Merge branch 'main' into feat/db/graceful-migrate-workflows

README.md

@@ -270,7 +270,7 @@ upgrade script.** See the next section for a Windows recipe.
 3. Select option [1] to upgrade to the latest release.
 
 4. Once the upgrade is finished you will be returned to the launcher
-menu. Select option [7] "Re-run the configure script to fix a broken
+menu. Select option [6] "Re-run the configure script to fix a broken
 install or to complete a major upgrade".
 
 This will run the configure script against the v2.3 directory and

docker/.env.sample

@@ -11,5 +11,5 @@ INVOKEAI_ROOT=
 # HUGGING_FACE_HUB_TOKEN=
 
 ## optional variables specific to the docker setup.
-# GPU_DRIVER=cuda # or rocm
+# GPU_DRIVER=nvidia #| rocm
 # CONTAINER_UID=1000

docker/README.md

@@ -1,6 +1,14 @@
 # InvokeAI Containerized
 
-All commands are to be run from the `docker` directory: `cd docker`
+All commands should be run within the `docker` directory: `cd docker`
+
+## Quickstart :rocket:
+
+On a known working Linux+Docker+CUDA (Nvidia) system, execute `./run.sh` in this directory. It will take a few minutes - depending on your internet speed - to install the core models. Once the application starts up, open `http://localhost:9090` in your browser to Invoke!
+
+For more configuration options (using an AMD GPU, custom root directory location, etc): read on.
+
+## Detailed setup
 
 #### Linux
 
@@ -18,7 +26,7 @@ All commands are to be run from the `docker` directory: `cd docker`
 
 This is done via Docker Desktop preferences
 
-## Quickstart
+### Configure Invoke environment
 
 1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
     a. the desired location of the InvokeAI runtime directory, or
@@ -37,19 +45,21 @@ The runtime directory (holding models and outputs) will be created in the locati
 
 The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker documentation for the most up-to-date instructions for using your GPU with Docker.
 
+To use an AMD GPU, set `GPU_DRIVER=rocm` in your `.env` file.
+
 ## Customize
 
 Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `run.sh`, your custom values will be used.
 
 You can also set these values in `docker-compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
 
-Example (values are optional, but setting `INVOKEAI_ROOT` is highly recommended):
+Values are optional, but setting `INVOKEAI_ROOT` is highly recommended. The default is `~/invokeai`. Example:
 
 ```bash
 INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
 HUGGINGFACE_TOKEN=the_actual_token
 CONTAINER_UID=1000
-GPU_DRIVER=cuda
+GPU_DRIVER=nvidia
 ```
 
 Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.

docker/run.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
-set -e
+set -e -o pipefail
 
 run() {
   local scriptdir=$(dirname "${BASH_SOURCE[0]}")
@@ -8,14 +8,18 @@ run() {
   local build_args=""
   local profile=""
 
-  [[ -f ".env" ]] &&
+  touch .env
-    build_args=$(awk '$1 ~ /=[^$]/ && $0 !~ /^#/ {print "--build-arg " $0 " "}' .env) &&
+  build_args=$(awk '$1 ~ /=[^$]/ && $0 !~ /^#/ {print "--build-arg " $0 " "}' .env) &&
-    profile="$(awk -F '=' '/GPU_DRIVER/ {print $2}' .env)"
+  profile="$(awk -F '=' '/GPU_DRIVER/ {print $2}' .env)"
+
+  [[ -z "$profile" ]] && profile="nvidia"
 
   local service_name="invokeai-$profile"
 
-  printf "%s\n" "docker compose build args:"
+  if [[ ! -z "$build_args" ]]; then
-  printf "%s\n" "$build_args"
+    printf "%s\n" "docker compose build args:"
+    printf "%s\n" "$build_args"
+  fi
 
   docker compose build $build_args
   unset build_args

docs/contributing/DOWNLOAD_QUEUE.md

@@ -0,0 +1,277 @@
+# The InvokeAI Download Queue
+
+The DownloadQueueService provides a multithreaded parallel download
+queue for arbitrary URLs, with queue prioritization, event handling,
+and restart capabilities.
+
+## Simple Example
+
+```
+from invokeai.app.services.download import DownloadQueueService, TqdmProgress
+
+download_queue = DownloadQueueService()
+for url in ['https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/a-painting-of-a-fire.png?raw=true',
+            'https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/birdhouse.png?raw=true',
+            'https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/missing.png',
+            'https://civitai.com/api/download/models/152309?type=Model&format=SafeTensor',
+            ]:
+
+    # urls start downloading as soon as download() is called
+    download_queue.download(source=url,
+                            dest='/tmp/downloads',
+                            on_progress=TqdmProgress().update
+                            )
+
+download_queue.join()  # wait for all downloads to finish
+for job in download_queue.list_jobs():
+    print(job.model_dump_json(exclude_none=True, indent=4),"\n")
+```
+
+Output:
+
+```
+{
+    "source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/a-painting-of-a-fire.png?raw=true",
+    "dest": "/tmp/downloads",
+    "id": 0,
+    "priority": 10,
+    "status": "completed",
+    "download_path": "/tmp/downloads/a-painting-of-a-fire.png",
+    "job_started": "2023-12-04T05:34:41.742174",
+    "job_ended": "2023-12-04T05:34:42.592035",
+    "bytes": 666734,
+    "total_bytes": 666734
+} 
+
+{
+    "source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/birdhouse.png?raw=true",
+    "dest": "/tmp/downloads",
+    "id": 1,
+    "priority": 10,
+    "status": "completed",
+    "download_path": "/tmp/downloads/birdhouse.png",
+    "job_started": "2023-12-04T05:34:41.741975",
+    "job_ended": "2023-12-04T05:34:42.652841",
+    "bytes": 774949,
+    "total_bytes": 774949
+}
+
+{
+    "source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/missing.png",
+    "dest": "/tmp/downloads",
+    "id": 2,
+    "priority": 10,
+    "status": "error",
+    "job_started": "2023-12-04T05:34:41.742079",
+    "job_ended": "2023-12-04T05:34:42.147625",
+    "bytes": 0,
+    "total_bytes": 0,
+    "error_type": "HTTPError(Not Found)",
+    "error": "Traceback (most recent call last):\n  File \"/home/lstein/Projects/InvokeAI/invokeai/app/services/download/download_default.py\", line 182, in _download_next_item\n    self._do_download(job)\n  File \"/home/lstein/Projects/InvokeAI/invokeai/app/services/download/download_default.py\", line 206, in _do_download\n    raise HTTPError(resp.reason)\nrequests.exceptions.HTTPError: Not Found\n"
+}
+
+{
+    "source": "https://civitai.com/api/download/models/152309?type=Model&format=SafeTensor",
+    "dest": "/tmp/downloads",
+    "id": 3,
+    "priority": 10,
+    "status": "completed",
+    "download_path": "/tmp/downloads/xl_more_art-full_v1.safetensors",
+    "job_started": "2023-12-04T05:34:42.147645",
+    "job_ended": "2023-12-04T05:34:43.735990",
+    "bytes": 719020768,
+    "total_bytes": 719020768
+} 
+```
+
+##  The API
+
+The default download queue is `DownloadQueueService`, an
+implementation of ABC `DownloadQueueServiceBase`. It juggles multiple
+background download requests and provides facilities for interrogating
+and cancelling the requests. Access to a current or past download task
+is mediated via `DownloadJob` objects which report the current status
+of a job request
+
+### The Queue Object
+
+A default download queue is located in
+`ApiDependencies.invoker.services.download_queue`. However, you can
+create additional instances if you need to isolate your queue from the
+main one.
+
+```
+queue = DownloadQueueService(event_bus=events)
+```
+
+`DownloadQueueService()` takes three optional arguments:
+
+| **Argument** | **Type**          |  **Default**  | **Description** |
+|----------------|-----------------|---------------|-----------------|
+| `max_parallel_dl`  | int                         | 5    | Maximum number of simultaneous downloads allowed |
+| `event_bus` | EventServiceBase   | None | System-wide FastAPI event bus for reporting download events |
+| `requests_session` | requests.sessions.Session   | None | An alternative requests Session object to use for the download |
+
+`max_parallel_dl` specifies how many download jobs are allowed to run
+simultaneously. Each will run in a different thread of execution.
+
+`event_bus` is an EventServiceBase, typically the one created at
+InvokeAI startup. If present, download events are periodically emitted
+on this bus to allow clients to follow download progress.
+
+`requests_session` is a url library requests Session object. It is
+used for testing.
+
+### The Job object
+
+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
+following fields:
+
+| **Field**      | **Type**        |  **Default**  | **Description** |
+|----------------|-----------------|---------------|-----------------|
+| _Fields passed in at job creation time_                               |
+| `source`         | AnyHttpUrl      |               | Where to download from |
+| `dest`           | Path            |               | Where to download to              |
+| `access_token`   | str             |               | [optional] string containing authentication token for access |
+| `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           |
+| `priority`       | int             | 10            | Job priority. Lower priorities run before higher priorities |
+|                                                                                                        |
+| _Fields updated over the course of the download task_
+| `status`         | DownloadJobStatus|              | Status code                                |
+| `download_path`  | Path |              | Path to the location of the downloaded file |
+| `job_started`    | float            |              | Timestamp for when the job started running |
+| `job_ended`      | float            |              | Timestamp for when the job completed or errored out |
+| `job_sequence`   | int              |              | A counter that is incremented each time a model is dequeued |
+| `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|
+
+When you create a job, you can assign it a `priority`. If multiple
+jobs are queued, the job with the lowest priority runs first.
+
+Every job has a `source` and a `dest`. `source` is a pydantic.networks AnyHttpUrl object.
+The `dest` is a path on the local filesystem that specifies the
+destination for the downloaded object. Its semantics are
+described below.
+
+When the job is submitted, it is assigned a numeric `id`. The id can
+then be used to fetch the job object from the queue.
+
+The `status` field is updated by the queue to indicate where the job
+is in its lifecycle. Values are defined in the string enum
+`DownloadJobStatus`, a symbol available from
+`invokeai.app.services.download_manager`. Possible values are:
+
+| **Value**    |   **String Value**  | ** Description ** |
+|--------------|---------------------|-------------------|
+| `WAITING`      | waiting           | Job is on the queue but not yet running|
+| `RUNNING`      | running           | The download is started                |
+| `COMPLETED`    | completed         | Job has finished its work without an error |
+| `ERROR`        | error             | Job encountered an error and will not run again|
+
+`job_started` and `job_ended` indicate when the job
+was started (using a python timestamp) and when it completed.
+
+In case of an error, the job's status will be set to `DownloadJobStatus.ERROR`, the text of the
+Exception that caused the error will be placed in the `error_type`
+field and the traceback that led to the error will be in `error`.
+
+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.
+
+### Callbacks
+
+Download jobs can be associated with a series of callbacks, each with
+the signature `Callable[["DownloadJob"], None]`. The callbacks are assigned
+using optional arguments `on_start`, `on_progress`, `on_complete` and
+`on_error`. When the corresponding event occurs, the callback wil be
+invoked and passed the job. The callback will be run in a `try:`
+context in the same thread as the download job. Any exceptions that
+occur during execution of the callback will be caught and converted
+into a log error message, thereby allowing the download to continue.
+
+#### `TqdmProgress`
+
+The `invokeai.app.services.download.download_default` module defines a
+class named `TqdmProgress` which can be used as an `on_progress`
+handler to display a completion bar in the console. Use as follows:
+
+```
+from invokeai.app.services.download import TqdmProgress
+
+download_queue.download(source='http://some.server.somewhere/some_file',
+                        dest='/tmp/downloads',
+                        on_progress=TqdmProgress().update
+                        )
+
+```
+
+### Events
+
+If the queue was initialized with the InvokeAI event bus (the case
+when using `ApiDependencies.invoker.services.download_queue`), then
+download events will also be issued on the bus. The events are:
+
+* `download_started` -- This is issued when a job is taken off the
+queue and a request is made to the remote server for the URL headers, but before any data
+has been downloaded. The event payload will contain the keys `source`
+and `download_path`. The latter contains the path that the URL will be
+downloaded to.
+
+* `download_progress -- This is issued periodically as the download
+runs. The payload contains the keys `source`, `download_path`,
+`current_bytes` and `total_bytes`. The latter two fields can be
+used to display the percent complete.
+
+* `download_complete` -- This is issued when the download completes
+successfully. The payload contains the keys `source`, `download_path`
+and `total_bytes`.
+
+* `download_error` -- This is issued when the download stops because
+of an error condition. The payload contains the fields `error_type`
+and `error`. The former is the text representation of the exception,
+and the latter is a traceback showing where the error occurred.
+
+### Job control
+
+To create a job call the queue's `download()` method. You can list all
+jobs using `list_jobs()`, fetch a single job by its with
+`id_to_job()`, cancel a running job with `cancel_job()`, cancel all
+running jobs with `cancel_all_jobs()`, and wait for all jobs to finish
+with `join()`.
+
+#### job = queue.download(source, dest, priority, access_token)
+
+Create a new download job and put it on the queue, returning the
+DownloadJob object.
+
+#### jobs = queue.list_jobs()
+
+Return a list of all active and inactive `DownloadJob`s.
+
+#### job = queue.id_to_job(id)
+
+Return the job corresponding to given ID.
+
+Return a list of all active and inactive `DownloadJob`s.
+
+#### queue.prune_jobs()
+
+Remove inactive (complete or errored) jobs from the listing returned
+by `list_jobs()`.
+
+#### queue.join()
+
+Block until all pending jobs have run to completion or errored out.
+

