Merge branch 'main' into lstein/threaded-download

.github/workflows/mkdocs-material.yml

@@ -43,7 +43,7 @@ jobs:
             --verbose
 
       - name: deploy to gh-pages
-        if: ${{ github.ref == 'refs/heads/main' }}
+        if: ${{ github.ref == 'refs/heads/v2.3' }}
         run: |
           python -m \
             mkdocs gh-deploy \

docs/CHANGELOG.md

@@ -617,6 +617,8 @@ sections describe what's new for InvokeAI.
 - `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains for
   backward compatibility.
 - Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
+- Support for [inpainting](deprecated/INPAINTING.md) and
+  [outpainting](features/OUTPAINTING.md)
 - img2img runs on all k\* samplers
 - Support for
   [negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)

docs/features/WEB.md

@@ -76,10 +76,10 @@ From top to bottom, these are:
    with outpainting,and modify interior portions of the image with
    inpainting, erase portions of a starting image and have the AI fill in
    the erased region from a text prompt.
-4. Node Editor - this panel allows you to create
+4. Workflow Management (not yet implemented) - this panel will allow you to create
    pipelines of common operations and combine them into workflows.
-5. Model Manager - this panel allows you to import and configure new
-   models using URLs, local paths, or HuggingFace diffusers repo_ids.
+5. Training (not yet implemented) - this panel will provide an interface to [textual
+   inversion training](TEXTUAL_INVERSION.md) and fine tuning.
 
 The inpainting, outpainting and postprocessing tabs are currently in
 development. However, limited versions of their features can already be accessed

docs/features/index.md

@@ -37,7 +37,7 @@ guide also covers optimizing models to load quickly.
 Teach an old model new tricks. Merge 2-3 models together to create a
 new model that combines characteristics of the originals.
 
-## * [Textual Inversion](TRAINING.md)
+## * [Textual Inversion](TEXTUAL_INVERSION.md)
 Personalize models by adding your own style or subjects.
 
 # Other Features

docs/index.md

@@ -146,6 +146,7 @@ This method is recommended for those familiar with running Docker containers
 - [Installing](installation/050_INSTALLING_MODELS.md)
 - [Model Merging](features/MODEL_MERGING.md)
 - [Style/Subject Concepts and Embeddings](features/CONCEPTS.md)
+- [Textual Inversion](features/TEXTUAL_INVERSION.md)
 - [Not Safe for Work (NSFW) Checker](features/NSFW.md)
 <!-- seperator -->
 ### Prompt Engineering

docs/installation/010_INSTALL_AUTOMATED.md

@@ -354,8 +354,8 @@ experimental versions later.
 
 12. **InvokeAI Options**: You can launch InvokeAI with several different command-line arguments that
     customize its behavior. For example, you can change the location of the
-    image output directory or balance memory usage vs performance. See
-    [Configuration](../features/CONFIGURATION.md) for a full list of the options.
+    image output directory, or select your favorite sampler. See the
+    [Command-Line Interface](../features/CLI.md) for a full list of the options.
 
     - To set defaults that will take effect every time you launch InvokeAI,
       use a text editor (e.g. Notepad) to exit the file

docs/installation/020_INSTALL_MANUAL.md

@@ -256,7 +256,7 @@ manager, please follow these steps:
 
 10.  Render away!
 
-    Browse the [features](../features/index.md) section to learn about all the
+    Browse the [features](../features/CLI.md) section to learn about all the
     things you can do with InvokeAI.
 
 
@@ -270,7 +270,7 @@ manager, please follow these steps:
 
 12. Other scripts
 
-    The [Textual Inversion](../features/TRAINING.md) script can be launched with the command:
+    The [Textual Inversion](../features/TEXTUAL_INVERSION.md) script can be launched with the command:
 
     ```bash
     invokeai-ti --gui

docs/installation/050_INSTALLING_MODELS.md

@@ -43,7 +43,24 @@ InvokeAI comes with support for a good set of starter models. You'll
 find them listed in the master models file
 `configs/INITIAL_MODELS.yaml` in the InvokeAI root directory. The
 subset that are currently installed are found in
-`configs/models.yaml`.
+`configs/models.yaml`. As of v2.3.1, the list of starter models is:
+
+|Model Name | HuggingFace Repo ID | Description | URL |
+|---------- | ---------- | ----------- | --- |
+|stable-diffusion-1.5|runwayml/stable-diffusion-v1-5|Stable Diffusion version 1.5 diffusers model (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-v1-5 |
+|sd-inpainting-1.5|runwayml/stable-diffusion-inpainting|RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-inpainting |
+|stable-diffusion-2.1|stabilityai/stable-diffusion-2-1|Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-1 |
+|sd-inpainting-2.0|stabilityai/stable-diffusion-2-inpainting|Stable Diffusion version 2.0 inpainting model (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-inpainting |
+|analog-diffusion-1.0|wavymulder/Analog-Diffusion|An SD-1.5 model trained on diverse analog photographs (2.13 GB)|https://huggingface.co/wavymulder/Analog-Diffusion |
+|deliberate-1.0|XpucT/Deliberate|Versatile model that produces detailed images up to 768px (4.27 GB)|https://huggingface.co/XpucT/Deliberate |
+|d&d-diffusion-1.0|0xJustin/Dungeons-and-Diffusion|Dungeons & Dragons characters (2.13 GB)|https://huggingface.co/0xJustin/Dungeons-and-Diffusion |
+|dreamlike-photoreal-2.0|dreamlike-art/dreamlike-photoreal-2.0|A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)|https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0 |
+|inkpunk-1.0|Envvi/Inkpunk-Diffusion|Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)|https://huggingface.co/Envvi/Inkpunk-Diffusion |
+|openjourney-4.0|prompthero/openjourney|An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)|https://huggingface.co/prompthero/openjourney |
+|portrait-plus-1.0|wavymulder/portraitplus|An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)|https://huggingface.co/wavymulder/portraitplus |
+|seek-art-mega-1.0|coreco/seek.art_MEGA|A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)|https://huggingface.co/coreco/seek.art_MEGA |
+|trinart-2.0|naclbit/trinart_stable_diffusion_v2|An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)|https://huggingface.co/naclbit/trinart_stable_diffusion_v2 |
+|waifu-diffusion-1.4|hakurei/waifu-diffusion|An SD-1.5 model trained on 680k anime/manga-style images (2.13 GB)|https://huggingface.co/hakurei/waifu-diffusion |
 
 Note that these files are covered by an "Ethical AI" license which
 forbids certain uses. When you initially download them, you are asked
@@ -54,7 +71,8 @@ with the model terms by visiting the URLs in the table above.
 
 ## Community-Contributed Models
 
-[HuggingFace](https://huggingface.co/models?library=diffusers)
+There are too many to list here and more are being contributed every
+day.  [HuggingFace](https://huggingface.co/models?library=diffusers)
 is a great resource for diffusers models, and is also the home of a
 [fast-growing repository](https://huggingface.co/sd-concepts-library)
 of embedding (".bin") models that add subjects and/or styles to your
@@ -68,106 +86,310 @@ only `.safetensors` and `.ckpt` models, but they can be easily loaded
 into InvokeAI and/or converted into optimized `diffusers` models. Be
 aware that CIVITAI hosts many models that generate NSFW content.
 
+!!! note
+
+    InvokeAI 2.3.x does not support directly importing and
+    running Stable Diffusion version 2 checkpoint models. You may instead
+    convert them into `diffusers` models using the conversion methods
+    described below.
+
 ## Installation
 
-There are two ways to install and manage models:
+There are multiple ways to install and manage models:
 
-1. The `invokeai-model-install` script which will download and install
-them for you.  In addition to supporting main models, you can install
-ControlNet, LoRA and Textual Inversion models.
+1. The `invokeai-configure` script which will download and install them for you.
 
-2. The web interface (WebUI) has a GUI for importing and managing
+2. The command-line tool (CLI) has commands that allows you to import, configure and modify
+   models files.
+
+3. The web interface (WebUI) has a GUI for importing and managing
    models.
 
-3. By placing models (or symbolic links to models) inside one of the
-InvokeAI root directory's `autoimport` folder.
+### Installation via `invokeai-configure`
 
-### Installation via `invokeai-model-install`
+From the `invoke` launcher, choose option (6) "re-run the configure
+script to download new models." This will launch the same script that
+prompted you to select models at install time. You can use this to add
+models that you skipped the first time around. It is all right to
+specify a model that was previously downloaded; the script will just
+confirm that the files are complete.
 
-From the `invoke` launcher, choose option [5] "Download and install
-models." This will launch the same script that prompted you to select
-models at install time. You can use this to add models that you
-skipped the first time around. It is all right to specify a model that
-was previously downloaded; the script will just confirm that the files
-are complete.
+### Installation via the CLI
 
-The installer has different panels for installing main models from
-HuggingFace, models from Civitai and other arbitrary web sites,
-ControlNet models, LoRA/LyCORIS models, and Textual Inversion
-embeddings. Each section has a text box in which you can enter a new
-model to install. You can refer to a model using its:
+You can install a new model, including any of the community-supported ones, via
+the command-line client's `!import_model` command.
 
-1. Local path to the .ckpt, .safetensors or diffusers folder on your local machine
-2. A directory on your machine that contains multiple models
-3. A URL that points to a downloadable model
-4. A HuggingFace repo id
+#### Installing individual `.ckpt` and `.safetensors` models
 
-Previously-installed models are shown with checkboxes. Uncheck a box
-to unregister the model from InvokeAI. Models that are physically
-installed inside the InvokeAI root directory will be deleted and
-purged (after a confirmation warning). Models that are located outside
-the InvokeAI root directory will be unregistered but not deleted.
+If the model is already downloaded to your local disk, use
+`!import_model /path/to/file.ckpt` to load it. For example:
 
