers.positivePrompt.recall) {
+    'negativePrompt',
-        return;
+    'sdxlPositiveStylePrompt',
-      }
+    'sdxlNegativeStylePrompt',
-      handlers.positivePrompt?.recall(positivePrompt);
+  ];
-    }),
+  const recalled = await recallKeys(keysToRecall, metadata);
-    handlers.negativePrompt.parse(metadata).then((negativePrompt) => {
+  if (size(recalled) > 0) {
-      if (!handlers.negativePrompt.recall) {
-        return;
-      }
-      handlers.negativePrompt?.recall(negativePrompt);
-    }),
-    handlers.sdxlPositiveStylePrompt.parse(metadata).then((sdxlPositiveStylePrompt) => {
-      if (!handlers.sdxlPositiveStylePrompt.recall) {
-        return;
-      }
-      handlers.sdxlPositiveStylePrompt?.recall(sdxlPositiveStylePrompt);
-    }),
-    handlers.sdxlNegativeStylePrompt.parse(metadata).then((sdxlNegativeStylePrompt) => {
-      if (!handlers.sdxlNegativeStylePrompt.recall) {
-        return;
-      }
-      handlers.sdxlNegativeStylePrompt?.recall(sdxlNegativeStylePrompt);
-    }),
-  ]);
-  if (results.some((result) => result.status === 'fulfilled')) {
     parameterSetToast(t('metadata.allPrompts'));
   }
 };
 
 export const parseAndRecallImageDimensions = async (metadata: unknown) => {
-  const results = await Promise.allSettled([
+  const recalled = recallKeys(['width', 'height'], metadata);
-    handlers.width.parse(metadata).then((width) => {
+  if (size(recalled) > 0) {
-      if (!handlers.width.recall) {
-        return;
-      }
-      handlers.width?.recall(width);
-    }),
-    handlers.height.parse(metadata).then((height) => {
-      if (!handlers.height.recall) {
-        return;
-      }
-      handlers.height?.recall(height);
-    }),
-  ]);
-  if (results.some((result) => result.status === 'fulfilled')) {
     parameterSetToast(t('metadata.imageDimensions'));
   }
 };
@@ -438,28 +413,20 @@ export const parseAndRecallAllMetadata = async (
   toControlLayers: boolean,
   skip: (keyof typeof handlers)[] = []
 ) => {
-  const skipKeys = skip ?? [];
+  const skipKeys = deepClone(skip);
   if (toControlLayers) {
     skipKeys.push(...TO_CONTROL_LAYERS_SKIP_KEYS);
   } else {
     skipKeys.push(...NOT_TO_CONTROL_LAYERS_SKIP_KEYS);
   }
-  const results = await Promise.allSettled(
-    objectKeys(handlers)
-      .filter((key) => !skipKeys.includes(key))
-      .map((key) => {
-        const { parse, recall } = handlers[key];
-        return parse(metadata).then((value) => {
-          if (!recall) {
-            return;
-          }
-          /* @ts-expect-error The return type of parse and the input type of recall are guaranteed to be compatible. */
-          recall(value);
-        });
-      })
-  );
 
-  if (results.some((result) => result.status === 'fulfilled')) {
+  // We may need to take some further action depending on what was recalled. For example, we need to disable SDXL prompt
+  // concat if the negative or positive style prompt was set. Because the recalling is all async, we need to collect all
+  // results
+  const keysToRecall = objectKeys(handlers).filter((key) => !skipKeys.includes(key));
+  const recalled = await recallKeys(keysToRecall, metadata);
+
+  if (size(recalled) > 0) {
     toast({
       id: 'PARAMETER_SET',
       title: t('toast.parametersSet'),
@@ -473,3 +440,43 @@ export const parseAndRecallAllMetadata = async (
     });
   }
 };
+
+/**
+ * Recalls a set of keys from metadata.
+ * Includes special handling for some metadata where recalling may have side effects. For example, recalling a "style"
+ * prompt that is different from the "positive" or "negative" prompt should disable prompt concatenation.
+ * @param keysToRecall An array of keys to recall.
+ * @param metadata The metadata to recall from
+ * @returns A promise that resolves to an object containing the recalled values.
+ */
+const recallKeys = async (keysToRecall: (keyof typeof handlers)[], metadata: unknown): Promise<RecallResults> => {
+  const { dispatch } = getStore();
+  const recalled: RecallResults = {};
+  for (const key of keysToRecall) {
+    const { parse, recall } = handlers[key];
+    if (!recall) {
+      continue;
+    }
+    try {
+      const value = await parse(metadata);
+      /* @ts-expect-error The return type of parse and the input type of recall are guaranteed to be compatible. */
+      await recall(value);
+      recalled[key] = value;
+    } catch {
+      // no-op
+    }
+  }
+
+  if (
+    (recalled['sdxlPositiveStylePrompt'] && recalled['sdxlPositiveStylePrompt'] !== recalled['positivePrompt']) ||
+    (recalled['sdxlNegativeStylePrompt'] && recalled['sdxlNegativeStylePrompt'] !== recalled['negativePrompt'])
+  ) {
+    // If we set the negative style prompt or positive style prompt, we should disable prompt concat
+    dispatch(shouldConcatPromptsChanged(false));
+  } else {
+    // Otherwise, we should enable prompt concat
+    dispatch(shouldConcatPromptsChanged(true));
+  }
+
+  return recalled;
+};