invokeai/app/api/dependencies.py

@@ -11,6 +11,7 @@ from ..services.board_images.board_images_default import BoardImagesService
 from ..services.board_records.board_records_sqlite import SqliteBoardRecordStorage
 from ..services.boards.boards_default import BoardService
 from ..services.config import InvokeAIAppConfig
+from ..services.download import DownloadQueueService
 from ..services.image_files.image_files_disk import DiskImageFileStorage
 from ..services.image_records.image_records_sqlite import SqliteImageRecordStorage
 from ..services.images.images_default import ImageService
@@ -29,8 +30,7 @@ from ..services.model_records import ModelRecordServiceSQL
 from ..services.names.names_default import SimpleNameService
 from ..services.session_processor.session_processor_default import DefaultSessionProcessor
 from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
-from ..services.shared.default_graphs import create_system_graphs
+from ..services.shared.graph import GraphExecutionState
-from ..services.shared.graph import GraphExecutionState, LibraryGraph
 from ..services.urls.urls_default import LocalUrlService
 from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from .events import FastAPIEventService
@@ -80,13 +80,13 @@ class ApiDependencies:
         boards = BoardService()
         events = FastAPIEventService(event_handler_id)
         graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
-        graph_library = SqliteItemStorage[LibraryGraph](db=db, table_name="graphs")
         image_records = SqliteImageRecordStorage(db=db)
         images = ImageService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
         model_manager = ModelManagerService(config, logger)
         model_record_service = ModelRecordServiceSQL(db=db)
+        download_queue_service = DownloadQueueService(event_bus=events)
         model_install_service = ModelInstallService(
             app_config=config, record_store=model_record_service, event_bus=events
         )
@@ -107,7 +107,6 @@ class ApiDependencies:
             configuration=configuration,
             events=events,
             graph_execution_manager=graph_execution_manager,
-            graph_library=graph_library,
             image_files=image_files,
             image_records=image_records,
             images=images,
@@ -116,6 +115,7 @@ class ApiDependencies:
             logger=logger,
             model_manager=model_manager,
             model_records=model_record_service,
+            download_queue=download_queue_service,
             model_install=model_install_service,
             names=names,
             performance_statistics=performance_statistics,
@@ -127,8 +127,6 @@ class ApiDependencies:
             workflow_records=workflow_records,
         )
 
-        create_system_graphs(services.graph_library)
-
         ApiDependencies.invoker = Invoker(services)
         db.clean()
 

invokeai/app/api/routers/download_queue.py

@@ -0,0 +1,111 @@
+# Copyright (c) 2023 Lincoln D. Stein
+"""FastAPI route for the download queue."""
+
+from typing import List, Optional
+
+from fastapi import Body, Path, Response
+from fastapi.routing import APIRouter
+from pydantic.networks import AnyHttpUrl
+from starlette.exceptions import HTTPException
+
+from invokeai.app.services.download import (
+    DownloadJob,
+    UnknownJobIDException,
+)
+
+from ..dependencies import ApiDependencies
+
+download_queue_router = APIRouter(prefix="/v1/download_queue", tags=["download_queue"])
+
+
+@download_queue_router.get(
+    "/",
+    operation_id="list_downloads",
+)
+async def list_downloads() -> List[DownloadJob]:
+    """Get a list of active and inactive jobs."""
+    queue = ApiDependencies.invoker.services.download_queue
+    return queue.list_jobs()
+
+
+@download_queue_router.patch(
+    "/",
+    operation_id="prune_downloads",
+    responses={
+        204: {"description": "All completed jobs have been pruned"},
+        400: {"description": "Bad request"},
+    },
+)
+async def prune_downloads():
+    """Prune completed and errored jobs."""
+    queue = ApiDependencies.invoker.services.download_queue
+    queue.prune_jobs()
+    return Response(status_code=204)
+
+
+@download_queue_router.post(
+    "/i/",
+    operation_id="download",
+)
+async def download(
+    source: AnyHttpUrl = Body(description="download source"),
+    dest: str = Body(description="download destination"),
+    priority: int = Body(default=10, description="queue priority"),
+    access_token: Optional[str] = Body(default=None, description="token for authorization to download"),
+) -> DownloadJob:
+    """Download the source URL to the file or directory indicted in dest."""
+    queue = ApiDependencies.invoker.services.download_queue
+    return queue.download(source, dest, priority, access_token)
+
+
+@download_queue_router.get(
+    "/i/{id}",
+    operation_id="get_download_job",
+    responses={
+        200: {"description": "Success"},
+        404: {"description": "The requested download JobID could not be found"},
+    },
+)
+async def get_download_job(
+    id: int = Path(description="ID of the download job to fetch."),
+) -> DownloadJob:
+    """Get a download job using its ID."""
+    try:
+        job = ApiDependencies.invoker.services.download_queue.id_to_job(id)
+        return job
+    except UnknownJobIDException as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+@download_queue_router.delete(
+    "/i/{id}",
+    operation_id="cancel_download_job",
+    responses={
+        204: {"description": "Job has been cancelled"},
+        404: {"description": "The requested download JobID could not be found"},
+    },
+)
+async def cancel_download_job(
+    id: int = Path(description="ID of the download job to cancel."),
+):
+    """Cancel a download job using its ID."""
+    try:
+        queue = ApiDependencies.invoker.services.download_queue
+        job = queue.id_to_job(id)
+        queue.cancel_job(job)
+        return Response(status_code=204)
+    except UnknownJobIDException as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+@download_queue_router.delete(
+    "/i",
+    operation_id="cancel_all_download_jobs",
+    responses={
+        204: {"description": "Download jobs have been cancelled"},
+    },
+)
+async def cancel_all_download_jobs():
+    """Cancel all download jobs."""
+    ApiDependencies.invoker.services.download_queue.cancel_all_jobs()
+    return Response(status_code=204)

invokeai/app/api_app.py

@@ -45,6 +45,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
         app_info,
         board_images,
         boards,
+        download_queue,
         images,
         model_records,
         models,
@@ -116,6 +117,7 @@ app.include_router(sessions.session_router, prefix="/api")
 app.include_router(utilities.utilities_router, prefix="/api")
 app.include_router(models.models_router, prefix="/api")
 app.include_router(model_records.model_records_router, prefix="/api")
+app.include_router(download_queue.download_queue_router, prefix="/api")
 app.include_router(images.images_router, prefix="/api")
 app.include_router(boards.boards_router, prefix="/api")
 app.include_router(board_images.board_images_router, prefix="/api")

invokeai/app/invocations/compel.py

@@ -1,4 +1,3 @@
-import re
 from dataclasses import dataclass
 from typing import List, Optional, Union
 
@@ -17,6 +16,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.model_management.models import ModelNotFoundException, ModelType
 from ...backend.util.devices import torch_dtype
+from ..util.ti_utils import extract_ti_triggers_from_prompt
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -87,7 +87,7 @@ class CompelInvocation(BaseInvocation):
         # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
-        for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
+        for trigger in extract_ti_triggers_from_prompt(self.prompt):
             name = trigger[1:-1]
             try:
                 ti_list.append(
@@ -210,7 +210,7 @@ class SDXLPromptInvocationBase:
         # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
-        for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
+        for trigger in extract_ti_triggers_from_prompt(prompt):
             name = trigger[1:-1]
             try:
                 ti_list.append(

invokeai/app/invocations/onnx.py

@@ -1,7 +1,6 @@
 # Copyright (c) 2023 Borisov Sergey (https://github.com/StAlKeR7779)
 
 import inspect
-import re
 
 # from contextlib import ExitStack
 from typing import List, Literal, Union
@@ -21,6 +20,7 @@ from invokeai.backend import BaseModelType, ModelType, SubModelType
 from ...backend.model_management import ONNXModelPatcher
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.util import choose_torch_device
+from ..util.ti_utils import extract_ti_triggers_from_prompt
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -78,7 +78,7 @@ class ONNXPromptInvocation(BaseInvocation):
             ]
 
             ti_list = []
-            for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
+            for trigger in extract_ti_triggers_from_prompt(self.prompt):
                 name = trigger[1:-1]
                 try:
                     ti_list.append(

invokeai/app/services/config/config_default.py

@@ -356,7 +356,7 @@ class InvokeAIAppConfig(InvokeAISettings):
         else:
             root = self.find_root().expanduser().absolute()
         self.root = root  # insulate ourselves from relative paths that may change
-        return root
+        return root.resolve()
 
     @property
     def root_dir(self) -> Path:

invokeai/app/services/download/__init__.py

@@ -0,0 +1,12 @@
+"""Init file for download queue."""
+from .download_base import DownloadJob, DownloadJobStatus, DownloadQueueServiceBase, UnknownJobIDException
+from .download_default import DownloadQueueService, TqdmProgress
+
+__all__ = [
+    "DownloadJob",
+    "DownloadQueueServiceBase",
+    "DownloadQueueService",
+    "TqdmProgress",
+    "DownloadJobStatus",
+    "UnknownJobIDException",
+]

invokeai/app/services/download/download_base.py

@@ -0,0 +1,217 @@
+# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
+"""Model download service."""
+
+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 pydantic import BaseModel, Field, PrivateAttr
+from pydantic.networks import AnyHttpUrl
+
+
+class DownloadJobStatus(str, Enum):
+    """State of a download job."""
+
+    WAITING = "waiting"  # not enqueued, will not run
+    RUNNING = "running"  # actively downloading
+    COMPLETED = "completed"  # finished running
+    CANCELLED = "cancelled"  # user cancelled
+    ERROR = "error"  # terminated with an error message
+
+
+class DownloadJobCancelledException(Exception):
+    """This exception is raised when a download job is cancelled."""
+
+
+class UnknownJobIDException(Exception):
+    """This exception is raised when an invalid job id is referened."""
+
+
+class ServiceInactiveException(Exception):
+    """This exception is raised when user attempts to initiate a download before the service is started."""
+
+
+DownloadEventHandler = Callable[["DownloadJob"], None]
+
+
+@total_ordering
+class DownloadJob(BaseModel):
+    """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
+    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)"
+    )
+    bytes: int = Field(default=0, description="Bytes downloaded so far")
+    total_bytes: int = Field(default=0, description="Total file size (bytes)")
+
+    # set when an error occurs
+    error_type: Optional[str] = Field(default=None, description="Name of exception that caused an error")
+    error: Optional[str] = Field(default=None, description="Traceback of the exception that caused an error")
+
+    # internal flag
+    _cancelled: bool = PrivateAttr(default=False)
+
+    # optional event handlers passed in on creation
+    _on_start: Optional[DownloadEventHandler] = PrivateAttr(default=None)
+    _on_progress: Optional[DownloadEventHandler] = PrivateAttr(default=None)
+    _on_complete: Optional[DownloadEventHandler] = PrivateAttr(default=None)
+    _on_cancelled: Optional[DownloadEventHandler] = PrivateAttr(default=None)
+    _on_error: Optional[DownloadEventHandler] = PrivateAttr(default=None)
+
+    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
+
+    # cancelled and the callbacks are private attributes in order to prevent
+    # them from being serialized and/or used in the Json Schema
+    @property
+    def cancelled(self) -> bool:
+        """Call to cancel the job."""
+        return self._cancelled
+
+    @property
+    def on_start(self) -> Optional[DownloadEventHandler]:
+        """Return the on_start event handler."""
+        return self._on_start
+
+    @property
+    def on_progress(self) -> Optional[DownloadEventHandler]:
+        """Return the on_progress event handler."""
+        return self._on_progress
+
+    @property
+    def on_complete(self) -> Optional[DownloadEventHandler]:
+        """Return the on_complete event handler."""
+        return self._on_complete
+
+    @property
+    def on_error(self) -> Optional[DownloadEventHandler]:
+        """Return the on_error event handler."""
+        return self._on_error
+
+    @property
+    def on_cancelled(self) -> Optional[DownloadEventHandler]:
+        """Return the on_cancelled event handler."""
+        return self._on_cancelled
+
+    def set_callbacks(
+        self,
+        on_start: Optional[DownloadEventHandler] = None,
+        on_progress: Optional[DownloadEventHandler] = None,
+        on_complete: Optional[DownloadEventHandler] = None,
+        on_cancelled: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadEventHandler] = None,
+    ) -> None:
+        """Set the callbacks for download events."""
+        self._on_start = on_start
+        self._on_progress = on_progress
+        self._on_complete = on_complete
+        self._on_error = on_error
+        self._on_cancelled = on_cancelled
+
+
+class DownloadQueueServiceBase(ABC):
+    """Multithreaded queue for downloading models via URL."""
+
+    @abstractmethod
+    def start(self, *args: Any, **kwargs: Any) -> None:
+        """Start the download worker threads."""
+
+    @abstractmethod
+    def stop(self, *args: Any, **kwargs: Any) -> None:
+        """Stop the download worker threads."""
+
+    @abstractmethod
+    def download(
+        self,
+        source: AnyHttpUrl,
+        dest: Path,
+        priority: int = 10,
+        access_token: Optional[str] = None,
+        on_start: Optional[DownloadEventHandler] = None,
+        on_progress: Optional[DownloadEventHandler] = None,
+        on_complete: Optional[DownloadEventHandler] = None,
+        on_cancelled: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadEventHandler] = None,
+    ) -> DownloadJob:
+        """
+        Create a download job.
+
+        :param source: Source of the download as a URL.
+        :param dest: Path to download to. See below.
+        :param on_start, on_progress, on_complete, on_error: Callbacks for the indicated
+         events.
+        :returns: A DownloadJob object for monitoring the state of the download.
+
+        The `dest` argument is a Path object. Its behavior is:
+
+        1. If the path exists and is a directory, then the URL contents will be downloaded
+            into that directory using the filename indicated in the response's `Content-Disposition` field.
+            If no content-disposition is present, then the last component of the URL will be used (similar to
+            wget's behavior).
+        2. If the path does not exist, then it is taken as the name of a new file to create with the downloaded
+           content.
+        3. If the path exists and is an existing file, then the downloader will try to resume the download from
+           the end of the existing file.
+
+        """
+        pass
+
+    @abstractmethod
+    def list_jobs(self) -> List[DownloadJob]:
+        """
+        List active download jobs.
+
+        :returns List[DownloadJob]: List of download jobs whose state is not "completed."
+        """
+        pass
+
+    @abstractmethod
+    def id_to_job(self, id: int) -> DownloadJob:
+        """
+        Return the DownloadJob corresponding to the integer ID.
+
+        :param id: ID of the DownloadJob.
+
+        Exceptions:
+        * UnknownJobIDException
+        """
+        pass
+
+    @abstractmethod
+    def cancel_all_jobs(self):
+        """Cancel all active and enquedjobs."""
+        pass
+
+    @abstractmethod
+    def prune_jobs(self):
+        """Prune completed and errored queue items from the job list."""
+        pass
+
+    @abstractmethod
+    def cancel_job(self, job: DownloadJob):
+        """Cancel the job, clearing partial downloads and putting it into ERROR state."""
+        pass
+
+    @abstractmethod
+    def join(self):
+        """Wait until all jobs are off the queue."""
+        pass