-Note: The installer script uses a console-based text interface that requires
-significant amounts of horizontal and vertical space. If the display
-looks messed up, just enlarge the terminal window and/or relaunch the
-script.
-
-If you wish you can script model addition and deletion, as well as
-listing installed models. Start the "developer's console" and give the
-command `invokeai-model-install --help`. This will give you a series
-of command-line parameters that will let you control model
-installation. Examples:
-
-```
-# (list all controlnet models)
-invokeai-model-install --list controlnet
-
-# (install the model at the indicated URL)
-invokeai-model-install --add http://civitai.com/2860
-
-# (delete the named model)
-invokeai-model-install --delete sd-1/main/analog-diffusion
+```bash
+invoke> !import_model C:/Users/fred/Downloads/martians.safetensors
 ```
 
-### Installation via the Web GUI
+!!! tip "Forward Slashes"
+    On Windows systems, use forward slashes rather than backslashes
+    in your file paths.
+    If you do use backslashes,
+    you must double them like this:
+    `C:\\Users\\fred\\Downloads\\martians.safetensors`
 
-To install a new model using the Web GUI, do the following:
+Alternatively you can directly import the file using its URL:
 
-1. Open the InvokeAI Model Manager (cube at the bottom of the
-left-hand panel) and navigate to *Import Models*
+```bash
+invoke> !import_model https://example.org/sd_models/martians.safetensors
+```
 
-2. In the field labeled *Location* type in the path to the model you
-wish to install. You may use a URL, HuggingFace repo id, or a path on
-your local disk.
+For this to work, the URL must not be password-protected. Otherwise
+you will receive a 404 error.
 
-3. Alternatively, the *Scan for Models* button allows you to paste in
-the path to a folder somewhere on your machine. It will be scanned for
-importable models and prompt you to add the ones of your choice.
+When you import a legacy model, the CLI will first ask you what type
+of model this is. You can indicate whether it is a model based on
+Stable Diffusion 1.x (1.4 or 1.5), one based on Stable Diffusion 2.x,
+or a 1.x inpainting model. Be careful to indicate the correct model
+type, or it will not load correctly. You can correct the model type
+after the fact using the `!edit_model` command.
 
-4. Press *Add Model* and wait for confirmation that the model
-was added.
+The system will then ask you a few other questions about the model,
+including what size image it was trained on (usually 512x512), what
+name and description you wish to use for it, and whether you would
+like to install a custom VAE (variable autoencoder) file for the
+model. For recent models, the answer to the VAE question is usually
+"no," but it won't hurt to answer "yes".
 
-To delete a model, Select *Model Manager* to list all the currently
-installed models. Press the trash can icons to delete any models you
-wish to get rid of. Models whose weights are located inside the
-InvokeAI `models` directory will be purged from disk, while those
-located outside will be unregistered from InvokeAI, but not deleted.
+After importing, the model will load. If this is successful, you will
+be asked if you want to keep the model loaded in memory to start
+generating immediately. You'll also be asked if you wish to make this
+the default model on startup. You can change this later using
+`!edit_model`.
 
-You can see where model weights are located by clicking on the model name.
-This will bring up an editable info panel showing the model's characteristics,
-including the `Model Location` of its files.
+#### Importing a batch of `.ckpt` and `.safetensors` models from a directory
 
-### Installation via the `autoimport` function
+You may also point `!import_model` to a directory containing a set of
+`.ckpt` or `.safetensors` files. They will be imported _en masse_.
 
-In the InvokeAI root directory you will find a series of folders under
-`autoimport`, one each for main models, controlnets, embeddings and
-Loras.  Any models that you add to these directories will be scanned
-at startup time and registered automatically.
+!!! example
 
-You may create symbolic links from these folders to models located
-elsewhere on disk and they will be autoimported. You can also create
-subfolders and organize them as you wish.
+    ```console
+    invoke> !import_model C:/Users/fred/Downloads/civitai_models/
+    ```
 
-The location of the autoimport directories are controlled by settings
-in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md).
+You will be given the option to import all models found in the
+directory, or select which ones to import. If there are subfolders
+within the directory, they will be searched for models to import.
+
+#### Installing `diffusers` models
+
+You can install a `diffusers` model from the HuggingFace site using
+`!import_model` and the HuggingFace repo_id for the model:
+
+```bash
+invoke> !import_model andite/anything-v4.0
+```
+
+Alternatively, you can download the model to disk and import it from
+there. The model may be distributed as a ZIP file, or as a Git
+repository:
+
+```bash
+invoke> !import_model C:/Users/fred/Downloads/andite--anything-v4.0
+```
+
+!!! tip "The CLI supports file path autocompletion"
+         Type a bit of the path name and hit ++tab++ in order to get a choice of
+         possible completions.
+
+!!! tip "On Windows, you can drag model files onto the command-line"
+         Once you have typed in `!import_model `, you can drag the
+         model  file or directory onto the command-line to insert the model path. This way, you don't need to
+         type it or copy/paste. However, you will need to reverse or
+         double backslashes as noted above.
+
+Before installing, the CLI will ask you for a short name and
+description for the model, whether to make this the default model that
+is loaded at InvokeAI startup time, and whether to replace its
+VAE. Generally the answer to the latter question is "no".
+
+### Converting legacy models into `diffusers`
+
+The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
+models file into `diffusers` and install it.This will enable the model
+to load and run faster without loss of image quality.
+
+The usage is identical to `!import_model`. You may point the command
+to either a downloaded model file on disk, or to a (non-password
+protected) URL:
+
+```bash
+invoke> !convert_model C:/Users/fred/Downloads/martians.safetensors
+```
+
+After a successful conversion, the CLI will offer you the option of
+deleting the original `.ckpt` or `.safetensors` file.
+
+### Optimizing a previously-installed model
+
+Lastly, if you have previously installed a `.ckpt` or `.safetensors`
+file and wish to convert it into a `diffusers` model, you can do this
+without re-downloading and converting the original file using the
+`!optimize_model` command. Simply pass the short name of an existing
+installed model:
+
+```bash
+invoke> !optimize_model martians-v1.0
+```
+
+The model will be converted into `diffusers` format and replace the
+previously installed version. You will again be offered the
+opportunity to delete the original `.ckpt` or `.safetensors` file.
+
+### Related CLI Commands
+
+There are a whole series of additional model management commands in
+the CLI that you can read about in [Command-Line
+Interface](../features/CLI.md). These include:
+
+* `!models` - List all installed models
+* `!switch <model name>` - Switch to the indicated model
+* `!edit_model <model name>` - Edit the indicated model to change its name, description or other properties
+* `!del_model <model name>` - Delete the indicated model
+
+### Manually editing `configs/models.yaml`
+
+
+If you are comfortable with a text editor then you may simply edit `models.yaml`
+directly.
+
+You will need to download the desired `.ckpt/.safetensors` file and
+place it somewhere on your machine's filesystem. Alternatively, for a
+`diffusers` model, record the repo_id or download the whole model
+directory. Then using a **text** editor (e.g. the Windows Notepad
+application), open the file `configs/models.yaml`, and add a new
+stanza that follows this model:
+
+#### A legacy model
+
+A legacy `.ckpt` or `.safetensors` entry will look like this:
+
+```yaml
+arabian-nights-1.0:
+  description: A great fine-tune in Arabian Nights style
+  weights: ./path/to/arabian-nights-1.0.ckpt
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  format: ckpt
+  width: 512
+  height: 512
+  default: false
+```
+
+Note that `format` is `ckpt` for both `.ckpt` and `.safetensors` files.
+
+#### A diffusers model
+
+A stanza for a `diffusers` model will look like this for a HuggingFace
+model with a repository ID:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  repo_id: captahab/arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+And for a downloaded directory:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  path: /path/to/captahab-arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+There is additional syntax for indicating an external VAE to use with
+this model. See `INITIAL_MODELS.yaml` and `models.yaml` for examples.
+
+After you save the modified `models.yaml` file relaunch
+`invokeai`. The new model will now be available for your use.
+
+### Installation via the WebUI
+
+To access the WebUI Model Manager, click on the button that looks like
+a cube in the upper right side of the browser screen. This will bring
+up a dialogue that lists the models you have already installed, and
+allows you to load, delete or edit them:
+
+<figure markdown>
+
+![model-manager](../assets/installing-models/webui-models-1.png)
+
+</figure>
+
+To add a new model, click on **+ Add New** and select to either a
+checkpoint/safetensors model, or a diffusers model:
+
+<figure markdown>
+
+![model-manager-add-new](../assets/installing-models/webui-models-2.png)
+
+</figure>
+
+In this example, we chose **Add Diffusers**. As shown in the figure
+below, a new dialogue prompts you to enter the name to use for the
+model, its description, and either the location of the `diffusers`
+model on disk, or its Repo ID on the HuggingFace web site. If you
+choose to enter a path to disk, the system will autocomplete for you
+as you type:
+
+<figure markdown>
+
+![model-manager-add-diffusers](../assets/installing-models/webui-models-3.png)
+
+</figure>
+
+Press **Add Model** at the bottom of the dialogue (scrolled out of
+site in the figure), and the model will be downloaded, imported, and
+registered in `models.yaml`.
+
+The **Add Checkpoint/Safetensor Model** option is similar, except that
+in this case you can choose to scan an entire folder for
+checkpoint/safetensors files to import. Simply type in the path of the
+directory and press the "Search" icon. This will display the
+`.ckpt` and `.safetensors` found inside the directory and its
+subfolders, and allow you to choose which ones to import:
+
+<figure markdown>
+
+![model-manager-add-checkpoint](../assets/installing-models/webui-models-4.png)
+
+</figure>
+
+## Model Management Startup Options
+
+The `invoke` launcher and the `invokeai` script accept a series of
+command-line arguments that modify InvokeAI's behavior when loading
+models. These can be provided on the command line, or added to the
+InvokeAI root directory's `invokeai.init` initialization file.
+
+The arguments are:
+
+* `--model <model name>` -- Start up with the indicated model loaded
+* `--ckpt_convert` -- When a checkpoint/safetensors model is loaded, convert it into a `diffusers` model in memory. This does not permanently save the converted model to disk.
+* `--autoconvert <path/to/directory>` -- Scan the indicated directory path for new checkpoint/safetensors files, convert them into `diffusers` models, and import them into InvokeAI.
+
+Here is an example of providing an argument on the command line using
+the `invoke.sh` launch script:
+
+```bash
+invoke.sh --autoconvert /home/fred/stable-diffusion-checkpoints
+```
+
+And here is what the same argument looks like in `invokeai.init`:
+
+```bash
+--outdir="/home/fred/invokeai/outputs
+--no-nsfw_checker
+--autoconvert /home/fred/stable-diffusion-checkpoints
+```

invokeai/app/api/events.py

@@ -9,7 +9,6 @@ from fastapi_events.dispatcher import dispatch
 
 from ..services.events import EventServiceBase
 
-
 class FastAPIEventService(EventServiceBase):
     event_handler_id: int
     __queue: Queue
@@ -28,6 +27,9 @@ class FastAPIEventService(EventServiceBase):
         self.__queue.put(None)
 
     def dispatch(self, event_name: str, payload: Any) -> None:
+        # TODO: Remove next two debugging lines
+        from .dependencies import ApiDependencies
+        ApiDependencies.invoker.services.logger.debug(f'dispatch {event_name} / {payload}')
         self.__queue.put(dict(event_name=event_name, payload=payload))
 
     async def __dispatch_from_queue(self, stop_event: threading.Event):

invokeai/app/api/routers/models.py

@@ -2,6 +2,7 @@
 
 
 import pathlib
+import threading
 from typing import Literal, List, Optional, Union
 
 from fastapi import Body, Path, Query, Response
@@ -127,54 +128,43 @@ async def update_model(
     "/import",
     operation_id="import_model",
     responses= {
-        201: {"description" : "The model imported successfully"},
-        404: {"description" : "The model could not be found"},
-        415: {"description" : "Unrecognized file/folder format"},
-        424: {"description" : "The model appeared to import successfully, but could not be found in the model manager"},
-        409: {"description" : "There is already a model corresponding to this path or repo_id"},
+        200: {"description" : "The path was queued for import"},
     },
-    status_code=201,
-    response_model=ImportModelResponse
+    status_code=200
 )
 async def import_model(
         location: str = Body(description="A model path, repo_id or URL to import"),
         prediction_type: Optional[Literal['v_prediction','epsilon','sample']] = \
                 Body(description='Prediction type for SDv2 checkpoint files', default="v_prediction"),
-) -> ImportModelResponse:
-    """ Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically """
+) -> str:
+    """ 
+    Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically.
+    This call launches a background thread to process the imported model and always succeeds. Results are reported in the background
+    as the following events:
+    - model_import_started(import_path:str)
+    - model_import_completed(import_path:str, import_info:AddModelResults, success:bool, error:str)
+    - download_started(url:str)
+    - download_progress(url:str, downloaded_bytes:int, total_bytes:int)
+    - download_completed(url:str, status_code:int, download_path:str)
+    """
     
     items_to_import = {location}
     prediction_types = { x.value: x for x in SchedulerPredictionType }
     logger = ApiDependencies.invoker.services.logger
+    events = ApiDependencies.invoker.services.events
 
     try:
-        installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
-            items_to_import = items_to_import,
-            prediction_type_helper = lambda x: prediction_types.get(prediction_type)
-        )
-        info = installed_models.get(location)
-
-        if not info:
-            logger.error("Import failed")
-            raise HTTPException(status_code=415)
-        
-        logger.info(f'Successfully imported {location}, got {info}')
-        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
-            model_name=info.name,
-            base_model=info.base_model,
-            model_type=info.model_type
-        )
-        return parse_obj_as(ImportModelResponse, model_raw)
-    
-    except ModelNotFoundException as e:
+        import_thread = threading.Thread(target = ApiDependencies.invoker.services.model_manager.heuristic_import,
+                                         kwargs = dict(items_to_import = items_to_import,
+                                                       prediction_type_helper = lambda x: prediction_types.get(prediction_type),
+                                                       event_bus = events,
+                                                       )
+                                         )
+        import_thread.start()
+        return 'request queued'
+    except Exception as e:
         logger.error(str(e))
-        raise HTTPException(status_code=404, detail=str(e))
-    except InvalidModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=415)
-    except ValueError as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
+        raise HTTPException(status_code=500, detail=str(e))
         
 @models_router.post(
     "/add",

invokeai/app/invocations/compel.py

@@ -1,14 +1,6 @@
 from typing import Literal, Optional, Union, List, Annotated
 from pydantic import BaseModel, Field
 import re
-
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
-from .model import ClipField
-
-from ...backend.util.devices import torch_dtype
-from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
-from ...backend.model_management import BaseModelType, ModelType, SubModelType, ModelPatcher
-
 import torch
 from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import (Blend, Conjunction,
@@ -339,8 +331,8 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     crop_left: int = Field(0, description="")
     target_width: int = Field(1024, description="")
     target_height: int = Field(1024, description="")
-    clip: ClipField = Field(None, description="Clip to use")
-    clip2: ClipField = Field(None, description="Clip2 to use")
+    clip1: ClipField = Field(None, description="Clip to use")
+    clip2: ClipField = Field(None, description="Clip to use")
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -356,7 +348,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
-        c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False)
+        c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip1, self.prompt, False)
         if self.style.strip() == "":
             c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True)
         else:
@@ -459,8 +451,8 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     crop_left: int = Field(0, description="")
     target_width: int = Field(1024, description="")
     target_height: int = Field(1024, description="")
-    clip: ClipField = Field(None, description="Clip to use")
-    clip2: ClipField = Field(None, description="Clip2 to use")
+    clip1: ClipField = Field(None, description="Clip to use")
+    clip2: ClipField = Field(None, description="Clip to use")
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -476,7 +468,7 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
-        c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip, self.prompt, False)
+        c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip1, self.prompt, False)
         if self.style.strip() == "":
             c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.prompt, True)
         else:

invokeai/app/invocations/image.py

@@ -518,8 +518,8 @@ class ImageScaleInvocation(BaseInvocation, PILInvocationConfig):
     type: Literal["img_scale"] = "img_scale"
 
     # Inputs
-    image:          Optional[ImageField] = Field(default=None, description="The image to scale")
-    scale_factor:        Optional[float] = Field(default=2.0, gt=0, description="The factor by which to scale the image")
+    image:       Optional[ImageField] = Field(default=None, description="The image to scale")
+    scale_factor:                  float = Field(gt=0, description="The factor by which to scale the image")
     resample_mode:  PIL_RESAMPLING_MODES = Field(default="bicubic", description="The resampling mode")
     # fmt: on
 

invokeai/app/invocations/latent.py

@@ -22,8 +22,7 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
     PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from ...backend.util.devices import choose_torch_device, torch_dtype
-from ...backend.model_management import ModelPatcher
+from ...backend.util.devices import torch_dtype
 from ..models.image import ImageCategory, ImageField, ResourceOrigin
 from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
                              InvocationConfig, InvocationContext)

invokeai/app/invocations/model.py

@@ -54,7 +54,6 @@ class MainModelField(BaseModel):
 
     model_name: str = Field(description="Name of the model")
     base_model: BaseModelType = Field(description="Base model")
-    model_type: ModelType = Field(description="Model Type")
 
 
 class LoRAModelField(BaseModel):
@@ -222,9 +221,6 @@ class LoraLoaderInvocation(BaseInvocation):
         base_model = self.lora.base_model
         lora_name = self.lora.model_name
 
-        # TODO: ui rewrite
-        base_model = BaseModelType.StableDiffusion1
-
         if not context.services.model_manager.model_exists(
             base_model=base_model,
             model_name=lora_name,

invokeai/app/invocations/onnx.py

@@ -1,591 +0,0 @@
-# Copyright (c) 2023 Borisov Sergey (https://github.com/StAlKeR7779)
-
-from contextlib import ExitStack
-from typing import List, Literal, Optional, Union
-
-import re
-import inspect
-
-from pydantic import BaseModel, Field, validator
-import torch
-import numpy as np
-from diffusers import ControlNetModel, DPMSolverMultistepScheduler
-from diffusers.image_processor import VaeImageProcessor
-from diffusers.schedulers import SchedulerMixin as Scheduler
-
-from ..models.image import ImageCategory, ImageField, ResourceOrigin
-from ...backend.model_management import ONNXModelPatcher
-from ...backend.util import choose_torch_device
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
-                             InvocationConfig, InvocationContext)
-from .compel import ConditioningField
-from .controlnet_image_processors import ControlField
-from .image import ImageOutput
-from .model import ModelInfo, UNetField, VaeField
-
-from invokeai.app.invocations.metadata import CoreMetadata
-from invokeai.backend import BaseModelType, ModelType, SubModelType
-from invokeai.app.util.step_callback import stable_diffusion_step_callback
-from ...backend.stable_diffusion import PipelineIntermediateState
-
-from tqdm import tqdm
-from .model import ClipField
-from .latent import LatentsField, LatentsOutput, build_latents_output, get_scheduler, SAMPLER_NAME_VALUES
-from .compel import CompelOutput
-
-
-ORT_TO_NP_TYPE = {
-    "tensor(bool)": np.bool_,
-    "tensor(int8)": np.int8,
-    "tensor(uint8)": np.uint8,
-    "tensor(int16)": np.int16,
-    "tensor(uint16)": np.uint16,
-    "tensor(int32)": np.int32,
-    "tensor(uint32)": np.uint32,
-    "tensor(int64)": np.int64,
-    "tensor(uint64)": np.uint64,
-    "tensor(float16)": np.float16,
-    "tensor(float)": np.float32,
-    "tensor(double)": np.float64,
-}
-
-
-class ONNXPromptInvocation(BaseInvocation):
-    type: Literal["prompt_onnx"] = "prompt_onnx"
-
-    prompt: str = Field(default="", description="Prompt")
-    clip: ClipField = Field(None, description="Clip to use")
-
-    def invoke(self, context: InvocationContext) -> CompelOutput:
-        tokenizer_info = context.services.model_manager.get_model(
-            **self.clip.tokenizer.dict(),
-        )
-        text_encoder_info = context.services.model_manager.get_model(
-            **self.clip.text_encoder.dict(),
-        )
-        with tokenizer_info as orig_tokenizer,\
-             text_encoder_info as text_encoder,\
-             ExitStack() as stack:
-
-            #loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.clip.loras]
-            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):
-                name = trigger[1:-1]
-                try:
-                    ti_list.append(
-                        #stack.enter_context(
-                        #    context.services.model_manager.get_model(
-                        #        model_name=name,
-                        #        base_model=self.clip.text_encoder.base_model,
-                        #        model_type=ModelType.TextualInversion,
-                        #    )
-                        #)
-                        context.services.model_manager.get_model(
-                            model_name=name,
-                            base_model=self.clip.text_encoder.base_model,
-                            model_type=ModelType.TextualInversion,
-                        ).context.model
-                    )
-                except Exception:
-                    #print(e)
-                    #import traceback
-                    #print(traceback.format_exc())
-                    print(f"Warn: trigger: \"{trigger}\" not found")
-
-            with ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras),\
-                 ONNXModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager):
-
-                text_encoder.create_session()
-
-                # copy from
-                # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L153
-                text_inputs = tokenizer(
-                    self.prompt,
-                    padding="max_length",
-                    max_length=tokenizer.model_max_length,
-                    truncation=True,
-                    return_tensors="np",
-                )
-                text_input_ids = text_inputs.input_ids
-                """
-                untruncated_ids = tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
-
-                if not np.array_equal(text_input_ids, untruncated_ids):
-                    removed_text = self.tokenizer.batch_decode(
-                        untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                    )
-                    logger.warning(
-                        "The following part of your input was truncated because CLIP can only handle sequences up to"
-                        f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                    )
-                """
-
-                prompt_embeds = text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
-
-                text_encoder.release_session()
-
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-
-        # TODO: hacky but works ;D maybe rename latents somehow?
-        context.services.latents.save(conditioning_name, (prompt_embeds, None))
-
-        return CompelOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
-
-# Text to image
-class ONNXTextToLatentsInvocation(BaseInvocation):
-    """Generates latents from conditionings."""
-
-    type: Literal["t2l_onnx"] = "t2l_onnx"
-
-    # Inputs
-    # fmt: off
-    positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
-    negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