invokeai/frontend/web/src/features/metadata/util/modelFetchingHelpers.ts

@@ -1,6 +1,7 @@
 import { getStore } from 'app/store/nanostores/store';
 import type { ModelIdentifierField } from 'features/nodes/types/common';
 import { isModelIdentifier, isModelIdentifierV2 } from 'features/nodes/types/common';
+import type { ModelIdentifier } from 'features/nodes/types/v2/common';
 import { modelsApi } from 'services/api/endpoints/models';
 import type { AnyModelConfig, BaseModelType, ModelType } from 'services/api/types';
 
@@ -107,19 +108,30 @@ export const fetchModelConfigWithTypeGuard = async <T extends AnyModelConfig>(
 
 /**
  * Fetches the model key from a model identifier. This includes fetching the key for MM1 format model identifiers.
- * @param modelIdentifier The model identifier. The MM2 format `{key: string}` simply extracts the key. The MM1 format
+ * @param modelIdentifier The model identifier. This can be a MM1 or MM2 identifier. In every case, we attempt to fetch
- * `{model_name: string, base_model: BaseModelType}` must do a network request to fetch the key.
+ * the model config from the server to ensure that the model identifier is valid and represents an installed model.
  * @param type The type of model to fetch. This is used to fetch the key for MM1 format model identifiers.
  * @param message An optional custom message to include in the error if the model identifier is invalid.
  * @returns A promise that resolves to the model key.
  * @throws {InvalidModelConfigError} If the model identifier is invalid.
  */
-export const getModelKey = async (modelIdentifier: unknown, type: ModelType, message?: string): Promise<string> => {
+export const getModelKey = async (
+  modelIdentifier: unknown | ModelIdentifierField | ModelIdentifier,
+  type: ModelType,
+  message?: string
+): Promise<string> => {
   if (isModelIdentifier(modelIdentifier)) {
-    return modelIdentifier.key;
+    try {
+      // Check if the model exists by key
+      return (await fetchModelConfig(modelIdentifier.key)).key;
+    } catch {
+      // If not, fetch the model key by name and base model
+      return (await fetchModelConfigByAttrs(modelIdentifier.name, modelIdentifier.base, type)).key;
     }
-  if (isModelIdentifierV2(modelIdentifier)) {
+  } else if (isModelIdentifierV2(modelIdentifier)) {
+    // Try by old-format model identifier
     return (await fetchModelConfigByAttrs(modelIdentifier.model_name, modelIdentifier.base_model, type)).key;
   }
+  // Nope, couldn't find it
   throw new InvalidModelConfigError(message || `Invalid model identifier: ${modelIdentifier}`);
 };

invokeai/frontend/web/src/features/metadata/util/parsers.ts

@@ -4,7 +4,7 @@ import {
   initialT2IAdapter,
 } from 'features/controlAdapters/util/buildControlAdapter';
 import { buildControlAdapterProcessor } from 'features/controlAdapters/util/buildControlAdapterProcessor';
-import { getCALayerId, getIPALayerId, INITIAL_IMAGE_LAYER_ID } from 'features/controlLayers/store/controlLayersSlice';
+import { getCALayerId, getIPALayerId, INITIAL_IMAGE_LAYER_ID } from 'features/controlLayers/konva/naming';
 import type { ControlAdapterLayer, InitialImageLayer, IPAdapterLayer, Layer } from 'features/controlLayers/store/types';
 import { zLayer } from 'features/controlLayers/store/types';
 import {

invokeai/frontend/web/src/features/metadata/util/recallers.ts

@@ -6,12 +6,10 @@ import {
   ipAdaptersReset,
   t2iAdaptersReset,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
+import { getCALayerId, getIPALayerId, getRGLayerId } from 'features/controlLayers/konva/naming';
 import {
   allLayersDeleted,
   caLayerRecalled,
-  getCALayerId,
-  getIPALayerId,
-  getRGLayerId,
   heightChanged,
   iiLayerRecalled,
   ipaLayerRecalled,

invokeai/frontend/web/src/features/nodes/util/graph/generation/addControlLayers.ts