invokeai/app/services/download/download_default.py

@@ -0,0 +1,418 @@
+# Copyright (c) 2023, Lincoln D. Stein
+"""Implementation of multithreaded download queue for invokeai."""
+
+import os
+import re
+import threading
+import traceback
+from logging import Logger
+from pathlib import Path
+from queue import Empty, PriorityQueue
+from typing import Any, Dict, List, Optional, Set
+
+import requests
+from pydantic.networks import AnyHttpUrl
+from requests import HTTPError
+from tqdm import tqdm
+
+from invokeai.app.services.events.events_base import EventServiceBase
+from invokeai.app.util.misc import get_iso_timestamp
+from invokeai.backend.util.logging import InvokeAILogger
+
+from .download_base import (
+    DownloadEventHandler,
+    DownloadJob,
+    DownloadJobCancelledException,
+    DownloadJobStatus,
+    DownloadQueueServiceBase,
+    ServiceInactiveException,
+    UnknownJobIDException,
+)
+
+# Maximum number of bytes to download during each call to requests.iter_content()
+DOWNLOAD_CHUNK_SIZE = 100000
+
+
+class DownloadQueueService(DownloadQueueServiceBase):
+    """Class for queued download of models."""
+
+    _jobs: Dict[int, DownloadJob]
+    _max_parallel_dl: int = 5
+    _worker_pool: Set[threading.Thread]
+    _queue: PriorityQueue[DownloadJob]
+    _stop_event: threading.Event
+    _lock: threading.Lock
+    _logger: Logger
+    _events: Optional[EventServiceBase] = None
+    _next_job_id: int = 0
+    _accept_download_requests: bool = False
+    _requests: requests.sessions.Session
+
+    def __init__(
+        self,
+        max_parallel_dl: int = 5,
+        event_bus: Optional[EventServiceBase] = None,
+        requests_session: Optional[requests.sessions.Session] = None,
+    ):
+        """
+        Initialize DownloadQueue.
+
+        :param max_parallel_dl: Number of simultaneous downloads allowed [5].
+        :param requests_session: Optional requests.sessions.Session object, for unit tests.
+        """
+        self._jobs = {}
+        self._next_job_id = 0
+        self._queue = PriorityQueue()
+        self._stop_event = threading.Event()
+        self._worker_pool = set()
+        self._lock = threading.Lock()
+        self._logger = InvokeAILogger.get_logger("DownloadQueueService")
+        self._event_bus = event_bus
+        self._requests = requests_session or requests.Session()
+        self._accept_download_requests = False
+        self._max_parallel_dl = max_parallel_dl
+
+    def start(self, *args: Any, **kwargs: Any) -> None:
+        """Start the download worker threads."""
+        with self._lock:
+            if self._worker_pool:
+                raise Exception("Attempt to start the download service twice")
+            self._stop_event.clear()
+            self._start_workers(self._max_parallel_dl)
+            self._accept_download_requests = True
+
+    def stop(self, *args: Any, **kwargs: Any) -> None:
+        """Stop the download worker threads."""
+        with self._lock:
+            if not self._worker_pool:
+                raise Exception("Attempt to stop the download service before it was started")
+            self._accept_download_requests = False  # reject attempts to add new jobs to queue
+            queued_jobs = [x for x in self.list_jobs() if x.status == DownloadJobStatus.WAITING]
+            active_jobs = [x for x in self.list_jobs() if x.status == DownloadJobStatus.RUNNING]
+            if queued_jobs:
+                self._logger.warning(f"Cancelling {len(queued_jobs)} queued downloads")
+            if active_jobs:
+                self._logger.info(f"Waiting for {len(active_jobs)} active download jobs to complete")
+            with self._queue.mutex:
+                self._queue.queue.clear()
+            self.join()  # wait for all active jobs to finish
+            self._stop_event.set()
+            self._worker_pool.clear()
+
+    def download(
+        self,
+        source: AnyHttpUrl,
+        dest: Path,
+        priority: int = 10,
+        access_token: Optional[str] = None,
+        on_start: Optional[DownloadEventHandler] = None,
+        on_progress: Optional[DownloadEventHandler] = None,
+        on_complete: Optional[DownloadEventHandler] = None,
+        on_cancelled: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadEventHandler] = None,
+    ) -> DownloadJob:
+        """Create a download job and return its ID."""
+        if not self._accept_download_requests:
+            raise ServiceInactiveException(
+                "The download service is not currently accepting requests. Please call start() to initialize the service."
+            )
+        with self._lock:
+            id = self._next_job_id
+            self._next_job_id += 1
+            job = DownloadJob(
+                id=id,
+                source=source,
+                dest=dest,
+                priority=priority,
+                access_token=access_token,
+            )
+            job.set_callbacks(
+                on_start=on_start,
+                on_progress=on_progress,
+                on_complete=on_complete,
+                on_cancelled=on_cancelled,
+                on_error=on_error,
+            )
+            self._jobs[id] = job
+            self._queue.put(job)
+        return job
+
+    def join(self) -> None:
+        """Wait for all jobs to complete."""
+        self._queue.join()
+
+    def list_jobs(self) -> List[DownloadJob]:
+        """List all the jobs."""
+        return list(self._jobs.values())
+
+    def prune_jobs(self) -> None:
+        """Prune completed and errored queue items from the job list."""
+        with self._lock:
+            to_delete = set()
+            for job_id, job in self._jobs.items():
+                if self._in_terminal_state(job):
+                    to_delete.add(job_id)
+            for job_id in to_delete:
+                del self._jobs[job_id]
+
+    def id_to_job(self, id: int) -> DownloadJob:
+        """Translate a job ID into a DownloadJob object."""
+        try:
+            return self._jobs[id]
+        except KeyError as excp:
+            raise UnknownJobIDException("Unrecognized job") from excp
+
+    def cancel_job(self, job: DownloadJob) -> None:
+        """
+        Cancel the indicated job.
+
+        If it is running it will be stopped.
+        job.status will be set to DownloadJobStatus.CANCELLED
+        """
+        with self._lock:
+            job.cancel()
+
+    def cancel_all_jobs(self, preserve_partial: bool = False) -> None:
+        """Cancel all jobs (those not in enqueued, running or paused state)."""
+        for job in self._jobs.values():
+            if not self._in_terminal_state(job):
+                self.cancel_job(job)
+
+    def _in_terminal_state(self, job: DownloadJob) -> bool:
+        return job.status in [
+            DownloadJobStatus.COMPLETED,
+            DownloadJobStatus.CANCELLED,
+            DownloadJobStatus.ERROR,
+        ]
+
+    def _start_workers(self, max_workers: int) -> None:
+        """Start the requested number of worker threads."""
+        self._stop_event.clear()
+        for i in range(0, max_workers):  # noqa B007
+            worker = threading.Thread(target=self._download_next_item, daemon=True)
+            self._logger.debug(f"Download queue worker thread {worker.name} starting.")
+            worker.start()
+            self._worker_pool.add(worker)
+
+    def _download_next_item(self) -> None:
+        """Worker thread gets next job on priority queue."""
+        done = False
+        while not done:
+            if self._stop_event.is_set():
+                done = True
+                continue
+            try:
+                job = self._queue.get(timeout=1)
+            except Empty:
+                continue
+
+            try:
+                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)
+            except DownloadJobCancelledException:
+                self._signal_job_cancelled(job)
+                self._cleanup_cancelled_job(job)
+
+            finally:
+                job.job_ended = get_iso_timestamp()
+                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"
+
+        # Make a streaming request. This will retrieve headers including
+        # content-length and content-disposition, but not fetch any content itself
+        resp = self._requests.get(str(url), headers=header, stream=True)
+        if not resp.ok:
+            raise HTTPError(resp.reason)
+        content_length = int(resp.headers.get("content-length", 0))
+        job.total_bytes = content_length
+
+        if job.dest.is_dir():
+            file_name = os.path.basename(str(url.path))  # default is to use the last bit of the URL
+
+            if match := re.search('filename="(.+)"', resp.headers.get("Content-Disposition", "")):
+                remote_name = match.group(1)
+                if self._validate_filename(job.dest.as_posix(), remote_name):
+                    file_name = remote_name
+
+            job.download_path = job.dest / file_name
+
+        else:
+            job.dest.parent.mkdir(parents=True, exist_ok=True)
+            job.download_path = job.dest
+
+        assert job.download_path
+
+        # Don't clobber an existing file. See commit 82c2c85202f88c6d24ff84710f297cfc6ae174af
+        # for code that instead resumes an interrupted download.
+        if job.download_path.exists():
+            raise OSError(f"[Errno 17] File {job.download_path} exists")
+
+        # append ".downloading" to the path
+        in_progress_path = self._in_progress_path(job.download_path)
+
+        # signal caller that the download is starting. At this point, key fields such as
+        # download_path and total_bytes will be populated. We call it here because the might
+        # discover that the local file is already complete and generate a COMPLETED status.
+        self._signal_job_started(job)
+
+        # "range not satisfiable" - local file is at least as large as the remote file
+        if resp.status_code == 416 or (content_length > 0 and job.bytes >= content_length):
+            self._logger.warning(f"{job.download_path}: complete file found. Skipping.")
+            return
+
+        # "partial content" - local file is smaller than remote file
+        elif resp.status_code == 206 or job.bytes > 0:
+            self._logger.warning(f"{job.download_path}: partial file found. Resuming")
+
+        # some other error
+        elif resp.status_code != 200:
+            raise HTTPError(resp.reason)
+
+        self._logger.debug(f"{job.source}: Downloading {job.download_path}")
+        report_delta = job.total_bytes / 100  # report every 1% change
+        last_report_bytes = 0
+
+        # DOWNLOAD LOOP
+        with open(in_progress_path, open_mode) as file:
+            for data in resp.iter_content(chunk_size=DOWNLOAD_CHUNK_SIZE):
+                if job.cancelled:
+                    raise DownloadJobCancelledException("Job was cancelled at caller's request")
+
+                job.bytes += file.write(data)
+                if (job.bytes - last_report_bytes >= report_delta) or (job.bytes >= job.total_bytes):
+                    last_report_bytes = job.bytes
+                    self._signal_job_progress(job)
+
+        # if we get here we are done and can rename the file to the original dest
+        in_progress_path.rename(job.download_path)
+
+    def _validate_filename(self, directory: str, filename: str) -> bool:
+        pc_name_max = os.pathconf(directory, "PC_NAME_MAX") if hasattr(os, "pathconf") else 260  # hardcoded for windows
+        pc_path_max = (
+            os.pathconf(directory, "PC_PATH_MAX") if hasattr(os, "pathconf") else 32767
+        )  # hardcoded for windows with long names enabled
+        if "/" in filename:
+            return False
+        if filename.startswith(".."):
+            return False
+        if len(filename) > pc_name_max:
+            return False
+        if len(os.path.join(directory, filename)) > pc_path_max:
+            return False
+        return True
+
+    def _in_progress_path(self, path: Path) -> Path:
+        return path.with_name(path.name + ".downloading")
+
+    def _signal_job_started(self, job: DownloadJob) -> None:
+        job.status = DownloadJobStatus.RUNNING
+        if job.on_start:
+            try:
+                job.on_start(job)
+            except Exception as e:
+                self._logger.error(e)
+        if self._event_bus:
+            assert job.download_path
+            self._event_bus.emit_download_started(str(job.source), job.download_path.as_posix())
+
+    def _signal_job_progress(self, job: DownloadJob) -> None:
+        if job.on_progress:
+            try:
+                job.on_progress(job)
+            except Exception as e:
+                self._logger.error(e)
+        if self._event_bus:
+            assert job.download_path
+            self._event_bus.emit_download_progress(
+                str(job.source),
+                download_path=job.download_path.as_posix(),
+                current_bytes=job.bytes,
+                total_bytes=job.total_bytes,
+            )
+
+    def _signal_job_complete(self, job: DownloadJob) -> None:
+        job.status = DownloadJobStatus.COMPLETED
+        if job.on_complete:
+            try:
+                job.on_complete(job)
+            except Exception as e:
+                self._logger.error(e)
+        if self._event_bus:
+            assert job.download_path
+            self._event_bus.emit_download_complete(
+                str(job.source), download_path=job.download_path.as_posix(), total_bytes=job.total_bytes
+            )
+
+    def _signal_job_cancelled(self, job: DownloadJob) -> None:
+        job.status = DownloadJobStatus.CANCELLED
+        if job.on_cancelled:
+            try:
+                job.on_cancelled(job)
+            except Exception as e:
+                self._logger.error(e)
+        if self._event_bus:
+            self._event_bus.emit_download_cancelled(str(job.source))
+
+    def _signal_job_error(self, job: DownloadJob) -> None:
+        job.status = DownloadJobStatus.ERROR
+        if job.on_error:
+            try:
+                job.on_error(job)
+            except Exception as e:
+                self._logger.error(e)
+        if self._event_bus:
+            assert job.error_type
+            assert job.error
+            self._event_bus.emit_download_error(str(job.source), error_type=job.error_type, error=job.error)
+
+    def _cleanup_cancelled_job(self, job: DownloadJob) -> None:
+        self._logger.warning(f"Cleaning up leftover files from cancelled download job {job.download_path}")
+        try:
+            if job.download_path:
+                partial_file = self._in_progress_path(job.download_path)
+                partial_file.unlink()
+        except OSError as excp:
+            self._logger.warning(excp)
+
+
+# Example on_progress event handler to display a TQDM status bar
+# Activate with:
+#   download_service.download('http://foo.bar/baz', '/tmp', on_progress=TqdmProgress().job_update
+class TqdmProgress(object):
+    """TQDM-based progress bar object to use in on_progress handlers."""
+
+    _bars: Dict[int, tqdm]  # the tqdm object
+    _last: Dict[int, int]  # last bytes downloaded
+
+    def __init__(self) -> None:  # noqa D107
+        self._bars = {}
+        self._last = {}
+
+    def update(self, job: DownloadJob) -> None:  # noqa D102
+        job_id = job.id
+        # new job
+        if job_id not in self._bars:
+            assert job.download_path
+            dest = Path(job.download_path).name
+            self._bars[job_id] = tqdm(
+                desc=dest,
+                initial=0,
+                total=job.total_bytes,
+                unit="iB",
+                unit_scale=True,
+            )
+            self._last[job_id] = 0
+        self._bars[job_id].update(job.bytes - self._last[job_id])
+        self._last[job_id] = job.bytes