-    noise: Optional[LatentsField] = Field(description="The noise to use")
-    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
-    cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
-    unet: UNetField = Field(default=None, description="UNet submodel")
-    #control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
-    #seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
-    #seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
-    # fmt: on
-
-    @validator("cfg_scale")
-    def ge_one(cls, v):
-        """validate that all cfg_scale values are >= 1"""
-        if isinstance(v, list):
-            for i in v:
-                if i < 1:
-                    raise ValueError('cfg_scale must be greater than 1')
-        else:
-            if v < 1:
-                raise ValueError('cfg_scale must be greater than 1')
-        return v
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents"],
-                "type_hints": {
-                  "model": "model",
-                  # "cfg_scale": "float",
-                  "cfg_scale": "number"
-                }
-            },
-        }
-
-    # based on
-    # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        c, _ = context.services.latents.get(self.positive_conditioning.conditioning_name)
-        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-        if isinstance(c, torch.Tensor):
-            c = c.cpu().numpy()
-        if isinstance(uc, torch.Tensor):
-            uc = uc.cpu().numpy()
-        device = torch.device(choose_torch_device())
-        prompt_embeds = np.concatenate([uc, c])
-
-        latents = context.services.latents.get(self.noise.latents_name)
-        if isinstance(latents, torch.Tensor):
-            latents = latents.cpu().numpy()
-
-        # TODO: better execution device handling
-        latents = latents.astype(np.float16)
-
-        # get the initial random noise unless the user supplied it
-        do_classifier_free_guidance = True
-        #latents_dtype = prompt_embeds.dtype
-        #latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
-        #if latents.shape != latents_shape:
-        #    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-
-        scheduler = get_scheduler(
-            context=context,
-            scheduler_info=self.unet.scheduler,
-            scheduler_name=self.scheduler,
-        )
-
-        def torch2numpy(latent: torch.Tensor):
-            return latent.cpu().numpy()
-
-        def numpy2torch(latent, device):
-            return torch.from_numpy(latent).to(device)
-
-        def dispatch_progress(
-                self, context: InvocationContext, source_node_id: str,
-                intermediate_state: PipelineIntermediateState) -> None:
-            stable_diffusion_step_callback(
-                context=context,
-                intermediate_state=intermediate_state,
-                node=self.dict(),
-                source_node_id=source_node_id,
-            )
-
-        scheduler.set_timesteps(self.steps)
-        latents = latents * np.float64(scheduler.init_noise_sigma)
-
-        extra_step_kwargs = dict()
-        if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
-            extra_step_kwargs.update(
-                eta=0.0,
-            )
-
-        unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
-
-        with unet_info as unet,\
-             ExitStack() as stack:
-
-            #loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
-            loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.unet.loras]
-
-            with ONNXModelPatcher.apply_lora_unet(unet, loras):
-                # TODO: 
-                unet.create_session()
-
-                timestep_dtype = next(
-                    (input.type for input in unet.session.get_inputs() if input.name == "timestep"), "tensor(float16)"
-                )
-                timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
-                import time
-                times = []
-                for i in tqdm(range(len(scheduler.timesteps))):
-                    t = scheduler.timesteps[i]
-                    # expand the latents if we are doing classifier free guidance
-                    latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
-                    latent_model_input = scheduler.scale_model_input(numpy2torch(latent_model_input, device), t)
-                    latent_model_input = latent_model_input.cpu().numpy()
-
-                    # predict the noise residual
-                    timestep = np.array([t], dtype=timestep_dtype)
-                    start_time = time.time()
-                    noise_pred = unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)
-                    times.append(time.time() - start_time)
-                    noise_pred = noise_pred[0]
-
-                    # perform guidance
-                    if do_classifier_free_guidance:
-                        noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
-                        noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
-
-                    # compute the previous noisy sample x_t -> x_t-1
-                    scheduler_output = scheduler.step(
-                        numpy2torch(noise_pred, device), t, numpy2torch(latents, device), **extra_step_kwargs
-                    )
-                    latents = torch2numpy(scheduler_output.prev_sample)
-
-                    state = PipelineIntermediateState(
-                        run_id= "test",
-                        step=i,
-                        timestep=timestep,
-                        latents=scheduler_output.prev_sample
-                    )
-                    dispatch_progress(
-                        self,
-                        context=context,
-                        source_node_id=source_node_id,
-                        intermediate_state=state
-                    )
-
-                    # call the callback, if provided
-                    #if callback is not None and i % callback_steps == 0:
-                    #    callback(i, t, latents)
-                print(times)
-                unet.release_session()
-
-        torch.cuda.empty_cache()
-
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.save(name, latents)
-        return build_latents_output(latents_name=name, latents=torch.from_numpy(latents))
-
-# Latent to image
-class ONNXLatentsToImageInvocation(BaseInvocation):
-    """Generates an image from latents."""
-
-    type: Literal["l2i_onnx"] = "l2i_onnx"
-
-    # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
-    vae: VaeField = Field(default=None, description="Vae submodel")
-    metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
-    #tiled: bool = Field(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents", "image"],
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        if self.vae.vae.submodel != SubModelType.VaeDecoder:
-            raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}")
-
-        vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.dict(),
-        )
-
-        # clear memory as vae decode can request a lot
-        torch.cuda.empty_cache()
-
-        with vae_info as vae:
-            vae.create_session()
-
-            # copied from
-            # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L427
-            latents = 1 / 0.18215 * latents
-            # image = self.vae_decoder(latent_sample=latents)[0]
-            # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
-            image = np.concatenate(
-                [vae(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
-            )
-
-            image = np.clip(image / 2 + 0.5, 0, 1)
-            image = image.transpose((0, 2, 3, 1))
-            image = VaeImageProcessor.numpy_to_pil(image)[0]
-
-            vae.release_session()
-
-        torch.cuda.empty_cache()
-
-        image_dto = context.services.images.create(
-            image=image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata.dict() if self.metadata else None,
-        )
-
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-class ONNXModelLoaderOutput(BaseInvocationOutput):
-    """Model loader output"""
-
-    #fmt: off
-    type: Literal["model_loader_output_onnx"] = "model_loader_output_onnx"
-
-    unet: UNetField = Field(default=None, description="UNet submodel")
-    clip: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
-    vae_decoder: VaeField = Field(default=None, description="Vae submodel")
-    vae_encoder: VaeField = Field(default=None, description="Vae submodel")
-    #fmt: on
-
-class ONNXSD1ModelLoaderInvocation(BaseInvocation):
-    """Loading submodels of selected model."""
-
-    type: Literal["sd1_model_loader_onnx"] = "sd1_model_loader_onnx"
-
-    model_name: str = Field(default="", description="Model to load")
-    # TODO: precision?
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["model", "loader"],
-                "type_hints": {
-                  "model_name": "model" # TODO: rename to model_name?
-                }
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> ONNXModelLoaderOutput:
-
-        model_name = "stable-diffusion-v1-5"
-        base_model = BaseModelType.StableDiffusion1
-
-        # TODO: not found exceptions
-        if not context.services.model_manager.model_exists(
-            model_name=model_name,
-            base_model=BaseModelType.StableDiffusion1,
-            model_type=ModelType.ONNX,
-        ):
-            raise Exception(f"Unkown model name: {model_name}!")
-
-
-        return ONNXModelLoaderOutput(
-            unet=UNetField(
-                unet=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=ModelType.ONNX,
-                    submodel=SubModelType.UNet,
-                ),
-                scheduler=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=ModelType.ONNX,
-                    submodel=SubModelType.Scheduler,
-                ),
-                loras=[],
-            ),
-            clip=ClipField(
-                tokenizer=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=ModelType.ONNX,
-                    submodel=SubModelType.Tokenizer,
-                ),
-                text_encoder=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=ModelType.ONNX,
-                    submodel=SubModelType.TextEncoder,
-                ),
-                loras=[],
-            ),
-            vae_decoder=VaeField(
-                vae=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=ModelType.ONNX,
-                    submodel=SubModelType.VaeDecoder,
-                ),
-            ),
-            vae_encoder=VaeField(
-                vae=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=ModelType.ONNX,
-                    submodel=SubModelType.VaeEncoder,
-                ),
-            )
-        )
-
-class OnnxModelField(BaseModel):
-    """Onnx model field"""
-
-    model_name: str = Field(description="Name of the model")
-    base_model: BaseModelType = Field(description="Base model")
-    model_type: ModelType = Field(description="Model Type")
-
-class OnnxModelLoaderInvocation(BaseInvocation):
-    """Loads a main model, outputting its submodels."""
-
-    type: Literal["onnx_model_loader"] = "onnx_model_loader"
-
-    model: OnnxModelField = Field(description="The model to load")
-    # TODO: precision?
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "title": "Onnx Model Loader",
-                "tags": ["model", "loader"],
-                "type_hints": {"model": "model"},
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> ONNXModelLoaderOutput:
-        base_model = self.model.base_model
-        model_name = self.model.model_name
-        model_type = ModelType.ONNX
-
-        # TODO: not found exceptions
-        if not context.services.model_manager.model_exists(
-            model_name=model_name,
-            base_model=base_model,
-            model_type=model_type,
-        ):
-            raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
-
-        """
-        if not context.services.model_manager.model_exists(
-            model_name=self.model_name,
-            model_type=SDModelType.Diffusers,
-            submodel=SDModelType.Tokenizer,
-        ):
-            raise Exception(
-                f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted"
-            )
-
-        if not context.services.model_manager.model_exists(
-            model_name=self.model_name,
-            model_type=SDModelType.Diffusers,
-            submodel=SDModelType.TextEncoder,
-        ):
-            raise Exception(
-                f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted"
-            )
-
-        if not context.services.model_manager.model_exists(
-            model_name=self.model_name,
-            model_type=SDModelType.Diffusers,
-            submodel=SDModelType.UNet,
-        ):
-            raise Exception(
-                f"Failed to find unet submodel from {self.model_name}! Check if model corrupted"
-            )
-        """
-
-        return ONNXModelLoaderOutput(
-            unet=UNetField(
-                unet=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.UNet,
-                ),
-                scheduler=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.Scheduler,
-                ),
-                loras=[],
-            ),
-            clip=ClipField(
-                tokenizer=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.Tokenizer,
-                ),
-                text_encoder=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.TextEncoder,
-                ),
-                loras=[],
-                skipped_layers=0,
-            ),
-            vae_decoder=VaeField(
-                vae=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.VaeDecoder,
-                ),
-            ),
-            vae_encoder=VaeField(
-                vae=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.VaeEncoder,
-                ),
-            )
-        )

invokeai/app/invocations/upscale.py

@@ -1,6 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
-from pathlib import Path
-from typing import Literal, Union
+from pathlib import Path, PosixPath
+from typing import Literal, Union, cast
 
 import cv2 as cv
 import numpy as np
@@ -16,20 +16,19 @@ from .image import ImageOutput
 
 # TODO: Populate this from disk?
 # TODO: Use model manager to load?
-ESRGAN_MODELS = Literal[
+REALESRGAN_MODELS = Literal[
     "RealESRGAN_x4plus.pth",
     "RealESRGAN_x4plus_anime_6B.pth",
     "ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
-    "RealESRGAN_x2plus.pth",
 ]
 
 
-class ESRGANInvocation(BaseInvocation):
+class RealESRGANInvocation(BaseInvocation):
     """Upscales an image using RealESRGAN."""
 
-    type: Literal["esrgan"] = "esrgan"
+    type: Literal["realesrgan"] = "realesrgan"
     image: Union[ImageField, None] = Field(default=None, description="The input image")
-    model_name: ESRGAN_MODELS = Field(
+    model_name: REALESRGAN_MODELS = Field(
         default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use"
     )
 
@@ -74,17 +73,19 @@ class ESRGANInvocation(BaseInvocation):
                 scale=4,
             )
             netscale = 4
-        elif self.model_name in ["RealESRGAN_x2plus.pth"]:
-            # x2 RRDBNet model
-            rrdbnet_model = RRDBNet(
-                num_in_ch=3,
-                num_out_ch=3,
-                num_feat=64,
-                num_block=23,
-                num_grow_ch=32,
-                scale=2,
-            )
-            netscale = 2
+        # TODO: add x2 models handling?
+        # elif self.model_name in ["RealESRGAN_x2plus"]:
+        #     # x2 RRDBNet model
+        #     model = RRDBNet(
+        #         num_in_ch=3,
+        #         num_out_ch=3,
+        #         num_feat=64,
+        #         num_block=23,
+        #         num_grow_ch=32,
+        #         scale=2,
+        #     )
+        #     model_path = Path()
+        #     netscale = 2
         else:
             msg = f"Invalid RealESRGAN model: {self.model_name}"
             context.services.logger.error(msg)

invokeai/app/services/events.py

@@ -1,9 +1,11 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from typing import Any, Optional
+from pathlib import Path
+
 from invokeai.app.models.image import ProgressImage
 from invokeai.app.util.misc import get_timestamp
-from invokeai.app.services.model_manager_service import BaseModelType, ModelType, SubModelType, ModelInfo
+from invokeai.app.services.model_manager_service import BaseModelType, ModelType, SubModelType, ModelInfo, AddModelResult
 
 class EventServiceBase:
     session_event: str = "session_event"
@@ -111,7 +113,7 @@ class EventServiceBase:
             submodel: SubModelType,
     ) -> None:
         """Emitted when a model is requested"""
-        self.__emit_session_event(
+        self.dispatch(
             event_name="model_load_started",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
@@ -132,7 +134,7 @@ class EventServiceBase:
             model_info: ModelInfo,
     ) -> None:
         """Emitted when a model is correctly loaded (returns model info)"""
-        self.__emit_session_event(
+        self.dispatch(
             event_name="model_load_completed",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
@@ -145,3 +147,92 @@ class EventServiceBase:
                 precision=str(model_info.precision),
             ),
         )
+
+    def emit_model_import_started (
+            self,
+            import_path: str,  # can be a local path, URL or repo_id
+    )->None:
+        """Emitted when a model import commences"""
+        self.dispatch(
+            event_name="model_import_started",
+            payload=dict(
+                import_path = import_path,
+            )
+        )
+        
+    def emit_model_import_completed (
+            self,
+            import_path: str,  # can be a local path, URL or repo_id
+            import_info: AddModelResult,
+            success: bool= True,
+            error: str = None,
+            
+    )->None:
+        """Emitted when a model import completes"""
+        self.dispatch(
+            event_name="model_import_completed",
+            payload=dict(
+                import_path = import_path,
+                import_info = import_info,
+                success = success,
+                error = error,
+            )
+        )
+    
+    def emit_download_started (
+            self,
+            url: str,
+            
+    )->None:
+        """Emitted when a download thread starts"""
+        self.dispatch(
+            event_name="download_started",
+            payload=dict(
+                url = url,
+            )
+        )
+
+    def emit_download_progress (
+            self,
+            url: str,
+            downloaded_size: int,
+            total_size: int,
+    )->None:
+        """
+        Emitted at intervals during a download process
+        :param url: Requested URL
+        :param downloaded_size: Bytes downloaded so far
+        :param total_size: Total bytes to download
+        """
+        self.dispatch(
+            event_name="download_progress",
+            payload=dict(
+                url = url,
+                downloaded_size = downloaded_size,
+                total_size = total_size,
+            )
+        )
+
+    def emit_download_completed (
+            self,
+            url: str,
+            status_code: int,
+            download_path: Path,
+            
+    )->None:
+        """
+        Emitted when a download thread completes. 
+        :param url: Requested URL
+        :param status_code: HTTP status code from request
+        :param download_path: Path to downloaded file
+        """
+        self.dispatch(
+            event_name="download_completed",
+            payload=dict(
+                url = url,
+                status_code = status_code,
+                download_path = download_path,
+            )
+        )
+
+    

invokeai/app/services/model_manager_service.py

@@ -26,6 +26,7 @@ import torch
 from invokeai.app.models.exceptions import CanceledException
 from ...backend.util import choose_precision, choose_torch_device
 from .config import InvokeAIAppConfig
+from .events import EventServiceBase
 
 if TYPE_CHECKING:
     from ..invocations.baseinvocation import BaseInvocation, InvocationContext
@@ -542,6 +543,7 @@ class ModelManagerService(ModelManagerServiceBase):
     def heuristic_import(self,
                          items_to_import: set[str],
                          prediction_type_helper: Optional[Callable[[Path],SchedulerPredictionType]]=None,
+                         event_bus: Optional[EventServiceBase]=None,
                          )->dict[str, AddModelResult]:
         '''Import a list of paths, repo_ids or URLs. Returns the set of
         successfully imported items.
@@ -559,7 +561,7 @@ class ModelManagerService(ModelManagerServiceBase):
         of the set is a dict corresponding to the newly-created OmegaConf stanza for
         that model.
         '''
-        return self.mgr.heuristic_import(items_to_import, prediction_type_helper)        
+        return self.mgr.heuristic_import(items_to_import, prediction_type_helper, event_bus=event_bus)
 
     def merge_models(
             self,

invokeai/backend/generator/base.py

@@ -466,6 +466,7 @@ class Generator:
             dtype=samples.dtype,
             device=samples.device,
         )
+
         latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
         latents_ubyte = (
             ((latent_image + 1) / 2)

invokeai/backend/install/invokeai_configure.py

@@ -222,7 +222,7 @@ def download_conversion_models():
 
 # ---------------------------------------------
 def download_realesrgan():
-    logger.info("Installing ESRGAN Upscaling models...")
+    logger.info("Installing RealESRGAN models...")
     URLs = [
         dict(
             url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
@@ -239,11 +239,6 @@ def download_realesrgan():
             dest= "core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
             description = "ESRGAN_SRx4_DF2KOST_official.pth",
         ),
-        dict(
-            url= "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
-            dest= "core/upscaling/realesrgan/RealESRGAN_x2plus.pth",
-            description = "RealESRGAN_x2plus.pth",
-        ),
     ]
     for model in URLs:
         download_with_progress_bar(model['url'], config.models_path / model['dest'], model['description'])

invokeai/backend/install/model_install_backend.py

@@ -10,7 +10,7 @@ from tempfile import TemporaryDirectory
 from typing import List, Dict, Callable, Union, Set
 
 import requests
-from diffusers import DiffusionPipeline
+from diffusers import StableDiffusionPipeline
 from diffusers import logging as dlogging
 from huggingface_hub import hf_hub_url, HfFolder, HfApi
 from omegaconf import OmegaConf
@@ -89,13 +89,16 @@ class ModelInstall(object):
                  config:InvokeAIAppConfig,
                  prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
                  model_manager: ModelManager = None,
-                 access_token:str = None):
+                 access_token:str = None,
+                 event_bus = None, # EventServicesBase - getting circular import errors
+                 ):
         self.config = config
         self.mgr = model_manager or ModelManager(config.model_conf_path)
         self.datasets = OmegaConf.load(Dataset_path)
         self.prediction_helper = prediction_type_helper
         self.access_token = access_token or HfFolder.get_token()
         self.reverse_paths = self._reverse_paths(self.datasets)
+        self.event_bus = event_bus
 
     def all_models(self)->Dict[str,ModelLoadInfo]:
         '''
@@ -197,39 +200,63 @@ class ModelInstall(object):
         Returns a set of dict objects corresponding to newly-created stanzas in models.yaml.
         '''
 
+        if self.event_bus:
+            self.event_bus.emit_model_import_started(str(model_path_id_or_url))
+
         if not models_installed:
             models_installed = dict()
             
         # A little hack to allow nested routines to retrieve info on the requested ID
         self.current_id = model_path_id_or_url
         path = Path(model_path_id_or_url)
-        # checkpoint file, or similar
-        if path.is_file():
-            models_installed.update({str(path):self._install_path(path)})
 
-        # folders style or similar
-        elif path.is_dir() and any([(path/x).exists() for x in \
-                                    {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}
-                                    ]
-                                   ):
-            models_installed.update({str(model_path_id_or_url): self._install_path(path)})
+        try:
+            # checkpoint file, or similar
+            if path.is_file():
+                models_installed.update({str(path):self._install_path(path)})
 
-        # recursive scan
-        elif path.is_dir():
-            for child in path.iterdir():
-                self.heuristic_import(child, models_installed=models_installed)
+            # folders style or similar
+            elif path.is_dir() and any([(path/x).exists() for x in \
+                                        {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}
+                                        ]
+                                       ):
+                models_installed.update({str(model_path_id_or_url): self._install_path(path)})
 
-        # huggingface repo
-        elif len(str(model_path_id_or_url).split('/')) == 2:
-            models_installed.update({str(model_path_id_or_url): self._install_repo(str(model_path_id_or_url))})
+            # recursive scan
+            elif path.is_dir():
+                for child in path.iterdir():
+                    self.heuristic_import(child, models_installed=models_installed)
 
-        # a URL
-        elif str(model_path_id_or_url).startswith(("http:", "https:", "ftp:")):
-            models_installed.update({str(model_path_id_or_url): self._install_url(model_path_id_or_url)})
+            # huggingface repo
+            elif len(str(model_path_id_or_url).split('/')) == 2:
+                models_installed.update({str(model_path_id_or_url): self._install_repo(str(model_path_id_or_url))})
 
-        else:
-            raise KeyError(f'{str(model_path_id_or_url)} is not recognized as a local path, repo ID or URL. Skipping')
+            # a URL
+            elif str(model_path_id_or_url).startswith(("http:", "https:", "ftp:")):
+                models_installed.update({str(model_path_id_or_url): self._install_url(model_path_id_or_url)})
 