@@ -1,6 +1,10 @@
 import { getStore } from 'app/store/nanostores/store';
 import type { RootState } from 'app/store/store';
 import { deepClone } from 'common/util/deepClone';
+import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
+import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
+import { RG_LAYER_NAME } from 'features/controlLayers/konva/naming';
+import { renderers } from 'features/controlLayers/konva/renderers';
 import {
   isControlAdapterLayer,
   isInitialImageLayer,
@@ -16,7 +20,6 @@ import type {
   ProcessorConfig,
   T2IAdapterConfigV2,
 } from 'features/controlLayers/util/controlAdapters';
-import { getRegionalPromptLayerBlobs } from 'features/controlLayers/util/getLayerBlobs';
 import type { ImageField } from 'features/nodes/types/common';
 import {
   CONTROL_NET_COLLECT,
@@ -31,11 +34,13 @@ import {
   T2I_ADAPTER_COLLECT,
 } from 'features/nodes/util/graph/constants';
 import type { Graph } from 'features/nodes/util/graph/generation/Graph';
+import Konva from 'konva';
 import { size } from 'lodash-es';
 import { getImageDTO, imagesApi } from 'services/api/endpoints/images';
 import type { BaseModelType, ImageDTO, Invocation } from 'services/api/types';
 import { assert } from 'tsafe';
 
+//#region addControlLayers
 /**
  * Adds the control layers to the graph
  * @param state The app root state
@@ -90,7 +95,7 @@ export const addControlLayers = async (
 
   const validRGLayers = validLayers.filter(isRegionalGuidanceLayer);
   const layerIds = validRGLayers.map((l) => l.id);
-  const blobs = await getRegionalPromptLayerBlobs(layerIds);
+  const blobs = await getRGLayerBlobs(layerIds);
   assert(size(blobs) === size(layerIds), 'Mismatch between layer IDs and blobs');
 
   for (const layer of validRGLayers) {
@@ -257,6 +262,7 @@ export const addControlLayers = async (
   g.upsertMetadata({ control_layers: { layers: validLayers, version: state.controlLayers.present._version } });
   return validLayers;
 };
+//#endregion
 
 //#region Control Adapters
 const addGlobalControlAdapterToGraph = (
@@ -509,7 +515,7 @@ const isValidLayer = (layer: Layer, base: BaseModelType) => {
 };
 //#endregion
 
-//#region Helpers
+//#region getMaskImage
 const getMaskImage = async (layer: RegionalGuidanceLayer, blob: Blob): Promise<ImageDTO> => {
   if (layer.uploadedMaskImage) {
     const imageDTO = await getImageDTO(layer.uploadedMaskImage.name);
@@ -529,7 +535,9 @@ const getMaskImage = async (layer: RegionalGuidanceLayer, blob: Blob): Promise<I
   dispatch(rgLayerMaskImageUploaded({ layerId: layer.id, imageDTO }));
   return imageDTO;
 };
+//#endregion
 
+//#region buildControlImage
 const buildControlImage = (
   image: ImageWithDims | null,
   processedImage: ImageWithDims | null,
@@ -549,3 +557,61 @@ const buildControlImage = (
   assert(false, 'Attempted to add unprocessed control image');
 };
 //#endregion
+
+//#region getRGLayerBlobs
+/**
+ * Get the blobs of all regional prompt layers. Only visible layers are returned.
+ * @param layerIds The IDs of the layers to get blobs for. If not provided, all regional prompt layers are used.
+ * @param preview Whether to open a new tab displaying each layer.
+ * @returns A map of layer IDs to blobs.
+ */
+const getRGLayerBlobs = async (layerIds?: string[], preview: boolean = false): Promise<Record<string, Blob>> => {
+  const state = getStore().getState();
+  const { layers } = state.controlLayers.present;
+  const { width, height } = state.controlLayers.present.size;
+  const reduxLayers = layers.filter(isRegionalGuidanceLayer);
+  const container = document.createElement('div');
+  const stage = new Konva.Stage({ container, width, height });
+  renderers.renderLayers(stage, reduxLayers, 1, 'brush', getImageDTO);
+
+  const konvaLayers = stage.find<Konva.Layer>(`.${RG_LAYER_NAME}`);
+  const blobs: Record<string, Blob> = {};
+
+  // First remove all layers
+  for (const layer of konvaLayers) {
+    layer.remove();
+  }
+
+  // Next render each layer to a blob
+  for (const layer of konvaLayers) {
+    if (layerIds && !layerIds.includes(layer.id())) {
+      continue;
+    }
+    const reduxLayer = reduxLayers.find((l) => l.id === layer.id());
+    assert(reduxLayer, `Redux layer ${layer.id()} not found`);
+    stage.add(layer);
+    const blob = await new Promise<Blob>((resolve) => {
+      stage.toBlob({
+        callback: (blob) => {
+          assert(blob, 'Blob is null');
+          resolve(blob);
+        },
+      });
+    });
+
+    if (preview) {
+      const base64 = await blobToDataURL(blob);
+      openBase64ImageInTab([
+        {
+          base64,
+          caption: `${reduxLayer.id}: ${reduxLayer.positivePrompt} / ${reduxLayer.negativePrompt}`,
+        },
+      ]);
+    }
+    layer.remove();
+    blobs[layer.id()] = blob;
+  }
+
+  return blobs;
+};
+//#endregion