invokeai/app/services/events/events_base.py

@@ -17,6 +17,7 @@ from invokeai.backend.model_management.models.base import BaseModelType, ModelTy
 
 class EventServiceBase:
     queue_event: str = "queue_event"
+    download_event: str = "download_event"
     model_event: str = "model_event"
 
     """Basic event bus, to have an empty stand-in when not needed"""
@@ -32,6 +33,13 @@ class EventServiceBase:
             payload={"event": event_name, "data": payload},
         )
 
+    def __emit_download_event(self, event_name: str, payload: dict) -> None:
+        payload["timestamp"] = get_timestamp()
+        self.dispatch(
+            event_name=EventServiceBase.download_event,
+            payload={"event": event_name, "data": payload},
+        )
+
     def __emit_model_event(self, event_name: str, payload: dict) -> None:
         payload["timestamp"] = get_timestamp()
         self.dispatch(
@@ -323,6 +331,79 @@ class EventServiceBase:
             payload={"queue_id": queue_id},
         )
 
+    def emit_download_started(self, source: str, download_path: str) -> None:
+        """
+        Emit when a download job is started.
+
+        :param url: The downloaded url
+        """
+        self.__emit_download_event(
+            event_name="download_started",
+            payload={"source": source, "download_path": download_path},
+        )
+
+    def emit_download_progress(self, source: str, download_path: str, current_bytes: int, total_bytes: int) -> None:
+        """
+        Emit "download_progress" events at regular intervals during a download job.
+
+        :param source: The downloaded source
+        :param download_path: The local downloaded file
+        :param current_bytes: Number of bytes downloaded so far
+        :param total_bytes: The size of the file being downloaded (if known)
+        """
+        self.__emit_download_event(
+            event_name="download_progress",
+            payload={
+                "source": source,
+                "download_path": download_path,
+                "current_bytes": current_bytes,
+                "total_bytes": total_bytes,
+            },
+        )
+
+    def emit_download_complete(self, source: str, download_path: str, total_bytes: int) -> None:
+        """
+        Emit a "download_complete" event at the end of a successful download.
+
+        :param source: Source URL
+        :param download_path: Path to the locally downloaded file
+        :param total_bytes: The size of the downloaded file
+        """
+        self.__emit_download_event(
+            event_name="download_complete",
+            payload={
+                "source": source,
+                "download_path": download_path,
+                "total_bytes": total_bytes,
+            },
+        )
+
+    def emit_download_cancelled(self, source: str) -> None:
+        """Emit a "download_cancelled" event in the event that the download was cancelled by user."""
+        self.__emit_download_event(
+            event_name="download_cancelled",
+            payload={
+                "source": source,
+            },
+        )
+
+    def emit_download_error(self, source: str, error_type: str, error: str) -> None:
+        """
+        Emit a "download_error" event when an download job encounters an exception.
+
+        :param source: Source URL
+        :param error_type: The name of the exception that raised the error
+        :param error: The traceback from this error
+        """
+        self.__emit_download_event(
+            event_name="download_error",
+            payload={
+                "source": source,
+                "error_type": error_type,
+                "error": error,
+            },
+        )
+
     def emit_model_install_started(self, source: str) -> None:
         """
         Emitted when an install job is started.

invokeai/app/services/invocation_services.py

@@ -11,6 +11,7 @@ if TYPE_CHECKING:
     from .board_records.board_records_base import BoardRecordStorageBase
     from .boards.boards_base import BoardServiceABC
     from .config import InvokeAIAppConfig
+    from .download import DownloadQueueServiceBase
     from .events.events_base import EventServiceBase
     from .image_files.image_files_base import ImageFileStorageBase
     from .image_records.image_records_base import ImageRecordStorageBase
@@ -27,7 +28,7 @@ if TYPE_CHECKING:
     from .names.names_base import NameServiceBase
     from .session_processor.session_processor_base import SessionProcessorBase
     from .session_queue.session_queue_base import SessionQueueBase
-    from .shared.graph import GraphExecutionState, LibraryGraph
+    from .shared.graph import GraphExecutionState
     from .urls.urls_base import UrlServiceBase
     from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
 
@@ -43,7 +44,6 @@ class InvocationServices:
     configuration: "InvokeAIAppConfig"
     events: "EventServiceBase"
     graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
-    graph_library: "ItemStorageABC[LibraryGraph]"
     images: "ImageServiceABC"
     image_records: "ImageRecordStorageBase"
     image_files: "ImageFileStorageBase"
@@ -51,6 +51,7 @@ class InvocationServices:
     logger: "Logger"
     model_manager: "ModelManagerServiceBase"
     model_records: "ModelRecordServiceBase"
+    download_queue: "DownloadQueueServiceBase"
     model_install: "ModelInstallServiceBase"
     processor: "InvocationProcessorABC"
     performance_statistics: "InvocationStatsServiceBase"
@@ -71,7 +72,6 @@ class InvocationServices:
         configuration: "InvokeAIAppConfig",
         events: "EventServiceBase",
         graph_execution_manager: "ItemStorageABC[GraphExecutionState]",
-        graph_library: "ItemStorageABC[LibraryGraph]",
         images: "ImageServiceABC",
         image_files: "ImageFileStorageBase",
         image_records: "ImageRecordStorageBase",
@@ -79,6 +79,7 @@ class InvocationServices:
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
         model_records: "ModelRecordServiceBase",
+        download_queue: "DownloadQueueServiceBase",
         model_install: "ModelInstallServiceBase",
         processor: "InvocationProcessorABC",
         performance_statistics: "InvocationStatsServiceBase",
@@ -97,7 +98,6 @@ class InvocationServices:
         self.configuration = configuration
         self.events = events
         self.graph_execution_manager = graph_execution_manager
-        self.graph_library = graph_library
         self.images = images
         self.image_files = image_files
         self.image_records = image_records
@@ -105,6 +105,7 @@ class InvocationServices:
         self.logger = logger
         self.model_manager = model_manager
         self.model_records = model_records
+        self.download_queue = download_queue
         self.model_install = model_install
         self.processor = processor
         self.performance_statistics = performance_statistics

invokeai/app/services/model_install/model_install_base.py

@@ -11,7 +11,6 @@ from typing_extensions import Annotated
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.events import EventServiceBase
-from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_records import ModelRecordServiceBase
 from invokeai.backend.model_manager import AnyModelConfig
 
@@ -157,12 +156,12 @@ class ModelInstallServiceBase(ABC):
         :param event_bus: InvokeAI event bus for reporting events to.
         """
 
-    def start(self, invoker: Invoker) -> None:
+    @abstractmethod
-        """Call at InvokeAI startup time."""
+    def start(self, *args: Any, **kwarg: Any) -> None:
-        self.sync_to_config()
+        """Start the installer service."""
 
     @abstractmethod
-    def stop(self) -> None:
+    def stop(self, *args: Any, **kwarg: Any) -> None:
         """Stop the model install service. After this the objection can be safely deleted."""
 
     @property

invokeai/app/services/model_install/model_install_default.py

@@ -71,7 +71,6 @@ class ModelInstallService(ModelInstallServiceBase):
         self._install_queue = Queue()
         self._cached_model_paths = set()
         self._models_installed = set()
-        self._start_installer_thread()
 
     @property
     def app_config(self) -> InvokeAIAppConfig:  # noqa D102
@@ -85,8 +84,13 @@ class ModelInstallService(ModelInstallServiceBase):
     def event_bus(self) -> Optional[EventServiceBase]:  # noqa D102
         return self._event_bus
 
-    def stop(self, *args, **kwargs) -> None:
+    def start(self, *args: Any, **kwarg: Any) -> None:
-        """Stop the install thread; after this the object can be deleted and garbage collected."""
+        """Start the installer thread."""
+        self._start_installer_thread()
+        self.sync_to_config()
+
+    def stop(self, *args: Any, **kwarg: Any) -> None:
+        """Stop the installer thread; after this the object can be deleted and garbage collected."""
         self._install_queue.put(STOP_JOB)
 
     def _start_installer_thread(self) -> None:

invokeai/app/util/ti_utils.py

@@ -0,0 +1,8 @@
+import re
+
+
+def extract_ti_triggers_from_prompt(prompt: str) -> list[str]:
+    ti_triggers = []
+    for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
+        ti_triggers.append(trigger)
+    return ti_triggers

invokeai/backend/install/check_root.py

@@ -28,7 +28,7 @@ def check_invokeai_root(config: InvokeAIAppConfig):
         print("== STARTUP ABORTED ==")
         print("** One or more necessary files is missing from your InvokeAI root directory **")
         print("** Please rerun the configuration script to fix this problem. **")
-        print("** From the launcher, selection option [7]. **")
+        print("** From the launcher, selection option [6]. **")
         print(
             '** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **'
         )

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

@@ -113,7 +113,8 @@
         "orderBy": "Ordinato per",
         "nextPage": "Pagina successiva",
         "saveAs": "Salva come",
-        "unsaved": "Non salvato"
+        "unsaved": "Non salvato",
+        "direction": "Direzione"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -1112,7 +1113,8 @@
         "betaDesc": "Questa invocazione è in versione beta. Fino a quando non sarà stabile, potrebbe subire modifiche importanti durante gli aggiornamenti dell'app. Abbiamo intenzione di supportare questa invocazione a lungo termine.",
         "newWorkflow": "Nuovo flusso di lavoro",
         "newWorkflowDesc": "Creare un nuovo flusso di lavoro?",
-        "newWorkflowDesc2": "Il flusso di lavoro attuale presenta modifiche non salvate."
+        "newWorkflowDesc2": "Il flusso di lavoro attuale presenta modifiche non salvate.",
+        "unsupportedAnyOfLength": "unione di troppi elementi ({{count}})"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",