+            else:
+                errmsg = f'{str(model_path_id_or_url)} is not recognized as a local path, repo ID or URL. Skipping'
+                raise KeyError(errmsg)
+            
+            if self.event_bus:
+                for path, add_model_result in models_installed.items():
+                    self.event_bus.emit_model_import_completed(
+                        str(path),
+                        import_info = add_model_result,
+                    )
+        except Exception as e:
+            if self.event_bus:
+                self.event_bus.emit_model_import_completed(
+                    str(path),
+                    import_info = None,
+                    success = False,
+                    error = str(e),
+                )
+                return models_installed
+            else:
+                raise
+                
         return models_installed
 
     # install a model from a local path. The optional info parameter is there to prevent
@@ -238,10 +265,14 @@ class ModelInstall(object):
         info = info or ModelProbe().heuristic_probe(path,self.prediction_helper)
         if not info:
             logger.warning(f'Unable to parse format of {path}')
-            return None
+            raise ValueError(f'Unable to parse format of {path}')
+        
         model_name = path.stem if path.is_file() else path.name
+        
         if self.mgr.model_exists(model_name, info.base_type, info.model_type):
-            raise ValueError(f'A model named "{model_name}" is already installed.')
+            errmsg = f'A model named "{model_name}" is already installed.'
+            raise ValueError(errmsg)
+            
         attributes = self._make_attributes(path,info)
         return self.mgr.add_model(model_name = model_name,
                                   base_model = info.base_type,
@@ -251,7 +282,7 @@ class ModelInstall(object):
 
     def _install_url(self, url: str)->AddModelResult:
         with TemporaryDirectory(dir=self.config.models_path) as staging:
-            location = download_with_resume(url,Path(staging))
+            location = download_with_resume(url,Path(staging),event_bus=self.event_bus)
             if not location:
                 logger.error(f'Unable to download {url}. Skipping.')
             info = ModelProbe().heuristic_probe(location)
@@ -310,8 +341,6 @@ class ModelInstall(object):
         if key := self.reverse_paths.get(path_name):
             (name, base, mtype) = ModelManager.parse_key(key)
             return name
-        elif location.is_dir():
-            return location.name
         else:
             return location.stem
 
@@ -367,7 +396,7 @@ class ModelInstall(object):
         model = None
         for revision in revisions:
             try:
-                model = DiffusionPipeline.from_pretrained(repo_id,revision=revision,safety_checker=None)
+                model = StableDiffusionPipeline.from_pretrained(repo_id,revision=revision,safety_checker=None)
             except:  # most errors are due to fp16 not being present. Fix this to catch other errors
                 pass
             if model:
@@ -386,7 +415,8 @@ class ModelInstall(object):
             p = hf_download_with_resume(repo_id,
                                         model_dir=location,
                                         model_name=filename,
-                                        access_token = self.access_token
+                                        access_token = self.access_token,
+                                        event_bus = self.event_bus,
                                         )
             if p:
                 paths.append(p)
@@ -427,12 +457,15 @@ def hf_download_from_pretrained(
     return destination
 
 # ---------------------------------------------
+# TODO: This function is almost identical to invokeai.backend.util.download_with_resume
+# and should be merged
 def hf_download_with_resume(
         repo_id: str,
         model_dir: str,
         model_name: str,
         model_dest: Path = None,
         access_token: str = None,
+        event_bus = None,
 ) -> Path:
     model_dest = model_dest or Path(os.path.join(model_dir, model_name))
     os.makedirs(model_dir, exist_ok=True)
@@ -449,15 +482,22 @@ def hf_download_with_resume(
         open_mode = "ab"
 
     resp = requests.get(url, headers=header, stream=True)
-    total = int(resp.headers.get("content-length", 0))
+    content_length = int(resp.headers.get("content-length", 0))
+
+    if event_bus:
+        event_bus.emit_download_started(url)
 
     if (
         resp.status_code == 416
     ):  # "range not satisfiable", which means nothing to return
         logger.info(f"{model_name}: complete file found. Skipping.")
+        if event_bus:
+            event_bus.emit_download_completed(url,resp.status_code,model_dest)
         return model_dest
     elif resp.status_code == 404:
         logger.warning("File not found")
+        if event_bus:
+            event_bus.emit_download_completed(url,resp.status_code,None)
         return None
     elif resp.status_code != 200:
         logger.warning(f"{model_name}: {resp.reason}")
@@ -466,11 +506,15 @@ def hf_download_with_resume(
     else:
         logger.info(f"{model_name}: Downloading...")
 
+    MB10 = 10 * 1048576
+    downloaded = exist_size
+    previous_interval = 0   
+    
     try:
         with open(model_dest, open_mode) as file, tqdm(
             desc=model_name,
             initial=exist_size,
-            total=total + exist_size,
+            total=content_length + exist_size,
             unit="iB",
             unit_scale=True,
             unit_divisor=1000,
@@ -478,9 +522,20 @@ def hf_download_with_resume(
             for data in resp.iter_content(chunk_size=1024):
                 size = file.write(data)
                 bar.update(size)
+                downloaded += size
+                if event_bus and downloaded // MB10 > previous_interval:
+                    previous_interval = downloaded // MB10
+                    event_bus.emit_download_progress(url, downloaded, content_length)
+                    
     except Exception as e:
         logger.error(f"An error occurred while downloading {model_name}: {str(e)}")
+        if event_bus:
+            event_bus.emit_download_completed(url,500,None)
         return None
+    
+    if event_bus:
+        event_bus.emit_download_completed(url,resp.status_code,model_dest)
+
     return model_dest
 
 

invokeai/backend/model_management/__init__.py

@@ -3,7 +3,6 @@ Initialization file for invokeai.backend.model_management
 """
 from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType
 from .model_cache import ModelCache
-from .lora import ModelPatcher, ONNXModelPatcher
 from .models import BaseModelType, ModelType, SubModelType, ModelVariantType, ModelNotFoundException
 from .model_merge import ModelMerger, MergeInterpolationMethod
 

invokeai/backend/model_management/lora.py

@@ -6,22 +6,11 @@ from typing import Optional, Dict, Tuple, Any, Union, List
 from pathlib import Path
 
 import torch
-from safetensors.torch import load_file
-from torch.utils.hooks import RemovableHandle
-
-from diffusers.models import UNet2DConditionModel
-from transformers import CLIPTextModel
-from onnx import numpy_helper
-from onnxruntime import OrtValue
-import numpy as np
-
 from compel.embeddings_provider import BaseTextualInversionManager
 from diffusers.models import UNet2DConditionModel
 from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
-# TODO: rename and split this file
-
 class LoRALayerBase:
     #rank: Optional[int]
     #alpha: Optional[float]
@@ -719,185 +708,3 @@ class TextualInversionManager(BaseTextualInversionManager):
 
         return new_token_ids
 
-
-class ONNXModelPatcher:
-
-    @classmethod
-    @contextmanager
-    def apply_lora_unet(
-        cls,
-        unet: OnnxRuntimeModel,
-        loras: List[Tuple[LoRAModel, float]],
-    ):
-        with cls.apply_lora(unet, loras, "lora_unet_"):
-            yield
-
-
-    @classmethod
-    @contextmanager
-    def apply_lora_text_encoder(
-        cls,
-        text_encoder: OnnxRuntimeModel,
-        loras: List[Tuple[LoRAModel, float]],
-    ):
-        with cls.apply_lora(text_encoder, loras, "lora_te_"):
-            yield
-
-    # based on
-    # https://github.com/ssube/onnx-web/blob/ca2e436f0623e18b4cfe8a0363fcfcf10508acf7/api/onnx_web/convert/diffusion/lora.py#L323
-    @classmethod
-    @contextmanager
-    def apply_lora(
-        cls,
-        model: IAIOnnxRuntimeModel,
-        loras: List[Tuple[LoraModel, float]],
-        prefix: str,
-    ):
-        from .models.base import IAIOnnxRuntimeModel
-        if not isinstance(model, IAIOnnxRuntimeModel):
-            raise Exception("Only IAIOnnxRuntimeModel models supported")
-
-        orig_weights = dict()
-
-        try:
-            blended_loras = dict()
-
-            for lora, lora_weight in loras:
-                for layer_key, layer in lora.layers.items():
-                    if not layer_key.startswith(prefix):
-                        continue
-
-                    layer_key = layer_key.replace(prefix, "")
-                    layer_weight = layer.get_weight().detach().cpu().numpy() * lora_weight
-                    if layer_key is blended_loras:
-                        blended_loras[layer_key] += layer_weight
-                    else:
-                        blended_loras[layer_key] = layer_weight
-
-            node_names = dict()
-            for node in model.nodes.values():
-                node_names[node.name.replace("/", "_").replace(".", "_").lstrip("_")] = node.name
-
-            for layer_key, lora_weight in blended_loras.items():
-                conv_key = layer_key + "_Conv"
-                gemm_key = layer_key + "_Gemm"
-                matmul_key = layer_key + "_MatMul"
-
-                if conv_key in node_names or gemm_key in node_names:
-                    if conv_key in node_names:
-                        conv_node = model.nodes[node_names[conv_key]]
-                    else:
-                        conv_node = model.nodes[node_names[gemm_key]]
-
-                    weight_name = [n for n in conv_node.input if ".weight" in n][0]
-                    orig_weight = model.tensors[weight_name]
-
-                    if orig_weight.shape[-2:] == (1, 1):
-                        if lora_weight.shape[-2:] == (1, 1):
-                            new_weight = orig_weight.squeeze((3, 2)) + lora_weight.squeeze((3, 2))
-                        else:
-                            new_weight = orig_weight.squeeze((3, 2)) + lora_weight
-
-                        new_weight = np.expand_dims(new_weight, (2, 3))
-                    else:
-                        if orig_weight.shape != lora_weight.shape:
-                            new_weight = orig_weight + lora_weight.reshape(orig_weight.shape)
-                        else:
-                            new_weight = orig_weight + lora_weight
-
-                    orig_weights[weight_name] = orig_weight
-                    model.tensors[weight_name] = new_weight.astype(orig_weight.dtype)
-
-                elif matmul_key in node_names:
-                    weight_node = model.nodes[node_names[matmul_key]]
-                    matmul_name = [n for n in weight_node.input if "MatMul" in n][0]
-
-                    orig_weight = model.tensors[matmul_name]
-                    new_weight = orig_weight + lora_weight.transpose()
-
-                    orig_weights[matmul_name] = orig_weight
-                    model.tensors[matmul_name] = new_weight.astype(orig_weight.dtype)
-
-                else:
-                    # warn? err?
-                    pass
-
-            yield
-
-        finally:
-            # restore original weights
-            for name, orig_weight in orig_weights.items():
-                model.tensors[name] = orig_weight
-
-
-
-    @classmethod
-    @contextmanager
-    def apply_ti(
-        cls,
-        tokenizer: CLIPTokenizer,
-        text_encoder: IAIOnnxRuntimeModel,
-        ti_list: List[Any],
-    ) -> Tuple[CLIPTokenizer, TextualInversionManager]:
-        from .models.base import IAIOnnxRuntimeModel
-        if not isinstance(text_encoder, IAIOnnxRuntimeModel):
-            raise Exception("Only IAIOnnxRuntimeModel models supported")
-
-        orig_embeddings = None
-
-        try:
-            ti_tokenizer = copy.deepcopy(tokenizer)
-            ti_manager = TextualInversionManager(ti_tokenizer)
-
-            def _get_trigger(ti, index):
-                trigger = ti.name
-                if index > 0:
-                    trigger += f"-!pad-{i}"
-                return f"<{trigger}>"
-
-            # modify tokenizer
-            new_tokens_added = 0
-            for ti in ti_list:
-                for i in range(ti.embedding.shape[0]):
-                    new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti, i))
-
-            # modify text_encoder
-            orig_embeddings = text_encoder.tensors["text_model.embeddings.token_embedding.weight"]
-
-            embeddings = np.concatenate(
-                (
-                    np.copy(orig_embeddings),
-                    np.zeros((new_tokens_added, orig_embeddings.shape[1]))
-                ),
-                axis=0,
-            )
-
-            for ti in ti_list:
-                ti_tokens = []
-                for i in range(ti.embedding.shape[0]):
-                    embedding = ti.embedding[i].detach().numpy()
-                    trigger = _get_trigger(ti, i)
-
-                    token_id = ti_tokenizer.convert_tokens_to_ids(trigger)
-                    if token_id == ti_tokenizer.unk_token_id:
-                        raise RuntimeError(f"Unable to find token id for token '{trigger}'")
-
-                    if embeddings[token_id].shape != embedding.shape:
-                        raise ValueError(
-                            f"Cannot load embedding for {trigger}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {embeddings[token_id].shape[0]}."
-                        )
-
-                    embeddings[token_id] = embedding
-                    ti_tokens.append(token_id)
-
-                if len(ti_tokens) > 1:
-                    ti_manager.pad_tokens[ti_tokens[0]] = ti_tokens[1:]
-
-            text_encoder.tensors["text_model.embeddings.token_embedding.weight"] = embeddings.astype(orig_embeddings.dtype)
-
-            yield ti_tokenizer, ti_manager
-
-        finally:
-            # restore
-            if orig_embeddings is not None:
-                text_encoder.tensors["text_model.embeddings.token_embedding.weight"] = orig_embeddings

invokeai/backend/model_management/model_cache.py

@@ -328,25 +328,6 @@ class ModelCache(object):
 
             refs = sys.getrefcount(cache_entry.model)
 
-            # manualy clear local variable references of just finished function calls
-            # for some reason python don't want to collect it even by gc.collect() immidiately
-            if refs > 2:
-                while True:
-                    cleared = False
-                    for referrer in gc.get_referrers(cache_entry.model):
-                        if type(referrer).__name__ == "frame":
-                            # RuntimeError: cannot clear an executing frame
-                            with suppress(RuntimeError):
-                                referrer.clear()
-                                cleared = True
-                                #break
-
-                    # repeat if referrers changes(due to frame clear), else exit loop
-                    if cleared:
-                        gc.collect()
-                    else:
-                        break
-
             device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
             self.logger.debug(f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}, refs: {refs}")
 
@@ -382,9 +363,6 @@ class ModelCache(object):
                 self.logger.debug(f'GPU VRAM freed: {(mem.vram_used/GIG):.2f} GB')
                 vram_in_use += mem.vram_used  # note vram_used is negative
                 self.logger.debug(f'{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB')
-
-        gc.collect()
-        torch.cuda.empty_cache()
         
     def _local_model_hash(self, model_path: Union[str, Path]) -> str:
         sha = hashlib.sha256()

invokeai/backend/model_management/model_manager.py

@@ -106,16 +106,16 @@ providing information about a model defined in models.yaml. For example:
 
    >>> models = mgr.list_models()
    >>> json.dumps(models[0])
-  {"path": "/home/lstein/invokeai-main/models/sd-1/controlnet/canny",
-    "model_format": "diffusers",
-    "name": "canny",
-    "base_model": "sd-1",
+  {"path": "/home/lstein/invokeai-main/models/sd-1/controlnet/canny", 
+    "model_format": "diffusers", 
+    "name": "canny", 
+    "base_model": "sd-1", 
     "type": "controlnet"
    }
 
 You can filter by model type and base model as shown here:
 
-
+  
   controlnets = mgr.list_models(model_type=ModelType.ControlNet,
                                 base_model=BaseModelType.StableDiffusion1)
   for c in controlnets:
@@ -140,14 +140,14 @@ Layout of the `models` directory:
 
  models
  ├── sd-1
- │   ├── controlnet
- │   ├── lora
- │   ├── main
- │   └── embedding
+ │   ├── controlnet
+ │   ├── lora
+ │   ├── main
+ │   └── embedding
  ├── sd-2
- │   ├── controlnet
- │   ├── lora
- │   ├── main
+ │   ├── controlnet
+ │   ├── lora
+ │   ├── main
  │   └── embedding
  └── core
      ├── face_reconstruction
@@ -195,7 +195,7 @@ name, base model, type and a dict of model attributes. See
 `invokeai/backend/model_management/models` for the attributes required
 by each model type.
 
-A model can be deleted using `del_model()`, providing the same
+A model can be deleted using `del_model()`, providing the same 
 identifying information as `get_model()`
 
 The `heuristic_import()` method will take a set of strings
@@ -304,7 +304,7 @@ class ModelManager(object):
         logger: types.ModuleType = logger,
     ):
         """
-        Initialize with the path to the models.yaml config file.
+        Initialize with the path to the models.yaml config file. 
         Optional parameters are the torch device type, precision, max_models,
         and sequential_offload boolean. Note that the default device
         type and precision are set up for a CUDA system running at half precision.
@@ -323,7 +323,7 @@ class ModelManager(object):
         self.config_meta = ConfigMeta(**config.pop("__metadata__"))
         # TODO: metadata not found
         # TODO: version check
-
+        
         self.app_config = InvokeAIAppConfig.get_config()
         self.logger = logger
         self.cache = ModelCache(
@@ -431,7 +431,7 @@ class ModelManager(object):
         :param model_name: symbolic name of the model in models.yaml
         :param model_type: ModelType enum indicating the type of model to return
         :param base_model: BaseModelType enum indicating the base model used by this model
-        :param submode_typel: an ModelType enum indicating the portion of
+        :param submode_typel: an ModelType enum indicating the portion of 
                the model to retrieve (e.g. ModelType.Vae)
         """
         model_class = MODEL_CLASSES[base_model][model_type]
@@ -456,7 +456,7 @@ class ModelManager(object):
                 raise ModelNotFoundException(f"Model not found - {model_key}")
 
         # vae/movq override
-        # TODO:
+        # TODO: 
         if submodel_type is not None and hasattr(model_config, submodel_type):
             override_path = getattr(model_config, submodel_type)
             if override_path:
@@ -489,7 +489,7 @@ class ModelManager(object):
         self.cache_keys[model_key].add(model_context.key)
 
         model_hash = "<NO_HASH>" # TODO:
-
+            
         return ModelInfo(
             context = model_context,
             name = model_name,
@@ -518,7 +518,7 @@ class ModelManager(object):
 
     def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
         """
-        Return a list of (str, BaseModelType, ModelType) corresponding to all models
+        Return a list of (str, BaseModelType, ModelType) corresponding to all models 
         known to the configuration.
         """
         return [(self.parse_key(x)) for x in self.models.keys()]
@@ -692,12 +692,12 @@ class ModelManager(object):
         if new_name is None and new_base is None:
             self.logger.error("rename_model() called with neither a new_name nor a new_base. {model_name} unchanged.")
             return
-
+        
         model_key = self.create_key(model_name, base_model, model_type)
         model_cfg = self.models.get(model_key, None)
         if not model_cfg:
             raise ModelNotFoundException(f"Unknown model: {model_key}")
-
+        
         old_path = self.app_config.root_path / model_cfg.path
         new_name = new_name or model_name
         new_base = new_base or base_model
@@ -726,7 +726,7 @@ class ModelManager(object):
         self.models.pop(model_key, None) # delete
         self.models[new_key] = model_cfg
         self.commit()
-
+        
     def convert_model (
             self,
             model_name: str,
@@ -776,12 +776,12 @@ class ModelManager(object):
             # something went wrong, so don't leave dangling diffusers model in directory or it will cause a duplicate model error!
             rmtree(new_diffusers_path)
             raise
-
+        
         if checkpoint_path.exists() and checkpoint_path.is_relative_to(self.app_config.models_path):
             checkpoint_path.unlink()
-
+        
         return result
-
+    
     def search_models(self, search_folder):
         self.logger.info(f"Finding Models In: {search_folder}")
         models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
@@ -824,14 +824,10 @@ class ModelManager(object):
         assert config_file_path is not None,'no config file path to write to'
         config_file_path = self.app_config.root_path / config_file_path
         tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp")
-        try:
-            with open(tmpfile, "w", encoding="utf-8") as outfile:
-                outfile.write(self.preamble())
-                outfile.write(yaml_str)
-            os.replace(tmpfile, config_file_path)
-        except OSError as err:
-            self.logger.warning(f"Could not modify the config file at {config_file_path}")
-            self.logger.warning(err)
+        with open(tmpfile, "w", encoding="utf-8") as outfile:
+            outfile.write(self.preamble())
+            outfile.write(yaml_str)
+        os.replace(tmpfile, config_file_path)
 
     def preamble(self) -> str:
         """
@@ -957,6 +953,7 @@ class ModelManager(object):
     def heuristic_import(self,
                          items_to_import: Set[str],
                          prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
+                         event_bus = None,   # EventServiceBase, with circular dependency issues
                          )->Dict[str, AddModelResult]:
         '''Import a list of paths, repo_ids or URLs. Returns the set of
         successfully imported items.
@@ -981,12 +978,16 @@ class ModelManager(object):
         # avoid circular import here
         from invokeai.backend.install.model_install_backend import ModelInstall
         successfully_installed = dict()
-
+        
         installer = ModelInstall(config = self.app_config,
                                  prediction_type_helper = prediction_type_helper,
-                                 model_manager = self)
+                                 model_manager = self,
+                                 event_bus = event_bus,
+                                 )
         for thing in items_to_import:
             installed = installer.heuristic_import(thing)
             successfully_installed.update(installed)
-        self.commit()
+                
+        self.commit()                
         return successfully_installed
+

invokeai/backend/model_management/model_probe.py

@@ -23,7 +23,7 @@ class ModelProbeInfo(object):
     variant_type: ModelVariantType
     prediction_type: SchedulerPredictionType
     upcast_attention: bool
-    format: Literal['diffusers','checkpoint', 'lycoris', 'olive']
+    format: Literal['diffusers','checkpoint', 'lycoris']
     image_size: int
 
 class ProbeBase(object):

invokeai/backend/model_management/models/__init__.py

@@ -10,11 +10,8 @@ from .lora import LoRAModel
 from .controlnet import ControlNetModel # TODO:
 from .textual_inversion import TextualInversionModel
 
-from .stable_diffusion_onnx import ONNXStableDiffusion1Model, ONNXStableDiffusion2Model
-
 MODEL_CLASSES = {
     BaseModelType.StableDiffusion1: {
-        ModelType.ONNX: ONNXStableDiffusion1Model,
         ModelType.Main: StableDiffusion1Model,
         ModelType.Vae: VaeModel,
         ModelType.Lora: LoRAModel,
@@ -22,7 +19,6 @@ MODEL_CLASSES = {
         ModelType.TextualInversion: TextualInversionModel,
     },
     BaseModelType.StableDiffusion2: {
-        ModelType.ONNX: ONNXStableDiffusion2Model,
         ModelType.Main: StableDiffusion2Model,
         ModelType.Vae: VaeModel,
         ModelType.Lora: LoRAModel,
@@ -36,7 +32,6 @@ MODEL_CLASSES = {
         ModelType.Lora: LoRAModel,
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
-        ModelType.ONNX: ONNXStableDiffusion2Model,
     },
     BaseModelType.StableDiffusionXLRefiner: {
         ModelType.Main: StableDiffusionXLModel,
@@ -45,7 +40,6 @@ MODEL_CLASSES = {
         ModelType.Lora: LoRAModel,
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
-        ModelType.ONNX: ONNXStableDiffusion2Model,
     },
     #BaseModelType.Kandinsky2_1: {
     #    ModelType.Main: Kandinsky2_1Model,

invokeai/backend/model_management/models/base.py

@@ -8,19 +8,13 @@ from abc import ABCMeta, abstractmethod
 from pathlib import Path
 from picklescan.scanner import scan_file_path
 import torch
-import numpy as np
 import safetensors.torch
-from pathlib import Path
-from diffusers import DiffusionPipeline, ConfigMixin, OnnxRuntimeModel
+from diffusers import DiffusionPipeline, ConfigMixin
 
 from contextlib import suppress
 from pydantic import BaseModel, Field
 from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
 
-import onnx
-from onnx import numpy_helper
-from onnx.external_data_helper import set_external_data
-from onnxruntime import InferenceSession, OrtValue, SessionOptions, ExecutionMode, GraphOptimizationLevel
 class InvalidModelException(Exception):
     pass
 
@@ -35,7 +29,6 @@ class BaseModelType(str, Enum):
     #Kandinsky2_1 = "kandinsky-2.1"
 
 class ModelType(str, Enum):
-    ONNX = "onnx"