invokeai/frontend/web/src/features/parameters/components/ImageSize/AspectRatioCanvasPreview.tsx

@@ -1,8 +1,17 @@
 import { Flex } from '@invoke-ai/ui-library';
+import { useStore } from '@nanostores/react';
 import { StageComponent } from 'features/controlLayers/components/StageComponent';
+import { $isPreviewVisible } from 'features/controlLayers/store/controlLayersSlice';
+import { AspectRatioIconPreview } from 'features/parameters/components/ImageSize/AspectRatioIconPreview';
 import { memo } from 'react';
 
 export const AspectRatioCanvasPreview = memo(() => {
+  const isPreviewVisible = useStore($isPreviewVisible);
+
+  if (!isPreviewVisible) {
+    return <AspectRatioIconPreview />;
+  }
+
   return (
     <Flex w="full" h="full" alignItems="center" justifyContent="center" position="relative">
       <StageComponent asPreview />

invokeai/frontend/web/src/features/settingsAccordions/components/ImageSettingsAccordion/ImageSizeLinear.tsx

@@ -3,15 +3,12 @@ import { aspectRatioChanged, heightChanged, widthChanged } from 'features/contro
 import { ParamHeight } from 'features/parameters/components/Core/ParamHeight';
 import { ParamWidth } from 'features/parameters/components/Core/ParamWidth';
 import { AspectRatioCanvasPreview } from 'features/parameters/components/ImageSize/AspectRatioCanvasPreview';
-import { AspectRatioIconPreview } from 'features/parameters/components/ImageSize/AspectRatioIconPreview';
 import { ImageSize } from 'features/parameters/components/ImageSize/ImageSize';
 import type { AspectRatioState } from 'features/parameters/components/ImageSize/types';
-import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { memo, useCallback } from 'react';
 
 export const ImageSizeLinear = memo(() => {
   const dispatch = useAppDispatch();
-  const tab = useAppSelector(activeTabNameSelector);
   const width = useAppSelector((s) => s.controlLayers.present.size.width);
   const height = useAppSelector((s) => s.controlLayers.present.size.height);
   const aspectRatioState = useAppSelector((s) => s.controlLayers.present.size.aspectRatio);
@@ -50,7 +47,7 @@ export const ImageSizeLinear = memo(() => {
       aspectRatioState={aspectRatioState}
       heightComponent={<ParamHeight />}
       widthComponent={<ParamWidth />}
-      previewComponent={tab === 'generation' ? <AspectRatioCanvasPreview /> : <AspectRatioIconPreview />}
+      previewComponent={<AspectRatioCanvasPreview />}
       onChangeAspectRatioState={onChangeAspectRatioState}
       onChangeWidth={onChangeWidth}
       onChangeHeight={onChangeHeight}

invokeai/frontend/web/src/features/ui/components/ParametersPanelTextToImage.tsx

@@ -3,6 +3,7 @@ import { Box, Flex, Tab, TabList, TabPanel, TabPanels, Tabs } from '@invoke-ai/u
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { overlayScrollbarsParams } from 'common/components/OverlayScrollbars/constants';
 import { ControlLayersPanelContent } from 'features/controlLayers/components/ControlLayersPanelContent';
+import { $isPreviewVisible } from 'features/controlLayers/store/controlLayersSlice';
 import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import { Prompts } from 'features/parameters/components/Prompts/Prompts';
 import QueueControls from 'features/queue/components/QueueControls';
@@ -53,6 +54,7 @@ const ParametersPanelTextToImage = () => {
       if (i === 1) {
         dispatch(isImageViewerOpenChanged(false));
       }
+      $isPreviewVisible.set(i === 0);
     },
     [dispatch]
   );
@@ -66,6 +68,7 @@ const ParametersPanelTextToImage = () => {
             <Flex gap={2} flexDirection="column" h="full" w="full">
               {isSDXL ? <SDXLPrompts /> : <Prompts />}
               <Tabs
+                defaultIndex={0}
                 variant="enclosed"
                 display="flex"
                 flexDir="column"