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

@@ -15,7 +15,7 @@
         "langBrPortuguese": "Português do Brasil",
         "langRussian": "Русский",
         "langSpanish": "Español",
-        "nodes": "노드",
+        "nodes": "Workflow Editor",
         "nodesDesc": "이미지 생성을 위한 노드 기반 시스템은 현재 개발 중입니다. 이 놀라운 기능에 대한 업데이트를 계속 지켜봐 주세요.",
         "postProcessing": "후처리",
         "postProcessDesc2": "보다 진보된 후처리 작업을 위한 전용 UI가 곧 출시될 예정입니다.",
@@ -25,7 +25,7 @@
         "trainingDesc2": "InvokeAI는 이미 메인 스크립트를 사용한 Textual Inversion를 이용한 Custom embedding 학습을 지원하고 있습니다.",
         "upload": "업로드",
         "close": "닫기",
-        "load": "로드",
+        "load": "불러오기",
         "back": "뒤로 가기",
         "statusConnected": "연결됨",
         "statusDisconnected": "연결 끊김",
@@ -58,7 +58,69 @@
         "statusGeneratingImageToImage": "이미지->이미지 생성",
         "statusProcessingComplete": "처리 완료",
         "statusIterationComplete": "반복(Iteration) 완료",
-        "statusSavingImage": "이미지 저장"
+        "statusSavingImage": "이미지 저장",
+        "t2iAdapter": "T2I 어댑터",
+        "communityLabel": "커뮤니티",
+        "txt2img": "텍스트->이미지",
+        "dontAskMeAgain": "다시 묻지 마세요",
+        "loadingInvokeAI": "Invoke AI 불러오는 중",
+        "checkpoint": "체크포인트",
+        "format": "형식",
+        "unknown": "알려지지 않음",
+        "areYouSure": "확실하나요?",
+        "folder": "폴더",
+        "inpaint": "inpaint",
+        "updated": "업데이트 됨",
+        "on": "켜기",
+        "save": "저장",
+        "langPortuguese": "Português",
+        "created": "생성됨",
+        "nodeEditor": "Node Editor",
+        "error": "에러",
+        "prevPage": "이전 페이지",
+        "ipAdapter": "IP 어댑터",
+        "controlAdapter": "제어 어댑터",
+        "installed": "설치됨",
+        "accept": "수락",
+        "ai": "인공지능",
+        "auto": "자동",
+        "file": "파일",
+        "openInNewTab": "새 탭에서 열기",
+        "delete": "삭제",
+        "template": "템플릿",
+        "cancel": "취소",
+        "controlNet": "컨트롤넷",
+        "outputs": "결과물",
+        "unknownError": "알려지지 않은 에러",
+        "statusProcessing": "처리 중",
+        "linear": "선형",
+        "imageFailedToLoad": "이미지를 로드할 수 없음",
+        "direction": "방향",
+        "data": "데이터",
+        "somethingWentWrong": "뭔가 잘못됐어요",
+        "imagePrompt": "이미지 프롬프트",
+        "modelManager": "Model Manager",
+        "lightMode": "라이트 모드",
+        "safetensors": "Safetensors",
+        "outpaint": "outpaint",
+        "langKorean": "한국어",
+        "orderBy": "정렬 기준",
+        "generate": "생성",
+        "copyError": "$t(gallery.copy) 에러",
+        "learnMore": "더 알아보기",
+        "nextPage": "다음 페이지",
+        "saveAs": "다른 이름으로 저장",
+        "darkMode": "다크 모드",
+        "loading": "불러오는 중",
+        "random": "랜덤",
+        "langHebrew": "Hebrew",
+        "batch": "Batch 매니저",
+        "postprocessing": "후처리",
+        "advanced": "고급",
+        "unsaved": "저장되지 않음",
+        "input": "입력",
+        "details": "세부사항",
+        "notInstalled": "설치되지 않음"
     },
     "gallery": {
         "showGenerations": "생성된 이미지 보기",
@@ -68,7 +130,35 @@
         "galleryImageSize": "이미지 크기",
         "galleryImageResetSize": "사이즈 리셋",
         "gallerySettings": "갤러리 설정",
-        "maintainAspectRatio": "종횡비 유지"
+        "maintainAspectRatio": "종횡비 유지",
+        "deleteSelection": "선택 항목 삭제",
+        "featuresWillReset": "이 이미지를 삭제하면 해당 기능이 즉시 재설정됩니다.",
+        "deleteImageBin": "삭제된 이미지는 운영 체제의 Bin으로 전송됩니다.",
+        "assets": "자산",
+        "problemDeletingImagesDesc": "하나 이상의 이미지를 삭제할 수 없습니다",
+        "noImagesInGallery": "보여줄 이미지가 없음",
+        "autoSwitchNewImages": "새로운 이미지로 자동 전환",
+        "loading": "불러오는 중",
+        "unableToLoad": "갤러리를 로드할 수 없음",
+        "preparingDownload": "다운로드 준비",
+        "preparingDownloadFailed": "다운로드 준비 중 발생한 문제",
+        "singleColumnLayout": "단일 열 레이아웃",
+        "image": "이미지",
+        "loadMore": "더 불러오기",
+        "drop": "드랍",
+        "problemDeletingImages": "이미지 삭제 중 발생한 문제",
+        "downloadSelection": "선택 항목 다운로드",
+        "deleteImage": "이미지 삭제",
+        "currentlyInUse": "이 이미지는 현재 다음 기능에서 사용되고 있습니다:",
+        "allImagesLoaded": "불러온 모든 이미지",
+        "dropOrUpload": "$t(gallery.drop) 또는 업로드",
+        "copy": "복사",
+        "download": "다운로드",
+        "deleteImagePermanent": "삭제된 이미지는 복원할 수 없습니다.",
+        "noImageSelected": "선택된 이미지 없음",
+        "autoAssignBoardOnClick": "클릭 시 Board로 자동 할당",
+        "setCurrentImage": "현재 이미지로 설정",
+        "dropToUpload": "업로드를 위해 $t(gallery.drop)"
     },
     "unifiedCanvas": {
         "betaPreserveMasked": "마스크 레이어 유지"
@@ -79,6 +169,752 @@
         "nextImage": "다음 이미지",
         "mode": "모드",
         "menu": "메뉴",
-        "modelSelect": "모델 선택"
+        "modelSelect": "모델 선택",
+        "zoomIn": "확대하기",
+        "rotateClockwise": "시계방향으로 회전",
+        "uploadImage": "이미지 업로드",
+        "showGalleryPanel": "갤러리 패널 표시",
+        "useThisParameter": "해당 변수 사용",
+        "reset": "리셋",
+        "loadMore": "더 불러오기",
+        "zoomOut": "축소하기",
+        "rotateCounterClockwise": "반시계방향으로 회전",
+        "showOptionsPanel": "사이드 패널 표시",
+        "toggleAutoscroll": "자동 스크롤 전환",
+        "toggleLogViewer": "Log Viewer 전환"
+    },
+    "modelManager": {
+        "pathToCustomConfig": "사용자 지정 구성 경로",
+        "importModels": "모델 가져오기",
+        "availableModels": "사용 가능한 모델",
+        "conversionNotSupported": "변환이 지원되지 않음",
+        "noCustomLocationProvided": "사용자 지정 위치가 제공되지 않음",
+        "onnxModels": "Onnx",
+        "vaeRepoID": "VAE Repo ID",
+        "modelExists": "모델 존재",
+        "custom": "사용자 지정",
+        "addModel": "모델 추가",
+        "none": "없음",
+        "modelConverted": "변환된 모델",
+        "width": "너비",
+        "weightedSum": "가중합",
+        "inverseSigmoid": "Inverse Sigmoid",
+        "invokeAIFolder": "Invoke AI 폴더",
+        "syncModelsDesc": "모델이 백엔드와 동기화되지 않은 경우 이 옵션을 사용하여 새로 고침할 수 있습니다. 이는 일반적으로 응용 프로그램이 부팅된 후 수동으로 모델.yaml 파일을 업데이트하거나 InvokeAI root 폴더에 모델을 추가하는 경우에 유용합니다.",
+        "convert": "변환",
+        "vae": "VAE",
+        "noModels": "모델을 찾을 수 없음",
+        "statusConverting": "변환중",
+        "sigmoid": "Sigmoid",
+        "deleteModel": "모델 삭제",
+        "modelLocation": "모델 위치",
+        "merge": "병합",
+        "v1": "v1",
+        "description": "Description",
+        "modelMergeInterpAddDifferenceHelp": "이 모드에서 모델 3은 먼저 모델 2에서 차감됩니다. 결과 버전은 위에 설정된 Alpha 비율로 모델 1과 혼합됩니다.",
+        "customConfig": "사용자 지정 구성",
+        "cannotUseSpaces": "공백을 사용할 수 없음",
+        "formMessageDiffusersModelLocationDesc": "적어도 하나 이상 입력해 주세요.",
+        "addDiffuserModel": "Diffusers 추가",
+        "search": "검색",
+        "predictionType": "예측 유형(안정 확산 2.x 모델 및 간혹 안정 확산 1.x 모델의 경우)",
+        "widthValidationMsg": "모형의 기본 너비.",
+        "selectAll": "모두 선택",
+        "vaeLocation": "VAE 위치",
+        "selectModel": "모델 선택",
+        "modelAdded": "추가된 모델",
+        "repo_id": "Repo ID",
+        "modelSyncFailed": "모델 동기화 실패",
+        "convertToDiffusersHelpText6": "이 모델을 변환하시겠습니까?",
+        "config": "구성",
+        "quickAdd": "빠른 추가",
+        "selected": "선택된",
+        "modelTwo": "모델 2",
+        "simpleModelDesc": "로컬 Difffusers 모델, 로컬 체크포인트/안전 센서 모델 HuggingFace Repo ID 또는 체크포인트/Diffusers 모델 URL의 경로를 제공합니다.",
+        "customSaveLocation": "사용자 정의 저장 위치",
+        "advanced": "고급",
+        "modelsFound": "발견된 모델",
+        "load": "불러오기",
+        "height": "높이",
+        "modelDeleted": "삭제된 모델",
+        "inpainting": "v1 Inpainting",
+        "vaeLocationValidationMsg": "VAE가 있는 경로.",
+        "convertToDiffusersHelpText2": "이 프로세스는 모델 관리자 항목을 동일한 모델의 Diffusers 버전으로 대체합니다.",
+        "modelUpdateFailed": "모델 업데이트 실패",
+        "modelUpdated": "업데이트된 모델",
+        "noModelsFound": "모델을 찾을 수 없음",
+        "useCustomConfig": "사용자 지정 구성 사용",
+        "formMessageDiffusersVAELocationDesc": "제공되지 않은 경우 호출AIA 파일을 위의 모델 위치 내에서 VAE 파일을 찾습니다.",
+        "formMessageDiffusersVAELocation": "VAE 위치",
+        "checkpointModels": "Checkpoints",
+        "modelOne": "모델 1",
+        "settings": "설정",
+        "heightValidationMsg": "모델의 기본 높이입니다.",
+        "selectAndAdd": "아래 나열된 모델 선택 및 추가",
+        "convertToDiffusersHelpText5": "디스크 공간이 충분한지 확인해 주세요. 모델은 일반적으로 2GB에서 7GB 사이로 다양합니다.",
+        "deleteConfig": "구성 삭제",
+        "deselectAll": "모두 선택 취소",
+        "modelConversionFailed": "모델 변환 실패",
+        "clearCheckpointFolder": "Checkpoint Folder 지우기",
+        "modelEntryDeleted": "모델 항목 삭제",
+        "deleteMsg1": "InvokeAI에서 이 모델을 삭제하시겠습니까?",
+        "syncModels": "동기화 모델",
+        "mergedModelSaveLocation": "위치 저장",
+        "checkpointOrSafetensors": "$t(common.checkpoint) / $t(common.safetensors)",
+        "modelType": "모델 유형",
+        "nameValidationMsg": "모델 이름 입력",
+        "cached": "cached",
+        "modelsMerged": "병합된 모델",
+        "formMessageDiffusersModelLocation": "Diffusers 모델 위치",
+        "modelsMergeFailed": "모델 병합 실패",
+        "convertingModelBegin": "모델 변환 중입니다. 잠시만 기다려 주십시오.",
+        "v2_base": "v2 (512px)",
+        "scanForModels": "모델 검색",
+        "modelLocationValidationMsg": "Diffusers 모델이 저장된 로컬 폴더의 경로 제공",
+        "name": "이름",
+        "selectFolder": "폴더 선택",
+        "updateModel": "모델 업데이트",
+        "addNewModel": "새로운 모델 추가",
+        "customConfigFileLocation": "사용자 지정 구성 파일 위치",
+        "descriptionValidationMsg": "모델에 대한 description 추가",
+        "safetensorModels": "SafeTensors",
+        "convertToDiffusersHelpText1": "이 모델은 🧨 Diffusers 형식으로 변환됩니다.",
+        "modelsSynced": "동기화된 모델",
+        "vaePrecision": "VAE 정밀도",
+        "invokeRoot": "InvokeAI 폴더",
+        "checkpointFolder": "Checkpoint Folder",
+        "mergedModelCustomSaveLocation": "사용자 지정 경로",
+        "mergeModels": "모델 병합",
+        "interpolationType": "Interpolation 타입",
+        "modelMergeHeaderHelp2": "Diffusers만 병합이 가능합니다. 체크포인트 모델 병합을 원하신다면 먼저 Diffusers로 변환해주세요.",
+        "convertToDiffusersSaveLocation": "위치 저장",
+        "deleteMsg2": "모델이 InvokeAI root 폴더에 있으면 디스크에서 모델이 삭제됩니다. 사용자 지정 위치를 사용하는 경우 모델이 디스크에서 삭제되지 않습니다.",
+        "oliveModels": "Olives",
+        "repoIDValidationMsg": "모델의 온라인 저장소",
+        "baseModel": "기본 모델",
+        "scanAgain": "다시 검색",
+        "pickModelType": "모델 유형 선택",
+        "sameFolder": "같은 폴더",
+        "addNew": "New 추가",
+        "manual": "매뉴얼",
+        "convertToDiffusersHelpText3": "디스크의 체크포인트 파일이 InvokeAI root 폴더에 있으면 삭제됩니다. 사용자 지정 위치에 있으면 삭제되지 않습니다.",
+        "addCheckpointModel": "체크포인트 / 안전 센서 모델 추가",
+        "configValidationMsg": "모델의 구성 파일에 대한 경로.",
+        "modelManager": "모델 매니저",
+        "variant": "Variant",
+        "vaeRepoIDValidationMsg": "VAE의 온라인 저장소",
+        "loraModels": "LoRAs",
+        "modelDeleteFailed": "모델을 삭제하지 못했습니다",
+        "convertToDiffusers": "Diffusers로 변환",
+        "allModels": "모든 모델",
+        "modelThree": "모델 3",
+        "findModels": "모델 찾기",
+        "notLoaded": "로드되지 않음",
+        "alpha": "Alpha",
+        "diffusersModels": "Diffusers",
+        "modelMergeAlphaHelp": "Alpha는 모델의 혼합 강도를 제어합니다. Alpha 값이 낮을수록 두 번째 모델의 영향력이 줄어듭니다.",
+        "addDifference": "Difference 추가",
+        "noModelSelected": "선택한 모델 없음",
+        "modelMergeHeaderHelp1": "최대 3개의 다른 모델을 병합하여 필요에 맞는 혼합물을 만들 수 있습니다.",
+        "ignoreMismatch": "선택한 모델 간의 불일치 무시",