     Main = "main"
     Vae = "vae"
     Lora = "lora"
@@ -49,8 +42,6 @@ class SubModelType(str, Enum):
     Tokenizer = "tokenizer"
     Tokenizer2 = "tokenizer_2"
     Vae = "vae"
-    VaeDecoder = "vae_decoder"
-    VaeEncoder = "vae_encoder"
     Scheduler = "scheduler"
     SafetyChecker = "safety_checker"
     #MoVQ = "movq"
@@ -263,18 +254,16 @@ class DiffusersModel(ModelBase):
             try:
                 # TODO: set cache_dir to /dev/null to be sure that cache not used?
                 model = self.child_types[child_type].from_pretrained(
-                    os.path.join(self.model_path, child_type.value),
-                    #subfolder=child_type.value,
+                    self.model_path,
+                    subfolder=child_type.value,
                     torch_dtype=torch_dtype,
                     variant=variant,
                     local_files_only=True,
                 )
                 break
             except Exception as e:
-                print("====ERR LOAD====")
-                print(f"{variant}: {e}")
-                import traceback
-                traceback.print_exc()
+                #print("====ERR LOAD====")
+                #print(f"{variant}: {e}")
                 pass
         else:
             raise Exception(f"Failed to load {self.base_model}:{self.model_type}:{child_type} model")
@@ -441,188 +430,3 @@ class SilenceWarnings(object):
         transformers_logging.set_verbosity(self.transformers_verbosity)
         diffusers_logging.set_verbosity(self.diffusers_verbosity)
         warnings.simplefilter('default')
-
-ONNX_WEIGHTS_NAME = "model.onnx"
-class IAIOnnxRuntimeModel:
-    class _tensor_access:
-
-        def __init__(self, model):
-            self.model = model
-            self.indexes = dict()
-            for idx, obj in enumerate(self.model.proto.graph.initializer):
-                self.indexes[obj.name] = idx
-
-        def __getitem__(self, key: str):
-            return self.model.data[key].numpy()
-
-        def __setitem__(self, key: str, value: np.ndarray):
-            new_node = numpy_helper.from_array(value)
-            # set_external_data(new_node, location="in-memory-location")
-            new_node.name = key
-            # new_node.ClearField("raw_data")
-            del self.model.proto.graph.initializer[self.indexes[key]]
-            self.model.proto.graph.initializer.insert(self.indexes[key], new_node)
-            self.model.data[key] = OrtValue.ortvalue_from_numpy(value)
-
-        # __delitem__
-
-        def __contains__(self, key: str):
-            return key in self.model.data
-
-        def items(self):
-            raise NotImplementedError("tensor.items")
-            #return [(obj.name, obj) for obj in self.raw_proto]
-
-        def keys(self):
-            return self.model.data.keys()
-
-        def values(self):
-            raise NotImplementedError("tensor.values")
-            #return [obj for obj in self.raw_proto]
-
-
-
-    class _access_helper:
-        def __init__(self, raw_proto):
-            self.indexes = dict()
-            self.raw_proto = raw_proto
-            for idx, obj in enumerate(raw_proto):
-                self.indexes[obj.name] = idx
-
-        def __getitem__(self, key: str):
-            return self.raw_proto[self.indexes[key]]
-
-        def __setitem__(self, key: str, value):
-            index = self.indexes[key]
-            del self.raw_proto[index]
-            self.raw_proto.insert(index, value)
-
-        # __delitem__
-
-        def __contains__(self, key: str):
-            return key in self.indexes
-
-        def items(self):
-            return [(obj.name, obj) for obj in self.raw_proto]
-
-        def keys(self):
-            return self.indexes.keys()
-
-        def values(self):
-            return [obj for obj in self.raw_proto]
-    
-    def __init__(self, model_path: str, provider: Optional[str]):
-        self.path = model_path
-        self.session = None
-        self.provider = provider or "CPUExecutionProvider"
-        """
-        self.data_path = self.path + "_data"
-        if not os.path.exists(self.data_path):
-            print(f"Moving model tensors to separate file: {self.data_path}")
-            tmp_proto = onnx.load(model_path, load_external_data=True)
-            onnx.save_model(tmp_proto, self.path, save_as_external_data=True, all_tensors_to_one_file=True, location=os.path.basename(self.data_path), size_threshold=1024, convert_attribute=False)
-            del tmp_proto
-            gc.collect()
-
-        self.proto = onnx.load(model_path, load_external_data=False)
-        """
-
-        self.proto = onnx.load(model_path, load_external_data=True)
-        self.data = dict()
-        for tensor in self.proto.graph.initializer:
-            name = tensor.name
-
-            if tensor.HasField("raw_data"):
-                npt = numpy_helper.to_array(tensor)
-                orv = OrtValue.ortvalue_from_numpy(npt)
-                self.data[name] = orv
-                # set_external_data(tensor, location="in-memory-location")
-                tensor.name = name
-                # tensor.ClearField("raw_data")
-
-        self.nodes = self._access_helper(self.proto.graph.node)
-        self.initializers = self._access_helper(self.proto.graph.initializer)
-        # print(self.proto.graph.input)
-        # print(self.proto.graph.initializer)
-
-        self.tensors = self._tensor_access(self)
-
-    # TODO: integrate with model manager/cache
-    def create_session(self):
-        if self.session is None:
-            #onnx.save(self.proto, "tmp.onnx")
-            #onnx.save_model(self.proto, "tmp.onnx", save_as_external_data=True, all_tensors_to_one_file=True, location="tmp.onnx_data", size_threshold=1024, convert_attribute=False)
-            # TODO: something to be able to get weight when they already moved outside of model proto
-            #(trimmed_model, external_data) = buffer_external_data_tensors(self.proto)
-            sess = SessionOptions()
-            #self._external_data.update(**external_data)
-            # sess.add_external_initializers(list(self.data.keys()), list(self.data.values()))
-            # sess.enable_profiling = True
-
-            # sess.intra_op_num_threads = 1
-            # sess.inter_op_num_threads = 1
-            # sess.execution_mode = ExecutionMode.ORT_SEQUENTIAL
-            # sess.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
-            # sess.enable_cpu_mem_arena = True
-            # sess.enable_mem_pattern = True
-            # sess.add_session_config_entry("session.intra_op.use_xnnpack_threadpool", "1") ########### It's the key code
-
-
-            sess.add_free_dimension_override_by_name("unet_sample_batch", 2)
-            sess.add_free_dimension_override_by_name("unet_sample_channels", 4)
-            sess.add_free_dimension_override_by_name("unet_hidden_batch", 2)
-            sess.add_free_dimension_override_by_name("unet_hidden_sequence", 77)
-            sess.add_free_dimension_override_by_name("unet_sample_height", 64)
-            sess.add_free_dimension_override_by_name("unet_sample_width", 64)
-            sess.add_free_dimension_override_by_name("unet_time_batch", 1)
-            self.session = InferenceSession(self.proto.SerializeToString(), providers=['CUDAExecutionProvider', 'CPUExecutionProvider'], sess_options=sess)
-            #self.session = InferenceSession("tmp.onnx", providers=[self.provider], sess_options=self.sess_options)
-            self.io_binding = self.session.io_binding()
-
-    def release_session(self):
-        self.session = None
-        import gc
-        gc.collect()
-
-    def __call__(self, **kwargs):
-        if self.session is None:
-            raise Exception("You should call create_session before running model")
-
-        inputs = {k: np.array(v) for k, v in kwargs.items()}
-        output_names = self.session.get_outputs()
-        for k in inputs:
-            self.io_binding.bind_cpu_input(k, inputs[k])
-        for name in output_names:
-            self.io_binding.bind_output(name.name)
-        self.session.run_with_iobinding(self.io_binding, None)
-        return self.io_binding.copy_outputs_to_cpu()
-
-    # compatability with diffusers load code
-    @classmethod
-    def from_pretrained(
-        cls,
-        model_id: Union[str, Path],
-        subfolder: Union[str, Path] = None,
-        file_name: Optional[str] = None,
-        provider: Optional[str] = None,
-        sess_options: Optional["SessionOptions"] = None,
-        **kwargs,
-    ):
-        file_name = file_name or ONNX_WEIGHTS_NAME
-
-        if os.path.isdir(model_id):
-            model_path = model_id
-            if subfolder is not None:
-                model_path = os.path.join(model_path, subfolder)
-            model_path = os.path.join(model_path, file_name)
-
-        else:
-            model_path = model_id
-
-        # load model from local directory
-        if not os.path.isfile(model_path):
-            raise Exception(f"Model not found: {model_path}")
-
-        # TODO: session options
-        return cls(model_path, provider=provider)
-

invokeai/backend/model_management/models/stable_diffusion_onnx.py

@@ -1,156 +0,0 @@
-import os
-import json
-from enum import Enum
-from pydantic import Field
-from pathlib import Path
-from typing import Literal, Optional, Union
-from .base import (
-    ModelBase,
-    ModelConfigBase,
-    BaseModelType,
-    ModelType,
-    SubModelType,
-    ModelVariantType,
-    DiffusersModel,
-    SchedulerPredictionType,
-    SilenceWarnings,
-    read_checkpoint_meta,
-    classproperty,
-    OnnxRuntimeModel,
-    IAIOnnxRuntimeModel,
-)
-from invokeai.app.services.config import InvokeAIAppConfig
-
-class ONNXStableDiffusion1Model(DiffusersModel):
-
-    class Config(ModelConfigBase):
-        model_format: None
-        variant: ModelVariantType
-
-
-    def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
-        assert base_model == BaseModelType.StableDiffusion1
-        assert model_type == ModelType.ONNX
-        super().__init__(
-            model_path=model_path,
-            base_model=BaseModelType.StableDiffusion1,
-            model_type=ModelType.ONNX,
-        )
-
-        for child_name, child_type in self.child_types.items():
-            if child_type is OnnxRuntimeModel:
-                self.child_types[child_name] = IAIOnnxRuntimeModel
-
-            # TODO: check that no optimum models provided
-
-    @classmethod
-    def probe_config(cls, path: str, **kwargs):
-        model_format = cls.detect_format(path)
-        in_channels = 4 # TODO:
-
-        if in_channels == 9:
-            variant = ModelVariantType.Inpaint
-        elif in_channels == 4:
-            variant = ModelVariantType.Normal
-        else:
-            raise Exception("Unkown stable diffusion 1.* model format")
-
-
-        return cls.create_config(
-            path=path,
-            model_format=model_format,
-
-            variant=variant,
-        )
-
-    @classproperty
-    def save_to_config(cls) -> bool:
-        return True
-
-    @classmethod
-    def detect_format(cls, model_path: str):
-        return None
-
-    @classmethod
-    def convert_if_required(
-        cls,
-        model_path: str,
-        output_path: str,
-        config: ModelConfigBase,
-        base_model: BaseModelType,
-    ) -> str:
-        return model_path
-
-class ONNXStableDiffusion2Model(DiffusersModel):
-
-    # TODO: check that configs overwriten properly
-    class Config(ModelConfigBase):
-        model_format: None
-        variant: ModelVariantType
-        prediction_type: SchedulerPredictionType
-        upcast_attention: bool
-
-
-    def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
-        assert base_model == BaseModelType.StableDiffusion2
-        assert model_type == ModelType.ONNX
-        super().__init__(
-            model_path=model_path,
-            base_model=BaseModelType.StableDiffusion2,
-            model_type=ModelType.ONNX,
-        )
-
-        for child_name, child_type in self.child_types.items():
-            if child_type is OnnxRuntimeModel:
-                self.child_types[child_name] = IAIOnnxRuntimeModel
-            # TODO: check that no optimum models provided
-
-    @classmethod
-    def probe_config(cls, path: str, **kwargs):
-        model_format = cls.detect_format(path)
-        in_channels = 4 # TODO:
-
-        if in_channels == 9:
-            variant = ModelVariantType.Inpaint
-        elif in_channels == 5:
-            variant = ModelVariantType.Depth
-        elif in_channels == 4:
-            variant = ModelVariantType.Normal
-        else:
-            raise Exception("Unkown stable diffusion 2.* model format")
-
-        if variant == ModelVariantType.Normal:
-            prediction_type = SchedulerPredictionType.VPrediction
-            upcast_attention = True
-
-        else:
-            prediction_type = SchedulerPredictionType.Epsilon
-            upcast_attention = False
-
-        return cls.create_config(
-            path=path,
-            model_format=model_format,
-
-            variant=variant,
-            prediction_type=prediction_type,
-            upcast_attention=upcast_attention,
-        )
-
-    @classproperty
-    def save_to_config(cls) -> bool:
-        return True
-
-    @classmethod
-    def detect_format(cls, model_path: str):
-        return None
-
-    @classmethod
-    def convert_if_required(
-        cls,
-        model_path: str,
-        output_path: str,
-        config: ModelConfigBase,
-        base_model: BaseModelType,
-    ) -> str:
-        return model_path
-

invokeai/backend/util/util.py

@@ -21,7 +21,6 @@ from tqdm import tqdm
 import invokeai.backend.util.logging as logger
 from .devices import torch_dtype
 
-
 def log_txt_as_img(wh, xc, size=10):
     # wh a tuple of (width, height)
     # xc a list of captions to plot
@@ -285,7 +284,11 @@ def ask_user(question: str, answers: list):
 
 
 # -------------------------------------
-def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path:
+def download_with_resume(url: str,
+                         dest: Path,
+                         access_token: str = None,
+                         event_bus = None, # EventServiceBase (circular import issues)
+                         ) -> Path:
     """
     Download a model file.
     :param url:  https, http or ftp URL
@@ -323,8 +326,13 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
         os.remove(dest)
         exist_size = 0
         
+    if event_bus:
+        event_bus.emit_download_started(url)
+        
     if resp.status_code == 416 or (content_length > 0 and exist_size == content_length):
         logger.warning(f"{dest}: complete file found. Skipping.")
+        if event_bus:
+            event_bus.emit_download_completed(url,resp.status_code,dest)
         return dest
     elif resp.status_code == 206 or exist_size > 0:
         logger.warning(f"{dest}: partial file found. Resuming...")
@@ -333,11 +341,20 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
     else:
         logger.info(f"{dest}: Downloading...")
 
-    try:
-        if content_length < 2000:
-            logger.error(f"ERROR DOWNLOADING {url}: {resp.text}")
+    # If less than 2K, it's not a model - usually an HTML document of some sort
+    if content_length < 2000:
+        logger.error(f"ERROR DOWNLOADING {url}: {resp.text}")
+        if event_bus:
+            event_bus.emit_download_completed(url, 500, None)
             return None
 
+    # these variables are used in progress reporting events
+    MB10 = 10 * 1048576
+    downloaded = exist_size
+    previous_interval = 0   
+    
+    try:
+
         with open(dest, open_mode) as file, tqdm(
             desc=str(dest),
             initial=exist_size,
@@ -349,10 +366,20 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
             for data in resp.iter_content(chunk_size=1024):
                 size = file.write(data)
                 bar.update(size)
+                downloaded += size
+                if event_bus and downloaded // MB10 > previous_interval:
+                    previous_interval = downloaded // MB10
+                    event_bus.emit_download_progress(url, downloaded, content_length)
+
     except Exception as e:
         logger.error(f"An error occurred while downloading {dest}: {str(e)}")
+        if event_bus:
+            event_bus.emit_download_completed(url,500,None)
         return None
 
+    if event_bus:
+        event_bus.emit_download_completed(url,resp.status_code,dest)
+        
     return dest
 
 

invokeai/configs/INITIAL_MODELS.yaml

@@ -1,4 +1,10 @@
 # This file predefines a few models that the user may want to install.
+sd-1/main/stable-diffusion-xdl-base:
+   description: Stable Diffusion XL base model - NOT YET RELEASED!! (70 GB)
+   repo_id: stabilityai/stable-diffusion-xl-base
+sd-1/main/stable-diffusion-xdl-refiner:
+   description: Stable Diffusion XL refiner model - NOT YET RELEASED!! (60 GB)
+   repo_id: stabilityai/stable-diffusion-xl-refiner
 sd-1/main/stable-diffusion-v1-5:
    description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
    repo_id: runwayml/stable-diffusion-v1-5
@@ -16,14 +22,6 @@ sd-2/main/stable-diffusion-2-inpainting:
    description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
    repo_id: stabilityai/stable-diffusion-2-inpainting
    recommended: False
-sdxl/main/stable-diffusion-xl-base-0-9:
-   description: Stable Diffusion XL base model (12 GB; access token required)
-   repo_id: stabilityai/stable-diffusion-xl-base-0.9
-   recommended: False
-sdxl-refiner/main/stable-diffusion-xl-refiner-0-9:
-   description: Stable Diffusion XL refiner model (12 GB; access token required)
-   repo_id: stabilityai/stable-diffusion-xl-refiner-0.9
-   recommended: False
 sd-1/main/Analog-Diffusion:
    description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
    repo_id: wavymulder/Analog-Diffusion

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-ba194473.js"></script>
+    <script type="module" crossorigin src="./assets/index-f1a5f9cf.js"></script>
   </head>
 
   <body dir="ltr">

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

@@ -399,8 +399,6 @@
         "deleteModel": "Delete Model",
         "deleteConfig": "Delete Config",
         "deleteMsg1": "Are you sure you want to delete this model from InvokeAI?",
-        "modelDeleted": "Model Deleted",
-        "modelDeleteFailed": "Failed to delete model",
         "deleteMsg2": "This WILL delete the model from disk if it is in the InvokeAI root folder. If you are using a custom location, then the model WILL NOT be deleted from disk.",
         "formMessageDiffusersModelLocation": "Diffusers Model Location",
         "formMessageDiffusersModelLocationDesc": "Please enter at least one.",
@@ -410,13 +408,11 @@
         "convertToDiffusers": "Convert To Diffusers",
         "convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
         "convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
-        "convertToDiffusersHelpText3": "Your checkpoint file on disk WILL be deleted if it is in InvokeAI root folder. If it is in a custom location, then it WILL NOT be deleted.",
+        "convertToDiffusersHelpText3": "Your checkpoint file on the disk will NOT be deleted or modified in anyway. You can add your checkpoint to the Model Manager again if you want to.",
         "convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
         "convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 2GB-7GB in size.",
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
         "convertToDiffusersSaveLocation": "Save Location",
-        "noCustomLocationProvided": "No Custom Location Provided",
-        "convertingModelBegin": "Converting Model. Please wait.",
         "v1": "v1",
         "v2_base": "v2 (512px)",
         "v2_768": "v2 (768px)",
@@ -454,8 +450,7 @@
         "none": "none",
         "addDifference": "Add Difference",
         "pickModelType": "Pick Model Type",
-        "selectModel": "Select Model",
-        "importModels": "Import Models"
+        "selectModel": "Select Model"
     },
     "parameters": {
         "general": "General",
@@ -577,7 +572,6 @@
         "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
         "downloadImageStarted": "Image Download Started",
         "imageCopied": "Image Copied",
-        "problemCopyingImage": "Unable to Copy Image",
         "imageLinkCopied": "Image Link Copied",
         "problemCopyingImageLink": "Unable to Copy Image Link",
         "imageNotLoaded": "No Image Loaded",
@@ -694,15 +688,6 @@
         "reloadSchema": "Reload Schema",
         "saveNodes": "Save Nodes",
         "loadNodes": "Load Nodes",
-        "clearNodes": "Clear Nodes",
-        "zoomInNodes": "Zoom In",
-        "zoomOutNodes": "Zoom Out",
-        "fitViewportNodes": "Fit View",
-        "hideGraphNodes": "Hide Graph Overlay",
-        "showGraphNodes": "Show Graph Overlay",
-        "hideLegendNodes": "Hide Field Type Legend",
-        "showLegendNodes": "Show Field Type Legend",
-        "hideMinimapnodes": "Hide MiniMap",
-        "showMinimapnodes": "Show MiniMap"
+        "clearNodes": "Clear Nodes"
     }
 }

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

@@ -59,8 +59,15 @@ export const SCHEDULER_LABEL_MAP: Record<SchedulerParam, string> = {
 
 export type Scheduler = (typeof SCHEDULER_NAMES)[number];
 
+// Valid upscaling levels
+export const UPSCALING_LEVELS: Array<{ label: string; value: string }> = [
+  { label: '2x', value: '2' },
+  { label: '4x', value: '4' },
+];
 export const NUMPY_RAND_MIN = 0;
 
 export const NUMPY_RAND_MAX = 2147483647;
 
+export const FACETOOL_TYPES = ['gfpgan', 'codeformer'] as const;
+
 export const NODE_MIN_WIDTH = 250;

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

@@ -90,7 +90,6 @@ import { addUserInvokedNodesListener } from './listeners/userInvokedNodes';
 import { addUserInvokedTextToImageListener } from './listeners/userInvokedTextToImage';
 import { addModelLoadStartedEventListener } from './listeners/socketio/socketModelLoadStarted';
 import { addModelLoadCompletedEventListener } from './listeners/socketio/socketModelLoadCompleted';
-import { addUpscaleRequestedListener } from './listeners/upscaleRequested';
 
 export const listenerMiddleware = createListenerMiddleware();
 
@@ -229,5 +228,3 @@ addModelSelectedListener();
 addAppStartedListener();
 addModelsLoadedListener();
 addAppConfigReceivedListener();
-
-addUpscaleRequestedListener();

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

@@ -1,7 +1,7 @@
 import { log } from 'app/logging/useLogger';
-import { serializeError } from 'serialize-error';
-import { sessionCreated } from 'services/api/thunks/session';
 import { startAppListening } from '..';
+import { sessionCreated } from 'services/api/thunks/session';
+import { serializeError } from 'serialize-error';
 
 const moduleLog = log.child({ namespace: 'session' });
 

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

@@ -1,37 +0,0 @@
-import { createAction } from '@reduxjs/toolkit';
-import { log } from 'app/logging/useLogger';
-import { buildAdHocUpscaleGraph } from 'features/nodes/util/graphBuilders/buildAdHocUpscaleGraph';
-import { sessionReadyToInvoke } from 'features/system/store/actions';
-import { sessionCreated } from 'services/api/thunks/session';
-import { startAppListening } from '..';
-
-const moduleLog = log.child({ namespace: 'upscale' });
-
-export const upscaleRequested = createAction<{ image_name: string }>(
-  `upscale/upscaleRequested`
-);
-
-export const addUpscaleRequestedListener = () => {
-  startAppListening({
-    actionCreator: upscaleRequested,
-    effect: async (
-      action,
-      { dispatch, getState, take, unsubscribe, subscribe }
-    ) => {
-      const { image_name } = action.payload;
-      const { esrganModelName } = getState().postprocessing;
-
-      const graph = buildAdHocUpscaleGraph({
-        image_name,
-        esrganModelName,
-      });
-
-      // Create a session to run the graph & wait til it's ready to invoke
-      dispatch(sessionCreated({ graph }));
-
-      await take(sessionCreated.fulfilled.match);
-
-      dispatch(sessionReadyToInvoke());
-    },
-  });
-};

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

@@ -11,7 +11,7 @@ interface ItemProps extends React.ComponentPropsWithoutRef<'div'> {
 
 const IAIMantineSelectItemWithTooltip = forwardRef<HTMLDivElement, ItemProps>(
   ({ label, tooltip, description, disabled, ...others }: ItemProps, ref) => (
-    <Tooltip label={tooltip} placement="top" hasArrow openDelay={500}>
+    <Tooltip label={tooltip} placement="top" hasArrow>
       <Box ref={ref} {...others}>
         <Box>
           <Text>{label}</Text>

invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx

@@ -5,26 +5,34 @@ import {
   ButtonGroup,
   Flex,
   FlexProps,
+  Link,
   Menu,
   MenuButton,
+  MenuItem,
   MenuList,
 } from '@chakra-ui/react';
 // import { runESRGAN, runFacetool } from 'app/socketio/actions';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIButton from 'common/components/IAIButton';
 import IAIIconButton from 'common/components/IAIIconButton';
+import IAIPopover from 'common/components/IAIPopover';
 
 import { skipToken } from '@reduxjs/toolkit/dist/query';
 import { useAppToaster } from 'app/components/Toaster';
-import { upscaleRequested } from 'app/store/middleware/listenerMiddleware/listeners/upscaleRequested';
 import { stateSelector } from 'app/store/store';
+import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
+import { requestCanvasRescale } from 'features/canvas/store/thunks/requestCanvasScale';
 import { DeleteImageButton } from 'features/imageDeletion/components/DeleteImageButton';
 import { imageToDeleteSelected } from 'features/imageDeletion/store/imageDeletionSlice';
-import ParamUpscalePopover from 'features/parameters/components/Parameters/Upscale/ParamUpscaleSettings';
+import FaceRestoreSettings from 'features/parameters/components/Parameters/FaceRestore/FaceRestoreSettings';
+import UpscaleSettings from 'features/parameters/components/Parameters/Upscale/UpscaleSettings';
 import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters';
 import { initialImageSelected } from 'features/parameters/store/actions';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { useCopyImageToClipboard } from 'features/ui/hooks/useCopyImageToClipboard';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import {
+  setActiveTab,
   setShouldShowImageDetails,
   setShouldShowProgressInViewer,
 } from 'features/ui/store/uiSlice';
@@ -34,25 +42,38 @@ import { useTranslation } from 'react-i18next';
 import {
   FaAsterisk,
   FaCode,
+  FaCopy,
+  FaDownload,
+  FaExpandArrowsAlt,
+  FaGrinStars,
   FaHourglassHalf,
   FaQuoteRight,
   FaSeedling,
+  FaShare,
   FaShareAlt,
 } from 'react-icons/fa';
 import {
   useGetImageDTOQuery,
   useGetImageMetadataQuery,
 } from 'services/api/endpoints/images';
-import { menuListMotionProps } from 'theme/components/menu';
 import { useDebounce } from 'use-debounce';
-import { sentImageToImg2Img } from '../../store/actions';
+import { sentImageToCanvas, sentImageToImg2Img } from '../../store/actions';
+import { menuListMotionProps } from 'theme/components/menu';
 import SingleSelectionMenuItems from '../ImageContextMenu/SingleSelectionMenuItems';
 
 const currentImageButtonsSelector = createSelector(
   [stateSelector, activeTabNameSelector],
-  ({ gallery, system, ui }, activeTabName) => {
-    const { isProcessing, isConnected, shouldConfirmOnDelete, progressImage } =
-      system;
+  ({ gallery, system, postprocessing, ui }, activeTabName) => {
+    const {
+      isProcessing,
+      isConnected,
+      isGFPGANAvailable,
+      isESRGANAvailable,
+      shouldConfirmOnDelete,
+      progressImage,
+    } = system;
+
+    const { upscalingLevel, facetoolStrength } = postprocessing;
 
     const {
       shouldShowImageDetails,
@@ -67,6 +88,10 @@ const currentImageButtonsSelector = createSelector(
       shouldConfirmOnDelete,
       isProcessing,
       isConnected,
+      isGFPGANAvailable,
+      isESRGANAvailable,
+      upscalingLevel,
+      facetoolStrength,
       shouldDisableToolbarButtons: Boolean(progressImage) || !lastSelectedImage,
       shouldShowImageDetails,
       activeTabName,
@@ -89,17 +114,27 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
   const {
     isProcessing,
     isConnected,
+    isGFPGANAvailable,
+    isESRGANAvailable,
+    upscalingLevel,
+    facetoolStrength,
     shouldDisableToolbarButtons,
     shouldShowImageDetails,
+    activeTabName,
     lastSelectedImage,
     shouldShowProgressInViewer,
   } = useAppSelector(currentImageButtonsSelector);
 
+  const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
   const isUpscalingEnabled = useFeatureStatus('upscaling').isFeatureEnabled;
+  const isFaceRestoreEnabled = useFeatureStatus('faceRestore').isFeatureEnabled;
 
   const toaster = useAppToaster();
   const { t } = useTranslation();
 
+  const { isClipboardAPIAvailable, copyImageToClipboard } =
+    useCopyImageToClipboard();
+
   const { recallBothPrompts, recallSeed, recallAllParameters } =
     useRecallParameters();
 
@@ -120,6 +155,42 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
 
   const metadata = metadataData?.metadata;
 
+  const handleCopyImageLink = useCallback(() => {
+    const getImageUrl = () => {
+      if (!imageDTO) {
+        return;
+      }
+
+      if (imageDTO.image_url.startsWith('http')) {
+        return imageDTO.image_url;
+      }
+
+      return window.location.toString() + imageDTO.image_url;
+    };
+
+    const url = getImageUrl();
+
+    if (!url) {
+      toaster({
+        title: t('toast.problemCopyingImageLink'),
+        status: 'error',
+        duration: 2500,
+        isClosable: true,
+      });
+
+      return;
+    }
+
+    navigator.clipboard.writeText(url).then(() => {
+      toaster({
+        title: t('toast.imageLinkCopied'),
+        status: 'success',
+        duration: 2500,
+        isClosable: true,
+      });
+    });
+  }, [toaster, t, imageDTO]);
+
   const handleClickUseAllParameters = useCallback(() => {
     recallAllParameters(metadata);
   }, [metadata, recallAllParameters]);
@@ -152,11 +223,8 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
   useHotkeys('shift+i', handleSendToImageToImage, [imageDTO]);
 
   const handleClickUpscale = useCallback(() => {
-    if (!imageDTO) {
-      return;
-    }
-    dispatch(upscaleRequested({ image_name: imageDTO.image_name }));
-  }, [dispatch, imageDTO]);
+    // selectedImage && dispatch(runESRGAN(selectedImage));
+  }, []);
 
   const handleDelete = useCallback(() => {
     if (!imageDTO) {
@@ -174,17 +242,53 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
       enabled: () =>
         Boolean(
           isUpscalingEnabled &&
+            isESRGANAvailable &&
             !shouldDisableToolbarButtons &&
             isConnected &&
-            !isProcessing
+            !isProcessing &&
+            upscalingLevel
         ),
     },
     [
       isUpscalingEnabled,
       imageDTO,
+      isESRGANAvailable,
       shouldDisableToolbarButtons,
       isConnected,
       isProcessing,
+      upscalingLevel,
+    ]
+  );
+
+  const handleClickFixFaces = useCallback(() => {
+    // selectedImage && dispatch(runFacetool(selectedImage));
+  }, []);
+
+  useHotkeys(
+    'Shift+R',
+    () => {
+      handleClickFixFaces();
+    },
+    {
+      enabled: () =>
+        Boolean(
+          isFaceRestoreEnabled &&
+            isGFPGANAvailable &&
+            !shouldDisableToolbarButtons &&
+            isConnected &&
+            !isProcessing &&
+            facetoolStrength
+        ),
+    },
+
+    [
+      isFaceRestoreEnabled,
+      imageDTO,
+      isGFPGANAvailable,
+      shouldDisableToolbarButtons,
+      isConnected,
+      isProcessing,
+      facetoolStrength,
     ]
   );
 
@@ -193,6 +297,25 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
     [dispatch, shouldShowImageDetails]
   );
 
+  const handleSendToCanvas = useCallback(() => {
+    if (!imageDTO) return;
+    dispatch(sentImageToCanvas());
+
+    dispatch(setInitialCanvasImage(imageDTO));
+    dispatch(requestCanvasRescale());
+
+    if (activeTabName !== 'unifiedCanvas') {
+      dispatch(setActiveTab('unifiedCanvas'));
+    }
+
+    toaster({
+      title: t('toast.sentToUnifiedCanvas'),
+      status: 'success',
+      duration: 2500,
+      isClosable: true,
+    });
+  }, [imageDTO, dispatch, activeTabName, toaster, t]);
+
   useHotkeys(
     'i',
     () => {
@@ -214,6 +337,13 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
     dispatch(setShouldShowProgressInViewer(!shouldShowProgressInViewer));
   }, [dispatch, shouldShowProgressInViewer]);
 
+  const handleCopyImage = useCallback(() => {
+    if (!imageDTO) {
+      return;
+    }
+    copyImageToClipboard(imageDTO.image_url);
+  }, [copyImageToClipboard, imageDTO]);
+
   return (
     <>
       <Flex
@@ -266,12 +396,72 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
           />
         </ButtonGroup>
 
-        {isUpscalingEnabled && (
+        {(isUpscalingEnabled || isFaceRestoreEnabled) && (
           <ButtonGroup
             isAttached={true}
             isDisabled={shouldDisableToolbarButtons}
           >
-            {isUpscalingEnabled && <ParamUpscalePopover imageDTO={imageDTO} />}
+            {isFaceRestoreEnabled && (
+              <IAIPopover
+                triggerComponent={
+                  <IAIIconButton
+                    icon={<FaGrinStars />}
+                    aria-label={t('parameters.restoreFaces')}
+                  />
+                }
+              >
+                <Flex
+                  sx={{
+                    flexDirection: 'column',
+                    rowGap: 4,
+                  }}
+                >
+                  <FaceRestoreSettings />
+                  <IAIButton
+                    isDisabled={
+                      !isGFPGANAvailable ||
+                      !imageDTO ||
+                      !(isConnected && !isProcessing) ||
+                      !facetoolStrength
+                    }
+                    onClick={handleClickFixFaces}
+                  >
+                    {t('parameters.restoreFaces')}
+                  </IAIButton>
+                </Flex>
+              </IAIPopover>
+            )}
+
+            {isUpscalingEnabled && (
+              <IAIPopover
+                triggerComponent={
+                  <IAIIconButton
+                    icon={<FaExpandArrowsAlt />}
+                    aria-label={t('parameters.upscale')}
+                  />
+                }
+              >
+                <Flex
+                  sx={{
+                    flexDirection: 'column',
+                    gap: 4,
+                  }}
+                >
+                  <UpscaleSettings />
+                  <IAIButton
+                    isDisabled={
+                      !isESRGANAvailable ||
+                      !imageDTO ||
+                      !(isConnected && !isProcessing) ||
+                      !upscalingLevel
+                    }
+                    onClick={handleClickUpscale}
+                  >
+                    {t('parameters.upscaleImage')}
+                  </IAIButton>
+                </Flex>
+              </IAIPopover>
+            )}
           </ButtonGroup>
         )}
 

invokeai/frontend/web/src/features/nodes/components/fields/ModelInputFieldComponent.tsx

@@ -12,10 +12,7 @@ import { modelIdToMainModelParam } from 'features/parameters/util/modelIdToMainM
 import { forEach } from 'lodash-es';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import {
-  useGetMainModelsQuery,
-  useGetOnnxModelsQuery,
-} from 'services/api/endpoints/models';
+import { useGetMainModelsQuery } from 'services/api/endpoints/models';
 import { FieldComponentProps } from './types';
 
 const ModelInputFieldComponent = (
@@ -26,7 +23,6 @@ const ModelInputFieldComponent = (
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
-  const { data: onnxModels } = useGetOnnxModelsQuery();
   const { data: mainModels, isLoading } = useGetMainModelsQuery();
 
   const data = useMemo(() => {
@@ -48,39 +44,17 @@ const ModelInputFieldComponent = (
       });
     });
 
-    if (onnxModels) {
-      forEach(onnxModels.entities, (model, id) => {
-        if (!model) {
-          return;
-        }
-
-        data.push({
-          value: id,
-          label: model.model_name,
-          group: BASE_MODEL_NAME_MAP[model.base_model],
-        });
-      });
-    }
     return data;
-  }, [mainModels, onnxModels]);
+  }, [mainModels]);
 
   // grab the full model entity from the RTK Query cache
   // TODO: maybe we should just store the full model entity in state?
   const selectedModel = useMemo(
     () =>
-      (mainModels?.entities[
+      mainModels?.entities[
         `${field.value?.base_model}/main/${field.value?.model_name}`
-      ] ||
-        onnxModels?.entities[
-          `${field.value?.base_model}/onnx/${field.value?.model_name}`
-        ]) ??
-      null,
-    [
-      field.value?.base_model,
-      field.value?.model_name,
-      mainModels?.entities,
-      onnxModels?.entities,
-    ]
+      ] ?? null,
+    [field.value?.base_model, field.value?.model_name, mainModels?.entities]
   );
 
   const handleChangeModel = useCallback(

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts

@@ -9,7 +9,6 @@ import {
   CLIP_SKIP,
   LORA_LOADER,
   MAIN_MODEL_LOADER,
-  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
@@ -18,8 +17,7 @@ import {
 export const addLoRAsToGraph = (
   state: RootState,
   graph: NonNullableGraph,
-  baseNodeId: string,
-  modelLoader: string = MAIN_MODEL_LOADER
+  baseNodeId: string
 ): void => {
   /**
    * LoRA nodes get the UNet and CLIP models from the main model loader and apply the LoRA to them.
@@ -42,10 +40,6 @@ export const addLoRAsToGraph = (
         !(
           e.source.node_id === MAIN_MODEL_LOADER &&
           ['unet'].includes(e.source.field)
-        ) &&
-        !(
-          e.source.node_id === ONNX_MODEL_LOADER &&
-          ['unet'].includes(e.source.field)
         )
     );
     // Remove CLIP_SKIP connections to conditionings to feed it through LoRAs
@@ -80,11 +74,12 @@ export const addLoRAsToGraph = (
 
     // add to graph
     graph.nodes[currentLoraNodeId] = loraLoaderNode;
+
     if (currentLoraIndex === 0) {
       // first lora = start the lora chain, attach directly to model loader
       graph.edges.push({
         source: {
-          node_id: modelLoader,
+          node_id: MAIN_MODEL_LOADER,
           field: 'unet',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts

@@ -9,15 +9,13 @@ import {
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
   METADATA_ACCUMULATOR,
-  ONNX_MODEL_LOADER,
   TEXT_TO_IMAGE_GRAPH,
   VAE_LOADER,
 } from './constants';
 
 export const addVAEToGraph = (
   state: RootState,
-  graph: NonNullableGraph,
-  modelLoader: string = MAIN_MODEL_LOADER
+  graph: NonNullableGraph
 ): void => {
   const { vae } = state.generation;
 
@@ -33,12 +31,12 @@ export const addVAEToGraph = (
       vae_model: vae,
     };
   }
-  const isOnnxModel = modelLoader == ONNX_MODEL_LOADER;
+
   if (graph.id === TEXT_TO_IMAGE_GRAPH || graph.id === IMAGE_TO_IMAGE_GRAPH) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? modelLoader : VAE_LOADER,
-        field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
+        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        field: 'vae',
       },
       destination: {
         node_id: LATENTS_TO_IMAGE,
@@ -50,8 +48,8 @@ export const addVAEToGraph = (
   if (graph.id === IMAGE_TO_IMAGE_GRAPH) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? modelLoader : VAE_LOADER,
-        field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
+        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        field: 'vae',
       },
       destination: {
         node_id: IMAGE_TO_LATENTS,
@@ -63,8 +61,8 @@ export const addVAEToGraph = (
   if (graph.id === INPAINT_GRAPH) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? modelLoader : VAE_LOADER,
-        field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
+        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        field: 'vae',
       },
       destination: {
         node_id: INPAINT,

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildAdHocUpscaleGraph.ts

@@ -1,32 +0,0 @@
-import { NonNullableGraph } from 'features/nodes/types/types';
-import { ESRGANModelName } from 'features/parameters/store/postprocessingSlice';
-import { Graph, ESRGANInvocation } from 'services/api/types';
-import { REALESRGAN as ESRGAN } from './constants';
-
-type Arg = {
-  image_name: string;
-  esrganModelName: ESRGANModelName;
-};
-
-export const buildAdHocUpscaleGraph = ({
-  image_name,
-  esrganModelName,
-}: Arg): Graph => {
-  const realesrganNode: ESRGANInvocation = {
-    id: ESRGAN,
-    type: 'esrgan',
-    image: { image_name },
-    model_name: esrganModelName,
-    is_intermediate: false,
-  };
-
-  const graph: NonNullableGraph = {
-    id: `adhoc-esrgan-graph`,
-    nodes: {
-      [ESRGAN]: realesrganNode,
-    },
-    edges: [],
-  };
-
-  return graph;
-};

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts

@@ -18,7 +18,6 @@ import {
   LATENTS_TO_IMAGE,
   LATENTS_TO_LATENTS,
   MAIN_MODEL_LOADER,
-  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
@@ -60,9 +59,6 @@ export const buildCanvasImageToImageGraph = (
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
 
-  const onnx_model_type = model.model_type.includes('onnx');
-  const model_loader = onnx_model_type ? ONNX_MODEL_LOADER : MAIN_MODEL_LOADER;
-
   /**
    * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
    * full graph here as a template. Then use the parameters from app state and set friendlier node
@@ -73,17 +69,16 @@ export const buildCanvasImageToImageGraph = (
    */
 
   // copy-pasted graph from node editor, filled in with state values & friendly node ids
-  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: IMAGE_TO_IMAGE_GRAPH,
     nodes: {
       [POSITIVE_CONDITIONING]: {
-        type: onnx_model_type ? 'prompt_onnx' : 'compel',
+        type: 'compel',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
-        type: onnx_model_type ? 'prompt_onnx' : 'compel',
+        type: 'compel',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
@@ -92,9 +87,9 @@ export const buildCanvasImageToImageGraph = (
         id: NOISE,
         use_cpu,
       },
-      [model_loader]: {
-        type: model_loader,
-        id: model_loader,
+      [MAIN_MODEL_LOADER]: {
+        type: 'main_model_loader',
+        id: MAIN_MODEL_LOADER,
         model,
       },
       [CLIP_SKIP]: {
@@ -103,11 +98,11 @@ export const buildCanvasImageToImageGraph = (
         skipped_layers: clipSkip,
       },
       [LATENTS_TO_IMAGE]: {
-        type: onnx_model_type ? 'l2i_onnx' : 'l2i',
+        type: 'l2i',
         id: LATENTS_TO_IMAGE,
       },
       [LATENTS_TO_LATENTS]: {
-        type: onnx_model_type ? 'l2l_onnx' : 'l2l',
+        type: 'l2l',
         id: LATENTS_TO_LATENTS,
         cfg_scale,
         scheduler,
@@ -115,7 +110,7 @@ export const buildCanvasImageToImageGraph = (
         strength,
       },
       [IMAGE_TO_LATENTS]: {
-        type: onnx_model_type ? 'i2l_onnx' : 'i2l',
+        type: 'i2l',
         id: IMAGE_TO_LATENTS,
         // must be set manually later, bc `fit` parameter may require a resize node inserted
         // image: {
@@ -126,7 +121,7 @@ export const buildCanvasImageToImageGraph = (
     edges: [
       {
         source: {
-          node_id: model_loader,
+          node_id: MAIN_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -186,7 +181,7 @@ export const buildCanvasImageToImageGraph = (
       },
       {
         source: {
-          node_id: model_loader,
+          node_id: MAIN_MODEL_LOADER,
           field: 'unet',
         },
         destination: {
@@ -318,10 +313,10 @@ export const buildCanvasImageToImageGraph = (
   });
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS, model_loader);
+  addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS);
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, model_loader);
+  addVAEToGraph(state, graph);
 
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts

@@ -15,7 +15,6 @@ import {
   INPAINT_GRAPH,
   ITERATE,
   MAIN_MODEL_LOADER,
-  ONNX_MODEL_LOADER,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
   RANDOM_INT,
@@ -64,11 +63,6 @@ export const buildCanvasInpaintGraph = (
   // We may need to set the inpaint width and height to scale the image
   const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
 
-  const model_loader = model.model_type.includes('onnx')
-    ? ONNX_MODEL_LOADER
-    : MAIN_MODEL_LOADER;
-
-  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: INPAINT_GRAPH,
     nodes: {
@@ -113,9 +107,9 @@ export const buildCanvasInpaintGraph = (
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
-      [model_loader]: {
-        type: model_loader,
-        id: model_loader,
+      [MAIN_MODEL_LOADER]: {
+        type: 'main_model_loader',
+        id: MAIN_MODEL_LOADER,
         model,
       },
       [CLIP_SKIP]: {
@@ -139,7 +133,7 @@ export const buildCanvasInpaintGraph = (
     edges: [
       {
         source: {
-          node_id: model_loader,
+          node_id: MAIN_MODEL_LOADER,
           field: 'unet',
         },
         destination: {
@@ -149,7 +143,7 @@ export const buildCanvasInpaintGraph = (
       },
       {
         source: {
-          node_id: model_loader,
+          node_id: MAIN_MODEL_LOADER,
           field: 'clip',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts

@@ -10,7 +10,6 @@ import {
   CLIP_SKIP,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
-  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
@@ -50,8 +49,7 @@ export const buildCanvasTextToImageGraph = (
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
-  const onnx_model_type = model.model_type.includes('onnx');
-  const model_loader = onnx_model_type ? ONNX_MODEL_LOADER : MAIN_MODEL_LOADER;
+
   /**
    * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
    * full graph here as a template. Then use the parameters from app state and set friendlier node
@@ -62,17 +60,16 @@ export const buildCanvasTextToImageGraph = (
    */
 
   // copy-pasted graph from node editor, filled in with state values & friendly node ids
-  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: TEXT_TO_IMAGE_GRAPH,
     nodes: {
       [POSITIVE_CONDITIONING]: {
-        type: onnx_model_type ? 'prompt_onnx' : 'compel',
+        type: 'compel',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
-        type: onnx_model_type ? 'prompt_onnx' : 'compel',
+        type: 'compel',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
@@ -84,15 +81,15 @@ export const buildCanvasTextToImageGraph = (
         use_cpu,
       },
       [TEXT_TO_LATENTS]: {
-        type: onnx_model_type ? 't2l_onnx' : 't2l',
+        type: 't2l',
         id: TEXT_TO_LATENTS,
         cfg_scale,
         scheduler,
         steps,
       },
-      [model_loader]: {
-        type: model_loader,
-        id: model_loader,
+      [MAIN_MODEL_LOADER]: {
+        type: 'main_model_loader',
+        id: MAIN_MODEL_LOADER,
         model,
       },
       [CLIP_SKIP]: {
@@ -101,7 +98,7 @@ export const buildCanvasTextToImageGraph = (
         skipped_layers: clipSkip,
       },
       [LATENTS_TO_IMAGE]: {
-        type: onnx_model_type ? 'l2i_onnx' : 'l2i',
+        type: 'l2i',
         id: LATENTS_TO_IMAGE,
       },
     },
@@ -128,7 +125,7 @@ export const buildCanvasTextToImageGraph = (
       },
       {
         source: {
-          node_id: model_loader,
+          node_id: MAIN_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -158,7 +155,7 @@ export const buildCanvasTextToImageGraph = (
       },
       {
         source: {
-          node_id: model_loader,
+          node_id: MAIN_MODEL_LOADER,
           field: 'unet',
         },
         destination: {
@@ -222,10 +219,10 @@ export const buildCanvasTextToImageGraph = (
   });
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, TEXT_TO_LATENTS, model_loader);
+  addLoRAsToGraph(state, graph, TEXT_TO_LATENTS);
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, model_loader);
+  addVAEToGraph(state, graph);
 
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts

@@ -17,7 +17,6 @@ import {
   LATENTS_TO_IMAGE,
   LATENTS_TO_LATENTS,
   MAIN_MODEL_LOADER,
-  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
@@ -83,17 +82,13 @@ export const buildLinearImageToImageGraph = (
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
 
-  const onnx_model_type = model.model_type.includes('onnx');
-  const model_loader = onnx_model_type ? ONNX_MODEL_LOADER : MAIN_MODEL_LOADER;
-
   // copy-pasted graph from node editor, filled in with state values & friendly node ids
-  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: IMAGE_TO_IMAGE_GRAPH,
     nodes: {
-      [model_loader]: {
-        type: model_loader,
-        id: model_loader,
+      [MAIN_MODEL_LOADER]: {
+        type: 'main_model_loader',
+        id: MAIN_MODEL_LOADER,
         model,
       },
       [CLIP_SKIP]: {
@@ -102,12 +97,12 @@ export const buildLinearImageToImageGraph = (
         skipped_layers: clipSkip,
       },
       [POSITIVE_CONDITIONING]: {
-        type: onnx_model_type ? 'prompt_onnx' : 'compel',
+        type: 'compel',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
-        type: onnx_model_type ? 'prompt_onnx' : 'compel',
+        type: 'compel',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
@@ -117,11 +112,11 @@ export const buildLinearImageToImageGraph = (
         use_cpu,
       },
       [LATENTS_TO_IMAGE]: {
-        type: onnx_model_type ? 'l2i_onnx' : 'l2i',
+        type: 'l2i',
         id: LATENTS_TO_IMAGE,
       },
       [LATENTS_TO_LATENTS]: {
-        type: onnx_model_type ? 'l2l_onnx' : 'l2l',
+        type: 'l2l',
         id: LATENTS_TO_LATENTS,
         cfg_scale,
         scheduler,
@@ -129,7 +124,7 @@ export const buildLinearImageToImageGraph = (
         strength,
       },
       [IMAGE_TO_LATENTS]: {
-        type: onnx_model_type ? 'i2l_onnx' : 'i2l',
+        type: 'i2l',
         id: IMAGE_TO_LATENTS,
         // must be set manually later, bc `fit` parameter may require a resize node inserted
         // image: {
@@ -140,7 +135,7 @@ export const buildLinearImageToImageGraph = (
     edges: [
       {
         source: {
-          node_id: model_loader,
+          node_id: MAIN_MODEL_LOADER,
           field: 'unet',
         },
         destination: {
@@ -150,7 +145,7 @@ export const buildLinearImageToImageGraph = (
       },
       {
         source: {
-          node_id: model_loader,
+          node_id: MAIN_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -371,10 +366,10 @@ export const buildLinearImageToImageGraph = (
   });
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS, model_loader);
+  addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS);
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, model_loader);
+  addVAEToGraph(state, graph);
 
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts

@@ -6,13 +6,10 @@ import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
-import { BaseModelType, OnnxModelField } from 'services/api/types';
-
 import {
   CLIP_SKIP,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
-  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
@@ -49,8 +46,6 @@ export const buildLinearTextToImageGraph = (
     throw new Error('No model found in state');
   }
 
-  const onnx_model_type = model.model_type.includes('onnx');
-  const model_loader = onnx_model_type ? ONNX_MODEL_LOADER : MAIN_MODEL_LOADER;
   /**
    * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
    * full graph here as a template. Then use the parameters from app state and set friendlier node
@@ -61,14 +56,12 @@ export const buildLinearTextToImageGraph = (
    */
 
   // copy-pasted graph from node editor, filled in with state values & friendly node ids
-
-  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: TEXT_TO_IMAGE_GRAPH,
     nodes: {
-      [model_loader]: {
-        type: model_loader,
-        id: model_loader,
+      [MAIN_MODEL_LOADER]: {
+        type: 'main_model_loader',
+        id: MAIN_MODEL_LOADER,
         model,
       },
       [CLIP_SKIP]: {
@@ -77,12 +70,12 @@ export const buildLinearTextToImageGraph = (
         skipped_layers: clipSkip,
       },
       [POSITIVE_CONDITIONING]: {
-        type: onnx_model_type ? 'prompt_onnx' : 'compel',
+        type: 'compel',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
-        type: onnx_model_type ? 'prompt_onnx' : 'compel',
+        type: 'compel',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
@@ -94,21 +87,21 @@ export const buildLinearTextToImageGraph = (
         use_cpu,
       },
       [TEXT_TO_LATENTS]: {
-        type: onnx_model_type ? 't2l_onnx' : 't2l',
+        type: 't2l',
         id: TEXT_TO_LATENTS,
         cfg_scale,
         scheduler,
         steps,
       },
       [LATENTS_TO_IMAGE]: {
-        type: onnx_model_type ? 'l2i_onnx' : 'l2i',
+        type: 'l2i',
         id: LATENTS_TO_IMAGE,
       },
     },
     edges: [
       {
         source: {