+        "v2_768": "v2 (768px)",
+        "convertToDiffusersHelpText4": "이것은 한 번의 과정일 뿐입니다. 컴퓨터 사양에 따라 30-60초 정도 소요될 수 있습니다.",
+        "model": "모델",
+        "addManually": "Manually 추가",
+        "addSelected": "Selected 추가",
+        "mergedModelName": "병합된 모델 이름",
+        "delete": "삭제"
+    },
+    "controlnet": {
+        "amult": "a_mult",
+        "resize": "크기 조정",
+        "showAdvanced": "고급 표시",
+        "contentShuffleDescription": "이미지에서 content 섞기",
+        "bgth": "bg_th",
+        "addT2IAdapter": "$t(common.t2iAdapter) 추가",
+        "pidi": "PIDI",
+        "importImageFromCanvas": "캔버스에서 이미지 가져오기",
+        "lineartDescription": "이미지->lineart 변환",
+        "normalBae": "Normal BAE",
+        "importMaskFromCanvas": "캔버스에서 Mask 가져오기",
+        "hed": "HED",
+        "contentShuffle": "Content Shuffle",
+        "controlNetEnabledT2IDisabled": "$t(common.controlNet) 사용 가능, $t(common.t2iAdapter) 사용 불가능",
+        "ipAdapterModel": "Adapter 모델",
+        "resetControlImage": "Control Image 재설정",
+        "beginEndStepPercent": "Begin / End Step Percentage",
+        "mlsdDescription": "Minimalist Line Segment Detector",
+        "duplicate": "복제",
+        "balanced": "Balanced",
+        "f": "F",
+        "h": "H",
+        "prompt": "프롬프트",
+        "depthMidasDescription": "Midas를 사용하여 Depth map 생성하기",
+        "openPoseDescription": "Openpose를 이용한 사람 포즈 추정",
+        "control": "Control",
+        "resizeMode": "크기 조정 모드",
+        "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) 사용 가능,$t(common.controlNet) 사용 불가능",
+        "coarse": "Coarse",
+        "weight": "Weight",
+        "selectModel": "모델 선택",
+        "crop": "Crop",
+        "depthMidas": "Depth (Midas)",
+        "w": "W",
+        "processor": "프로세서",
+        "addControlNet": "$t(common.controlNet) 추가",
+        "none": "해당없음",
+        "incompatibleBaseModel": "호환되지 않는 기본 모델:",
+        "enableControlnet": "사용 가능한 ControlNet",
+        "detectResolution": "해상도 탐지",
+        "controlNetT2IMutexDesc": "$t(common.controlNet)와 $t(common.t2iAdapter)는 현재 동시에 지원되지 않습니다.",
+        "pidiDescription": "PIDI image 처리",
+        "mediapipeFace": "Mediapipe Face",
+        "mlsd": "M-LSD",
+        "controlMode": "Control Mode",
+        "fill": "채우기",
+        "cannyDescription": "Canny 모서리 삭제",
+        "addIPAdapter": "$t(common.ipAdapter) 추가",
+        "lineart": "Lineart",
+        "colorMapDescription": "이미지에서 color map을 생성합니다",
+        "lineartAnimeDescription": "Anime-style lineart 처리",
+        "minConfidence": "Min Confidence",
+        "imageResolution": "이미지 해상도",
+        "megaControl": "Mega Control",
+        "depthZoe": "Depth (Zoe)",
+        "colorMap": "색",
+        "lowThreshold": "Low Threshold",
+        "autoConfigure": "프로세서 자동 구성",
+        "highThreshold": "High Threshold",
+        "normalBaeDescription": "Normal BAE 처리",
+        "noneDescription": "처리되지 않음",
+        "saveControlImage": "Control Image 저장",
+        "openPose": "Openpose",
+        "toggleControlNet": "해당 ControlNet으로 전환",
+        "delete": "삭제",
+        "controlAdapter_other": "Control Adapter(s)",
+        "safe": "Safe",
+        "colorMapTileSize": "타일 크기",
+        "lineartAnime": "Lineart Anime",
+        "ipAdapterImageFallback": "IP Adapter Image가 선택되지 않음",
+        "mediapipeFaceDescription": "Mediapipe를 사용하여 Face 탐지",
+        "canny": "Canny",
+        "depthZoeDescription": "Zoe를 사용하여 Depth map 생성하기",
+        "hedDescription": "Holistically-Nested 모서리 탐지",
+        "setControlImageDimensions": "Control Image Dimensions를 W/H로 설정",
+        "scribble": "scribble",
+        "resetIPAdapterImage": "IP Adapter Image 재설정",
+        "handAndFace": "Hand and Face",
+        "enableIPAdapter": "사용 가능한 IP Adapter",
+        "maxFaces": "Max Faces"
+    },
+    "hotkeys": {
+        "toggleGalleryPin": {
+            "title": "Gallery Pin 전환",
+            "desc": "갤러리를 UI에 고정했다가 풉니다"
+        },
+        "toggleSnap": {
+            "desc": "Snap을 Grid로 전환",
+            "title": "Snap 전환"
+        },
+        "setSeed": {
+            "title": "시드 설정",
+            "desc": "현재 이미지의 시드 사용"
+        },
+        "keyboardShortcuts": "키보드 바로 가기",
+        "decreaseGalleryThumbSize": {
+            "desc": "갤러리 미리 보기 크기 축소",
+            "title": "갤러리 이미지 크기 축소"
+        },
+        "previousStagingImage": {
+            "title": "이전 스테이징 이미지",
+            "desc": "이전 스테이징 영역 이미지"
+        },
+        "decreaseBrushSize": {
+            "title": "브러시 크기 줄이기",
+            "desc": "캔버스 브러시/지우개 크기 감소"
+        },
+        "consoleToggle": {
+            "desc": "콘솔 열고 닫기",
+            "title": "콘솔 전환"
+        },
+        "selectBrush": {
+            "desc": "캔버스 브러시를 선택",
+            "title": "브러시 선택"
+        },
+        "upscale": {
+            "desc": "현재 이미지를 업스케일",
+            "title": "업스케일"
+        },
+        "previousImage": {
+            "title": "이전 이미지",
+            "desc": "갤러리에 이전 이미지 표시"
+        },
+        "unifiedCanvasHotkeys": "Unified Canvas Hotkeys",
+        "toggleOptions": {
+            "desc": "옵션 패널을 열고 닫기",
+            "title": "옵션 전환"
+        },
+        "selectEraser": {
+            "title": "지우개 선택",
+            "desc": "캔버스 지우개를 선택"
+        },
+        "setPrompt": {
+            "title": "프롬프트 설정",
+            "desc": "현재 이미지의 프롬프트 사용"
+        },
+        "acceptStagingImage": {
+            "desc": "현재 준비 영역 이미지 허용",
+            "title": "준비 이미지 허용"
+        },
+        "resetView": {
+            "desc": "Canvas View 초기화",
+            "title": "View 초기화"
+        },
+        "hideMask": {
+            "title": "Mask 숨김",
+            "desc": "mask 숨김/숨김 해제"
+        },
+        "pinOptions": {
+            "title": "옵션 고정",
+            "desc": "옵션 패널을 고정"
+        },
+        "toggleGallery": {
+            "desc": "gallery drawer 열기 및 닫기",
+            "title": "Gallery 전환"
+        },
+        "quickToggleMove": {
+            "title": "빠른 토글 이동",
+            "desc": "일시적으로 이동 모드 전환"
+        },
+        "generalHotkeys": "General Hotkeys",
+        "showHideBoundingBox": {
+            "desc": "bounding box 표시 전환",
+            "title": "Bounding box 표시/숨김"
+        },
+        "showInfo": {
+            "desc": "현재 이미지의 metadata 정보 표시",
+            "title": "정보 표시"
+        },
+        "copyToClipboard": {
+            "title": "클립보드로 복사",
+            "desc": "현재 캔버스를 클립보드로 복사"
+        },
+        "restoreFaces": {
+            "title": "Faces 복원",
+            "desc": "현재 이미지 복원"
+        },
+        "fillBoundingBox": {
+            "title": "Bounding Box 채우기",
+            "desc": "bounding box를 브러시 색으로 채웁니다"
+        },
+        "closePanels": {
+            "desc": "열린 panels 닫기",
+            "title": "panels 닫기"
+        },
+        "downloadImage": {
+            "desc": "현재 캔버스 다운로드",
+            "title": "이미지 다운로드"
+        },
+        "setParameters": {
+            "title": "매개 변수 설정",
+            "desc": "현재 이미지의 모든 매개 변수 사용"
+        },
+        "maximizeWorkSpace": {
+            "desc": "패널을 닫고 작업 면적을 극대화",
+            "title": "작업 공간 극대화"
+        },
+        "galleryHotkeys": "Gallery Hotkeys",
+        "cancel": {
+            "desc": "이미지 생성 취소",
+            "title": "취소"
+        },
+        "saveToGallery": {
+            "title": "갤러리에 저장",
+            "desc": "현재 캔버스를 갤러리에 저장"
+        },
+        "eraseBoundingBox": {
+            "desc": "bounding box 영역을 지웁니다",
+            "title": "Bounding Box 지우기"
+        },
+        "nextImage": {
+            "title": "다음 이미지",
+            "desc": "갤러리에 다음 이미지 표시"
+        },
+        "colorPicker": {
+            "desc": "canvas color picker 선택",
+            "title": "Color Picker 선택"
+        },
+        "invoke": {
+            "desc": "이미지 생성",
+            "title": "불러오기"
+        },
+        "sendToImageToImage": {
+            "desc": "현재 이미지를 이미지로 보내기"
+        },
+        "toggleLayer": {
+            "desc": "mask/base layer 선택 전환",
+            "title": "Layer 전환"
+        },
+        "increaseBrushSize": {
+            "title": "브러시 크기 증가",
+            "desc": "캔버스 브러시/지우개 크기 증가"
+        },
+        "appHotkeys": "App Hotkeys",
+        "deleteImage": {
+            "title": "이미지 삭제",
+            "desc": "현재 이미지 삭제"
+        },
+        "moveTool": {
+            "desc": "캔버스 탐색 허용",
+            "title": "툴 옮기기"
+        },
+        "clearMask": {
+            "desc": "전체 mask 제거",
+            "title": "Mask 제거"
+        },
+        "increaseGalleryThumbSize": {
+            "title": "갤러리 이미지 크기 증가",
+            "desc": "갤러리 미리 보기 크기를 늘립니다"
+        },
+        "increaseBrushOpacity": {
+            "desc": "캔버스 브러시의 불투명도를 높입니다",
+            "title": "브러시 불투명도 증가"
+        },
+        "focusPrompt": {
+            "desc": "프롬프트 입력 영역에 초점을 맞춥니다",
+            "title": "프롬프트에 초점 맞추기"
+        },
+        "decreaseBrushOpacity": {
+            "desc": "캔버스 브러시의 불투명도를 줄입니다",
+            "title": "브러시 불투명도 감소"
+        },
+        "nextStagingImage": {
+            "desc": "다음 스테이징 영역 이미지",
+            "title": "다음 스테이징 이미지"
+        },
+        "redoStroke": {
+            "title": "Stroke 다시 실행",
+            "desc": "brush stroke 다시 실행"
+        },
+        "nodesHotkeys": "Nodes Hotkeys",
+        "addNodes": {
+            "desc": "노드 추가 메뉴 열기",
+            "title": "노드 추가"
+        },
+        "toggleViewer": {
+            "desc": "이미지 뷰어 열기 및 닫기",
+            "title": "Viewer 전환"
+        },
+        "undoStroke": {
+            "title": "Stroke 실행 취소",
+            "desc": "brush stroke 실행 취소"
+        },
+        "changeTabs": {
+            "desc": "다른 workspace으로 전환",
+            "title": "탭 바꾸기"
+        },
+        "mergeVisible": {
+            "desc": "캔버스의 보이는 모든 레이어 병합"
+        }
+    },
+    "nodes": {
+        "inputField": "입력 필드",
+        "controlFieldDescription": "노드 간에 전달된 Control 정보입니다.",
+        "latentsFieldDescription": "노드 사이에 Latents를 전달할 수 있습니다.",
+        "denoiseMaskFieldDescription": "노드 간에 Denoise Mask가 전달될 수 있음",
+        "floatCollectionDescription": "실수 컬렉션.",
+        "missingTemplate": "잘못된 노드: {{type}} 유형의 {{node}} 템플릿 누락(설치되지 않으셨나요?)",
+        "outputSchemaNotFound": "Output schema가 발견되지 않음",
+        "ipAdapterPolymorphicDescription": "IP-Adapters 컬렉션.",
+        "latentsPolymorphicDescription": "노드 사이에 Latents를 전달할 수 있습니다.",
+        "colorFieldDescription": "RGBA 색.",
+        "mainModelField": "모델",
+        "ipAdapterCollection": "IP-Adapters 컬렉션",
+        "conditioningCollection": "Conditioning 컬렉션",
+        "maybeIncompatible": "설치된 것과 호환되지 않을 수 있음",
+        "ipAdapterPolymorphic": "IP-Adapter 다형성",
+        "noNodeSelected": "선택한 노드 없음",
+        "addNode": "노드 추가",
+        "hideGraphNodes": "그래프 오버레이 숨기기",
+        "enum": "Enum",
+        "loadWorkflow": "Workflow 불러오기",
+        "integerPolymorphicDescription": "정수 컬렉션.",
+        "noOutputRecorded": "기록된 출력 없음",
+        "conditioningCollectionDescription": "노드 간에 Conditioning을 전달할 수 있습니다.",
+        "colorPolymorphic": "색상 다형성",
+        "colorCodeEdgesHelp": "연결된 필드에 따른 색상 코드 선",
+        "collectionDescription": "해야 할 일",
+        "hideLegendNodes": "필드 유형 범례 숨기기",
+        "addLinearView": "Linear View에 추가",