-          node_id: model_loader,
+          node_id: MAIN_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -118,7 +111,7 @@ export const buildLinearTextToImageGraph = (
       },
       {
         source: {
-          node_id: model_loader,
+          node_id: MAIN_MODEL_LOADER,
           field: 'unet',
         },
         destination: {
@@ -222,10 +215,10 @@ export const buildLinearTextToImageGraph = (
   });
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, TEXT_TO_LATENTS, model_loader);
+  addLoRAsToGraph(state, graph, TEXT_TO_LATENTS);
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, model_loader);
+  addVAEToGraph(state, graph);
 
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts

@@ -8,7 +8,6 @@ export const RANDOM_INT = 'rand_int';
 export const RANGE_OF_SIZE = 'range_of_size';
 export const ITERATE = 'iterate';
 export const MAIN_MODEL_LOADER = 'main_model_loader';
-export const ONNX_MODEL_LOADER = 'onnx_model_loader';
 export const VAE_LOADER = 'vae_loader';
 export const LORA_LOADER = 'lora_loader';
 export const CLIP_SKIP = 'clip_skip';
@@ -21,9 +20,6 @@ export const DYNAMIC_PROMPT = 'dynamic_prompt';
 export const IMAGE_COLLECTION = 'image_collection';
 export const IMAGE_COLLECTION_ITERATE = 'image_collection_iterate';
 export const METADATA_ACCUMULATOR = 'metadata_accumulator';
-export const REALESRGAN = 'esrgan';
-export const DIVIDE = 'divide';
-export const SCALE = 'scale_image';
 
 // friendly graph ids
 export const TEXT_TO_IMAGE_GRAPH = 'text_to_image_graph';

invokeai/frontend/web/src/features/nodes/util/modelIdToMainModelField.ts

@@ -1,17 +0,0 @@
-import { BaseModelType, MainModelField, ModelType } from 'services/api/types';
-
-/**
- * Crudely converts a model id to a main model field
- * TODO: Make better
- */
-export const modelIdToMainModelField = (modelId: string): MainModelField => {
-  const [base_model, model_type, model_name] = modelId.split('/');
-
-  const field: MainModelField = {
-    base_model: base_model as BaseModelType,
-    model_type: model_type as ModelType,
-    model_name,
-  };
-
-  return field;
-};

invokeai/frontend/web/src/features/nodes/util/modelIdToOnnxModelField.ts

@@ -1,17 +0,0 @@
-import { BaseModelType, OnnxModelField, ModelType } from 'services/api/types';
-
-/**
- * Crudely converts a model id to a main model field
- * TODO: Make better
- */
-export const modelIdToOnnxModelField = (modelId: string): OnnxModelField => {
-  const [base_model, model_type, model_name] = modelId.split('/');
-
-  const field: OnnxModelField = {
-    base_model: base_model as BaseModelType,
-    model_name,
-    model_type: model_type as ModelType,
-  };
-
-  return field;
-};

invokeai/frontend/web/src/features/parameters/components/Parameters/FaceRestore/CodeformerFidelity.tsx

@@ -0,0 +1,34 @@
+import type { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISlider from 'common/components/IAISlider';
+import { setCodeformerFidelity } from 'features/parameters/store/postprocessingSlice';
+import { useTranslation } from 'react-i18next';
+
+export default function CodeformerFidelity() {
+  const isGFPGANAvailable = useAppSelector(
+    (state: RootState) => state.system.isGFPGANAvailable
+  );
+
+  const codeformerFidelity = useAppSelector(
+    (state: RootState) => state.postprocessing.codeformerFidelity
+  );
+
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+
+  return (
+    <IAISlider
+      isDisabled={!isGFPGANAvailable}
+      label={t('parameters.codeformerFidelity')}
+      step={0.05}
+      min={0}
+      max={1}
+      onChange={(v) => dispatch(setCodeformerFidelity(v))}
+      handleReset={() => dispatch(setCodeformerFidelity(1))}
+      value={codeformerFidelity}
+      withReset
+      withSliderMarks
+      withInput
+    />
+  );
+}

invokeai/frontend/web/src/features/parameters/components/Parameters/FaceRestore/FaceRestoreSettings.tsx

@@ -0,0 +1,25 @@
+import { VStack } from '@chakra-ui/react';
+import { useAppSelector } from 'app/store/storeHooks';
+import type { RootState } from 'app/store/store';
+import FaceRestoreType from './FaceRestoreType';
+import FaceRestoreStrength from './FaceRestoreStrength';
+import CodeformerFidelity from './CodeformerFidelity';
+
+/**
+ * Displays face-fixing/GFPGAN options (strength).
+ */
+const FaceRestoreSettings = () => {
+  const facetoolType = useAppSelector(
+    (state: RootState) => state.postprocessing.facetoolType
+  );
+
+  return (
+    <VStack gap={2} alignItems="stretch">
+      <FaceRestoreType />
+      <FaceRestoreStrength />
+      {facetoolType === 'codeformer' && <CodeformerFidelity />}
+    </VStack>
+  );
+};
+
+export default FaceRestoreSettings;

invokeai/frontend/web/src/features/parameters/components/Parameters/FaceRestore/FaceRestoreStrength.tsx

@@ -0,0 +1,34 @@
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISlider from 'common/components/IAISlider';
+import { setFacetoolStrength } from 'features/parameters/store/postprocessingSlice';
+import { useTranslation } from 'react-i18next';
+
+export default function FaceRestoreStrength() {
+  const isGFPGANAvailable = useAppSelector(
+    (state: RootState) => state.system.isGFPGANAvailable
+  );
+
+  const facetoolStrength = useAppSelector(
+    (state: RootState) => state.postprocessing.facetoolStrength
+  );
+
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+
+  return (
+    <IAISlider
+      isDisabled={!isGFPGANAvailable}
+      label={t('parameters.strength')}
+      step={0.05}
+      min={0}
+      max={1}
+      onChange={(v) => dispatch(setFacetoolStrength(v))}
+      handleReset={() => dispatch(setFacetoolStrength(0.75))}
+      value={facetoolStrength}
+      withReset
+      withSliderMarks
+      withInput
+    />
+  );
+}

invokeai/frontend/web/src/features/parameters/components/Parameters/FaceRestore/FaceRestoreToggle.tsx

@@ -0,0 +1,28 @@
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISwitch from 'common/components/IAISwitch';
+import { setShouldRunFacetool } from 'features/parameters/store/postprocessingSlice';
+import { ChangeEvent } from 'react';
+
+export default function FaceRestoreToggle() {
+  const isGFPGANAvailable = useAppSelector(
+    (state: RootState) => state.system.isGFPGANAvailable
+  );
+
+  const shouldRunFacetool = useAppSelector(
+    (state: RootState) => state.postprocessing.shouldRunFacetool
+  );
+
+  const dispatch = useAppDispatch();
+
+  const handleChangeShouldRunFacetool = (e: ChangeEvent<HTMLInputElement>) =>
+    dispatch(setShouldRunFacetool(e.target.checked));
+
+  return (
+    <IAISwitch
+      isDisabled={!isGFPGANAvailable}
+      isChecked={shouldRunFacetool}
+      onChange={handleChangeShouldRunFacetool}
+    />
+  );
+}

invokeai/frontend/web/src/features/parameters/components/Parameters/FaceRestore/FaceRestoreType.tsx

@@ -0,0 +1,30 @@
+import { FACETOOL_TYPES } from 'app/constants';
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIMantineSearchableSelect from 'common/components/IAIMantineSearchableSelect';
+import {
+  FacetoolType,
+  setFacetoolType,
+} from 'features/parameters/store/postprocessingSlice';
+import { useTranslation } from 'react-i18next';
+
+export default function FaceRestoreType() {
+  const facetoolType = useAppSelector(
+    (state: RootState) => state.postprocessing.facetoolType
+  );
+
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const handleChangeFacetoolType = (v: string) =>
+    dispatch(setFacetoolType(v as FacetoolType));
+
+  return (
+    <IAIMantineSearchableSelect
+      label={t('parameters.type')}
+      data={FACETOOL_TYPES.concat()}
+      value={facetoolType}
+      onChange={handleChangeFacetoolType}
+    />
+  );
+}

invokeai/frontend/web/src/features/parameters/components/Parameters/Hires/ParamHiresCollapse.tsx

@@ -0,0 +1,43 @@
+import { Flex } from '@chakra-ui/react';
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAICollapse from 'common/components/IAICollapse';
+import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { ParamHiresStrength } from './ParamHiresStrength';
+import { ParamHiresToggle } from './ParamHiresToggle';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const activeLabel = state.postprocessing.hiresFix ? 'Enabled' : undefined;
+
+    return { activeLabel };
+  },
+  defaultSelectorOptions
+);
+
+const ParamHiresCollapse = () => {
+  const { t } = useTranslation();
+  const { activeLabel } = useAppSelector(selector);
+
+  const isHiresEnabled = useFeatureStatus('hires').isFeatureEnabled;
+
+  if (!isHiresEnabled) {
+    return null;
+  }
+
+  return (
+    <IAICollapse label={t('parameters.hiresOptim')} activeLabel={activeLabel}>
+      <Flex sx={{ gap: 2, flexDirection: 'column' }}>
+        <ParamHiresToggle />
+        <ParamHiresStrength />
+      </Flex>
+    </IAICollapse>
+  );
+};
+
+export default memo(ParamHiresCollapse);

invokeai/frontend/web/src/features/parameters/components/Parameters/Hires/ParamHiresHeight.tsx

@@ -0,0 +1,3 @@
+// TODO
+
+export default {};

invokeai/frontend/web/src/features/parameters/components/Parameters/Hires/ParamHiresSteps.tsx

@@ -0,0 +1,3 @@
+// TODO
+
+export default {};

invokeai/frontend/web/src/features/parameters/components/Parameters/Hires/ParamHiresStrength.tsx

@@ -0,0 +1,51 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISlider from 'common/components/IAISlider';
+import { postprocessingSelector } from 'features/parameters/store/postprocessingSelectors';
+import { setHiresStrength } from 'features/parameters/store/postprocessingSlice';
+import { isEqual } from 'lodash-es';
+import { useTranslation } from 'react-i18next';
+
+const hiresStrengthSelector = createSelector(
+  [postprocessingSelector],
+  ({ hiresFix, hiresStrength }) => ({ hiresFix, hiresStrength }),
+  {
+    memoizeOptions: {
+      resultEqualityCheck: isEqual,
+    },
+  }
+);
+
+export const ParamHiresStrength = () => {
+  const { hiresFix, hiresStrength } = useAppSelector(hiresStrengthSelector);
+
+  const dispatch = useAppDispatch();
+
+  const { t } = useTranslation();
+
+  const handleHiresStrength = (v: number) => {
+    dispatch(setHiresStrength(v));
+  };
+
+  const handleHiResStrengthReset = () => {
+    dispatch(setHiresStrength(0.75));
+  };
+
+  return (
+    <IAISlider
+      label={t('parameters.hiresStrength')}
+      step={0.01}
+      min={0.01}
+      max={0.99}
+      onChange={handleHiresStrength}
+      value={hiresStrength}
+      isInteger={false}
+      withInput
+      withSliderMarks
+      // inputWidth={22}
+      withReset
+      handleReset={handleHiResStrengthReset}
+      isDisabled={!hiresFix}
+    />
+  );
+};

invokeai/frontend/web/src/features/parameters/components/Parameters/Hires/ParamHiresToggle.tsx

@@ -0,0 +1,30 @@
+import type { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISwitch from 'common/components/IAISwitch';
+import { setHiresFix } from 'features/parameters/store/postprocessingSlice';
+import { ChangeEvent } from 'react';
+import { useTranslation } from 'react-i18next';
+
+/**
+ * Hires Fix Toggle
+ */
+export const ParamHiresToggle = () => {
+  const dispatch = useAppDispatch();
+
+  const hiresFix = useAppSelector(
+    (state: RootState) => state.postprocessing.hiresFix
+  );
+
+  const { t } = useTranslation();
+
+  const handleChangeHiresFix = (e: ChangeEvent<HTMLInputElement>) =>
+    dispatch(setHiresFix(e.target.checked));
+
+  return (
+    <IAISwitch
+      label={t('parameters.hiresOptim')}
+      isChecked={hiresFix}
+      onChange={handleChangeHiresFix}
+    />
+  );
+};

invokeai/frontend/web/src/features/parameters/components/Parameters/Hires/ParamHiresWidth.tsx

@@ -0,0 +1,3 @@
+// TODO
+
+export default {};

invokeai/frontend/web/src/features/parameters/components/Parameters/MainModel/ParamMainModelSelect.tsx

@@ -12,11 +12,7 @@ import { modelSelected } from 'features/parameters/store/actions';
 import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
 import { modelIdToMainModelParam } from 'features/parameters/util/modelIdToMainModelParam';
 import { forEach } from 'lodash-es';
-import {
-  useGetMainModelsQuery,
-  useGetOnnxModelsQuery,
-} from 'services/api/endpoints/models';
-import { modelIdToOnnxModelField } from 'features/nodes/util/modelIdToOnnxModelField';
+import { useGetMainModelsQuery } from 'services/api/endpoints/models';
 
 const selector = createSelector(
   stateSelector,
@@ -31,7 +27,6 @@ const ParamMainModelSelect = () => {
   const { model } = useAppSelector(selector);
 
   const { data: mainModels, isLoading } = useGetMainModelsQuery();
-  const { data: onnxModels, isLoading: onnxLoading } = useGetOnnxModelsQuery();
 
   const data = useMemo(() => {
     if (!mainModels) {
@@ -51,31 +46,17 @@ const ParamMainModelSelect = () => {
         group: MODEL_TYPE_MAP[model.base_model],
       });
     });
-    forEach(onnxModels?.entities, (model, id) => {
-      if (!model) {
-        return;
-      }
-
-      data.push({
-        value: id,
-        label: model.model_name,
-        group: MODEL_TYPE_MAP[model.base_model],
-      });
-    });
 
     return data;
-  }, [mainModels, onnxModels]);
+  }, [mainModels]);
 
   // grab the full model entity from the RTK Query cache
   // TODO: maybe we should just store the full model entity in state?
   const selectedModel = useMemo(
     () =>
-      (mainModels?.entities[`${model?.base_model}/main/${model?.model_name}`] ||
-        onnxModels?.entities[
-          `${model?.base_model}/onnx/${model?.model_name}`
-        ]) ??
+      mainModels?.entities[`${model?.base_model}/main/${model?.model_name}`] ??
       null,
-    [mainModels?.entities, model, onnxModels?.entities]
+    [mainModels?.entities, model]
   );
 
   const handleChangeModel = useCallback(
@@ -84,11 +65,7 @@ const ParamMainModelSelect = () => {
         return;
       }
 
-      let newModel = modelIdToMainModelParam(v);
-
-      if (v.includes('onnx')) {
-        newModel = modelIdToOnnxModelField(v);
-      }
+      const newModel = modelIdToMainModelParam(v);
 
       if (!newModel) {
         return;
@@ -99,7 +76,7 @@ const ParamMainModelSelect = () => {
     [dispatch]
   );
 
-  return isLoading || onnxLoading ? (
+  return isLoading ? (
     <IAIMantineSearchableSelect
       label={t('modelManager.model')}
       placeholder="Loading..."

invokeai/frontend/web/src/features/parameters/components/Parameters/Upscale/ParamRealESRGANModel.tsx

@@ -1,58 +0,0 @@
-import { SelectItem } from '@mantine/core';
-import type { RootState } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIMantineSelect from 'common/components/IAIMantineSelect';
-import IAIMantineSelectItemWithTooltip from 'common/components/IAIMantineSelectItemWithTooltip';
-import {
-  ESRGANModelName,
-  esrganModelNameChanged,
-} from 'features/parameters/store/postprocessingSlice';
-import { useTranslation } from 'react-i18next';
-
-export const ESRGAN_MODEL_NAMES: SelectItem[] = [
-  {
-    label: 'RealESRGAN x2 Plus',
-    value: 'RealESRGAN_x2plus.pth',
-    tooltip: 'Attempts to retain sharpness, low smoothing',
-    group: 'x2 Upscalers',
-  },
-  {
-    label: 'RealESRGAN x4 Plus',
-    value: 'RealESRGAN_x4plus.pth',
-    tooltip: 'Best for photos and highly detailed images, medium smoothing',
-    group: 'x4 Upscalers',
-  },
-  {
-    label: 'RealESRGAN x4 Plus (anime 6B)',
-    value: 'RealESRGAN_x4plus_anime_6B.pth',
-    tooltip: 'Best for anime/manga, high smoothing',
-    group: 'x4 Upscalers',
-  },
-  {
-    label: 'ESRGAN SRx4',
-    value: 'ESRGAN_SRx4_DF2KOST_official-ff704c30.pth',
-    tooltip: 'Retains sharpness, low smoothing',
-    group: 'x4 Upscalers',
-  },
-];
-
-export default function ParamESRGANModel() {
-  const esrganModelName = useAppSelector(
-    (state: RootState) => state.postprocessing.esrganModelName
-  );
-
-  const dispatch = useAppDispatch();
-
-  const handleChange = (v: string) =>
-    dispatch(esrganModelNameChanged(v as ESRGANModelName));
-
-  return (
-    <IAIMantineSelect
-      label="ESRGAN Model"
-      value={esrganModelName}
-      itemComponent={IAIMantineSelectItemWithTooltip}
-      onChange={handleChange}
-      data={ESRGAN_MODEL_NAMES}
-    />
-  );
-}

invokeai/frontend/web/src/features/parameters/components/Parameters/Upscale/ParamUpscaleSettings.tsx

@@ -1,62 +0,0 @@
-import { Flex, useDisclosure } from '@chakra-ui/react';
-import { upscaleRequested } from 'app/store/middleware/listenerMiddleware/listeners/upscaleRequested';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIButton from 'common/components/IAIButton';
-import IAIIconButton from 'common/components/IAIIconButton';
-import IAIPopover from 'common/components/IAIPopover';
-import { selectIsBusy } from 'features/system/store/systemSelectors';
-import { useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { FaExpandArrowsAlt } from 'react-icons/fa';
-import { ImageDTO } from 'services/api/types';
-import ParamESRGANModel from './ParamRealESRGANModel';
-
-type Props = { imageDTO?: ImageDTO };
-
-const ParamUpscalePopover = (props: Props) => {
-  const { imageDTO } = props;
-  const dispatch = useAppDispatch();
-  const isBusy = useAppSelector(selectIsBusy);
-  const { t } = useTranslation();
-  const { isOpen, onOpen, onClose } = useDisclosure();
-
-  const handleClickUpscale = useCallback(() => {
-    onClose();
-    if (!imageDTO) {
-      return;
-    }
-    dispatch(upscaleRequested({ image_name: imageDTO.image_name }));
-  }, [dispatch, imageDTO, onClose]);
-
-  return (
-    <IAIPopover
-      isOpen={isOpen}
-      onClose={onClose}
-      triggerComponent={
-        <IAIIconButton
-          onClick={onOpen}
-          icon={<FaExpandArrowsAlt />}
-          aria-label={t('parameters.upscale')}
-        />
-      }
-    >
-      <Flex
-        sx={{
-          flexDirection: 'column',
-          gap: 4,
-        }}
-      >
-        <ParamESRGANModel />
-        <IAIButton
-          size="sm"
-          isDisabled={!imageDTO || isBusy}
-          onClick={handleClickUpscale}
-        >
-          {t('parameters.upscaleImage')}
-        </IAIButton>
-      </Flex>
-    </IAIPopover>
-  );
-};
-
-export default ParamUpscalePopover;

invokeai/frontend/web/src/features/parameters/components/Parameters/Upscale/UpscaleDenoisingStrength.tsx

@@ -0,0 +1,36 @@
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISlider from 'common/components/IAISlider';
+import { setUpscalingDenoising } from 'features/parameters/store/postprocessingSlice';
+import { useTranslation } from 'react-i18next';
+
+export default function UpscaleDenoisingStrength() {
+  const isESRGANAvailable = useAppSelector(
+    (state: RootState) => state.system.isESRGANAvailable
+  );
+
+  const upscalingDenoising = useAppSelector(
+    (state: RootState) => state.postprocessing.upscalingDenoising
+  );
+
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+
+  return (
+    <IAISlider
+      label={t('parameters.denoisingStrength')}
+      value={upscalingDenoising}
+      min={0}
+      max={1}
+      step={0.01}
+      onChange={(v) => {
+        dispatch(setUpscalingDenoising(v));
+      }}
+      handleReset={() => dispatch(setUpscalingDenoising(0.75))}
+      withSliderMarks
+      withInput
+      withReset
+      isDisabled={!isESRGANAvailable}
+    />
+  );
+}

invokeai/frontend/web/src/features/parameters/components/Parameters/Upscale/UpscaleScale.tsx

@@ -0,0 +1,35 @@
+import { UPSCALING_LEVELS } from 'app/constants';
+import type { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIMantineSearchableSelect from 'common/components/IAIMantineSearchableSelect';
+import {
+  UpscalingLevel,
+  setUpscalingLevel,
+} from 'features/parameters/store/postprocessingSlice';
+import { useTranslation } from 'react-i18next';
+
+export default function UpscaleScale() {
+  const isESRGANAvailable = useAppSelector(
+    (state: RootState) => state.system.isESRGANAvailable
+  );
+
+  const upscalingLevel = useAppSelector(
+    (state: RootState) => state.postprocessing.upscalingLevel
+  );
+
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+
+  const handleChangeLevel = (v: string) =>
+    dispatch(setUpscalingLevel(Number(v) as UpscalingLevel));
+
+  return (
+    <IAIMantineSearchableSelect
+      disabled={!isESRGANAvailable}
+      label={t('parameters.scale')}
+      value={String(upscalingLevel)}
+      onChange={handleChangeLevel}
+      data={UPSCALING_LEVELS}
+    />
+  );
+}

invokeai/frontend/web/src/features/parameters/components/Parameters/Upscale/UpscaleSettings.tsx

@@ -0,0 +1,19 @@
+import { VStack } from '@chakra-ui/react';
+import UpscaleDenoisingStrength from './UpscaleDenoisingStrength';
+import UpscaleStrength from './UpscaleStrength';
+import UpscaleScale from './UpscaleScale';
+
+/**
+ * Displays upscaling/ESRGAN options (level and strength).
+ */
+const UpscaleSettings = () => {
+  return (
+    <VStack gap={2} alignItems="stretch">
+      <UpscaleScale />
+      <UpscaleDenoisingStrength />
+      <UpscaleStrength />
+    </VStack>
+  );
+};
+
+export default UpscaleSettings;

invokeai/frontend/web/src/features/parameters/components/Parameters/Upscale/UpscaleStrength.tsx

@@ -0,0 +1,33 @@
+import type { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISlider from 'common/components/IAISlider';
+import { setUpscalingStrength } from 'features/parameters/store/postprocessingSlice';
+import { useTranslation } from 'react-i18next';
+
+export default function UpscaleStrength() {
+  const isESRGANAvailable = useAppSelector(
+    (state: RootState) => state.system.isESRGANAvailable
+  );
+  const upscalingStrength = useAppSelector(
+    (state: RootState) => state.postprocessing.upscalingStrength
+  );
+
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+
+  return (
+    <IAISlider
+      label={`${t('parameters.upscale')} ${t('parameters.strength')}`}
+      value={upscalingStrength}
+      min={0}
+      max={1}
+      step={0.05}
+      onChange={(v) => dispatch(setUpscalingStrength(v))}
+      handleReset={() => dispatch(setUpscalingStrength(0.75))}
+      withSliderMarks
+      withInput
+      withReset
+      isDisabled={!isESRGANAvailable}
+    />
+  );
+}

invokeai/frontend/web/src/features/parameters/components/Parameters/Upscale/UpscaleToggle.tsx

@@ -0,0 +1,26 @@
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISwitch from 'common/components/IAISwitch';
+import { setShouldRunESRGAN } from 'features/parameters/store/postprocessingSlice';
+import { ChangeEvent } from 'react';
+
+export default function UpscaleToggle() {
+  const isESRGANAvailable = useAppSelector(
+    (state: RootState) => state.system.isESRGANAvailable
+  );
+
+  const shouldRunESRGAN = useAppSelector(
+    (state: RootState) => state.postprocessing.shouldRunESRGAN
+  );
+
+  const dispatch = useAppDispatch();
+  const handleChangeShouldRunESRGAN = (e: ChangeEvent<HTMLInputElement>) =>
+    dispatch(setShouldRunESRGAN(e.target.checked));
+  return (
+    <IAISwitch
+      isDisabled={!isESRGANAvailable}
+      isChecked={shouldRunESRGAN}
+      onChange={handleChangeShouldRunESRGAN}
+    />
+  );
+}

invokeai/frontend/web/src/features/parameters/store/actions.ts

@@ -1,10 +1,10 @@
 import { createAction } from '@reduxjs/toolkit';
-import { ImageDTO, MainModelField, OnnxModelField } from 'services/api/types';
+import { ImageDTO, MainModelField } from 'services/api/types';
 
 export const initialImageSelected = createAction<ImageDTO | string | undefined>(
   'generation/initialImageSelected'
 );
 
-export const modelSelected = createAction<MainModelField | OnnxModelField>(
+export const modelSelected = createAction<MainModelField>(
   'generation/modelSelected'
 );

invokeai/frontend/web/src/features/parameters/store/generationSlice.ts

@@ -8,7 +8,7 @@ import {
   setShouldShowAdvancedOptions,
 } from 'features/ui/store/uiSlice';
 import { clamp } from 'lodash-es';
-import { ImageDTO, MainModelField, OnnxModelField } from 'services/api/types';
+import { ImageDTO, MainModelField } from 'services/api/types';
 import { clipSkipMap } from '../components/Parameters/Advanced/ParamClipSkip';
 import {
   CfgScaleParam,
@@ -54,7 +54,7 @@ export interface GenerationState {
   shouldUseSymmetry: boolean;
   horizontalSymmetrySteps: number;
   verticalSymmetrySteps: number;
-  model: MainModelField | OnnxModelField | null;
+  model: MainModelField | null;
   vae: VaeModelParam | null;
   seamlessXAxis: boolean;
   seamlessYAxis: boolean;
@@ -227,10 +227,7 @@ export const generationSlice = createSlice({
       const { image_name, width, height } = action.payload;
       state.initialImage = { imageName: image_name, width, height };
     },
-    modelChanged: (
-      state,
-      action: PayloadAction<MainModelParam | OnnxModelField | null>
-    ) => {
+    modelChanged: (state, action: PayloadAction<MainModelParam | null>) => {
       state.model = action.payload;
 
       if (state.model === null) {
@@ -262,7 +259,6 @@ export const generationSlice = createSlice({
         const result = zMainModel.safeParse({
           model_name,
           base_model,
-          model_type,
         });
 
         if (result.success) {

invokeai/frontend/web/src/features/parameters/store/postprocessingSlice.ts

@@ -1,27 +1,98 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
-import { ESRGANInvocation } from 'services/api/types';
+import { FACETOOL_TYPES } from 'app/constants';
 
-export type ESRGANModelName = NonNullable<ESRGANInvocation['model_name']>;
+export type UpscalingLevel = 2 | 4;
+
+export type FacetoolType = (typeof FACETOOL_TYPES)[number];
 
 export interface PostprocessingState {
-  esrganModelName: ESRGANModelName;
+  codeformerFidelity: number;
+  facetoolStrength: number;
+  facetoolType: FacetoolType;
+  hiresFix: boolean;
+  hiresStrength: number;
+  shouldLoopback: boolean;
+  shouldRunESRGAN: boolean;
+  shouldRunFacetool: boolean;
+  upscalingLevel: UpscalingLevel;
+  upscalingDenoising: number;
+  upscalingStrength: number;
 }
 
 export const initialPostprocessingState: PostprocessingState = {
-  esrganModelName: 'RealESRGAN_x4plus.pth',
+  codeformerFidelity: 0.75,
+  facetoolStrength: 0.75,
+  facetoolType: 'gfpgan',
+  hiresFix: false,
+  hiresStrength: 0.75,
+  shouldLoopback: false,
+  shouldRunESRGAN: false,
+  shouldRunFacetool: false,
+  upscalingLevel: 4,
+  upscalingDenoising: 0.75,
+  upscalingStrength: 0.75,
 };
 
 export const postprocessingSlice = createSlice({
   name: 'postprocessing',
   initialState: initialPostprocessingState,
   reducers: {
-    esrganModelNameChanged: (state, action: PayloadAction<ESRGANModelName>) => {
-      state.esrganModelName = action.payload;
+    setFacetoolStrength: (state, action: PayloadAction<number>) => {
+      state.facetoolStrength = action.payload;
+    },
+    setCodeformerFidelity: (state, action: PayloadAction<number>) => {
+      state.codeformerFidelity = action.payload;
+    },