+        "float": "실수",
+        "targetNodeFieldDoesNotExist": "잘못된 모서리: 대상/입력 필드 {{node}}. {{field}}이(가) 없습니다",
+        "animatedEdges": "애니메이션 모서리",
+        "conditioningPolymorphic": "Conditioning 다형성",
+        "integer": "정수",
+        "colorField": "색",
+        "boardField": "Board",
+        "nodeTemplate": "노드 템플릿",
+        "latentsCollection": "Latents 컬렉션",
+        "nodeOpacity": "노드 불투명도",
+        "sourceNodeDoesNotExist": "잘못된 모서리: 소스/출력 노드 {{node}}이(가) 없습니다",
+        "pickOne": "하나 고르기",
+        "collectionItemDescription": "해야 할 일",
+        "integerDescription": "정수는 소수점이 없는 숫자입니다.",
+        "outputField": "출력 필드",
+        "conditioningPolymorphicDescription": "노드 간에 Conditioning을 전달할 수 있습니다.",
+        "noFieldsLinearview": "Linear View에 추가된 필드 없음",
+        "imagePolymorphic": "이미지 다형성",
+        "nodeSearch": "노드 검색",
+        "imagePolymorphicDescription": "이미지 컬렉션.",
+        "floatPolymorphic": "실수 다형성",
+        "outputFieldInInput": "입력 중 출력필드",
+        "doesNotExist": "존재하지 않음",
+        "ipAdapterCollectionDescription": "IP-Adapters 컬렉션.",
+        "controlCollection": "Control 컬렉션",
+        "inputMayOnlyHaveOneConnection": "입력에 하나의 연결만 있을 수 있습니다",
+        "notes": "메모",
+        "nodeOutputs": "노드 결과물",
+        "currentImageDescription": "Node Editor에 현재 이미지를 표시합니다",
+        "downloadWorkflow": "Workflow JSON 다운로드",
+        "ipAdapter": "IP-Adapter",
+        "integerCollection": "정수 컬렉션",
+        "collectionItem": "컬렉션 아이템",
+        "noConnectionInProgress": "진행중인 연결이 없습니다",
+        "controlCollectionDescription": "노드 간에 전달된 Control 정보입니다.",
+        "noConnectionData": "연결 데이터 없음",
+        "outputFields": "출력 필드",
+        "fieldTypesMustMatch": "필드 유형은 일치해야 합니다",
+        "edge": "Edge",
+        "inputNode": "입력 노드",
+        "enumDescription": "Enums은 여러 옵션 중 하나일 수 있는 값입니다.",
+        "sourceNodeFieldDoesNotExist": "잘못된 모서리: 소스/출력 필드 {{node}}. {{field}}이(가) 없습니다",
+        "loRAModelFieldDescription": "해야 할 일",
+        "imageField": "이미지",
+        "animatedEdgesHelp": "선택한 노드에 연결된 선택한 가장자리 및 가장자리를 애니메이션화합니다",
+        "cannotDuplicateConnection": "중복 연결을 만들 수 없습니다",
+        "booleanPolymorphic": "Boolean 다형성",
+        "noWorkflow": "Workflow 없음",
+        "colorCollectionDescription": "해야 할 일",
+        "integerCollectionDescription": "정수 컬렉션.",
+        "colorPolymorphicDescription": "색의 컬렉션.",
+        "denoiseMaskField": "Denoise Mask",
+        "missingCanvaInitImage": "캔버스 init 이미지 누락",
+        "conditioningFieldDescription": "노드 간에 Conditioning을 전달할 수 있습니다.",
+        "clipFieldDescription": "Tokenizer 및 text_encoder 서브모델.",
+        "fullyContainNodesHelp": "선택하려면 노드가 선택 상자 안에 완전히 있어야 합니다",
+        "noImageFoundState": "상태에서 초기 이미지를 찾을 수 없습니다",
+        "clipField": "Clip",
+        "nodePack": "Node pack",
+        "nodeType": "노드 유형",
+        "noMatchingNodes": "일치하는 노드 없음",
+        "fullyContainNodes": "선택할 노드 전체 포함",
+        "integerPolymorphic": "정수 다형성",
+        "executionStateInProgress": "진행중",
+        "noFieldType": "필드 유형 없음",
+        "colorCollection": "색의 컬렉션.",
+        "executionStateError": "에러",
+        "noOutputSchemaName": "ref 개체에 output schema 이름이 없습니다",
+        "ipAdapterModel": "IP-Adapter 모델",
+        "latentsPolymorphic": "Latents 다형성",
+        "ipAdapterDescription": "이미지 프롬프트 어댑터(IP-Adapter).",
+        "boolean": "Booleans",
+        "missingCanvaInitMaskImages": "캔버스 init 및 mask 이미지 누락",
+        "problemReadingMetadata": "이미지에서 metadata를 읽는 중 문제가 발생했습니다",
+        "hideMinimapnodes": "미니맵 숨기기",
+        "oNNXModelField": "ONNX 모델",
+        "executionStateCompleted": "완료된",
+        "node": "노드",
+        "currentImage": "현재 이미지",
+        "controlField": "Control",
+        "booleanDescription": "Booleans은 참 또는 거짓입니다.",
+        "collection": "컬렉션",
+        "ipAdapterModelDescription": "IP-Adapter 모델 필드",
+        "cannotConnectInputToInput": "입력을 입력에 연결할 수 없습니다",
+        "invalidOutputSchema": "잘못된 output schema",
+        "boardFieldDescription": "A gallery board",
+        "floatDescription": "실수는 소수점이 있는 숫자입니다.",
+        "floatPolymorphicDescription": "실수 컬렉션.",
+        "conditioningField": "Conditioning",
+        "collectionFieldType": "{{name}} 컬렉션",
+        "floatCollection": "실수 컬렉션",
+        "latentsField": "Latents",
+        "cannotConnectOutputToOutput": "출력을 출력에 연결할 수 없습니다",
+        "booleanCollection": "Boolean 컬렉션",
+        "connectionWouldCreateCycle": "연결하면 주기가 생성됩니다",
+        "cannotConnectToSelf": "자체에 연결할 수 없습니다",
+        "notesDescription": "Workflow에 대한 메모 추가",
+        "inputFields": "입력 필드",
+        "colorCodeEdges": "색상-코드 선",
+        "targetNodeDoesNotExist": "잘못된 모서리: 대상/입력 노드 {{node}}이(가) 없습니다",
+        "imageCollectionDescription": "이미지 컬렉션.",
+        "mismatchedVersion": "잘못된 노드: {{type}} 유형의 {{node}} 노드에 일치하지 않는 버전이 있습니다(업데이트 해보시겠습니까?)",
+        "imageFieldDescription": "노드 간에 이미지를 전달할 수 있습니다.",
+        "outputNode": "출력노드",
+        "addNodeToolTip": "노드 추가(Shift+A, Space)",
+        "collectionOrScalarFieldType": "{{name}} 컬렉션|Scalar",
+        "nodeVersion": "노드 버전",
+        "loadingNodes": "노드 로딩중...",
+        "mainModelFieldDescription": "해야 할 일",
+        "loRAModelField": "LoRA",
+        "deletedInvalidEdge": "잘못된 모서리 {{source}} -> {{target}} 삭제",
+        "latentsCollectionDescription": "노드 사이에 Latents를 전달할 수 있습니다.",
+        "oNNXModelFieldDescription": "ONNX 모델 필드.",
+        "imageCollection": "이미지 컬렉션"
+    },
+    "queue": {
+        "status": "상태",
+        "pruneSucceeded": "Queue로부터 {{item_count}} 완성된 항목 잘라내기",
+        "cancelTooltip": "현재 항목 취소",
+        "queueEmpty": "비어있는 Queue",
+        "pauseSucceeded": "중지된 프로세서",
+        "in_progress": "진행 중",
+        "queueFront": "Front of Queue에 추가",
+        "notReady": "Queue를 생성할 수 없음",
+        "batchFailedToQueue": "Queue Batch에 실패",
+        "completed": "완성된",
+        "queueBack": "Queue에 추가",
+        "batchValues": "Batch 값들",
+        "cancelFailed": "항목 취소 중 발생한 문제",
+        "queueCountPrediction": "Queue에 {{predicted}} 추가",
+        "batchQueued": "Batch Queued",
+        "pauseFailed": "프로세서 중지 중 발생한 문제",
+        "clearFailed": "Queue 제거 중 발생한 문제",
+        "queuedCount": "{{pending}} Pending",
+        "front": "front",
+        "clearSucceeded": "제거된 Queue",
+        "pause": "중지",
+        "pruneTooltip": "{{item_count}} 완성된 항목 잘라내기",
+        "cancelSucceeded": "취소된 항목",
+        "batchQueuedDesc_other": "queue의 {{direction}}에 추가된 {{count}}세션",
+        "queue": "Queue",
+        "batch": "Batch",
+        "clearQueueAlertDialog": "Queue를 지우면 처리 항목이 즉시 취소되고 Queue가 완전히 지워집니다.",
+        "resumeFailed": "프로세서 재개 중 발생한 문제",
+        "clear": "제거하다",
+        "prune": "잘라내다",
+        "total": "총 개수",
+        "canceled": "취소된",
+        "pruneFailed": "Queue 잘라내는 중 발생한 문제",
+        "cancelBatchSucceeded": "취소된 Batch",
+        "clearTooltip": "모든 항목을 취소하고 제거",
+        "current": "최근",
+        "pauseTooltip": "프로세서 중지",
+        "failed": "실패한",
+        "cancelItem": "항목 취소",
+        "next": "다음",
+        "cancelBatch": "Batch 취소",
+        "back": "back",
+        "batchFieldValues": "Batch 필드 값들",
+        "cancel": "취소",
+        "session": "세션",
+        "time": "시간",
+        "queueTotal": "{{total}} Total",
+        "resumeSucceeded": "재개된 프로세서",
+        "enqueueing": "Queueing Batch",
+        "resumeTooltip": "프로세서 재개",
+        "resume": "재개",
+        "cancelBatchFailed": "Batch 취소 중 발생한 문제",
+        "clearQueueAlertDialog2": "Queue를 지우시겠습니까?",
+        "item": "항목",
+        "graphFailedToQueue": "queue graph에 실패"
+    },
+    "metadata": {
+        "positivePrompt": "긍정적 프롬프트",
+        "negativePrompt": "부정적인 프롬프트",
+        "generationMode": "Generation Mode",
+        "Threshold": "Noise Threshold",
+        "metadata": "Metadata",
+        "seed": "시드",
+        "imageDetails": "이미지 세부 정보",
+        "perlin": "Perlin Noise",
+        "model": "모델",
+        "noImageDetails": "이미지 세부 정보를 찾을 수 없습니다",
+        "hiresFix": "고해상도 최적화",
+        "cfgScale": "CFG scale",
+        "initImage": "초기이미지",
+        "recallParameters": "매개변수 호출",
+        "height": "Height",
+        "variations": "Seed-weight 쌍",
+        "noMetaData": "metadata를 찾을 수 없습니다",
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
+        "width": "너비",
+        "vae": "VAE",
+        "createdBy": "~에 의해 생성된",
+        "workflow": "작업의 흐름",
+        "steps": "단계",
+        "scheduler": "스케줄러",
+        "noRecallParameters": "호출할 매개 변수가 없습니다"
+    },
+    "invocationCache": {
+        "useCache": "캐시 사용",
+        "disable": "이용 불가능한",
+        "misses": "캐시 미스",
+        "enableFailed": "Invocation 캐시를 사용하도록 설정하는 중 발생한 문제",
+        "invocationCache": "Invocation 캐시",
+        "clearSucceeded": "제거된 Invocation 캐시",
+        "enableSucceeded": "이용 가능한 Invocation 캐시",
+        "clearFailed": "Invocation 캐시 제거 중 발생한 문제",
+        "hits": "캐시 적중",
+        "disableSucceeded": "이용 불가능한 Invocation 캐시",
+        "disableFailed": "Invocation 캐시를 이용하지 못하게 설정 중 발생한 문제",
+        "enable": "이용 가능한",
+        "clear": "제거",
+        "maxCacheSize": "최대 캐시 크기",
+        "cacheSize": "캐시 크기"
+    },
+    "embedding": {
+        "noEmbeddingsLoaded": "불러온 Embeddings이 없음",
+        "noMatchingEmbedding": "일치하는 Embeddings이 없음",
+        "addEmbedding": "Embedding 추가",
+        "incompatibleModel": "호환되지 않는 기본 모델:"
+    },
+    "hrf": {
+        "enableHrf": "이용 가능한 고해상도 고정",
+        "upscaleMethod": "업스케일 방법",
+        "enableHrfTooltip": "낮은 초기 해상도로 생성하고 기본 해상도로 업스케일한 다음 Image-to-Image를 실행합니다.",
+        "metadata": {
+            "strength": "고해상도 고정 강도",
+            "enabled": "고해상도 고정 사용",
+            "method": "고해상도 고정 방법"
+        },
+        "hrf": "고해상도 고정",
+        "hrfStrength": "고해상도 고정 강도"
+    },
+    "models": {
+        "noLoRAsLoaded": "로드된 LoRA 없음",
+        "noMatchingModels": "일치하는 모델 없음",
+        "esrganModel": "ESRGAN 모델",
+        "loading": "로딩중",
+        "noMatchingLoRAs": "일치하는 LoRA 없음",
+        "noLoRAsAvailable": "사용 가능한 LoRA 없음",
+        "noModelsAvailable": "사용 가능한 모델이 없음",
+        "addLora": "LoRA 추가",
+        "selectModel": "모델 선택",
+        "noRefinerModelsInstalled": "SDXL Refiner 모델이 설치되지 않음",
+        "noLoRAsInstalled": "설치된 LoRA 없음",
+        "selectLoRA": "LoRA 선택"
+    },
+    "boards": {
+        "autoAddBoard": "자동 추가 Board",
+        "topMessage": "이 보드에는 다음 기능에 사용되는 이미지가 포함되어 있습니다:",
+        "move": "이동",
+        "menuItemAutoAdd": "해당 Board에 자동 추가",
+        "myBoard": "나의 Board",
+        "searchBoard": "Board 찾는 중...",
+        "deleteBoardOnly": "Board만 삭제",
+        "noMatching": "일치하는 Board들이 없음",
+        "movingImagesToBoard_other": "{{count}}이미지를 Board로 이동시키기",
+        "selectBoard": "Board 선택",
+        "cancel": "취소",
+        "addBoard": "Board 추가",
+        "bottomMessage": "이 보드와 이미지를 삭제하면 현재 사용 중인 모든 기능이 재설정됩니다.",
+        "uncategorized": "미분류",
+        "downloadBoard": "Board 다운로드",
+        "changeBoard": "Board 바꾸기",
+        "loading": "불러오는 중...",
+        "clearSearch": "검색 지우기",
+        "deleteBoard": "Board 삭제",
+        "deleteBoardAndImages": "Board와 이미지 삭제",
+        "deletedBoardsCannotbeRestored": "삭제된 Board는 복원할 수 없습니다"
     }
 }