+    setUpscalingLevel: (state, action: PayloadAction<UpscalingLevel>) => {
+      state.upscalingLevel = action.payload;
+    },
+    setUpscalingDenoising: (state, action: PayloadAction<number>) => {
+      state.upscalingDenoising = action.payload;
+    },
+    setUpscalingStrength: (state, action: PayloadAction<number>) => {
+      state.upscalingStrength = action.payload;
+    },
+    setHiresFix: (state, action: PayloadAction<boolean>) => {
+      state.hiresFix = action.payload;
+    },
+    setHiresStrength: (state, action: PayloadAction<number>) => {
+      state.hiresStrength = action.payload;
+    },
+    resetPostprocessingState: (state) => {
+      return {
+        ...state,
+        ...initialPostprocessingState,
+      };
+    },
+    setShouldRunFacetool: (state, action: PayloadAction<boolean>) => {
+      state.shouldRunFacetool = action.payload;
+    },
+    setFacetoolType: (state, action: PayloadAction<FacetoolType>) => {
+      state.facetoolType = action.payload;
+    },
+    setShouldRunESRGAN: (state, action: PayloadAction<boolean>) => {
+      state.shouldRunESRGAN = action.payload;
+    },
+    setShouldLoopback: (state, action: PayloadAction<boolean>) => {
+      state.shouldLoopback = action.payload;
     },
   },
 });
 
-export const { esrganModelNameChanged } = postprocessingSlice.actions;
+export const {
+  resetPostprocessingState,
+  setCodeformerFidelity,
+  setFacetoolStrength,
+  setFacetoolType,
+  setHiresFix,
+  setHiresStrength,
+  setShouldLoopback,
+  setShouldRunESRGAN,
+  setShouldRunFacetool,
+  setUpscalingLevel,
+  setUpscalingDenoising,
+  setUpscalingStrength,
+} = postprocessingSlice.actions;
 
 export default postprocessingSlice.reducer;

invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts

@@ -126,14 +126,6 @@ export type HeightParam = z.infer<typeof zHeight>;
 export const isValidHeight = (val: unknown): val is HeightParam =>
   zHeight.safeParse(val).success;
 
-const zModelType = z.enum([
-  'vae',
-  'lora',
-  'onnx',
-  'main',
-  'controlnet',
-  'embedding',
-]);
 const zBaseModel = z.enum(['sd-1', 'sd-2', 'sdxl', 'sdxl-refiner']);
 
 export type BaseModelParam = z.infer<typeof zBaseModel>;
@@ -145,7 +137,6 @@ export type BaseModelParam = z.infer<typeof zBaseModel>;
 export const zMainModel = z.object({
   model_name: z.string().min(1),
   base_model: zBaseModel,
-  model_type: zModelType,
 });
 
 /**

invokeai/frontend/web/src/features/parameters/util/modelIdToMainModelParam.ts

@@ -14,7 +14,6 @@ export const modelIdToMainModelParam = (
   const result = zMainModel.safeParse({
     base_model,
     model_name,
-    model_type,
   });
 
   if (!result.success) {

invokeai/frontend/web/src/features/system/components/InvokeAILogoComponent.tsx

@@ -5,11 +5,7 @@ import { useRef } from 'react';
 import { useHoverDirty } from 'react-use';
 import { useGetAppVersionQuery } from 'services/api/endpoints/appInfo';
 
-interface Props {
-  showVersion?: boolean;
-}
-
-const InvokeAILogoComponent = ({ showVersion = true }: Props) => {
+const InvokeAILogoComponent = () => {
   const { data: appVersion } = useGetAppVersionQuery();
   const ref = useRef(null);
   const isHovered = useHoverDirty(ref);
@@ -32,7 +28,7 @@ const InvokeAILogoComponent = ({ showVersion = true }: Props) => {
           invoke <strong>ai</strong>
         </Text>
         <AnimatePresence>
-          {showVersion && isHovered && appVersion && (
+          {isHovered && appVersion && (
             <motion.div
               key="statusText"
               initial={{

invokeai/frontend/web/src/features/system/components/ModelSelect.tsx

@@ -1,119 +0,0 @@
-import { memo, useCallback, useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
-
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIMantineSelect from 'common/components/IAIMantineSelect';
-
-import { SelectItem } from '@mantine/core';
-import { createSelector } from '@reduxjs/toolkit';
-import { stateSelector } from 'app/store/store';
-import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import { modelIdToMainModelField } from 'features/nodes/util/modelIdToMainModelField';
-import { modelSelected } from 'features/parameters/store/actions';
-import { forEach } from 'lodash-es';
-import {
-  useGetMainModelsQuery,
-  useGetOnnxModelsQuery,
-} from 'services/api/endpoints/models';
-import { modelIdToOnnxModelField } from 'features/nodes/util/modelIdToOnnxModelField';
-
-export const MODEL_TYPE_MAP = {
-  'sd-1': 'Stable Diffusion 1.x',
-  'sd-2': 'Stable Diffusion 2.x',
-};
-
-const selector = createSelector(
-  stateSelector,
-  (state) => ({ currentModel: state.generation.model }),
-  defaultSelectorOptions
-);
-
-const ModelSelect = () => {
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-
-  const { currentModel } = useAppSelector(selector);
-
-  const { data: mainModels, isLoading } = useGetMainModelsQuery();
-  const { data: onnxModels, isLoading: onnxLoading } = useGetOnnxModelsQuery();
-
-  const data = useMemo(() => {
-    if (!mainModels) {
-      return [];
-    }
-
-    const data: SelectItem[] = [];
-
-    forEach(mainModels.entities, (model, id) => {
-      if (!model) {
-        return;
-      }
-
-      data.push({
-        value: id,
-        label: model.model_name,
-        group: MODEL_TYPE_MAP[model.base_model],
-      });
-    });
-    forEach(onnxModels?.entities, (model, id) => {
-      if (!model) {
-        return;
-      }
-
-      data.push({
-        value: id,
-        label: model.model_name,
-        group: MODEL_TYPE_MAP[model.base_model],
-      });
-    });
-
-    return data;
-  }, [mainModels, onnxModels]);
-
-  const selectedModel = useMemo(
-    () =>
-      mainModels?.entities[
-        `${currentModel?.base_model}/main/${currentModel?.model_name}`
-      ] ||
-      onnxModels?.entities[
-        `${currentModel?.base_model}/onnx/${currentModel?.model_name}`
-      ],
-    [mainModels?.entities, onnxModels?.entities, currentModel]
-  );
-
-  const handleChangeModel = useCallback(
-    (v: string | null) => {
-      if (!v) {
-        return;
-      }
-      let modelField = modelIdToMainModelField(v);
-      if (v.includes('onnx')) {
-        modelField = modelIdToOnnxModelField(v);
-      }
-      dispatch(modelSelected(modelField));
-    },
-    [dispatch]
-  );
-
-  return isLoading || onnxLoading ? (
-    <IAIMantineSelect
-      label={t('modelManager.model')}
-      placeholder="Loading..."
-      disabled={true}
-      data={[]}
-    />
-  ) : (
-    <IAIMantineSelect
-      tooltip={selectedModel?.description}
-      label={t('modelManager.model')}
-      value={selectedModel?.id}
-      placeholder={data.length > 0 ? 'Select a model' : 'No models available'}
-      data={data}
-      error={data.length === 0}
-      disabled={data.length === 0}
-      onChange={handleChangeModel}
-    />
-  );
-};
-
-export default memo(ModelSelect);

invokeai/frontend/web/src/features/system/store/models/sd1PipelineModelSlice.ts

@@ -1,56 +0,0 @@
-import { createEntityAdapter, createSlice } from '@reduxjs/toolkit';
-import { RootState } from 'app/store/store';
-import {
-  StableDiffusion1ModelCheckpointConfig,
-  StableDiffusion1ModelDiffusersConfig,
-} from 'services/api';
-
-import { receivedModels } from 'services/thunks/model';
-
-export type SD1PipelineModel = (
-  | StableDiffusion1ModelCheckpointConfig
-  | StableDiffusion1ModelDiffusersConfig
-) & {
-  name: string;
-};
-
-export const sd1PipelineModelsAdapter = createEntityAdapter<SD1PipelineModel>({
-  selectId: (model) => model.name,
-  sortComparer: (a, b) => a.name.localeCompare(b.name),
-});
-
-export const sd1InitialPipelineModelsState =
-  sd1PipelineModelsAdapter.getInitialState();
-
-export type SD1PipelineModelState = typeof sd1InitialPipelineModelsState;
-
-export const sd1PipelineModelsSlice = createSlice({
-  name: 'sd1PipelineModels',
-  initialState: sd1InitialPipelineModelsState,
-  reducers: {
-    modelAdded: sd1PipelineModelsAdapter.upsertOne,
-  },
-  extraReducers(builder) {
-    /**
-     * Received Models - FULFILLED
-     */
-    builder.addCase(receivedModels.fulfilled, (state, action) => {
-      if (action.meta.arg.baseModel !== 'sd-1') return;
-      sd1PipelineModelsAdapter.setAll(state, action.payload);
-    });
-  },
-});
-
-export const {
-  selectAll: selectAllSD1PipelineModels,
-  selectById: selectByIdSD1PipelineModels,
-  selectEntities: selectEntitiesSD1PipelineModels,
-  selectIds: selectIdsSD1PipelineModels,
-  selectTotal: selectTotalSD1PipelineModels,
-} = sd1PipelineModelsAdapter.getSelectors<RootState>(
-  (state) => state.sd1pipelinemodels
-);
-
-export const { modelAdded } = sd1PipelineModelsSlice.actions;
-
-export default sd1PipelineModelsSlice.reducer;

invokeai/frontend/web/src/features/system/store/models/sd2PipelineModelSlice.ts

@@ -1,56 +0,0 @@
-import { createEntityAdapter, createSlice } from '@reduxjs/toolkit';
-import { RootState } from 'app/store/store';
-import {
-  StableDiffusion2ModelCheckpointConfig,
-  StableDiffusion2ModelDiffusersConfig,
-} from 'services/api';
-
-import { receivedModels } from 'services/thunks/model';
-
-export type SD2PipelineModel = (
-  | StableDiffusion2ModelCheckpointConfig
-  | StableDiffusion2ModelDiffusersConfig
-) & {
-  name: string;
-};
-
-export const sd2PipelineModelsAdapater = createEntityAdapter<SD2PipelineModel>({
-  selectId: (model) => model.name,
-  sortComparer: (a, b) => a.name.localeCompare(b.name),
-});
-
-export const sd2InitialPipelineModelsState =
-  sd2PipelineModelsAdapater.getInitialState();
-
-export type SD2PipelineModelState = typeof sd2InitialPipelineModelsState;
-
-export const sd2PipelineModelsSlice = createSlice({
-  name: 'sd2PipelineModels',
-  initialState: sd2InitialPipelineModelsState,
-  reducers: {
-    modelAdded: sd2PipelineModelsAdapater.upsertOne,
-  },
-  extraReducers(builder) {
-    /**
-     * Received Models - FULFILLED
-     */
-    builder.addCase(receivedModels.fulfilled, (state, action) => {
-      if (action.meta.arg.baseModel !== 'sd-2') return;
-      sd2PipelineModelsAdapater.setAll(state, action.payload);
-    });
-  },
-});
-
-export const {
-  selectAll: selectAllSD2PipelineModels,
-  selectById: selectByIdSD2PipelineModels,
-  selectEntities: selectEntitiesSD2PipelineModels,
-  selectIds: selectIdsSD2PipelineModels,
-  selectTotal: selectTotalSD2PipelineModels,
-} = sd2PipelineModelsAdapater.getSelectors<RootState>(
-  (state) => state.sd2pipelinemodels
-);
-
-export const { modelAdded } = sd2PipelineModelsSlice.actions;
-
-export default sd2PipelineModelsSlice.reducer;

invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/AddModelsPanel/SimpleAddModels.tsx

@@ -85,7 +85,7 @@ export default function SimpleAddModels() {
       <Flex flexDirection="column" width="100%" gap={4}>
         <IAIMantineTextInput
           label="Model Location"
-          placeholder="Provide a path to a local Diffusers model, local checkpoint / safetensors model a HuggingFace Repo ID, or a checkpoint/diffusers model URL."
+          placeholder="Provide a path to a local Diffusers model, local checkpoint / safetensors model or a HuggingFace Repo ID"
           w="100%"
           {...addModelForm.getInputProps('location')}
         />

invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelList.tsx

@@ -17,7 +17,7 @@ type ModelListProps = {
   setSelectedModelId: (name: string | undefined) => void;
 };
 
-type ModelFormat = 'all' | 'checkpoint' | 'diffusers' | 'olive';
+type ModelFormat = 'all' | 'checkpoint' | 'diffusers';
 
 const ModelList = (props: ModelListProps) => {
   const { selectedModelId, setSelectedModelId } = props;

invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTabParameters.tsx

@@ -4,6 +4,7 @@ import ParamAdvancedCollapse from 'features/parameters/components/Parameters/Adv
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
 import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
 import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
+import ParamHiresCollapse from 'features/parameters/components/Parameters/Hires/ParamHiresCollapse';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
 import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
@@ -25,6 +26,7 @@ const TextToImageTabParameters = () => {
       <ParamVariationCollapse />
       <ParamNoiseCollapse />
       <ParamSymmetryCollapse />
+      <ParamHiresCollapse />
       <ParamSeamlessCollapse />
       <ParamAdvancedCollapse />
     </>

invokeai/frontend/web/src/services/api/endpoints/models.ts

@@ -10,7 +10,6 @@ import {
   ImportModelConfig,
   LoRAModelConfig,
   MainModelConfig,
-  OnnxModelConfig,
   MergeModelConfig,
   TextualInversionModelConfig,
   VaeModelConfig,
@@ -28,8 +27,6 @@ export type MainModelConfigEntity =
   | DiffusersModelConfigEntity
   | CheckpointModelConfigEntity;
 
-export type OnnxModelConfigEntity = OnnxModelConfig & { id: string };
-
 export type LoRAModelConfigEntity = LoRAModelConfig & { id: string };
 
 export type ControlNetModelConfigEntity = ControlNetModelConfig & {
@@ -44,7 +41,6 @@ export type VaeModelConfigEntity = VaeModelConfig & { id: string };
 
 type AnyModelConfigEntity =
   | MainModelConfigEntity
-  | OnnxModelConfigEntity
   | LoRAModelConfigEntity
   | ControlNetModelConfigEntity
   | TextualInversionModelConfigEntity
@@ -108,10 +104,6 @@ type SearchFolderArg = operations['search_for_models']['parameters']['query'];
 const mainModelsAdapter = createEntityAdapter<MainModelConfigEntity>({
   sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
-
-const onnxModelsAdapter = createEntityAdapter<OnnxModelConfigEntity>({
-  sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
-});
 const loraModelsAdapter = createEntityAdapter<LoRAModelConfigEntity>({
   sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
@@ -149,38 +141,6 @@ const createModelEntities = <T extends AnyModelConfigEntity>(
 
 export const modelsApi = api.injectEndpoints({
   endpoints: (build) => ({
-    getOnnxModels: build.query<EntityState<OnnxModelConfigEntity>, void>({
-      query: () => ({ url: 'models/', params: { model_type: 'onnx' } }),
-      providesTags: (result, error, arg) => {
-        const tags: ApiFullTagDescription[] = [
-          { id: 'OnnxModel', type: LIST_TAG },
-        ];
-
-        if (result) {
-          tags.push(
-            ...result.ids.map((id) => ({
-              type: 'OnnxModel' as const,
-              id,
-            }))
-          );
-        }
-
-        return tags;
-      },
-      transformResponse: (
-        response: { models: OnnxModelConfig[] },
-        meta,
-        arg
-      ) => {
-        const entities = createModelEntities<OnnxModelConfigEntity>(
-          response.models
-        );
-        return onnxModelsAdapter.setAll(
-          onnxModelsAdapter.getInitialState(),
-          entities
-        );
-      },
-    }),
     getMainModels: build.query<EntityState<MainModelConfigEntity>, void>({
       query: () => ({ url: 'models/', params: { model_type: 'main' } }),
       providesTags: (result, error, arg) => {
@@ -453,7 +413,6 @@ export const modelsApi = api.injectEndpoints({
 
 export const {
   useGetMainModelsQuery,
-  useGetOnnxModelsQuery,
   useGetControlNetModelsQuery,
   useGetLoRAModelsQuery,
   useGetTextualInversionModelsQuery,

invokeai/frontend/web/src/services/api/index.ts

@@ -8,132 +8,6 @@ import {
 } from '@reduxjs/toolkit/query/react';
 import { $authToken, $baseUrl } from 'services/api/client';
 
-export type { AddInvocation } from './models/AddInvocation';
-export type { BoardChanges } from './models/BoardChanges';
-export type { BoardDTO } from './models/BoardDTO';
-export type { Body_create_board_image } from './models/Body_create_board_image';
-export type { Body_remove_board_image } from './models/Body_remove_board_image';
-export type { Body_upload_image } from './models/Body_upload_image';
-export type { CannyImageProcessorInvocation } from './models/CannyImageProcessorInvocation';
-export type { CkptModelInfo } from './models/CkptModelInfo';
-export type { ClipField } from './models/ClipField';
-export type { CollectInvocation } from './models/CollectInvocation';
-export type { CollectInvocationOutput } from './models/CollectInvocationOutput';
-export type { ColorField } from './models/ColorField';
-export type { CompelInvocation } from './models/CompelInvocation';
-export type { CompelOutput } from './models/CompelOutput';
-export type { ConditioningField } from './models/ConditioningField';
-export type { ContentShuffleImageProcessorInvocation } from './models/ContentShuffleImageProcessorInvocation';
-export type { ControlField } from './models/ControlField';
-export type { ControlNetInvocation } from './models/ControlNetInvocation';
-export type { ControlNetModelConfig } from './models/ControlNetModelConfig';
-export type { ControlOutput } from './models/ControlOutput';
-export type { CreateModelRequest } from './models/CreateModelRequest';
-export type { CvInpaintInvocation } from './models/CvInpaintInvocation';
-export type { DiffusersModelInfo } from './models/DiffusersModelInfo';
-export type { DivideInvocation } from './models/DivideInvocation';
-export type { DynamicPromptInvocation } from './models/DynamicPromptInvocation';
-export type { Edge } from './models/Edge';
-export type { EdgeConnection } from './models/EdgeConnection';
-export type { FloatCollectionOutput } from './models/FloatCollectionOutput';
-export type { FloatLinearRangeInvocation } from './models/FloatLinearRangeInvocation';
-export type { FloatOutput } from './models/FloatOutput';
-export type { Graph } from './models/Graph';
-export type { GraphExecutionState } from './models/GraphExecutionState';
-export type { GraphInvocation } from './models/GraphInvocation';
-export type { GraphInvocationOutput } from './models/GraphInvocationOutput';
-export type { HTTPValidationError } from './models/HTTPValidationError';
-export type { ImageBlurInvocation } from './models/ImageBlurInvocation';
-export type { ImageChannelInvocation } from './models/ImageChannelInvocation';
-export type { ImageConvertInvocation } from './models/ImageConvertInvocation';
-export type { ImageCropInvocation } from './models/ImageCropInvocation';
-export type { ImageDTO } from './models/ImageDTO';
-export type { ImageField } from './models/ImageField';
-export type { ImageInverseLerpInvocation } from './models/ImageInverseLerpInvocation';
-export type { ImageLerpInvocation } from './models/ImageLerpInvocation';
-export type { ImageMetadata } from './models/ImageMetadata';
-export type { ImageMultiplyInvocation } from './models/ImageMultiplyInvocation';
-export type { ImageOutput } from './models/ImageOutput';
-export type { ImagePasteInvocation } from './models/ImagePasteInvocation';
-export type { ImageProcessorInvocation } from './models/ImageProcessorInvocation';
-export type { ImageRecordChanges } from './models/ImageRecordChanges';
-export type { ImageResizeInvocation } from './models/ImageResizeInvocation';
-export type { ImageScaleInvocation } from './models/ImageScaleInvocation';
-export type { ImageToLatentsInvocation } from './models/ImageToLatentsInvocation';
-export type { ImageUrlsDTO } from './models/ImageUrlsDTO';
-export type { InfillColorInvocation } from './models/InfillColorInvocation';
-export type { InfillPatchMatchInvocation } from './models/InfillPatchMatchInvocation';
-export type { InfillTileInvocation } from './models/InfillTileInvocation';
-export type { InpaintInvocation } from './models/InpaintInvocation';
-export type { IntCollectionOutput } from './models/IntCollectionOutput';
-export type { IntOutput } from './models/IntOutput';
-export type { IterateInvocation } from './models/IterateInvocation';
-export type { IterateInvocationOutput } from './models/IterateInvocationOutput';
-export type { LatentsField } from './models/LatentsField';
-export type { LatentsOutput } from './models/LatentsOutput';
-export type { LatentsToImageInvocation } from './models/LatentsToImageInvocation';
-export type { LatentsToLatentsInvocation } from './models/LatentsToLatentsInvocation';
-export type { LineartAnimeImageProcessorInvocation } from './models/LineartAnimeImageProcessorInvocation';
-export type { LineartImageProcessorInvocation } from './models/LineartImageProcessorInvocation';
-export type { LoadImageInvocation } from './models/LoadImageInvocation';
-export type { LoraInfo } from './models/LoraInfo';
-export type { LoraLoaderInvocation } from './models/LoraLoaderInvocation';
-export type { LoraLoaderOutput } from './models/LoraLoaderOutput';
-export type { MaskFromAlphaInvocation } from './models/MaskFromAlphaInvocation';
-export type { MaskOutput } from './models/MaskOutput';
-export type { MediapipeFaceProcessorInvocation } from './models/MediapipeFaceProcessorInvocation';
-export type { MidasDepthImageProcessorInvocation } from './models/MidasDepthImageProcessorInvocation';
-export type { MlsdImageProcessorInvocation } from './models/MlsdImageProcessorInvocation';
-export type { ModelInfo } from './models/ModelInfo';
-export type { ModelLoaderOutput } from './models/ModelLoaderOutput';
-export type { ModelsList } from './models/ModelsList';
-export type { ModelType } from './models/ModelType';
-export type { MultiplyInvocation } from './models/MultiplyInvocation';
-export type { NoiseInvocation } from './models/NoiseInvocation';
-export type { NoiseOutput } from './models/NoiseOutput';
-export type { NormalbaeImageProcessorInvocation } from './models/NormalbaeImageProcessorInvocation';
-export type { OffsetPaginatedResults_BoardDTO_ } from './models/OffsetPaginatedResults_BoardDTO_';
-export type { OffsetPaginatedResults_ImageDTO_ } from './models/OffsetPaginatedResults_ImageDTO_';
-export type { ONNXLatentsToImageInvocation } from './models/ONNXLatentsToImageInvocation';
-export type { ONNXModelLoaderOutput } from './models/ONNXModelLoaderOutput';
-export type { ONNXPromptInvocation } from './models/ONNXPromptInvocation';
-export type { ONNXSD1ModelLoaderInvocation } from './models/ONNXSD1ModelLoaderInvocation';
-export type { ONNXStableDiffusion1ModelConfig } from './models/ONNXStableDiffusion1ModelConfig';
-export type { ONNXStableDiffusion2ModelConfig } from './models/ONNXStableDiffusion2ModelConfig';
-export type { ONNXTextToLatentsInvocation } from './models/ONNXTextToLatentsInvocation';
-export type { OpenposeImageProcessorInvocation } from './models/OpenposeImageProcessorInvocation';
-export type { PaginatedResults_GraphExecutionState_ } from './models/PaginatedResults_GraphExecutionState_';
-export type { ParamFloatInvocation } from './models/ParamFloatInvocation';
-export type { ParamIntInvocation } from './models/ParamIntInvocation';
-export type { PidiImageProcessorInvocation } from './models/PidiImageProcessorInvocation';
-export type { PipelineModelField } from './models/PipelineModelField';
-export type { PipelineModelLoaderInvocation } from './models/PipelineModelLoaderInvocation';
-export type { PromptCollectionOutput } from './models/PromptCollectionOutput';
-export type { PromptOutput } from './models/PromptOutput';
-export type { RandomIntInvocation } from './models/RandomIntInvocation';
-export type { RandomRangeInvocation } from './models/RandomRangeInvocation';
-export type { RangeInvocation } from './models/RangeInvocation';
-export type { RangeOfSizeInvocation } from './models/RangeOfSizeInvocation';
-export type { ResizeLatentsInvocation } from './models/ResizeLatentsInvocation';
-export type { RestoreFaceInvocation } from './models/RestoreFaceInvocation';
-export type { ScaleLatentsInvocation } from './models/ScaleLatentsInvocation';
-export type { ShowImageInvocation } from './models/ShowImageInvocation';
-export type { StableDiffusion1ModelCheckpointConfig } from './models/StableDiffusion1ModelCheckpointConfig';
-export type { StableDiffusion1ModelDiffusersConfig } from './models/StableDiffusion1ModelDiffusersConfig';
-export type { StableDiffusion2ModelCheckpointConfig } from './models/StableDiffusion2ModelCheckpointConfig';
-export type { StableDiffusion2ModelDiffusersConfig } from './models/StableDiffusion2ModelDiffusersConfig';
-export type { StepParamEasingInvocation } from './models/StepParamEasingInvocation';
-export type { SubModelType } from './models/SubModelType';
-export type { SubtractInvocation } from './models/SubtractInvocation';
-export type { TextToLatentsInvocation } from './models/TextToLatentsInvocation';
-export type { TextualInversionModelConfig } from './models/TextualInversionModelConfig';
-export type { UNetField } from './models/UNetField';
-export type { UpscaleInvocation } from './models/UpscaleInvocation';
-export type { VaeField } from './models/VaeField';
-export type { VaeModelConfig } from './models/VaeModelConfig';
-export type { VaeRepo } from './models/VaeRepo';
-export type { ValidationError } from './models/ValidationError';
-export type { ZoeDepthImageProcessorInvocation } from './models/ZoeDepthImageProcessorInvocation';
 export const tagTypes = ['Board', 'Image', 'ImageMetadata', 'Model'];
 export type ApiFullTagDescription = FullTagDescription<
   (typeof tagTypes)[number]

invokeai/frontend/web/src/services/api/models/AddInvocation.ts

@@ -1,26 +0,0 @@
-/* istanbul ignore file */
-/* tslint:disable */
-/* eslint-disable */
-
-/**
- * Adds two numbers
- */
-export type AddInvocation = {
-  /**
-   * The id of this node. Must be unique among all nodes.
-   */
-  id: string;
-  /**
-   * Whether or not this node is an intermediate node.
-   */
-  is_intermediate?: boolean;
-  type?: 'add';
-  /**
-   * The first number
-   */
-  'a'?: number;
-  /**
-   * The second number
-   */
-  'b'?: number;
-};

invokeai/frontend/web/src/services/api/models/BoardChanges.ts

@@ -1,14 +0,0 @@
-/* istanbul ignore file */
-/* tslint:disable */
-/* eslint-disable */
-
-export type BoardChanges = {
-  /**
-   * The board's new name.
-   */
-  board_name?: string;
-  /**
-   * The name of the board's new cover image.
-   */
-  cover_image_name?: string;
-};

invokeai/frontend/web/src/services/api/models/BoardDTO.ts

@@ -1,37 +0,0 @@
-/* istanbul ignore file */
-/* tslint:disable */
-/* eslint-disable */
-
-/**
- * Deserialized board record with cover image URL and image count.
- */
-export type BoardDTO = {
-  /**
-   * The unique ID of the board.
-   */
-  board_id: string;
-  /**
-   * The name of the board.
-   */
-  board_name: string;
-  /**
-   * The created timestamp of the board.
-   */
-  created_at: string;
-  /**
-   * The updated timestamp of the board.
-   */
-  updated_at: string;
-  /**
-   * The deleted timestamp of the board.
-   */
-  deleted_at?: string;
-  /**
-   * The name of the board's cover image.
-   */
-  cover_image_name?: string;
-  /**
-   * The number of images in the board.
-   */
-  image_count: number;
-};