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

@@ -120,7 +120,8 @@
         "orderBy": "排序方式：",
         "nextPage": "下一页",
         "saveAs": "保存为",
-        "unsaved": "未保存"
+        "unsaved": "未保存",
+        "ai": "ai"
     },
     "gallery": {
         "generations": "生成的图像",
@@ -1122,7 +1123,8 @@
         "betaDesc": "此调用尚处于测试阶段。在稳定之前，它可能会在项目更新期间发生破坏性更改。本项目计划长期支持这种调用。",
         "newWorkflow": "新建工作流",
         "newWorkflowDesc": "是否创建一个新的工作流？",
-        "newWorkflowDesc2": "当前工作流有未保存的更改。"
+        "newWorkflowDesc2": "当前工作流有未保存的更改。",
+        "unsupportedAnyOfLength": "联合（union）数据类型数目过多 ({{count}})"
     },
     "controlnet": {
         "resize": "直接缩放",

mkdocs.yml

@@ -172,6 +172,8 @@ nav:
         - Adding Tests: 'contributing/TESTS.md'
       - Documentation: 'contributing/contribution_guides/documentation.md'
       - Nodes: 'contributing/INVOCATIONS.md'
+      - Model Manager: 'contributing/MODEL_MANAGER.md'
+      - Download Queue: 'contributing/DOWNLOAD_QUEUE.md'
       - Translation: 'contributing/contribution_guides/translation.md'
       - Tutorials: 'contributing/contribution_guides/tutorials.md'
     - Changelog: 'CHANGELOG.md'

pyproject.toml

@@ -105,6 +105,7 @@ dependencies = [
   "pytest>6.0.0",
   "pytest-cov",
   "pytest-datadir",
+  "requests_testadapter",
 ]
 "xformers" = [
   "xformers==0.0.23; sys_platform!='darwin'",

tests/aa_nodes/test_graph_execution_state.py

@@ -26,7 +26,6 @@ from invokeai.app.services.shared.graph import (
     Graph,
     GraphExecutionState,
     IterateInvocation,
-    LibraryGraph,
 )
 from invokeai.backend.util.logging import InvokeAILogger
 from tests.fixtures.sqlite_database import create_mock_sqlite_database
@@ -61,7 +60,6 @@ def mock_services() -> InvocationServices:
         configuration=configuration,
         events=TestEventService(),
         graph_execution_manager=graph_execution_manager,
-        graph_library=SqliteItemStorage[LibraryGraph](db=db, table_name="graphs"),
         image_files=None,  # type: ignore
         image_records=None,  # type: ignore
         images=None,  # type: ignore
@@ -70,6 +68,7 @@ def mock_services() -> InvocationServices:
         logger=logging,  # type: ignore
         model_manager=None,  # type: ignore
         model_records=None,  # type: ignore
+        download_queue=None,  # type: ignore
         model_install=None,  # type: ignore
         names=None,  # type: ignore
         performance_statistics=InvocationStatsService(),

tests/aa_nodes/test_invoker.py

@@ -24,7 +24,7 @@ from invokeai.app.services.invocation_stats.invocation_stats_default import Invo
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.item_storage.item_storage_sqlite import SqliteItemStorage
 from invokeai.app.services.session_queue.session_queue_common import DEFAULT_QUEUE_ID
-from invokeai.app.services.shared.graph import Graph, GraphExecutionState, GraphInvocation, LibraryGraph
+from invokeai.app.services.shared.graph import Graph, GraphExecutionState, GraphInvocation
 
 
 @pytest.fixture
@@ -66,7 +66,6 @@ def mock_services() -> InvocationServices:
         configuration=configuration,
         events=TestEventService(),
         graph_execution_manager=graph_execution_manager,
-        graph_library=SqliteItemStorage[LibraryGraph](db=db, table_name="graphs"),
         image_files=None,  # type: ignore
         image_records=None,  # type: ignore
         images=None,  # type: ignore
@@ -75,6 +74,7 @@ def mock_services() -> InvocationServices:
         logger=logging,  # type: ignore
         model_manager=None,  # type: ignore
         model_records=None,  # type: ignore
+        download_queue=None,  # type: ignore
         model_install=None,  # type: ignore
         names=None,  # type: ignore
         performance_statistics=InvocationStatsService(),

tests/app/services/download/test_download_queue.py

@@ -0,0 +1,223 @@
+"""Test the queued download facility"""
+import re
+import time
+from pathlib import Path
+from typing import Any, Dict, List
+
+import pytest
+import requests
+from pydantic import BaseModel
+from pydantic.networks import AnyHttpUrl
+from requests.sessions import Session
+from requests_testadapter import TestAdapter
+
+from invokeai.app.services.download import DownloadJob, DownloadJobStatus, DownloadQueueService
+from invokeai.app.services.events.events_base import EventServiceBase
+
+# Prevent pytest deprecation warnings
+TestAdapter.__test__ = False
+
+
+@pytest.fixture
+def session() -> requests.sessions.Session:
+    sess = requests.Session()
+    for i in ["12345", "9999", "54321"]:
+        content = (
+            b"I am a safetensors file " + bytearray(i, "utf-8") + bytearray(32_000)
+        )  # for pause tests, must make content large
+        sess.mount(
+            f"http://www.civitai.com/models/{i}",
+            TestAdapter(
+                content,
+                headers={
+                    "Content-Length": len(content),
+                    "Content-Disposition": f'filename="mock{i}.safetensors"',
+                },
+            ),
+        )
+
+    # here are some malformed URLs to test
+    # missing the content length
+    sess.mount(
+        "http://www.civitai.com/models/missing",
+        TestAdapter(
+            b"Missing content length",
+            headers={
+                "Content-Disposition": 'filename="missing.txt"',
+            },
+        ),
+    )
+    # not found test
+    sess.mount("http://www.civitai.com/models/broken", TestAdapter(b"Not found", status=404))
+
+    return sess
+
+
+class DummyEvent(BaseModel):
+    """Dummy Event to use with Dummy Event service."""
+
+    event_name: str
+    payload: Dict[str, Any]
+
+
+# A dummy event service for testing event issuing
+class DummyEventService(EventServiceBase):
+    """Dummy event service for testing."""
+
+    events: List[DummyEvent]
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.events = []
+
+    def dispatch(self, event_name: str, payload: Any) -> None:
+        """Dispatch an event by appending it to self.events."""
+        self.events.append(DummyEvent(event_name=payload["event"], payload=payload["data"]))
+
+
+def test_basic_queue_download(tmp_path: Path, session: Session) -> None:
+    events = set()
+
+    def event_handler(job: DownloadJob) -> None:
+        events.add(job.status)
+
+    queue = DownloadQueueService(
+        requests_session=session,
+    )
+    queue.start()
+    job = queue.download(
+        source=AnyHttpUrl("http://www.civitai.com/models/12345"),
+        dest=tmp_path,
+        on_start=event_handler,
+        on_progress=event_handler,
+        on_complete=event_handler,
+        on_error=event_handler,
+    )
+    assert isinstance(job, DownloadJob), "expected the job to be of type DownloadJobBase"
+    assert isinstance(job.id, int), "expected the job id to be numeric"
+    queue.join()
+
+    assert job.status == DownloadJobStatus("completed"), "expected job status to be completed"
+    assert Path(tmp_path, "mock12345.safetensors").exists(), f"expected {tmp_path}/mock12345.safetensors to exist"
+
+    assert events == {DownloadJobStatus.RUNNING, DownloadJobStatus.COMPLETED}
+    queue.stop()
+
+
+def test_errors(tmp_path: Path, session: Session) -> None:
+    queue = DownloadQueueService(
+        requests_session=session,
+    )
+    queue.start()
+
+    for bad_url in ["http://www.civitai.com/models/broken", "http://www.civitai.com/models/missing"]:
+        queue.download(AnyHttpUrl(bad_url), dest=tmp_path)
+
+    queue.join()
+    jobs = queue.list_jobs()
+    print(jobs)
+    assert len(jobs) == 2
+    jobs_dict = {str(x.source): x for x in jobs}
+    assert jobs_dict["http://www.civitai.com/models/broken"].status == DownloadJobStatus.ERROR
+    assert jobs_dict["http://www.civitai.com/models/broken"].error_type == "HTTPError(NOT FOUND)"
+    assert jobs_dict["http://www.civitai.com/models/missing"].status == DownloadJobStatus.COMPLETED
+    assert jobs_dict["http://www.civitai.com/models/missing"].total_bytes == 0
+    queue.stop()
+
+
+def test_event_bus(tmp_path: Path, session: Session) -> None:
+    event_bus = DummyEventService()
+
+    queue = DownloadQueueService(requests_session=session, event_bus=event_bus)
+    queue.start()
+    queue.download(
+        source=AnyHttpUrl("http://www.civitai.com/models/12345"),
+        dest=tmp_path,
+    )
+    queue.join()
+    events = event_bus.events
+    assert len(events) == 3
+    assert events[0].payload["timestamp"] <= events[1].payload["timestamp"]
+    assert events[1].payload["timestamp"] <= events[2].payload["timestamp"]
+    assert events[0].event_name == "download_started"
+    assert events[1].event_name == "download_progress"
+    assert events[1].payload["total_bytes"] > 0
+    assert events[1].payload["current_bytes"] <= events[1].payload["total_bytes"]
+    assert events[2].event_name == "download_complete"
+    assert events[2].payload["total_bytes"] == 32029
+
+    # test a failure
+    event_bus.events = []  # reset our accumulator
+    queue.download(source=AnyHttpUrl("http://www.civitai.com/models/broken"), dest=tmp_path)
+    queue.join()
+    events = event_bus.events
+    print("\n".join([x.model_dump_json() for x in events]))
+    assert len(events) == 1
+    assert events[0].event_name == "download_error"
+    assert events[0].payload["error_type"] == "HTTPError(NOT FOUND)"
+    assert events[0].payload["error"] is not None
+    assert re.search(r"requests.exceptions.HTTPError: NOT FOUND", events[0].payload["error"])
+    queue.stop()
+
+
+def test_broken_callbacks(tmp_path: Path, session: requests.sessions.Session, capsys) -> None:
+    queue = DownloadQueueService(
+        requests_session=session,
+    )
+    queue.start()
+
+    callback_ran = False
+
+    def broken_callback(job: DownloadJob) -> None:
+        nonlocal callback_ran
+        callback_ran = True
+        print(1 / 0)  # deliberate error here
+
+    job = queue.download(
+        source=AnyHttpUrl("http://www.civitai.com/models/12345"),
+        dest=tmp_path,
+        on_progress=broken_callback,
+    )
+
+    queue.join()
+    assert job.status == DownloadJobStatus.COMPLETED  # should complete even though the callback is borked
+    assert Path(tmp_path, "mock12345.safetensors").exists()
+    assert callback_ran
+    # LS: The pytest capsys fixture does not seem to be working. I can see the
+    # correct stderr message in the pytest log, but it is not appearing in
+    # capsys.readouterr().
+    # captured = capsys.readouterr()
+    # assert re.search("division by zero", captured.err)
+    queue.stop()
+
+
+def test_cancel(tmp_path: Path, session: requests.sessions.Session) -> None:
+    event_bus = DummyEventService()
+
+    queue = DownloadQueueService(requests_session=session, event_bus=event_bus)
+    queue.start()
+
+    cancelled = False
+
+    def slow_callback(job: DownloadJob) -> None:
+        time.sleep(2)
+
+    def cancelled_callback(job: DownloadJob) -> None:
+        nonlocal cancelled
+        cancelled = True
+
+    job = queue.download(
+        source=AnyHttpUrl("http://www.civitai.com/models/12345"),
+        dest=tmp_path,
+        on_start=slow_callback,
+        on_cancelled=cancelled_callback,
+    )
+    queue.cancel_job(job)
+    queue.join()
+
+    assert job.status == DownloadJobStatus.CANCELLED
+    assert cancelled
+    events = event_bus.events
+    assert events[-1].event_name == "download_cancelled"
+    assert events[-1].payload["source"] == "http://www.civitai.com/models/12345"
+    queue.stop()

tests/app/services/model_install/test_model_install.py

@@ -48,11 +48,13 @@ def store(
 
 @pytest.fixture
 def installer(app_config: InvokeAIAppConfig, store: ModelRecordServiceBase) -> ModelInstallServiceBase:
-    return ModelInstallService(
+    installer = ModelInstallService(
         app_config=app_config,
         record_store=store,
         event_bus=DummyEventService(),
     )
+    installer.start()
+    return installer
 
 
 class DummyEvent(BaseModel):